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+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-completeness/expr_dt/index.html b/api-completeness/expr_dt/index.html new file mode 100644 index 000000000..6eca703b6 --- /dev/null +++ b/api-completeness/expr_dt/index.html @@ -0,0 +1,2029 @@ + + + + + + + + + + + + + + + + + + + + + + + Supported Expr.dt methods - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ +
+ + + + + + +
+ + +
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Expr_dt

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ClassMethodarrowdaskduckdbpandas-like
ExprDateTimeNamespaceconvert_time_zone✅✅❌✅
ExprDateTimeNamespacedate✅✅❌✅
ExprDateTimeNamespaceday✅✅✅✅
ExprDateTimeNamespacehour✅✅✅✅
ExprDateTimeNamespacemicrosecond✅✅✅✅
ExprDateTimeNamespacemillisecond✅✅✅✅
ExprDateTimeNamespaceminute✅✅✅✅
ExprDateTimeNamespacemonth✅✅✅✅
ExprDateTimeNamespacenanosecond✅✅✅✅
ExprDateTimeNamespaceordinal_day✅✅❌✅
ExprDateTimeNamespacereplace_time_zone✅✅❌✅
ExprDateTimeNamespacesecond✅✅✅✅
ExprDateTimeNamespacetimestamp✅✅❌✅
ExprDateTimeNamespaceto_string✅✅❌✅
ExprDateTimeNamespacetotal_microseconds✅✅❌✅
ExprDateTimeNamespacetotal_milliseconds✅✅❌✅
ExprDateTimeNamespacetotal_minutes✅✅❌✅
ExprDateTimeNamespacetotal_nanoseconds✅✅❌✅
ExprDateTimeNamespacetotal_seconds✅✅❌✅
ExprDateTimeNamespaceweekday✅✅❌✅
ExprDateTimeNamespaceyear✅✅✅✅
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-completeness/expr_list/index.html b/api-completeness/expr_list/index.html new file mode 100644 index 000000000..6ebb5f35a --- /dev/null +++ b/api-completeness/expr_list/index.html @@ -0,0 +1,1865 @@ + + + + + + + + + + + + + + + + + + + + + + + Supported Expr.list methods - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
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+
+ + + + + +
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+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Expr_list

+ + + + + + + + + + + + + + + + + +
ClassMethodarrowpandas-like
ExprListNamespacelen✅✅
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-completeness/expr_name/index.html b/api-completeness/expr_name/index.html new file mode 100644 index 000000000..74abf2ec6 --- /dev/null +++ b/api-completeness/expr_name/index.html @@ -0,0 +1,1902 @@ + + + + + + + + + + + + + + + + + + + + + + + Supported Expr.name methods - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
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+
+ + + +
+
+ + + + + + + +

Expr_name

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ClassMethodarrowdaskpandas-like
ExprNameNamespacekeep✅✅✅
ExprNameNamespacemap✅✅✅
ExprNameNamespaceprefix✅✅✅
ExprNameNamespacesuffix✅✅✅
ExprNameNamespaceto_lowercase✅✅✅
ExprNameNamespaceto_uppercase✅✅✅
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-completeness/expr_str/index.html b/api-completeness/expr_str/index.html new file mode 100644 index 000000000..ce827001c --- /dev/null +++ b/api-completeness/expr_str/index.html @@ -0,0 +1,1979 @@ + + + + + + + + + + + + + + + + + + + + + + + Supported Expr.str methods - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Expr_str

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ClassMethodarrowdaskduckdbpandas-likespark-like
ExprStringNamespacecontains✅✅✅✅✅
ExprStringNamespaceends_with✅✅✅✅✅
ExprStringNamespacehead❌❌❌❌❌
ExprStringNamespacelen_chars✅✅✅✅✅
ExprStringNamespacereplace✅✅✅✅❌
ExprStringNamespacereplace_all✅✅✅✅✅
ExprStringNamespaceslice✅✅✅✅✅
ExprStringNamespacestarts_with✅✅✅✅✅
ExprStringNamespacestrip_chars✅✅✅✅✅
ExprStringNamespacetail❌❌❌❌❌
ExprStringNamespaceto_datetime✅✅❌✅❌
ExprStringNamespaceto_lowercase✅✅✅✅✅
ExprStringNamespaceto_uppercase✅✅✅✅✅
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-completeness/index.html b/api-completeness/index.html new file mode 100644 index 000000000..d56cf7cb1 --- /dev/null +++ b/api-completeness/index.html @@ -0,0 +1,1849 @@ + + + + + + + + + + + + + + + + + + + + + + + API Completeness - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
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+ + + + + + + +

API Completeness

+

Narwhals has two different level of support for libraries: "full" and "interchange".

+

Libraries for which we have full support we intend to support the whole Narwhals API, +however this is a continuous work in progress.

+

In the following section it is possible to check which method is implemented for which +class and backend.

+
+

Info

+
    +
  • By design, Polars supports all the methods of the Narwhals API.
    • +
  • "pandas-like" means pandas, cuDF and Modin.
    • +
+
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-completeness/lazyframe/index.html b/api-completeness/lazyframe/index.html new file mode 100644 index 000000000..4419e427a --- /dev/null +++ b/api-completeness/lazyframe/index.html @@ -0,0 +1,2042 @@ + + + + + + + + + + + + + + + + + + + + + + + Supported LazyFrame methods - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
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+
+ + + +
+
+ + + + + + + +

LazyFrame

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ClassMethoddaskduckdbspark-like
LazyFrameclone❌❌❌
LazyFramecollect✅✅✅
LazyFramecollect_schema✅✅✅
LazyFramecolumns✅✅✅
LazyFramedrop✅✅✅
LazyFramedrop_nulls✅❌✅
LazyFrameexplode❌❌❌
LazyFramefilter✅✅✅
LazyFramegather_every✅❌❌
LazyFramegroup_by✅✅✅
LazyFramehead✅✅✅
LazyFrameimplementation✅✅✅
LazyFramejoin✅✅✅
LazyFramejoin_asof✅❌❌
LazyFramelazy❌✅❌
LazyFramepipe✅✅✅
LazyFramerename✅✅✅
LazyFrameschema✅✅✅
LazyFrameselect✅✅✅
LazyFramesort✅✅✅
LazyFrametail✅❌❌
LazyFrameto_native✅✅✅
LazyFrameunique✅✅✅
LazyFrameunpivot✅❌❌
LazyFramewith_columns✅✅✅
LazyFramewith_row_index✅❌❌
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-completeness/series/index.html b/api-completeness/series/index.html new file mode 100644 index 000000000..32cd97e57 --- /dev/null +++ b/api-completeness/series/index.html @@ -0,0 +1,2333 @@ + + + + + + + + + + + + + + + + + + + + + + + Supported Series methods - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
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Series

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ClassMethodarrowpandas-like
Seriesabs✅✅
Seriesalias✅✅
Seriesall✅✅
Seriesany✅✅
Seriesarg_max✅✅
Seriesarg_min✅✅
Seriesarg_true✅✅
Seriescast✅✅
Seriescat✅✅
Seriesclip✅✅
Seriescount✅✅
Seriescum_count✅✅
Seriescum_max✅✅
Seriescum_min✅✅
Seriescum_prod✅✅
Seriescum_sum✅✅
Seriesdiff✅✅
Seriesdrop_nulls✅✅
Seriesdt✅✅
Seriesdtype✅✅
Seriesewm_mean❌✅
Seriesfill_null✅✅
Seriesfilter✅✅
Seriesgather_every✅✅
Serieshead✅✅
Seriesimplementation✅✅
Seriesis_between✅✅
Seriesis_duplicated✅✅
Seriesis_empty✅✅
Seriesis_finite✅✅
Seriesis_first_distinct✅✅
Seriesis_in✅✅
Seriesis_last_distinct✅✅
Seriesis_nan✅✅
Seriesis_null✅✅
Seriesis_sorted✅✅
Seriesis_unique✅✅
Seriesitem✅✅
Serieslen✅✅
Serieslist✅✅
Seriesmax✅✅
Seriesmean✅✅
Seriesmedian✅✅
Seriesmin✅✅
Seriesmode✅✅
Seriesn_unique✅✅
Seriesname✅✅
Seriesnull_count✅✅
Seriespipe✅✅
Seriesquantile✅✅
Seriesrank✅✅
Seriesrename❌❌
Seriesreplace_strict✅✅
Seriesrolling_mean✅✅
Seriesrolling_std✅✅
Seriesrolling_sum✅✅
Seriesrolling_var✅✅
Seriesround✅✅
Seriessample✅✅
Seriesscatter✅✅
Seriesshape✅✅
Seriesshift✅✅
Seriesskew✅✅
Seriessort✅✅
Seriesstd✅✅
Seriesstr✅✅
Seriessum✅✅
Seriestail✅✅
Seriesto_arrow✅✅
Seriesto_dummies✅✅
Seriesto_frame✅✅
Seriesto_list✅✅
Seriesto_native✅✅
Seriesto_numpy✅✅
Seriesto_pandas✅✅
Seriesunique✅✅
Seriesvalue_counts✅✅
Seriesvar✅✅
Serieszip_with✅✅
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-completeness/series_cat/index.html b/api-completeness/series_cat/index.html new file mode 100644 index 000000000..3b893ddbb --- /dev/null +++ b/api-completeness/series_cat/index.html @@ -0,0 +1,1865 @@ + + + + + + + + + + + + + + + + + + + + + + + Supported Series.cat methods - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Series_cat

+ + + + + + + + + + + + + + + + + +
ClassMethodarrowpandas-like
SeriesCatNamespaceget_categories✅✅
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-completeness/series_dt/index.html b/api-completeness/series_dt/index.html new file mode 100644 index 000000000..3868e4b41 --- /dev/null +++ b/api-completeness/series_dt/index.html @@ -0,0 +1,1985 @@ + + + + + + + + + + + + + + + + + + + + + + + Supported Series.dt methods - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Series_dt

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ClassMethodarrowpandas-like
SeriesDateTimeNamespaceconvert_time_zone✅✅
SeriesDateTimeNamespacedate✅✅
SeriesDateTimeNamespaceday✅✅
SeriesDateTimeNamespacehour✅✅
SeriesDateTimeNamespacemicrosecond✅✅
SeriesDateTimeNamespacemillisecond✅✅
SeriesDateTimeNamespaceminute✅✅
SeriesDateTimeNamespacemonth✅✅
SeriesDateTimeNamespacenanosecond✅✅
SeriesDateTimeNamespaceordinal_day✅✅
SeriesDateTimeNamespacereplace_time_zone✅✅
SeriesDateTimeNamespacesecond✅✅
SeriesDateTimeNamespacetimestamp✅✅
SeriesDateTimeNamespaceto_string✅✅
SeriesDateTimeNamespacetotal_microseconds✅✅
SeriesDateTimeNamespacetotal_milliseconds✅✅
SeriesDateTimeNamespacetotal_minutes✅✅
SeriesDateTimeNamespacetotal_nanoseconds✅✅
SeriesDateTimeNamespacetotal_seconds✅✅
SeriesDateTimeNamespaceweekday✅✅
SeriesDateTimeNamespaceyear✅✅
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-completeness/series_list/index.html b/api-completeness/series_list/index.html new file mode 100644 index 000000000..390a7e0e4 --- /dev/null +++ b/api-completeness/series_list/index.html @@ -0,0 +1,1865 @@ + + + + + + + + + + + + + + + + + + + + + + + Supported Series.list methods - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Series_list

+ + + + + + + + + + + + + + + + + +
ClassMethodarrowpandas-like
SeriesListNamespacelen✅✅
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-completeness/series_str/index.html b/api-completeness/series_str/index.html new file mode 100644 index 000000000..99eced990 --- /dev/null +++ b/api-completeness/series_str/index.html @@ -0,0 +1,1937 @@ + + + + + + + + + + + + + + + + + + + + + + + Supported Series.str methods - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
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+
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+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Series_str

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ClassMethodarrowpandas-like
SeriesStringNamespacecontains✅✅
SeriesStringNamespaceends_with✅✅
SeriesStringNamespacehead❌❌
SeriesStringNamespacelen_chars✅✅
SeriesStringNamespacereplace✅✅
SeriesStringNamespacereplace_all✅✅
SeriesStringNamespaceslice✅✅
SeriesStringNamespacestarts_with✅✅
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narwhals.DataFrame

+ + +
+ + + + +
+ + +

Narwhals DataFrame, backed by a native eager dataframe.

+
+

Warning

+

This class is not meant to be instantiated directly - instead:

+
    +
  • +

    If the native object is a eager dataframe from one of the supported + backend (e.g. pandas.DataFrame, polars.DataFrame, pyarrow.Table), + you can use narwhals.from_native: + 

    narwhals.from_native(native_dataframe)
+narwhals.from_native(native_dataframe, eager_only=True)
+

    +
    • +
  • +

    If the object is a dictionary of column names and generic sequences mapping + (e.g. dict[str, list]), you can create a DataFrame via + narwhals.from_dict: + 

    narwhals.from_dict(
+    data={"a": [1, 2, 3]},
+    native_namespace=narwhals.get_native_namespace(another_object),
+)
+

    +
    • +
+
+ + + + + + + + + +
+ + + + + + + +
+ + + +

+ columns + + + property + + +

+ + +
+ +

Get column names.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ list[str] + +
+

The column names stored in a list.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrame
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_columns(df_native: IntoFrame) -> list[str]:
+...     df = nw.from_native(df_native)
+...     return df.columns
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_columns:

+ 
>>> agnostic_columns(df_pd)
+['foo', 'bar', 'ham']
+>>> agnostic_columns(df_pl)
+['foo', 'bar', 'ham']
+>>> agnostic_columns(df_pa)
+['foo', 'bar', 'ham']
+
+
+ +
+ +
+ + + +

+ implementation + + + property + + +

+ + +
+ +

Return implementation of native frame.

+

This can be useful when you need to use special-casing for features outside of +Narwhals' scope - for example, when dealing with pandas' Period Dtype.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Implementation + +
+

Implementation.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> df_native = pd.DataFrame({"a": [1, 2, 3]})
+>>> df = nw.from_native(df_native)
+>>> df.implementation
+<Implementation.PANDAS: 1>
+>>> df.implementation.is_pandas()
+True
+>>> df.implementation.is_pandas_like()
+True
+>>> df.implementation.is_polars()
+False
+
+
+ +
+ +
+ + + +

+ schema + + + property + + +

+ + +
+ +

Get an ordered mapping of column names to their data type.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Schema + +
+

A Narwhals Schema object that displays the mapping of column names.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.schema import Schema
+>>> from narwhals.typing import IntoFrame
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6.0, 7.0, 8.0],
+...     "ham": ["a", "b", "c"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_schema(df_native: IntoFrame) -> Schema:
+...     df = nw.from_native(df_native)
+...     return df.schema
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_schema:

+ 
>>> agnostic_schema(df_pd)
+Schema({'foo': Int64, 'bar': Float64, 'ham': String})
+>>> agnostic_schema(df_pl)
+Schema({'foo': Int64, 'bar': Float64, 'ham': String})
+>>> agnostic_schema(df_pa)
+Schema({'foo': Int64, 'bar': Float64, 'ham': String})
+
+
+ +
+ +
+ + + +

+ shape + + + property + + +

+ + +
+ +

Get the shape of the DataFrame.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ tuple[int, int] + +
+

The shape of the dataframe as a tuple.

+
+
+ + +

Examples:

+

Construct pandas and polars DataFrames:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> data = {"foo": [1, 2, 3, 4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_shape(df_native: IntoDataFrame) -> tuple[int, int]:
+...     df = nw.from_native(df_native)
+...     return df.shape
+
+

We can then pass either pandas, Polars or PyArrow to agnostic_shape:

+ 
>>> agnostic_shape(df_pd)
+(5, 1)
+>>> agnostic_shape(df_pl)
+(5, 1)
+>>> agnostic_shape(df_pa)
+(5, 1)
+
+
+ +
+ + + +
+ + +

+ __arrow_c_stream__(requested_schema=None) + +

+ + +
+ +

Export a DataFrame via the Arrow PyCapsule Interface.

+
    +
  • if the underlying dataframe implements the interface, it'll return that
    • +
  • else, it'll call to_arrow and then defer to PyArrow's implementation
    • +
+

See PyCapsule Interface +for more.

+ +
+ +
+ +
+ + +

+ __getitem__(item) + +

+ + +
+ +

Extract column or slice of DataFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ item + + str | slice | Sequence[int] | Sequence[str] | tuple[Sequence[int], str | int] | tuple[slice, str | int] | tuple[slice | Sequence[int], Sequence[int] | Sequence[str] | slice] | tuple[slice, slice] + +
+

How to slice dataframe. What happens depends on what is passed. It's easiest +to explain by example. Suppose we have a Dataframe df:

+
    +
  • df['a'] extracts column 'a' and returns a Series.
    • +
  • df[0:2] extracts the first two rows and returns a DataFrame.
    • +
  • df[0:2, 'a'] extracts the first two rows from column 'a' and returns + a Series.
    • +
  • df[0:2, 0] extracts the first two rows from the first column and returns + a Series.
    • +
  • df[[0, 1], [0, 1, 2]] extracts the first two rows and the first three columns + and returns a DataFrame
    • +
  • df[:, [0, 1, 2]] extracts all rows from the first three columns and returns a + DataFrame.
    • +
  • df[:, ['a', 'c']] extracts all rows and columns 'a' and 'c' and returns a + DataFrame.
    • +
  • df[['a', 'c']] extracts all rows and columns 'a' and 'c' and returns a + DataFrame.
    • +
  • df[0: 2, ['a', 'c']] extracts the first two rows and columns 'a' and 'c' and + returns a DataFrame
    • +
  • df[:, 0: 2] extracts all rows from the first two columns and returns a DataFrame
    • +
  • df[:, 'a': 'c'] extracts all rows and all columns positioned between 'a' and 'c' + inclusive and returns a DataFrame. For example, if the columns are + 'a', 'd', 'c', 'b', then that would extract columns 'a', 'd', and 'c'.
    • +
+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Series[Any] | Self + +
+

A Narwhals Series, backed by a native series.

+
+
+ + +
+ Notes +
    +
  • Integers are always interpreted as positions
    • +
  • Strings are always interpreted as column names.
    • +
+

In contrast with Polars, pandas allows non-string column names. +If you don't know whether the column name you're trying to extract +is definitely a string (e.g. df[df.columns[0]]) then you should +use DataFrame.get_column instead.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> from narwhals.typing import IntoSeries
+>>> data = {"a": [1, 2], "b": [3, 4]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_slice(df_native: IntoDataFrame) -> IntoSeries:
+...     df = nw.from_native(df_native)
+...     return df["a"].to_native()
+
+

We can then pass either pandas, Polars or PyArrow to agnostic_slice:

+ 
>>> agnostic_slice(df_pd)
+0    1
+1    2
+Name: a, dtype: int64
+>>> agnostic_slice(df_pl)
+shape: (2,)
+Series: 'a' [i64]
+[
+    1
+    2
+]
+>>> agnostic_slice(df_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    2
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ clone() + +

+ + +
+ +

Create a copy of this DataFrame.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

An identical copy of the original dataframe.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {"a": [1, 2], "b": [3, 4]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function in which we clone the DataFrame:

+ 
>>> def agnostic_clone(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.clone().to_native()
+
+

We can then pass any supported library such as Pandas or Polars +to agnostic_clone:

+ 
>>> agnostic_clone(df_pd)
+   a  b
+0  1  3
+1  2  4
+
+ 
>>> agnostic_clone(df_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 3   │
+│ 2   ┆ 4   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ collect_schema() + +

+ + +
+ +

Get an ordered mapping of column names to their data type.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Schema + +
+

A Narwhals Schema object that displays the mapping of column names.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.schema import Schema
+>>> from narwhals.typing import IntoFrame
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6.0, 7.0, 8.0],
+...     "ham": ["a", "b", "c"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_collect_schema(df_native: IntoFrame) -> Schema:
+...     df = nw.from_native(df_native)
+...     return df.collect_schema()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_collect_schema:

+ 
>>> agnostic_collect_schema(df_pd)
+Schema({'foo': Int64, 'bar': Float64, 'ham': String})
+>>> agnostic_collect_schema(df_pl)
+Schema({'foo': Int64, 'bar': Float64, 'ham': String})
+>>> agnostic_collect_schema(df_pa)
+Schema({'foo': Int64, 'bar': Float64, 'ham': String})
+
+ +
+ +
+ +
+ + +

+ drop(*columns, strict=True) + +

+ + +
+ +

Remove columns from the dataframe.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The dataframe with the specified columns removed.

+
+
+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ *columns + + str | Iterable[str] + +
+

Names of the columns that should be removed from the dataframe.

+
+ 		+ () +
+ strict + + bool + +
+

Validate that all column names exist in the schema and throw an +exception if a column name does not exist in the schema.

+
+ 		+ True +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_drop(df_native: IntoFrameT) -> IntoFrameT:
+...     return nw.from_native(df_native).drop("ham").to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_drop:

+ 
>>> agnostic_drop(df_pd)
+   foo  bar
+0    1  6.0
+1    2  7.0
+2    3  8.0
+>>> agnostic_drop(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ f64 │
+╞═════╪═════╡
+│ 1   ┆ 6.0 │
+│ 2   ┆ 7.0 │
+│ 3   ┆ 8.0 │
+└─────┴─────┘
+>>> agnostic_drop(df_pa)
+pyarrow.Table
+foo: int64
+bar: double
+----
+foo: [[1,2,3]]
+bar: [[6,7,8]]
+
+

Use positional arguments to drop multiple columns.

+ 
>>> def agnostic_drop_multi(df_native: IntoFrameT) -> IntoFrameT:
+...     return nw.from_native(df_native).drop("foo", "ham").to_native()
+
+ 
>>> agnostic_drop_multi(df_pd)
+   bar
+0  6.0
+1  7.0
+2  8.0
+>>> agnostic_drop_multi(df_pl)
+shape: (3, 1)
+┌─────┐
+│ bar │
+│ --- │
+│ f64 │
+╞═════╡
+│ 6.0 │
+│ 7.0 │
+│ 8.0 │
+└─────┘
+>>> agnostic_drop_multi(df_pa)
+pyarrow.Table
+bar: double
+----
+bar: [[6,7,8]]
+
+ +
+ +
+ +
+ + +

+ drop_nulls(subset=None) + +

+ + +
+ +

Drop rows that contain null values.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ subset + + str | list[str] | None + +
+

Column name(s) for which null values are considered. If set to None +(default), use all columns.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The original object with the rows removed that contained the null values.

+
+
+ + +
+ Notes +

pandas handles null values differently from Polars and PyArrow. +See null_handling +for reference.

+
+ +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {"a": [1.0, 2.0, None], "ba": [1.0, None, 2.0]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_drop_nulls(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.drop_nulls().to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_drop_nulls:

+ 
>>> agnostic_drop_nulls(df_pd)
+     a   ba
+0  1.0  1.0
+>>> agnostic_drop_nulls(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ ba  │
+│ --- ┆ --- │
+│ f64 ┆ f64 │
+╞═════╪═════╡
+│ 1.0 ┆ 1.0 │
+└─────┴─────┘
+>>> agnostic_drop_nulls(df_pa)
+pyarrow.Table
+a: double
+ba: double
+----
+a: [[1]]
+ba: [[1]]
+
+ +
+ +
+ +
+ + +

+ estimated_size(unit='b') + +

+ + +
+ +

Return an estimation of the total (heap) allocated size of the DataFrame.

+

Estimated size is given in the specified unit (bytes by default).

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ unit + + SizeUnit + +
+

'b', 'kb', 'mb', 'gb', 'tb', 'bytes', 'kilobytes', 'megabytes', +'gigabytes', or 'terabytes'.

+
+ 		+ 'b' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ int | float + +
+

Integer or Float.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrameT
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6.0, 7.0, 8.0],
+...     "ham": ["a", "b", "c"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_estimated_size(df_native: IntoDataFrameT) -> int | float:
+...     df = nw.from_native(df_native)
+...     return df.estimated_size()
+
+

We can then pass either pandas, Polars or PyArrow to agnostic_estimated_size:

+ 
>>> agnostic_estimated_size(df_pd)
+np.int64(330)
+>>> agnostic_estimated_size(df_pl)
+51
+>>> agnostic_estimated_size(df_pa)
+63
+
+ +
+ +
+ +
+ + +

+ explode(columns, *more_columns) + +

+ + +
+ +

Explode the dataframe to long format by exploding the given columns.

+ + +
+ Notes +

It is possible to explode multiple columns only if these columns must have +matching element counts.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ columns + + str | Sequence[str] + +
+

Column names. The underlying columns being exploded must be of the List data type.

+
+ 		+ required +
+ *more_columns + + str + +
+

Additional names of columns to explode, specified as positional arguments.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

New DataFrame

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {
+...     "a": ["x", "y", "z", "w"],
+...     "lst1": [[1, 2], None, [None], []],
+...     "lst2": [[3, None], None, [42], []],
+... }
+
+

We define a library agnostic function:

+ 
>>> def agnostic_explode(df_native: IntoFrameT) -> IntoFrameT:
+...     return (
+...         nw.from_native(df_native)
+...         .with_columns(nw.col("lst1", "lst2").cast(nw.List(nw.Int32())))
+...         .explode("lst1", "lst2")
+...         .to_native()
+...     )
+
+

We can then pass any supported library such as pandas, Polars (eager), +or PyArrow to agnostic_explode:

+ 
>>> agnostic_explode(pd.DataFrame(data))
+   a  lst1  lst2
+0  x     1     3
+0  x     2  <NA>
+1  y  <NA>  <NA>
+2  z  <NA>    42
+3  w  <NA>  <NA>
+>>> agnostic_explode(pl.DataFrame(data))
+shape: (5, 3)
+┌─────┬──────┬──────┐
+│ a   ┆ lst1 ┆ lst2 │
+│ --- ┆ ---  ┆ ---  │
+│ str ┆ i32  ┆ i32  │
+╞═════╪══════╪══════╡
+│ x   ┆ 1    ┆ 3    │
+│ x   ┆ 2    ┆ null │
+│ y   ┆ null ┆ null │
+│ z   ┆ null ┆ 42   │
+│ w   ┆ null ┆ null │
+└─────┴──────┴──────┘
+
+ +
+ +
+ +
+ + +

+ filter(*predicates, **constraints) + +

+ + +
+ +

Filter the rows in the DataFrame based on one or more predicate expressions.

+

The original order of the remaining rows is preserved.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ *predicates + + IntoExpr | Iterable[IntoExpr] | list[bool] + +
+

Expression(s) that evaluates to a boolean Series. Can +also be a (single!) boolean list.

+
+ 		+ () +
+ **constraints + + Any + +
+

Column filters; use name = value to filter columns by the supplied value. +Each constraint will behave the same as nw.col(name).eq(value), and will be implicitly +joined with the other filter conditions using &.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The filtered dataframe.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6, 7, 8],
+...     "ham": ["a", "b", "c"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function in which we filter on +one condition.

+ 
>>> def agnostic_filter(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.filter(nw.col("foo") > 1).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_filter:

+ 
>>> agnostic_filter(df_pd)
+   foo  bar ham
+1    2    7   b
+2    3    8   c
+>>> agnostic_filter(df_pl)
+shape: (2, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 2   ┆ 7   ┆ b   │
+│ 3   ┆ 8   ┆ c   │
+└─────┴─────┴─────┘
+>>> agnostic_filter(df_pa)
+pyarrow.Table
+foo: int64
+bar: int64
+ham: string
+----
+foo: [[2,3]]
+bar: [[7,8]]
+ham: [["b","c"]]
+
+

Filter on multiple conditions, combined with and/or operators:

+ 
>>> def agnostic_filter(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.filter((nw.col("foo") < 3) & (nw.col("ham") == "a")).to_native()
+>>> agnostic_filter(df_pd)
+   foo  bar ham
+0    1    6   a
+>>> agnostic_filter(df_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+└─────┴─────┴─────┘
+>>> agnostic_filter(df_pa)
+pyarrow.Table
+foo: int64
+bar: int64
+ham: string
+----
+foo: [[1]]
+bar: [[6]]
+ham: [["a"]]
+
+ 
>>> def agnostic_filter(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     dframe = df.filter(
+...         (nw.col("foo") == 1) | (nw.col("ham") == "c")
+...     ).to_native()
+...     return dframe
+>>> agnostic_filter(df_pd)
+   foo  bar ham
+0    1    6   a
+2    3    8   c
+>>> agnostic_filter(df_pl)
+shape: (2, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+│ 3   ┆ 8   ┆ c   │
+└─────┴─────┴─────┘
+>>> agnostic_filter(df_pa)
+pyarrow.Table
+foo: int64
+bar: int64
+ham: string
+----
+foo: [[1,3]]
+bar: [[6,8]]
+ham: [["a","c"]]
+
+

Provide multiple filters using *args syntax:

+ 
>>> def agnostic_filter(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     dframe = df.filter(
+...         nw.col("foo") <= 2,
+...         ~nw.col("ham").is_in(["b", "c"]),
+...     ).to_native()
+...     return dframe
+>>> agnostic_filter(df_pd)
+   foo  bar ham
+0    1    6   a
+>>> agnostic_filter(df_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+└─────┴─────┴─────┘
+>>> agnostic_filter(df_pa)
+pyarrow.Table
+foo: int64
+bar: int64
+ham: string
+----
+foo: [[1]]
+bar: [[6]]
+ham: [["a"]]
+
+

Provide multiple filters using **kwargs syntax:

+ 
>>> def agnostic_filter(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.filter(foo=2, ham="b").to_native()
+>>> agnostic_filter(df_pd)
+   foo  bar ham
+1    2    7   b
+>>> agnostic_filter(df_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 2   ┆ 7   ┆ b   │
+└─────┴─────┴─────┘
+>>> agnostic_filter(df_pa)
+pyarrow.Table
+foo: int64
+bar: int64
+ham: string
+----
+foo: [[2]]
+bar: [[7]]
+ham: [["b"]]
+
+ +
+ +
+ +
+ + +

+ gather_every(n, offset=0) + +

+ + +
+ +

Take every nth row in the DataFrame and return as a new DataFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Gather every n-th row.

+
+ 		+ required +
+ offset + + int + +
+

Starting index.

+
+ 		+ 0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The dataframe containing only the selected rows.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function in which gather every 2 rows, +starting from a offset of 1:

+ 
>>> def agnostic_gather_every(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.gather_every(n=2, offset=1).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_gather_every:

+ 
>>> agnostic_gather_every(df_pd)
+   a  b
+1  2  6
+3  4  8
+
+ 
>>> agnostic_gather_every(df_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 2   ┆ 6   │
+│ 4   ┆ 8   │
+└─────┴─────┘
+>>> agnostic_gather_every(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[2,4]]
+b: [[6,8]]
+
+ +
+ +
+ +
+ + +

+ get_column(name) + +

+ + +
+ +

Get a single column by name.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ name + + str + +
+

The column name as a string.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Series[Any] + +
+

A Narwhals Series, backed by a native series.

+
+
+ + +
+ Notes +

Although name is typed as str, pandas does allow non-string column +names, and they will work when passed to this function if the +narwhals.DataFrame is backed by a pandas dataframe with non-string +columns. This function can only be used to extract a column by name, so +there is no risk of ambiguity.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> from narwhals.typing import IntoSeries
+>>> data = {"a": [1, 2], "b": [3, 4]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_get_column(df_native: IntoDataFrame) -> IntoSeries:
+...     df = nw.from_native(df_native)
+...     name = df.columns[0]
+...     return df.get_column(name).to_native()
+
+

We can then pass either pandas, Polars or PyArrow to agnostic_get_column:

+ 
>>> agnostic_get_column(df_pd)
+0    1
+1    2
+Name: a, dtype: int64
+>>> agnostic_get_column(df_pl)
+shape: (2,)
+Series: 'a' [i64]
+[
+    1
+    2
+]
+>>> agnostic_get_column(df_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    2
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ group_by(*keys, drop_null_keys=False) + +

+ + +
+ +

Start a group by operation.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ *keys + + str | Iterable[str] + +
+

Column(s) to group by. Accepts multiple columns names as a list.

+
+ 		+ () +
+ drop_null_keys + + bool + +
+

if True, then groups where any key is null won't be included +in the result.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
Name TypeDescription
GroupBy + GroupBy[Self] + +
+

Object which can be used to perform aggregations.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrameT
+>>> data = {
+...     "a": ["a", "b", "a", "b", "c"],
+...     "b": [1, 2, 1, 3, 3],
+...     "c": [5, 4, 3, 2, 1],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function in which we group by one column +and call agg to compute the grouped sum of another column.

+ 
>>> def agnostic_group_by_agg(df_native: IntoDataFrameT) -> IntoDataFrameT:
+...     df = nw.from_native(df_native, eager_only=True)
+...     return df.group_by("a").agg(nw.col("b").sum()).sort("a").to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_group_by_agg:

+ 
>>> agnostic_group_by_agg(df_pd)
+   a  b
+0  a  2
+1  b  5
+2  c  3
+>>> agnostic_group_by_agg(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ str ┆ i64 │
+╞═════╪═════╡
+│ a   ┆ 2   │
+│ b   ┆ 5   │
+│ c   ┆ 3   │
+└─────┴─────┘
+>>> agnostic_group_by_agg(df_pa)
+pyarrow.Table
+a: string
+b: int64
+----
+a: [["a","b","c"]]
+b: [[2,5,3]]
+
+

Group by multiple columns by passing a list of column names.

+ 
>>> def agnostic_group_by_agg(df_native: IntoDataFrameT) -> IntoDataFrameT:
+...     df = nw.from_native(df_native, eager_only=True)
+...     return df.group_by(["a", "b"]).agg(nw.max("c")).sort("a", "b").to_native()
+
+ 
>>> agnostic_group_by_agg(df_pd)
+   a  b  c
+0  a  1  5
+1  b  2  4
+2  b  3  2
+3  c  3  1
+>>> agnostic_group_by_agg(df_pl)
+shape: (4, 3)
+┌─────┬─────┬─────┐
+│ a   ┆ b   ┆ c   │
+│ --- ┆ --- ┆ --- │
+│ str ┆ i64 ┆ i64 │
+╞═════╪═════╪═════╡
+│ a   ┆ 1   ┆ 5   │
+│ b   ┆ 2   ┆ 4   │
+│ b   ┆ 3   ┆ 2   │
+│ c   ┆ 3   ┆ 1   │
+└─────┴─────┴─────┘
+>>> agnostic_group_by_agg(df_pa)
+pyarrow.Table
+a: string
+b: int64
+c: int64
+----
+a: [["a","b","b","c"]]
+b: [[1,2,3,3]]
+c: [[5,4,2,1]]
+
+ +
+ +
+ +
+ + +

+ head(n=5) + +

+ + +
+ +

Get the first n rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of rows to return. If a negative value is passed, return all rows +except the last abs(n).

+
+ 		+ 5 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A subset of the dataframe of shape (n, n_columns).

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {
+...     "foo": [1, 2, 3, 4, 5],
+...     "bar": [6, 7, 8, 9, 10],
+...     "ham": ["a", "b", "c", "d", "e"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function that gets the first 3 rows.

+ 
>>> def agnostic_head(df_native: IntoFrameT) -> IntoFrameT:
+...     return nw.from_native(df_native).head(3).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_head:

+ 
>>> agnostic_head(df_pd)
+   foo  bar ham
+0    1    6   a
+1    2    7   b
+2    3    8   c
+>>> agnostic_head(df_pl)
+shape: (3, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+│ 2   ┆ 7   ┆ b   │
+│ 3   ┆ 8   ┆ c   │
+└─────┴─────┴─────┘
+>>> agnostic_head(df_pa)
+pyarrow.Table
+foo: int64
+bar: int64
+ham: string
+----
+foo: [[1,2,3]]
+bar: [[6,7,8]]
+ham: [["a","b","c"]]
+
+ +
+ +
+ +
+ + +

+ is_duplicated() + +

+ + +
+ +

Get a mask of all duplicated rows in this DataFrame.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Series[Any] + +
+

A new Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> from narwhals.typing import IntoSeries
+>>> data = {
+...     "a": [1, 2, 3, 1],
+...     "b": ["x", "y", "z", "x"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_duplicated(df_native: IntoDataFrame) -> IntoSeries:
+...     df = nw.from_native(df_native, eager_only=True)
+...     return df.is_duplicated().to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_is_duplicated:

+ 
>>> agnostic_is_duplicated(df_pd)
+0     True
+1    False
+2    False
+3     True
+dtype: bool
+
+ 
>>> agnostic_is_duplicated(df_pl)
+shape: (4,)
+Series: '' [bool]
+[
+    true
+    false
+    false
+    true
+]
+>>> agnostic_is_duplicated(df_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    true,
+    false,
+    false,
+    true
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ is_empty() + +

+ + +
+ +

Check if the dataframe is empty.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ bool + +
+

A boolean indicating whether the dataframe is empty (True) or not (False).

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+
+

Let's define a dataframe-agnostic function that filters rows in which "foo" +values are greater than 10, and then checks if the result is empty or not:

+ 
>>> def agnostic_is_empty(df_native: IntoDataFrame) -> bool:
+...     df = nw.from_native(df_native, eager_only=True)
+...     return df.filter(nw.col("foo") > 10).is_empty()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_is_empty:

+ 
>>> data = {"foo": [1, 2, 3], "bar": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+>>> agnostic_is_empty(df_pd), agnostic_is_empty(df_pl), agnostic_is_empty(df_pa)
+(True, True, True)
+
+ 
>>> data = {"foo": [100, 2, 3], "bar": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+>>> agnostic_is_empty(df_pd), agnostic_is_empty(df_pl), agnostic_is_empty(df_pa)
+(False, False, False)
+
+ +
+ +
+ +
+ + +

+ is_unique() + +

+ + +
+ +

Get a mask of all unique rows in this DataFrame.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Series[Any] + +
+

A new Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> from narwhals.typing import IntoSeries
+>>> data = {
+...     "a": [1, 2, 3, 1],
+...     "b": ["x", "y", "z", "x"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_unique(df_native: IntoDataFrame) -> IntoSeries:
+...     df = nw.from_native(df_native, eager_only=True)
+...     return df.is_unique().to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_is_unique:

+ 
>>> agnostic_is_unique(df_pd)
+0    False
+1     True
+2     True
+3    False
+dtype: bool
+
+ 
>>> agnostic_is_unique(df_pl)
+shape: (4,)
+Series: '' [bool]
+[
+    false
+     true
+     true
+    false
+]
+>>> agnostic_is_unique(df_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    false,
+    true,
+    true,
+    false
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ item(row=None, column=None) + +

+ + +
+ +

Return the DataFrame as a scalar, or return the element at the given row/column.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ row + + int | None + +
+

The n-th row.

+
+ 		+ None +
+ column + + int | str | None + +
+

The column selected via an integer or a string (column name).

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

A scalar or the specified element in the dataframe.

+
+
+ + +
+ Notes +

If row/col not provided, this is equivalent to df[0,0], with a check that the shape is (1,1). +With row/col, this is equivalent to df[row,col].

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function that returns item at given row/column

+ 
>>> def agnostic_item(
+...     df_native: IntoDataFrame, row: int | None, column: int | str | None
+... ):
+...     df = nw.from_native(df_native, eager_only=True)
+...     return df.item(row, column)
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_item:

+ 
>>> agnostic_item(df_pd, 1, 1), agnostic_item(df_pd, 2, "b")
+(np.int64(5), np.int64(6))
+>>> agnostic_item(df_pl, 1, 1), agnostic_item(df_pl, 2, "b")
+(5, 6)
+>>> agnostic_item(df_pa, 1, 1), agnostic_item(df_pa, 2, "b")
+(5, 6)
+
+ +
+ +
+ +
+ + +

+ iter_rows(*, named=False, buffer_size=512) + +

+ + +
+ +

Returns an iterator over the DataFrame of rows of python-native values.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ named + + bool + +
+

By default, each row is returned as a tuple of values given +in the same order as the frame columns. Setting named=True will +return rows of dictionaries instead.

+
+ 		+ False +
+ buffer_size + + int + +
+

Determines the number of rows that are buffered +internally while iterating over the data. +See https://docs.pola.rs/api/python/stable/reference/dataframe/api/polars.DataFrame.iter_rows.html

+
+ 		+ 512 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Iterator[tuple[Any, ...]] | Iterator[dict[str, Any]] + +
+

An iterator over the DataFrame of rows.

+
+
+ + +
+ Notes +

cuDF doesn't support this method.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_iter_rows(df_native: IntoDataFrame, *, named: bool):
+...     return nw.from_native(df_native, eager_only=True).iter_rows(named=named)
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_iter_rows:

+ 
>>> [row for row in agnostic_iter_rows(df_pd, named=False)]
+[(1, 6.0, 'a'), (2, 7.0, 'b'), (3, 8.0, 'c')]
+>>> [row for row in agnostic_iter_rows(df_pd, named=True)]
+[{'foo': 1, 'bar': 6.0, 'ham': 'a'}, {'foo': 2, 'bar': 7.0, 'ham': 'b'}, {'foo': 3, 'bar': 8.0, 'ham': 'c'}]
+>>> [row for row in agnostic_iter_rows(df_pl, named=False)]
+[(1, 6.0, 'a'), (2, 7.0, 'b'), (3, 8.0, 'c')]
+>>> [row for row in agnostic_iter_rows(df_pl, named=True)]
+[{'foo': 1, 'bar': 6.0, 'ham': 'a'}, {'foo': 2, 'bar': 7.0, 'ham': 'b'}, {'foo': 3, 'bar': 8.0, 'ham': 'c'}]
+>>> [row for row in agnostic_iter_rows(df_pa, named=False)]
+[(1, 6.0, 'a'), (2, 7.0, 'b'), (3, 8.0, 'c')]
+>>> [row for row in agnostic_iter_rows(df_pa, named=True)]
+[{'foo': 1, 'bar': 6.0, 'ham': 'a'}, {'foo': 2, 'bar': 7.0, 'ham': 'b'}, {'foo': 3, 'bar': 8.0, 'ham': 'c'}]
+
+ +
+ +
+ +
+ + +

+ join(other, on=None, how='inner', *, left_on=None, right_on=None, suffix='_right') + +

+ + +
+ +

Join in SQL-like fashion.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ other + + Self + +
+

DataFrame to join with.

+
+ 		+ required +
+ on + + str | list[str] | None + +
+

Name(s) of the join columns in both DataFrames. If set, left_on and +right_on should be None.

+
+ 		+ None +
+ how + + Literal['inner', 'left', 'cross', 'semi', 'anti'] + +
+

Join strategy.

+
    +
  • inner: Returns rows that have matching values in both tables.
    • +
  • left: Returns all rows from the left table, and the matched rows from the right table.
    • +
  • cross: Returns the Cartesian product of rows from both tables.
    • +
  • semi: Filter rows that have a match in the right table.
    • +
  • anti: Filter rows that do not have a match in the right table.
    • +
+
+ 		+ 'inner' +
+ left_on + + str | list[str] | None + +
+

Join column of the left DataFrame.

+
+ 		+ None +
+ right_on + + str | list[str] | None + +
+

Join column of the right DataFrame.

+
+ 		+ None +
+ suffix + + str + +
+

Suffix to append to columns with a duplicate name.

+
+ 		+ '_right' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new joined DataFrame

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6.0, 7.0, 8.0],
+...     "ham": ["a", "b", "c"],
+... }
+>>> data_other = {
+...     "apple": ["x", "y", "z"],
+...     "ham": ["a", "b", "d"],
+... }
+
+ 
>>> df_pd = pd.DataFrame(data)
+>>> other_pd = pd.DataFrame(data_other)
+
+ 
>>> df_pl = pl.DataFrame(data)
+>>> other_pl = pl.DataFrame(data_other)
+
+ 
>>> df_pa = pa.table(data)
+>>> other_pa = pa.table(data_other)
+
+

Let's define a dataframe-agnostic function in which we join over "ham" column:

+ 
>>> def agnostic_join_on_ham(
+...     df_native: IntoFrameT, other_native: IntoFrameT
+... ) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     other = nw.from_native(other_native)
+...     return df.join(other, left_on="ham", right_on="ham").to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_join_on_ham:

+ 
>>> agnostic_join_on_ham(df_pd, other_pd)
+   foo  bar ham apple
+0    1  6.0   a     x
+1    2  7.0   b     y
+
+ 
>>> agnostic_join_on_ham(df_pl, other_pl)
+shape: (2, 4)
+┌─────┬─────┬─────┬───────┐
+│ foo ┆ bar ┆ ham ┆ apple │
+│ --- ┆ --- ┆ --- ┆ ---   │
+│ i64 ┆ f64 ┆ str ┆ str   │
+╞═════╪═════╪═════╪═══════╡
+│ 1   ┆ 6.0 ┆ a   ┆ x     │
+│ 2   ┆ 7.0 ┆ b   ┆ y     │
+└─────┴─────┴─────┴───────┘
+>>> agnostic_join_on_ham(df_pa, other_pa)
+pyarrow.Table
+foo: int64
+bar: double
+ham: string
+apple: string
+----
+foo: [[1,2]]
+bar: [[6,7]]
+ham: [["a","b"]]
+apple: [["x","y"]]
+
+ +
+ +
+ +
+ + +

+ join_asof(other, *, left_on=None, right_on=None, on=None, by_left=None, by_right=None, by=None, strategy='backward') + +

+ + +
+ +

Perform an asof join.

+

This is similar to a left-join except that we match on nearest key rather than equal keys.

+

Both DataFrames must be sorted by the asof_join key.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ other + + Self + +
+

DataFrame to join with.

+
+ 		+ required +
+ left_on + + str | None + +
+

Name(s) of the left join column(s).

+
+ 		+ None +
+ right_on + + str | None + +
+

Name(s) of the right join column(s).

+
+ 		+ None +
+ on + + str | None + +
+

Join column of both DataFrames. If set, left_on and right_on should be None.

+
+ 		+ None +
+ by_left + + str | list[str] | None + +
+

join on these columns before doing asof join.

+
+ 		+ None +
+ by_right + + str | list[str] | None + +
+

join on these columns before doing asof join.

+
+ 		+ None +
+ by + + str | list[str] | None + +
+

join on these columns before doing asof join.

+
+ 		+ None +
+ strategy + + Literal['backward', 'forward', 'nearest'] + +
+

Join strategy. The default is "backward".

+
    +
  • backward: selects the last row in the right DataFrame whose "on" key is less than or equal to the left's key.
    • +
  • forward: selects the first row in the right DataFrame whose "on" key is greater than or equal to the left's key.
    • +
  • nearest: search selects the last row in the right DataFrame whose value is nearest to the left's key.
    • +
+
+ 		+ 'backward' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new joined DataFrame

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> from typing import Literal
+>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data_gdp = {
+...     "datetime": [
+...         datetime(2016, 1, 1),
+...         datetime(2017, 1, 1),
+...         datetime(2018, 1, 1),
+...         datetime(2019, 1, 1),
+...         datetime(2020, 1, 1),
+...     ],
+...     "gdp": [4164, 4411, 4566, 4696, 4827],
+... }
+>>> data_population = {
+...     "datetime": [
+...         datetime(2016, 3, 1),
+...         datetime(2018, 8, 1),
+...         datetime(2019, 1, 1),
+...     ],
+...     "population": [82.19, 82.66, 83.12],
+... }
+>>> gdp_pd = pd.DataFrame(data_gdp)
+>>> population_pd = pd.DataFrame(data_population)
+
+ 
>>> gdp_pl = pl.DataFrame(data_gdp).sort("datetime")
+>>> population_pl = pl.DataFrame(data_population).sort("datetime")
+
+

Let's define a dataframe-agnostic function in which we join over "datetime" column:

+ 
>>> def agnostic_join_asof_datetime(
+...     df_native: IntoFrameT,
+...     other_native: IntoFrameT,
+...     strategy: Literal["backward", "forward", "nearest"],
+... ) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     other = nw.from_native(other_native)
+...     return df.join_asof(other, on="datetime", strategy=strategy).to_native()
+
+

We can then pass any supported library such as Pandas or Polars +to agnostic_join_asof_datetime:

+ 
>>> agnostic_join_asof_datetime(population_pd, gdp_pd, strategy="backward")
+    datetime  population   gdp
+0 2016-03-01       82.19  4164
+1 2018-08-01       82.66  4566
+2 2019-01-01       83.12  4696
+
+ 
>>> agnostic_join_asof_datetime(population_pl, gdp_pl, strategy="backward")
+shape: (3, 3)
+┌─────────────────────┬────────────┬──────┐
+│ datetime            ┆ population ┆ gdp  │
+│ ---                 ┆ ---        ┆ ---  │
+│ datetime[μs]        ┆ f64        ┆ i64  │
+╞═════════════════════╪════════════╪══════╡
+│ 2016-03-01 00:00:00 ┆ 82.19      ┆ 4164 │
+│ 2018-08-01 00:00:00 ┆ 82.66      ┆ 4566 │
+│ 2019-01-01 00:00:00 ┆ 83.12      ┆ 4696 │
+└─────────────────────┴────────────┴──────┘
+
+

Here is a real-world times-series example that uses by argument.

+ 
>>> from datetime import datetime
+>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data_quotes = {
+...     "datetime": [
+...         datetime(2016, 5, 25, 13, 30, 0, 23),
+...         datetime(2016, 5, 25, 13, 30, 0, 23),
+...         datetime(2016, 5, 25, 13, 30, 0, 30),
+...         datetime(2016, 5, 25, 13, 30, 0, 41),
+...         datetime(2016, 5, 25, 13, 30, 0, 48),
+...         datetime(2016, 5, 25, 13, 30, 0, 49),
+...         datetime(2016, 5, 25, 13, 30, 0, 72),
+...         datetime(2016, 5, 25, 13, 30, 0, 75),
+...     ],
+...     "ticker": [
+...         "GOOG",
+...         "MSFT",
+...         "MSFT",
+...         "MSFT",
+...         "GOOG",
+...         "AAPL",
+...         "GOOG",
+...         "MSFT",
+...     ],
+...     "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
+...     "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03],
+... }
+>>> data_trades = {
+...     "datetime": [
+...         datetime(2016, 5, 25, 13, 30, 0, 23),
+...         datetime(2016, 5, 25, 13, 30, 0, 38),
+...         datetime(2016, 5, 25, 13, 30, 0, 48),
+...         datetime(2016, 5, 25, 13, 30, 0, 48),
+...         datetime(2016, 5, 25, 13, 30, 0, 48),
+...     ],
+...     "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
+...     "price": [51.95, 51.95, 720.77, 720.92, 98.0],
+...     "quantity": [75, 155, 100, 100, 100],
+... }
+>>> quotes_pd = pd.DataFrame(data_quotes)
+>>> trades_pd = pd.DataFrame(data_trades)
+>>> quotes_pl = pl.DataFrame(data_quotes).sort("datetime")
+>>> trades_pl = pl.DataFrame(data_trades).sort("datetime")
+
+

Let's define a dataframe-agnostic function in which we join over "datetime" and by "ticker" columns:

+ 
>>> def agnostic_join_asof_datetime_by_ticker(
+...     df_native: IntoFrameT, other_native: IntoFrameT
+... ) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     other = nw.from_native(other_native)
+...     return df.join_asof(other, on="datetime", by="ticker").to_native()
+
+

We can now pass either pandas or Polars to the function:

+ 
>>> agnostic_join_asof_datetime_by_ticker(trades_pd, quotes_pd)
+                    datetime ticker   price  quantity     bid     ask
+0 2016-05-25 13:30:00.000023   MSFT   51.95        75   51.95   51.96
+1 2016-05-25 13:30:00.000038   MSFT   51.95       155   51.97   51.98
+2 2016-05-25 13:30:00.000048   GOOG  720.77       100  720.50  720.93
+3 2016-05-25 13:30:00.000048   GOOG  720.92       100  720.50  720.93
+4 2016-05-25 13:30:00.000048   AAPL   98.00       100     NaN     NaN
+
+ 
>>> agnostic_join_asof_datetime_by_ticker(trades_pl, quotes_pl)
+shape: (5, 6)
+┌────────────────────────────┬────────┬────────┬──────────┬───────┬────────┐
+│ datetime                   ┆ ticker ┆ price  ┆ quantity ┆ bid   ┆ ask    │
+│ ---                        ┆ ---    ┆ ---    ┆ ---      ┆ ---   ┆ ---    │
+│ datetime[μs]               ┆ str    ┆ f64    ┆ i64      ┆ f64   ┆ f64    │
+╞════════════════════════════╪════════╪════════╪══════════╪═══════╪════════╡
+│ 2016-05-25 13:30:00.000023 ┆ MSFT   ┆ 51.95  ┆ 75       ┆ 51.95 ┆ 51.96  │
+│ 2016-05-25 13:30:00.000038 ┆ MSFT   ┆ 51.95  ┆ 155      ┆ 51.97 ┆ 51.98  │
+│ 2016-05-25 13:30:00.000048 ┆ GOOG   ┆ 720.77 ┆ 100      ┆ 720.5 ┆ 720.93 │
+│ 2016-05-25 13:30:00.000048 ┆ GOOG   ┆ 720.92 ┆ 100      ┆ 720.5 ┆ 720.93 │
+│ 2016-05-25 13:30:00.000048 ┆ AAPL   ┆ 98.0   ┆ 100      ┆ null  ┆ null   │
+└────────────────────────────┴────────┴────────┴──────────┴───────┴────────┘
+
+ +
+ +
+ +
+ + +

+ lazy() + +

+ + +
+ +

Lazify the DataFrame (if possible).

+

If a library does not support lazy execution, then this is a no-op.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ LazyFrame[Any] + +
+

A new LazyFrame.

+
+
+ + +

Examples:

+

Construct pandas, Polars and PyArrow DataFrames:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrame
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_lazy(df_native: IntoFrame) -> IntoFrame:
+...     df = nw.from_native(df_native)
+...     return df.lazy().to_native()
+
+

Note that then, pandas and pyarrow dataframe stay eager, but Polars DataFrame +becomes a Polars LazyFrame:

+ 
>>> agnostic_lazy(df_pd)
+   foo  bar ham
+0    1  6.0   a
+1    2  7.0   b
+2    3  8.0   c
+>>> agnostic_lazy(df_pl)
+<LazyFrame ...>
+>>> agnostic_lazy(df_pa)
+pyarrow.Table
+foo: int64
+bar: double
+ham: string
+----
+foo: [[1,2,3]]
+bar: [[6,7,8]]
+ham: [["a","b","c"]]
+
+ +
+ +
+ +
+ + +

+ null_count() + +

+ + +
+ +

Create a new DataFrame that shows the null counts per column.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A dataframe of shape (1, n_columns).

+
+
+ + +
+ Notes +

pandas handles null values differently from Polars and PyArrow. +See null_handling +for reference.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {
+...     "foo": [1, None, 3],
+...     "bar": [6, 7, None],
+...     "ham": ["a", "b", "c"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function that returns the null count of +each columns:

+ 
>>> def agnostic_null_count(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.null_count().to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_null_count:

+ 
>>> agnostic_null_count(df_pd)
+   foo  bar  ham
+0    1    1    0
+
+ 
>>> agnostic_null_count(df_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ u32 ┆ u32 ┆ u32 │
+╞═════╪═════╪═════╡
+│ 1   ┆ 1   ┆ 0   │
+└─────┴─────┴─────┘
+
+ 
>>> agnostic_null_count(df_pa)
+pyarrow.Table
+foo: int64
+bar: int64
+ham: int64
+----
+foo: [[1]]
+bar: [[1]]
+ham: [[0]]
+
+ +
+ +
+ +
+ + +

+ pipe(function, *args, **kwargs) + +

+ + +
+ +

Pipe function call.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ function + + Callable[[Any], Self] + +
+

Function to apply.

+
+ 		+ required +
+ args + + Any + +
+

Positional arguments to pass to function.

+
+ 		+ () +
+ kwargs + + Any + +
+

Keyword arguments to pass to function.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The original object with the function applied.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {"a": [1, 2, 3], "ba": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_pipe(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.pipe(
+...         lambda _df: _df.select(
+...             [x for x in _df.columns if len(x) == 1]
+...         ).to_native()
+...     )
+
+

We can then pass either pandas, Polars or PyArrow to agnostic_pipe:

+ 
>>> agnostic_pipe(df_pd)
+   a
+0  1
+1  2
+2  3
+>>> agnostic_pipe(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 2   │
+│ 3   │
+└─────┘
+>>> agnostic_pipe(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[1,2,3]]
+
+ +
+ +
+ +
+ + +

+ pivot(on, *, index=None, values=None, aggregate_function=None, maintain_order=None, sort_columns=False, separator='_') + +

+ + +
+ +

Create a spreadsheet-style pivot table as a DataFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ on + + str | list[str] + +
+

Name of the column(s) whose values will be used as the header of the +output DataFrame.

+
+ 		+ required +
+ index + + str | list[str] | None + +
+

One or multiple keys to group by. If None, all remaining columns not +specified on on and values will be used. At least one of index and +values must be specified.

+
+ 		+ None +
+ values + + str | list[str] | None + +
+

One or multiple keys to group by. If None, all remaining columns not +specified on on and index will be used. At least one of index and +values must be specified.

+
+ 		+ None +
+ aggregate_function + + Literal['min', 'max', 'first', 'last', 'sum', 'mean', 'median', 'len'] | None + +
+

Choose from:

+
    +
  • None: no aggregation takes place, will raise error if multiple values + are in group.
    • +
  • A predefined aggregate function string, one of + {'min', 'max', 'first', 'last', 'sum', 'mean', 'median', 'len'}
    • +
+
+ 		+ None +
+ maintain_order + + bool | None + +
+

Has no effect and is kept around only for backwards-compatibility.

+
+ 		+ None +
+ sort_columns + + bool + +
+

Sort the transposed columns by name. Default is by order of +discovery.

+
+ 		+ False +
+ separator + + str + +
+

Used as separator/delimiter in generated column names in case of +multiple values columns.

+
+ 		+ '_' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new dataframe.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrameT
+>>> data = {
+...     "ix": [1, 1, 2, 2, 1, 2],
+...     "col": ["a", "a", "a", "a", "b", "b"],
+...     "foo": [0, 1, 2, 2, 7, 1],
+...     "bar": [0, 2, 0, 0, 9, 4],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_pivot(df_native: IntoDataFrameT) -> IntoDataFrameT:
+...     df = nw.from_native(df_native, eager_only=True)
+...     return df.pivot("col", index="ix", aggregate_function="sum").to_native()
+
+

We can then pass any supported library such as Pandas or Polars +to agnostic_pivot:

+ 
>>> agnostic_pivot(df_pd)
+   ix  foo_a  foo_b  bar_a  bar_b
+0   1      1      7      2      9
+1   2      4      1      0      4
+>>> agnostic_pivot(df_pl)
+shape: (2, 5)
+┌─────┬───────┬───────┬───────┬───────┐
+│ ix  ┆ foo_a ┆ foo_b ┆ bar_a ┆ bar_b │
+│ --- ┆ ---   ┆ ---   ┆ ---   ┆ ---   │
+│ i64 ┆ i64   ┆ i64   ┆ i64   ┆ i64   │
+╞═════╪═══════╪═══════╪═══════╪═══════╡
+│ 1   ┆ 1     ┆ 7     ┆ 2     ┆ 9     │
+│ 2   ┆ 4     ┆ 1     ┆ 0     ┆ 4     │
+└─────┴───────┴───────┴───────┴───────┘
+
+ +
+ +
+ +
+ + +

+ rename(mapping) + +

+ + +
+ +

Rename column names.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ mapping + + dict[str, str] + +
+

Key value pairs that map from old name to new name.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The dataframe with the specified columns renamed.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {"foo": [1, 2, 3], "bar": [6, 7, 8], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_rename(df_native: IntoFrameT) -> IntoFrameT:
+...     return nw.from_native(df_native).rename({"foo": "apple"}).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_rename:

+ 
>>> agnostic_rename(df_pd)
+   apple  bar ham
+0      1    6   a
+1      2    7   b
+2      3    8   c
+>>> agnostic_rename(df_pl)
+shape: (3, 3)
+┌───────┬─────┬─────┐
+│ apple ┆ bar ┆ ham │
+│ ---   ┆ --- ┆ --- │
+│ i64   ┆ i64 ┆ str │
+╞═══════╪═════╪═════╡
+│ 1     ┆ 6   ┆ a   │
+│ 2     ┆ 7   ┆ b   │
+│ 3     ┆ 8   ┆ c   │
+└───────┴─────┴─────┘
+>>> agnostic_rename(df_pa)
+pyarrow.Table
+apple: int64
+bar: int64
+ham: string
+----
+apple: [[1,2,3]]
+bar: [[6,7,8]]
+ham: [["a","b","c"]]
+
+ +
+ +
+ +
+ + +

+ row(index) + +

+ + +
+ +

Get values at given row.

+
+

Warning

+

You should NEVER use this method to iterate over a DataFrame; +if you require row-iteration you should strongly prefer use of iter_rows() +instead.

+
+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ index + + int + +
+

Row number.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ tuple[Any, ...] + +
+

A tuple of the values in the selected row.

+
+
+ + +
+ Notes +

cuDF doesn't support this method.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> from narwhals.typing import IntoDataFrame
+>>> from typing import Any
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a library-agnostic function to get the second row.

+ 
>>> def agnostic_row(df_native: IntoDataFrame) -> tuple[Any, ...]:
+...     return nw.from_native(df_native).row(1)
+
+

We can then pass either pandas, Polars or PyArrow to agnostic_row:

+ 
>>> agnostic_row(df_pd)
+(2, 5)
+>>> agnostic_row(df_pl)
+(2, 5)
+>>> agnostic_row(df_pa)
+(<pyarrow.Int64Scalar: 2>, <pyarrow.Int64Scalar: 5>)
+
+ +
+ +
+ +
+ + +

+ rows(*, named=False) + +

+ + +
+ +

Returns all data in the DataFrame as a list of rows of python-native values.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ named + + bool + +
+

By default, each row is returned as a tuple of values given +in the same order as the frame columns. Setting named=True will +return rows of dictionaries instead.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ list[tuple[Any, ...]] | list[dict[str, Any]] + +
+

The data as a list of rows.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_rows(df_native: IntoDataFrame, *, named: bool):
+...     return nw.from_native(df_native, eager_only=True).rows(named=named)
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_rows:

+ 
>>> agnostic_rows(df_pd, named=False)
+[(1, 6.0, 'a'), (2, 7.0, 'b'), (3, 8.0, 'c')]
+>>> agnostic_rows(df_pd, named=True)
+[{'foo': 1, 'bar': 6.0, 'ham': 'a'}, {'foo': 2, 'bar': 7.0, 'ham': 'b'}, {'foo': 3, 'bar': 8.0, 'ham': 'c'}]
+>>> agnostic_rows(df_pl, named=False)
+[(1, 6.0, 'a'), (2, 7.0, 'b'), (3, 8.0, 'c')]
+>>> agnostic_rows(df_pl, named=True)
+[{'foo': 1, 'bar': 6.0, 'ham': 'a'}, {'foo': 2, 'bar': 7.0, 'ham': 'b'}, {'foo': 3, 'bar': 8.0, 'ham': 'c'}]
+>>> agnostic_rows(df_pa, named=False)
+[(1, 6.0, 'a'), (2, 7.0, 'b'), (3, 8.0, 'c')]
+>>> agnostic_rows(df_pa, named=True)
+[{'foo': 1, 'bar': 6.0, 'ham': 'a'}, {'foo': 2, 'bar': 7.0, 'ham': 'b'}, {'foo': 3, 'bar': 8.0, 'ham': 'c'}]
+
+ +
+ +
+ +
+ + +

+ sample(n=None, *, fraction=None, with_replacement=False, seed=None) + +

+ + +
+ +

Sample from this DataFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int | None + +
+

Number of items to return. Cannot be used with fraction.

+
+ 		+ None +
+ fraction + + float | None + +
+

Fraction of items to return. Cannot be used with n.

+
+ 		+ None +
+ with_replacement + + bool + +
+

Allow values to be sampled more than once.

+
+ 		+ False +
+ seed + + int | None + +
+

Seed for the random number generator. If set to None (default), a random +seed is generated for each sample operation.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new dataframe.

+
+
+ + +
+ Notes +

The results may not be consistent across libraries.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrameT
+>>> data = {"a": [1, 2, 3, 4], "b": ["x", "y", "x", "y"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_sample(df_native: IntoDataFrameT) -> IntoDataFrameT:
+...     df = nw.from_native(df_native, eager_only=True)
+...     return df.sample(n=2, seed=123).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_sample:

+ 
>>> agnostic_sample(df_pd)
+   a  b
+3  4  y
+0  1  x
+>>> agnostic_sample(df_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ str │
+╞═════╪═════╡
+│ 2   ┆ y   │
+│ 3   ┆ x   │
+└─────┴─────┘
+>>> agnostic_sample(df_pa)
+pyarrow.Table
+a: int64
+b: string
+----
+a: [[1,3]]
+b: [["x","x"]]
+
+

As you can see, by using the same seed, the result will be consistent within +the same backend, but not necessarely across different backends.

+ +
+ +
+ +
+ + +

+ select(*exprs, **named_exprs) + +

+ + +
+ +

Select columns from this DataFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ *exprs + + IntoExpr | Iterable[IntoExpr] + +
+

Column(s) to select, specified as positional arguments. + Accepts expression input. Strings are parsed as column names, + other non-expression inputs are parsed as literals.

+
+ 		+ () +
+ **named_exprs + + IntoExpr + +
+

Additional columns to select, specified as keyword arguments. + The columns will be renamed to the keyword used.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The dataframe containing only the selected columns.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6, 7, 8],
+...     "ham": ["a", "b", "c"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function in which we pass the name of a +column to select that column.

+ 
>>> def agnostic_single_select(df_native: IntoFrameT) -> IntoFrameT:
+...     return nw.from_native(df_native).select("foo").to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_single_select:

+ 
>>> agnostic_single_select(df_pd)
+   foo
+0    1
+1    2
+2    3
+>>> agnostic_single_select(df_pl)
+shape: (3, 1)
+┌─────┐
+│ foo │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 2   │
+│ 3   │
+└─────┘
+>>> agnostic_single_select(df_pa)
+pyarrow.Table
+foo: int64
+----
+foo: [[1,2,3]]
+
+

Multiple columns can be selected by passing a list of column names.

+ 
>>> def agnostic_multi_select(df_native: IntoFrameT) -> IntoFrameT:
+...     return nw.from_native(df_native).select(["foo", "bar"]).to_native()
+
+ 
>>> agnostic_multi_select(df_pd)
+   foo  bar
+0    1    6
+1    2    7
+2    3    8
+>>> agnostic_multi_select(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 6   │
+│ 2   ┆ 7   │
+│ 3   ┆ 8   │
+└─────┴─────┘
+>>> agnostic_multi_select(df_pa)
+pyarrow.Table
+foo: int64
+bar: int64
+----
+foo: [[1,2,3]]
+bar: [[6,7,8]]
+
+

Multiple columns can also be selected using positional arguments instead of a +list. Expressions are also accepted.

+ 
>>> def agnostic_select(df_native: IntoFrameT) -> IntoFrameT:
+...     return (
+...         nw.from_native(df_native)
+...         .select(nw.col("foo"), nw.col("bar") + 1)
+...         .to_native()
+...     )
+
+ 
>>> agnostic_select(df_pd)
+   foo  bar
+0    1    7
+1    2    8
+2    3    9
+>>> agnostic_select(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 7   │
+│ 2   ┆ 8   │
+│ 3   ┆ 9   │
+└─────┴─────┘
+>>> agnostic_select(df_pa)
+pyarrow.Table
+foo: int64
+bar: int64
+----
+foo: [[1,2,3]]
+bar: [[7,8,9]]
+
+

Use keyword arguments to easily name your expression inputs.

+ 
>>> def agnostic_select_w_kwargs(df_native: IntoFrameT) -> IntoFrameT:
+...     return (
+...         nw.from_native(df_native)
+...         .select(threshold=nw.col("foo") * 2)
+...         .to_native()
+...     )
+
+ 
>>> agnostic_select_w_kwargs(df_pd)
+   threshold
+0          2
+1          4
+2          6
+>>> agnostic_select_w_kwargs(df_pl)
+shape: (3, 1)
+┌───────────┐
+│ threshold │
+│ ---       │
+│ i64       │
+╞═══════════╡
+│ 2         │
+│ 4         │
+│ 6         │
+└───────────┘
+>>> agnostic_select_w_kwargs(df_pa)
+pyarrow.Table
+threshold: int64
+----
+threshold: [[2,4,6]]
+
+ +
+ +
+ +
+ + +

+ sort(by, *more_by, descending=False, nulls_last=False) + +

+ + +
+ +

Sort the dataframe by the given columns.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ by + + str | Iterable[str] + +
+

Column(s) names to sort by.

+
+ 		+ required +
+ *more_by + + str + +
+

Additional columns to sort by, specified as positional arguments.

+
+ 		+ () +
+ descending + + bool | Sequence[bool] + +
+

Sort in descending order. When sorting by multiple columns, can be +specified per column by passing a sequence of booleans.

+
+ 		+ False +
+ nulls_last + + bool + +
+

Place null values last.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The sorted dataframe.

+
+
+ + +
+ Warning +

Unlike Polars, it is not possible to specify a sequence of booleans for +nulls_last in order to control per-column behaviour. Instead a single +boolean is applied for all by columns.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {
+...     "a": [1, 2, None],
+...     "b": [6.0, 5.0, 4.0],
+...     "c": ["a", "c", "b"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function in which we sort by multiple +columns in different orders

+ 
>>> def agnostic_sort(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.sort("c", "a", descending=[False, True]).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_sort:

+ 
>>> agnostic_sort(df_pd)
+     a    b  c
+0  1.0  6.0  a
+2  NaN  4.0  b
+1  2.0  5.0  c
+>>> agnostic_sort(df_pl)
+shape: (3, 3)
+┌──────┬─────┬─────┐
+│ a    ┆ b   ┆ c   │
+│ ---  ┆ --- ┆ --- │
+│ i64  ┆ f64 ┆ str │
+╞══════╪═════╪═════╡
+│ 1    ┆ 6.0 ┆ a   │
+│ null ┆ 4.0 ┆ b   │
+│ 2    ┆ 5.0 ┆ c   │
+└──────┴─────┴─────┘
+>>> agnostic_sort(df_pa)
+pyarrow.Table
+a: int64
+b: double
+c: string
+----
+a: [[1,null,2]]
+b: [[6,4,5]]
+c: [["a","b","c"]]
+
+ +
+ +
+ +
+ + +

+ tail(n=5) + +

+ + +
+ +

Get the last n rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of rows to return. If a negative value is passed, return all rows +except the first abs(n).

+
+ 		+ 5 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A subset of the dataframe of shape (n, n_columns).

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {
+...     "foo": [1, 2, 3, 4, 5],
+...     "bar": [6, 7, 8, 9, 10],
+...     "ham": ["a", "b", "c", "d", "e"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function that gets the last 3 rows.

+ 
>>> def agnostic_tail(df_native: IntoFrameT) -> IntoFrameT:
+...     return nw.from_native(df_native).tail(3).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_tail:

+ 
>>> agnostic_tail(df_pd)
+   foo  bar ham
+2    3    8   c
+3    4    9   d
+4    5   10   e
+>>> agnostic_tail(df_pl)
+shape: (3, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 3   ┆ 8   ┆ c   │
+│ 4   ┆ 9   ┆ d   │
+│ 5   ┆ 10  ┆ e   │
+└─────┴─────┴─────┘
+>>> agnostic_tail(df_pa)
+pyarrow.Table
+foo: int64
+bar: int64
+ham: string
+----
+foo: [[3,4,5]]
+bar: [[8,9,10]]
+ham: [["c","d","e"]]
+
+ +
+ +
+ +
+ + +

+ to_arrow() + +

+ + +
+ +

Convert to arrow table.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Table + +
+

A new PyArrow table.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> data = {"foo": [1, 2, 3], "bar": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function that converts to arrow table:

+ 
>>> def agnostic_to_arrow(df_native: IntoDataFrame) -> pa.Table:
+...     df = nw.from_native(df_native, eager_only=True)
+...     return df.to_arrow()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_to_arrow:

+ 
>>> agnostic_to_arrow(df_pd)
+pyarrow.Table
+foo: int64
+bar: string
+----
+foo: [[1,2,3]]
+bar: [["a","b","c"]]
+
+ 
>>> agnostic_to_arrow(df_pl)
+pyarrow.Table
+foo: int64
+bar: large_string
+----
+foo: [[1,2,3]]
+bar: [["a","b","c"]]
+
+ 
>>> agnostic_to_arrow(df_pa)
+pyarrow.Table
+foo: int64
+bar: string
+----
+foo: [[1,2,3]]
+bar: [["a","b","c"]]
+
+ +
+ +
+ +
+ + +

+ to_dict(*, as_series=True) + +

+ + +
+ +

Convert DataFrame to a dictionary mapping column name to values.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ as_series + + bool + +
+

If set to true True, then the values are Narwhals Series, + otherwise the values are Any.

+
+ 		+ True +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ dict[str, Series[Any]] | dict[str, list[Any]] + +
+

A mapping from column name to values / Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> data = {
+...     "A": [1, 2, 3, 4, 5],
+...     "fruits": ["banana", "banana", "apple", "apple", "banana"],
+...     "B": [5, 4, 3, 2, 1],
+...     "animals": ["beetle", "fly", "beetle", "beetle", "beetle"],
+...     "optional": [28, 300, None, 2, -30],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_to_dict(
+...     df_native: IntoDataFrame,
+... ) -> dict[str, list[int | str | float | None]]:
+...     df = nw.from_native(df_native)
+...     return df.to_dict(as_series=False)
+
+

We can then pass either pandas, Polars or PyArrow to agnostic_to_dict:

+ 
>>> agnostic_to_dict(df_pd)
+{'A': [1, 2, 3, 4, 5], 'fruits': ['banana', 'banana', 'apple', 'apple', 'banana'], 'B': [5, 4, 3, 2, 1], 'animals': ['beetle', 'fly', 'beetle', 'beetle', 'beetle'], 'optional': [28.0, 300.0, nan, 2.0, -30.0]}
+>>> agnostic_to_dict(df_pl)
+{'A': [1, 2, 3, 4, 5], 'fruits': ['banana', 'banana', 'apple', 'apple', 'banana'], 'B': [5, 4, 3, 2, 1], 'animals': ['beetle', 'fly', 'beetle', 'beetle', 'beetle'], 'optional': [28, 300, None, 2, -30]}
+>>> agnostic_to_dict(df_pa)
+{'A': [1, 2, 3, 4, 5], 'fruits': ['banana', 'banana', 'apple', 'apple', 'banana'], 'B': [5, 4, 3, 2, 1], 'animals': ['beetle', 'fly', 'beetle', 'beetle', 'beetle'], 'optional': [28, 300, None, 2, -30]}
+
+ +
+ +
+ +
+ + +

+ to_native() + +

+ + +
+ +

Convert Narwhals DataFrame to native one.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DataFrameT + +
+

Object of class that user started with.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Calling to_native on a Narwhals DataFrame returns the native object:

+ 
>>> nw.from_native(df_pd).to_native()
+   foo  bar ham
+0    1  6.0   a
+1    2  7.0   b
+2    3  8.0   c
+>>> nw.from_native(df_pl).to_native()
+shape: (3, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ f64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6.0 ┆ a   │
+│ 2   ┆ 7.0 ┆ b   │
+│ 3   ┆ 8.0 ┆ c   │
+└─────┴─────┴─────┘
+>>> nw.from_native(df_pa).to_native()
+pyarrow.Table
+foo: int64
+bar: double
+ham: string
+----
+foo: [[1,2,3]]
+bar: [[6,7,8]]
+ham: [["a","b","c"]]
+
+ +
+ +
+ +
+ + +

+ to_numpy() + +

+ + +
+ +

Convert this DataFrame to a NumPy ndarray.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ndarray + +
+

A NumPy ndarray array.

+
+
+ + +

Examples:

+

Construct pandas and polars DataFrames:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> import numpy as np
+>>> from narwhals.typing import IntoDataFrame
+>>> data = {"foo": [1, 2, 3], "bar": [6.5, 7.0, 8.5], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_to_numpy(df_native: IntoDataFrame) -> np.ndarray:
+...     df = nw.from_native(df_native)
+...     return df.to_numpy()
+
+

We can then pass either pandas, Polars or PyArrow to agnostic_to_numpy:

+ 
>>> agnostic_to_numpy(df_pd)
+array([[1, 6.5, 'a'],
+       [2, 7.0, 'b'],
+       [3, 8.5, 'c']], dtype=object)
+>>> agnostic_to_numpy(df_pl)
+array([[1, 6.5, 'a'],
+       [2, 7.0, 'b'],
+       [3, 8.5, 'c']], dtype=object)
+>>> agnostic_to_numpy(df_pa)
+array([[1, 6.5, 'a'],
+       [2, 7.0, 'b'],
+       [3, 8.5, 'c']], dtype=object)
+
+ +
+ +
+ +
+ + +

+ to_pandas() + +

+ + +
+ +

Convert this DataFrame to a pandas DataFrame.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DataFrame + +
+

A pandas DataFrame.

+
+
+ + +

Examples:

+

Construct pandas, Polars (eager) and PyArrow DataFrames:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_to_pandas(df_native: IntoDataFrame) -> pd.DataFrame:
+...     df = nw.from_native(df_native)
+...     return df.to_pandas()
+
+

We can then pass any supported library such as pandas, Polars (eager), or +PyArrow to agnostic_to_pandas:

+ 
>>> agnostic_to_pandas(df_pd)
+   foo  bar ham
+0    1  6.0   a
+1    2  7.0   b
+2    3  8.0   c
+>>> agnostic_to_pandas(df_pl)
+   foo  bar ham
+0    1  6.0   a
+1    2  7.0   b
+2    3  8.0   c
+>>> agnostic_to_pandas(df_pa)
+   foo  bar ham
+0    1  6.0   a
+1    2  7.0   b
+2    3  8.0   c
+
+ +
+ +
+ +
+ + +

+ unique(subset=None, *, keep='any', maintain_order=False) + +

+ + +
+ +

Drop duplicate rows from this dataframe.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ subset + + str | list[str] | None + +
+

Column name(s) to consider when identifying duplicate rows.

+
+ 		+ None +
+ keep + + Literal['any', 'first', 'last', 'none'] + +
+

{'first', 'last', 'any', 'none'} +Which of the duplicate rows to keep.

+
    +
  • 'any': Does not give any guarantee of which row is kept. + This allows more optimizations.
    • +
  • 'none': Don't keep duplicate rows.
    • +
  • 'first': Keep first unique row.
    • +
  • 'last': Keep last unique row.
    • +
+
+ 		+ 'any' +
+ maintain_order + + bool + +
+

Keep the same order as the original DataFrame. This may be more +expensive to compute.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The dataframe with the duplicate rows removed.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {
+...     "foo": [1, 2, 3, 1],
+...     "bar": ["a", "a", "a", "a"],
+...     "ham": ["b", "b", "b", "b"],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_unique(df_native: IntoFrameT) -> IntoFrameT:
+...     return nw.from_native(df_native).unique(["bar", "ham"]).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_unique:

+ 
>>> agnostic_unique(df_pd)
+   foo bar ham
+0    1   a   b
+>>> agnostic_unique(df_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ str ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ a   ┆ b   │
+└─────┴─────┴─────┘
+>>> agnostic_unique(df_pa)
+pyarrow.Table
+foo: int64
+bar: string
+ham: string
+----
+foo: [[1]]
+bar: [["a"]]
+ham: [["b"]]
+
+ +
+ +
+ +
+ + +

+ unpivot(on=None, *, index=None, variable_name=None, value_name=None) + +

+ + +
+ +

Unpivot a DataFrame from wide to long format.

+

Optionally leaves identifiers set.

+

This function is useful to massage a DataFrame into a format where one or more +columns are identifier variables (index) while all other columns, considered +measured variables (on), are "unpivoted" to the row axis leaving just +two non-identifier columns, 'variable' and 'value'.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ on + + str | list[str] | None + +
+

Column(s) to use as values variables; if on is empty all columns that +are not in index will be used.

+
+ 		+ None +
+ index + + str | list[str] | None + +
+

Column(s) to use as identifier variables.

+
+ 		+ None +
+ variable_name + + str | None + +
+

Name to give to the variable column. Defaults to "variable".

+
+ 		+ None +
+ value_name + + str | None + +
+

Name to give to the value column. Defaults to "value".

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The unpivoted dataframe.

+
+
+ + +
+ Notes +

If you're coming from pandas, this is similar to pandas.DataFrame.melt, +but with index replacing id_vars and on replacing value_vars. +In other frameworks, you might know this operation as pivot_longer.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {
+...     "a": ["x", "y", "z"],
+...     "b": [1, 3, 5],
+...     "c": [2, 4, 6],
+... }
+
+

We define a library agnostic function:

+ 
>>> def agnostic_unpivot(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.unpivot(on=["b", "c"], index="a").to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_unpivot:

+ 
>>> agnostic_unpivot(pl.DataFrame(data))
+shape: (6, 3)
+┌─────┬──────────┬───────┐
+│ a   ┆ variable ┆ value │
+│ --- ┆ ---      ┆ ---   │
+│ str ┆ str      ┆ i64   │
+╞═════╪══════════╪═══════╡
+│ x   ┆ b        ┆ 1     │
+│ y   ┆ b        ┆ 3     │
+│ z   ┆ b        ┆ 5     │
+│ x   ┆ c        ┆ 2     │
+│ y   ┆ c        ┆ 4     │
+│ z   ┆ c        ┆ 6     │
+└─────┴──────────┴───────┘
+
+ 
>>> agnostic_unpivot(pd.DataFrame(data))
+   a variable  value
+0  x        b      1
+1  y        b      3
+2  z        b      5
+3  x        c      2
+4  y        c      4
+5  z        c      6
+
+ 
>>> agnostic_unpivot(pa.table(data))
+pyarrow.Table
+a: string
+variable: string
+value: int64
+----
+a: [["x","y","z"],["x","y","z"]]
+variable: [["b","b","b"],["c","c","c"]]
+value: [[1,3,5],[2,4,6]]
+
+ +
+ +
+ +
+ + +

+ with_columns(*exprs, **named_exprs) + +

+ + +
+ +

Add columns to this DataFrame.

+

Added columns will replace existing columns with the same name.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ *exprs + + IntoExpr | Iterable[IntoExpr] + +
+

Column(s) to add, specified as positional arguments. + Accepts expression input. Strings are parsed as column names, other + non-expression inputs are parsed as literals.

+
+ 		+ () +
+ **named_exprs + + IntoExpr + +
+

Additional columns to add, specified as keyword arguments. + The columns will be renamed to the keyword used.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
Name TypeDescription
DataFrame + Self + +
+

A new DataFrame with the columns added.

+
+
+ + +
+ Note +

Creating a new DataFrame using this method does not create a new copy of +existing data.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {
+...     "a": [1, 2, 3, 4],
+...     "b": [0.5, 4, 10, 13],
+...     "c": [True, True, False, True],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function in which we pass an expression +to add it as a new column:

+ 
>>> def agnostic_with_columns(df_native: IntoFrameT) -> IntoFrameT:
+...     return (
+...         nw.from_native(df_native)
+...         .with_columns((nw.col("a") * 2).alias("a*2"))
+...         .to_native()
+...     )
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_with_columns:

+ 
>>> agnostic_with_columns(df_pd)
+   a     b      c  a*2
+0  1   0.5   True    2
+1  2   4.0   True    4
+2  3  10.0  False    6
+3  4  13.0   True    8
+>>> agnostic_with_columns(df_pl)
+shape: (4, 4)
+┌─────┬──────┬───────┬─────┐
+│ a   ┆ b    ┆ c     ┆ a*2 │
+│ --- ┆ ---  ┆ ---   ┆ --- │
+│ i64 ┆ f64  ┆ bool  ┆ i64 │
+╞═════╪══════╪═══════╪═════╡
+│ 1   ┆ 0.5  ┆ true  ┆ 2   │
+│ 2   ┆ 4.0  ┆ true  ┆ 4   │
+│ 3   ┆ 10.0 ┆ false ┆ 6   │
+│ 4   ┆ 13.0 ┆ true  ┆ 8   │
+└─────┴──────┴───────┴─────┘
+>>> agnostic_with_columns(df_pa)
+pyarrow.Table
+a: int64
+b: double
+c: bool
+a*2: int64
+----
+a: [[1,2,3,4]]
+b: [[0.5,4,10,13]]
+c: [[true,true,false,true]]
+a*2: [[2,4,6,8]]
+
+ +
+ +
+ +
+ + +

+ with_row_index(name='index') + +

+ + +
+ +

Insert column which enumerates rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ name + + str + +
+

The name of the column as a string. The default is "index".

+
+ 		+ 'index' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The original object with the column added.

+
+
+ + +

Examples:

+

Construct pandas as polars DataFrames:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_with_row_index(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_row_index().to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_with_row_index:

+ 
>>> agnostic_with_row_index(df_pd)
+   index  a  b
+0      0  1  4
+1      1  2  5
+2      2  3  6
+>>> agnostic_with_row_index(df_pl)
+shape: (3, 3)
+┌───────┬─────┬─────┐
+│ index ┆ a   ┆ b   │
+│ ---   ┆ --- ┆ --- │
+│ u32   ┆ i64 ┆ i64 │
+╞═══════╪═════╪═════╡
+│ 0     ┆ 1   ┆ 4   │
+│ 1     ┆ 2   ┆ 5   │
+│ 2     ┆ 3   ┆ 6   │
+└───────┴─────┴─────┘
+>>> agnostic_with_row_index(df_pa)
+pyarrow.Table
+index: int64
+a: int64
+b: int64
+----
+index: [[0,1,2]]
+a: [[1,2,3]]
+b: [[4,5,6]]
+
+ +
+ +
+ +
+ + +

+ write_csv(file=None) + +

+ + +
+ +

Write dataframe to comma-separated values (CSV) file.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ file + + str | Path | BytesIO | None + +
+

String, path object or file-like object to which the dataframe will be +written. If None, the resulting csv format is returned as a string.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ str | None + +
+

String or None.

+
+
+ + +

Examples:

+

Construct pandas, Polars (eager) and PyArrow DataFrames:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_write_csv(df_native: IntoDataFrame) -> str:
+...     df = nw.from_native(df_native)
+...     return df.write_csv()
+
+

We can pass any supported library such as pandas, Polars or PyArrow to agnostic_write_csv:

+ 
>>> agnostic_write_csv(df_pd)
+'foo,bar,ham\n1,6.0,a\n2,7.0,b\n3,8.0,c\n'
+>>> agnostic_write_csv(df_pl)
+'foo,bar,ham\n1,6.0,a\n2,7.0,b\n3,8.0,c\n'
+>>> agnostic_write_csv(df_pa)
+'"foo","bar","ham"\n1,6,"a"\n2,7,"b"\n3,8,"c"\n'
+
+

If we had passed a file name to write_csv, it would have been +written to that file.

+ +
+ +
+ +
+ + +

+ write_parquet(file) + +

+ + +
+ +

Write dataframe to parquet file.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ file + + str | Path | BytesIO + +
+

String, path object or file-like object to which the dataframe will be +written.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ None + +
+

None.

+
+
+ + +

Examples:

+

Construct pandas, Polars and PyArrow DataFrames:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_write_parquet(df_native: IntoDataFrame):
+...     df = nw.from_native(df_native)
+...     df.write_parquet("foo.parquet")
+
+

We can then pass either pandas, Polars or PyArrow to agnostic_write_parquet:

+ 
>>> agnostic_write_parquet(df_pd)
+>>> agnostic_write_parquet(df_pl)
+>>> agnostic_write_parquet(df_pa)
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/dependencies/index.html b/api-reference/dependencies/index.html new file mode 100644 index 000000000..37452447e --- /dev/null +++ b/api-reference/dependencies/index.html @@ -0,0 +1,3065 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.dependencies - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.dependencies

+ + +
+ + + + +
+ + + + + + + + +
+ + + + + + + + + +
+ + +

+ get_cudf() + +

+ + +
+ +

Get cudf module (if already imported - else return None).

+ +
+ +
+ +
+ + +

+ get_ibis() + +

+ + +
+ +

Get ibis module (if already imported - else return None).

+ +
+ +
+ +
+ + +

+ get_modin() + +

+ + +
+ +

Get modin.pandas module (if already imported - else return None).

+ +
+ +
+ +
+ + +

+ get_pandas() + +

+ + +
+ +

Get pandas module (if already imported - else return None).

+ +
+ +
+ +
+ + +

+ get_polars() + +

+ + +
+ +

Get Polars module (if already imported - else return None).

+ +
+ +
+ +
+ + +

+ get_pyarrow() + +

+ + +
+ +

Get pyarrow module (if already imported - else return None).

+ +
+ +
+ +
+ + +

+ is_cudf_dataframe(df) + +

+ + +
+ +

Check whether df is a cudf DataFrame without importing cudf.

+ +
+ +
+ +
+ + +

+ is_cudf_index(index) + +

+ + +
+ +

Check whether index is a cudf Index without importing cudf.

+ +
+ +
+ +
+ + +

+ is_cudf_series(ser) + +

+ + +
+ +

Check whether ser is a cudf Series without importing cudf.

+ +
+ +
+ +
+ + +

+ is_dask_dataframe(df) + +

+ + +
+ +

Check whether df is a Dask DataFrame without importing Dask.

+ +
+ +
+ +
+ + +

+ is_ibis_table(df) + +

+ + +
+ +

Check whether df is a Ibis Table without importing Ibis.

+ +
+ +
+ +
+ + +

+ is_into_dataframe(native_dataframe) + +

+ + +
+ +

Check whether native_dataframe can be converted to a Narwhals DataFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ native_dataframe + + Any + +
+

The object to check.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ bool + +
+

True if native_dataframe can be converted to a Narwhals DataFrame, False otherwise.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import numpy as np
+>>> from narwhals.dependencies import is_into_dataframe
+
+ 
>>> df_pd = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+>>> df_pl = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
+>>> np_arr = np.array([[1, 4], [2, 5], [3, 6]])
+
+ 
>>> is_into_dataframe(df_pd)
+True
+>>> is_into_dataframe(df_pl)
+True
+>>> is_into_dataframe(np_arr)
+False
+
+ +
+ +
+ +
+ + +

+ is_into_series(native_series) + +

+ + +
+ +

Check whether native_series can be converted to a Narwhals Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ native_series + + IntoSeries + +
+

The object to check.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ bool + +
+

True if native_series can be converted to a Narwhals Series, False otherwise.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import numpy as np
+>>> import narwhals as nw
+
+ 
>>> s_pd = pd.Series([1, 2, 3])
+>>> s_pl = pl.Series([1, 2, 3])
+>>> np_arr = np.array([1, 2, 3])
+
+ 
>>> nw.dependencies.is_into_series(s_pd)
+True
+>>> nw.dependencies.is_into_series(s_pl)
+True
+>>> nw.dependencies.is_into_series(np_arr)
+False
+
+ +
+ +
+ +
+ + +

+ is_modin_dataframe(df) + +

+ + +
+ +

Check whether df is a modin DataFrame without importing modin.

+ +
+ +
+ +
+ + +

+ is_modin_index(index) + +

+ + +
+ +

Check whether index is a modin Index without importing modin.

+ +
+ +
+ +
+ + +

+ is_modin_series(ser) + +

+ + +
+ +

Check whether ser is a modin Series without importing modin.

+ +
+ +
+ +
+ + +

+ is_numpy_array(arr) + +

+ + +
+ +

Check whether arr is a NumPy Array without importing NumPy.

+ +
+ +
+ +
+ + +

+ is_pandas_dataframe(df) + +

+ + +
+ +

Check whether df is a pandas DataFrame without importing pandas.

+ +
+ +
+ +
+ + +

+ is_pandas_index(index) + +

+ + +
+ +

Check whether index is a pandas Index without importing pandas.

+ +
+ +
+ +
+ + +

+ is_pandas_like_dataframe(df) + +

+ + +
+ +

Check whether df is a pandas-like DataFrame without doing any imports.

+

By "pandas-like", we mean: pandas, Modin, cuDF.

+ +
+ +
+ +
+ + +

+ is_pandas_like_index(index) + +

+ + +
+ +

Check whether index is a pandas-like Index without doing any imports.

+

By "pandas-like", we mean: pandas, Modin, cuDF.

+ +
+ +
+ +
+ + +

+ is_pandas_like_series(ser) + +

+ + +
+ +

Check whether ser is a pandas-like Series without doing any imports.

+

By "pandas-like", we mean: pandas, Modin, cuDF.

+ +
+ +
+ +
+ + +

+ is_pandas_series(ser) + +

+ + +
+ +

Check whether ser is a pandas Series without importing pandas.

+ +
+ +
+ +
+ + +

+ is_polars_dataframe(df) + +

+ + +
+ +

Check whether df is a Polars DataFrame without importing Polars.

+ +
+ +
+ +
+ + +

+ is_polars_lazyframe(df) + +

+ + +
+ +

Check whether df is a Polars LazyFrame without importing Polars.

+ +
+ +
+ +
+ + +

+ is_polars_series(ser) + +

+ + +
+ +

Check whether ser is a Polars Series without importing Polars.

+ +
+ +
+ +
+ + +

+ is_pyarrow_chunked_array(ser) + +

+ + +
+ +

Check whether ser is a PyArrow ChunkedArray without importing PyArrow.

+ +
+ +
+ +
+ + +

+ is_pyarrow_table(df) + +

+ + +
+ +

Check whether df is a PyArrow Table without importing PyArrow.

+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/dtypes/index.html b/api-reference/dtypes/index.html new file mode 100644 index 000000000..d850538b2 --- /dev/null +++ b/api-reference/dtypes/index.html @@ -0,0 +1,4230 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.dtypes - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.dtypes

+ + +
+ + + + +
+ + + + + + + + +
+ + + + + + + + +
+ + + +

+ Array + + +

+ + +
+ + +

Fixed length list type.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ inner + + DType | type[DType] + +
+

The datatype of the values within each array.

+
+ 		+ required +
+ width + + int | None + +
+

the length of each array.

+
+ 		+ None +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [[1, 2], [3, 4], [5, 6]]
+>>> ser_pd = pd.Series(data, dtype=pd.ArrowDtype(pa.list_(pa.int32(), 2)))
+>>> ser_pl = pl.Series(data, dtype=pl.Array(pl.Int32, 2))
+>>> ser_pa = pa.chunked_array([data], type=pa.list_(pa.int32(), 2))
+
+ 
>>> nw.from_native(ser_pd, series_only=True).dtype
+Array(Int32, 2)
+>>> nw.from_native(ser_pl, series_only=True).dtype
+Array(Int32, 2)
+>>> nw.from_native(ser_pa, series_only=True).dtype
+Array(Int32, 2)
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Decimal + + +

+ + +
+ + +

Decimal type.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import narwhals as nw
+>>> s = pl.Series(["1.5"], dtype=pl.Decimal)
+>>> nw.from_native(s, series_only=True).dtype
+Decimal
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ List + + +

+ + +
+ + +

Variable length list type.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [["narwhal", "orca"], ["beluga", "vaquita"]]
+>>> ser_pd = pd.Series(data, dtype=pd.ArrowDtype(pa.large_list(pa.large_string())))
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> nw.from_native(ser_pd, series_only=True).dtype
+List(String)
+>>> nw.from_native(ser_pl, series_only=True).dtype
+List(String)
+>>> nw.from_native(ser_pa, series_only=True).dtype
+List(String)
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Int128 + + +

+ + +
+ + +

128-bit signed integer type.

+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Int64 + + +

+ + +
+ + +

64-bit signed integer type.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [2, 1, 3, 7]
+>>> ser_pd = pd.Series(data)
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> nw.from_native(ser_pd, series_only=True).dtype
+Int64
+>>> nw.from_native(ser_pl, series_only=True).dtype
+Int64
+>>> nw.from_native(ser_pa, series_only=True).dtype
+Int64
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Int32 + + +

+ + +
+ + +

32-bit signed integer type.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [2, 1, 3, 7]
+>>> ser_pd = pd.Series(data)
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> def func(ser):
+...     ser_nw = nw.from_native(ser, series_only=True)
+...     return ser_nw.cast(nw.Int32).dtype
+
+ 
>>> func(ser_pd)
+Int32
+>>> func(ser_pl)
+Int32
+>>> func(ser_pa)
+Int32
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Int16 + + +

+ + +
+ + +

16-bit signed integer type.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [2, 1, 3, 7]
+>>> ser_pd = pd.Series(data)
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> def func(ser):
+...     ser_nw = nw.from_native(ser, series_only=True)
+...     return ser_nw.cast(nw.Int16).dtype
+
+ 
>>> func(ser_pd)
+Int16
+>>> func(ser_pl)
+Int16
+>>> func(ser_pa)
+Int16
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Int8 + + +

+ + +
+ + +

8-bit signed integer type.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [2, 1, 3, 7]
+>>> ser_pd = pd.Series(data)
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> def func(ser):
+...     ser_nw = nw.from_native(ser, series_only=True)
+...     return ser_nw.cast(nw.Int8).dtype
+
+ 
>>> func(ser_pd)
+Int8
+>>> func(ser_pl)
+Int8
+>>> func(ser_pa)
+Int8
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ UInt128 + + +

+ + +
+ + +

128-bit unsigned integer type.

+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ UInt64 + + +

+ + +
+ + +

64-bit unsigned integer type.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [2, 1, 3, 7]
+>>> ser_pd = pd.Series(data)
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> def func(ser):
+...     ser_nw = nw.from_native(ser, series_only=True)
+...     return ser_nw.cast(nw.UInt64).dtype
+
+ 
>>> func(ser_pd)
+UInt64
+>>> func(ser_pl)
+UInt64
+>>> func(ser_pa)
+UInt64
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ UInt32 + + +

+ + +
+ + +

32-bit unsigned integer type.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [2, 1, 3, 7]
+>>> ser_pd = pd.Series(data)
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> def func(ser):
+...     ser_nw = nw.from_native(ser, series_only=True)
+...     return ser_nw.cast(nw.UInt32).dtype
+
+ 
>>> func(ser_pd)
+UInt32
+>>> func(ser_pl)
+UInt32
+>>> func(ser_pa)
+UInt32
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ UInt16 + + +

+ + +
+ + +

16-bit unsigned integer type.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [2, 1, 3, 7]
+>>> ser_pd = pd.Series(data)
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> def func(ser):
+...     ser_nw = nw.from_native(ser, series_only=True)
+...     return ser_nw.cast(nw.UInt16).dtype
+
+ 
>>> func(ser_pd)
+UInt16
+>>> func(ser_pl)
+UInt16
+>>> func(ser_pa)
+UInt16
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ UInt8 + + +

+ + +
+ + +

8-bit unsigned integer type.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [2, 1, 3, 7]
+>>> ser_pd = pd.Series(data)
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> def func(ser):
+...     ser_nw = nw.from_native(ser, series_only=True)
+...     return ser_nw.cast(nw.UInt8).dtype
+
+ 
>>> func(ser_pd)
+UInt8
+>>> func(ser_pl)
+UInt8
+>>> func(ser_pa)
+UInt8
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Field + + +

+ + +
+ + +

Definition of a single field within a Struct DataType.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ name + + str + +
+

The name of the field within its parent Struct.

+
+ 		+ required +
+ dtype + + type[DType] | DType + +
+

The DataType of the field's values.

+
+ 		+ required +
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Float64 + + +

+ + +
+ + +

64-bit floating point type.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [0.001, 0.1, 0.01, 0.1]
+>>> ser_pd = pd.Series(data)
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> nw.from_native(ser_pd, series_only=True).dtype
+Float64
+>>> nw.from_native(ser_pl, series_only=True).dtype
+Float64
+>>> nw.from_native(ser_pa, series_only=True).dtype
+Float64
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Float32 + + +

+ + +
+ + +

32-bit floating point type.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [0.001, 0.1, 0.01, 0.1]
+>>> ser_pd = pd.Series(data)
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> def func(ser):
+...     ser_nw = nw.from_native(ser, series_only=True)
+...     return ser_nw.cast(nw.Float32).dtype
+
+ 
>>> func(ser_pd)
+Float32
+>>> func(ser_pl)
+Float32
+>>> func(ser_pa)
+Float32
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Boolean + + +

+ + +
+ + +

Boolean type.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [True, False, False, True]
+>>> ser_pd = pd.Series(data)
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> nw.from_native(ser_pd, series_only=True).dtype
+Boolean
+>>> nw.from_native(ser_pl, series_only=True).dtype
+Boolean
+>>> nw.from_native(ser_pa, series_only=True).dtype
+Boolean
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Categorical + + +

+ + +
+ + +

A categorical encoding of a set of strings.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = ["beluga", "narwhal", "orca", "vaquita"]
+>>> ser_pd = pd.Series(data)
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> nw.from_native(ser_pd, series_only=True).cast(nw.Categorical).dtype
+Categorical
+>>> nw.from_native(ser_pl, series_only=True).cast(nw.Categorical).dtype
+Categorical
+>>> nw.from_native(ser_pa, series_only=True).cast(nw.Categorical).dtype
+Categorical
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Enum + + +

+ + +
+ + +

A fixed categorical encoding of a unique set of strings.

+

Polars has an Enum data type, while pandas and PyArrow do not.

+ + +

Examples:

+ 
>>> import polars as pl
+>>> import narwhals as nw
+>>> data = ["beluga", "narwhal", "orca", "vaquita"]
+>>> ser_pl = pl.Series(data, dtype=pl.Enum(data))
+
+ 
>>> nw.from_native(ser_pl, series_only=True).dtype
+Enum
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ String + + +

+ + +
+ + +

UTF-8 encoded string type.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = ["beluga", "narwhal", "orca", "vaquita"]
+>>> ser_pd = pd.Series(data)
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> nw.from_native(ser_pd, series_only=True).dtype
+String
+>>> nw.from_native(ser_pl, series_only=True).dtype
+String
+>>> nw.from_native(ser_pa, series_only=True).dtype
+String
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Struct + + +

+ + +
+ + +

Struct composite type.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ fields + + Sequence[Field] | Mapping[str, DType | type[DType]] + +
+

The fields that make up the struct. Can be either a sequence of Field +objects or a mapping of column names to data types.

+
+ 		+ required +
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> data = [{"a": 1, "b": ["narwhal", "beluga"]}, {"a": 2, "b": ["orca"]}]
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> nw.from_native(ser_pl, series_only=True).dtype
+Struct({'a': Int64, 'b': List(String)})
+>>> nw.from_native(ser_pa, series_only=True).dtype
+Struct({'a': Int64, 'b': List(String)})
+
+ + + + + + + + + +
+ + + + + + + + + +
+ + +

+ to_schema() + +

+ + +
+ +

Return Struct dtype as a schema dict.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ OrderedDict[str, DType | type[DType]] + +
+

Mapping from column name to dtype.

+
+
+ +
+ +
+ + + +
+ +
+ +
+ +
+ + + +

+ Date + + +

+ + +
+ + +

Data type representing a calendar date.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from datetime import date, timedelta
+>>> data = [date(2024, 12, 1) + timedelta(days=d) for d in range(4)]
+>>> ser_pd = pd.Series(data, dtype="date32[pyarrow]")
+>>> ser_pl = pl.Series(data)
+>>> ser_pa = pa.chunked_array([data])
+
+ 
>>> nw.from_native(ser_pd, series_only=True).dtype
+Date
+>>> nw.from_native(ser_pl, series_only=True).dtype
+Date
+>>> nw.from_native(ser_pa, series_only=True).dtype
+Date
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Datetime + + +

+ + +
+ + +

Data type representing a calendar date and time of day.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ time_unit + + Literal['us', 'ns', 'ms', 's'] + +
+

Unit of time. Defaults to 'us' (microseconds).

+
+ 		+ 'us' +
+ time_zone + + str | timezone | None + +
+

Time zone string, as defined in zoneinfo (to see valid strings run +import zoneinfo; zoneinfo.available_timezones() for a full list).

+
+ 		+ None +
+ + +
+ Notes +

Adapted from Polars implementation

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import pyarrow.compute as pc
+>>> import narwhals as nw
+>>> from datetime import datetime, timedelta
+>>> data = [datetime(2024, 12, 9) + timedelta(days=n) for n in range(5)]
+>>> ser_pd = (
+...     pd.Series(data)
+...     .dt.tz_localize("Africa/Accra")
+...     .astype("datetime64[ms, Africa/Accra]")
+... )
+>>> ser_pl = (
+...     pl.Series(data).cast(pl.Datetime("ms")).dt.replace_time_zone("Africa/Accra")
+... )
+>>> ser_pa = pc.assume_timezone(
+...     pa.chunked_array([data], type=pa.timestamp("ms")), "Africa/Accra"
+... )
+
+ 
>>> nw.from_native(ser_pd, series_only=True).dtype
+Datetime(time_unit='ms', time_zone='Africa/Accra')
+>>> nw.from_native(ser_pl, series_only=True).dtype
+Datetime(time_unit='ms', time_zone='Africa/Accra')
+>>> nw.from_native(ser_pa, series_only=True).dtype
+Datetime(time_unit='ms', time_zone='Africa/Accra')
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Duration + + +

+ + +
+ + +

Data type representing a time duration.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ time_unit + + Literal['us', 'ns', 'ms', 's'] + +
+

Unit of time. Defaults to 'us' (microseconds).

+
+ 		+ 'us' +
+ + +
+ Notes +

Adapted from Polars implementation

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from datetime import timedelta
+>>> data = [timedelta(seconds=d) for d in range(1, 4)]
+>>> ser_pd = pd.Series(data).astype("timedelta64[ms]")
+>>> ser_pl = pl.Series(data).cast(pl.Duration("ms"))
+>>> ser_pa = pa.chunked_array([data], type=pa.duration("ms"))
+
+ 
>>> nw.from_native(ser_pd, series_only=True).dtype
+Duration(time_unit='ms')
+>>> nw.from_native(ser_pl, series_only=True).dtype
+Duration(time_unit='ms')
+>>> nw.from_native(ser_pa, series_only=True).dtype
+Duration(time_unit='ms')
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Object + + +

+ + +
+ + +

Data type for wrapping arbitrary Python objects.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> class Foo: ...
+>>> ser_pd = pd.Series([Foo(), Foo()])
+>>> ser_pl = pl.Series([Foo(), Foo()])
+
+ 
>>> nw.from_native(ser_pd, series_only=True).dtype
+Object
+>>> nw.from_native(ser_pl, series_only=True).dtype
+Object
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ Unknown + + +

+ + +
+ + +

Type representing DataType values that could not be determined statically.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import narwhals as nw
+>>> data = pd.period_range("2000-01", periods=4, freq="M")
+>>> ser_pd = pd.Series(data)
+
+ 
>>> nw.from_native(ser_pd, series_only=True).dtype
+Unknown
+
+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/exceptions/index.html b/api-reference/exceptions/index.html new file mode 100644 index 000000000..ee3a108da --- /dev/null +++ b/api-reference/exceptions/index.html @@ -0,0 +1,2144 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.exceptions - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.exceptions

+ + +
+ + + + +
+ + + + + + + + +
+ + + + + + + + +
+ + + +

+ ColumnNotFoundError + + +

+ + +
+ + +

Exception raised when column name isn't present.

+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ InvalidIntoExprError + + +

+ + +
+ + +

Exception raised when object can't be converted to expression.

+ + + + + + + + + +
+ + + + + + + + + + + +
+ +
+ +
+ +
+ + + +

+ InvalidOperationError + + +

+ + +
+ + +

Exception raised during invalid operations.

+ + + + + + + +
+ +
+ +
+ + + +

+ NarwhalsUnstableWarning + + +

+ + +
+ + +

Warning issued when a method or function is considered unstable in the stable api.

+ + + + + + + +
+ +
+ + + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/expr/index.html b/api-reference/expr/index.html new file mode 100644 index 000000000..9f2481ec3 --- /dev/null +++ b/api-reference/expr/index.html @@ -0,0 +1,10087 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Expr - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Expr

+ + +
+ + + + +
+ + + + + + + + + + +
+ + + + + + + + + +
+ + +

+ abs() + +

+ + +
+ +

Return absolute value of each element.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, -2], "b": [-3, 4]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_abs(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a", "b").abs()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_abs:

+ 
>>> agnostic_abs(df_pd)
+   a  b
+0  1  3
+1  2  4
+
+ 
>>> agnostic_abs(df_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 3   │
+│ 2   ┆ 4   │
+└─────┴─────┘
+
+ 
>>> agnostic_abs(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[1,2]]
+b: [[3,4]]
+
+ +
+ +
+ +
+ + +

+ alias(name) + +

+ + +
+ +

Rename the expression.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ name + + str + +
+

The new name.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2], "b": [4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_alias(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select((nw.col("b") + 10).alias("c")).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_alias:

+ 
>>> agnostic_alias(df_pd)
+    c
+0  14
+1  15
+
+ 
>>> agnostic_alias(df_pl)
+shape: (2, 1)
+┌─────┐
+│ c   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 14  │
+│ 15  │
+└─────┘
+
+ 
>>> agnostic_alias(df_pa)
+pyarrow.Table
+c: int64
+----
+c: [[14,15]]
+
+ +
+ +
+ +
+ + +

+ all() + +

+ + +
+ +

Return whether all values in the column are True.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [True, False], "b": [True, True]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_all(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a", "b").all()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_all:

+ 
>>> agnostic_all(df_pd)
+       a     b
+0  False  True
+
+ 
>>> agnostic_all(df_pl)
+shape: (1, 2)
+┌───────┬──────┐
+│ a     ┆ b    │
+│ ---   ┆ ---  │
+│ bool  ┆ bool │
+╞═══════╪══════╡
+│ false ┆ true │
+└───────┴──────┘
+
+ 
>>> agnostic_all(df_pa)
+pyarrow.Table
+a: bool
+b: bool
+----
+a: [[false]]
+b: [[true]]
+
+ +
+ +
+ +
+ + +

+ any() + +

+ + +
+ +

Return whether any of the values in the column are True.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [True, False], "b": [True, True]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_any(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a", "b").any()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_any:

+ 
>>> agnostic_any(df_pd)
+      a     b
+0  True  True
+
+ 
>>> agnostic_any(df_pl)
+shape: (1, 2)
+┌──────┬──────┐
+│ a    ┆ b    │
+│ ---  ┆ ---  │
+│ bool ┆ bool │
+╞══════╪══════╡
+│ true ┆ true │
+└──────┴──────┘
+
+ 
>>> agnostic_any(df_pa)
+pyarrow.Table
+a: bool
+b: bool
+----
+a: [[true]]
+b: [[true]]
+
+ +
+ +
+ +
+ + +

+ arg_max() + +

+ + +
+ +

Returns the index of the maximum value.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [10, 20], "b": [150, 100]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_arg_max(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.col("a", "b").arg_max().name.suffix("_arg_max")
+...     ).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or +PyArrow to agnostic_arg_max:

+ 
>>> agnostic_arg_max(df_pd)
+   a_arg_max  b_arg_max
+0          1          0
+
+ 
>>> agnostic_arg_max(df_pl)
+shape: (1, 2)
+┌───────────┬───────────┐
+│ a_arg_max ┆ b_arg_max │
+│ ---       ┆ ---       │
+│ u32       ┆ u32       │
+╞═══════════╪═══════════╡
+│ 1         ┆ 0         │
+└───────────┴───────────┘
+
+ 
>>> agnostic_arg_max(df_pa)
+pyarrow.Table
+a_arg_max: int64
+b_arg_max: int64
+----
+a_arg_max: [[1]]
+b_arg_max: [[0]]
+
+ +
+ +
+ +
+ + +

+ arg_min() + +

+ + +
+ +

Returns the index of the minimum value.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [10, 20], "b": [150, 100]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_arg_min(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.col("a", "b").arg_min().name.suffix("_arg_min")
+...     ).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or +PyArrow to agnostic_arg_min:

+ 
>>> agnostic_arg_min(df_pd)
+   a_arg_min  b_arg_min
+0          0          1
+
+ 
>>> agnostic_arg_min(df_pl)
+shape: (1, 2)
+┌───────────┬───────────┐
+│ a_arg_min ┆ b_arg_min │
+│ ---       ┆ ---       │
+│ u32       ┆ u32       │
+╞═══════════╪═══════════╡
+│ 0         ┆ 1         │
+└───────────┴───────────┘
+
+ 
>>> agnostic_arg_min(df_pa)
+pyarrow.Table
+a_arg_min: int64
+b_arg_min: int64
+----
+a_arg_min: [[0]]
+b_arg_min: [[1]]
+
+ +
+ +
+ +
+ + +

+ arg_true() + +

+ + +
+ +

Find elements where boolean expression is True.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, None, None, 2]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_arg_true(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").is_null().arg_true()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_arg_true:

+ 
>>> agnostic_arg_true(df_pd)
+   a
+1  1
+2  2
+
+ 
>>> agnostic_arg_true(df_pl)
+shape: (2, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ u32 │
+╞═════╡
+│ 1   │
+│ 2   │
+└─────┘
+
+ 
>>> agnostic_arg_true(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[1,2]]
+
+ +
+ +
+ +
+ + +

+ cast(dtype) + +

+ + +
+ +

Redefine an object's data type.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ dtype + + DType | type[DType] + +
+

Data type that the object will be cast into.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_cast(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.col("foo").cast(nw.Float32), nw.col("bar").cast(nw.UInt8)
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_cast:

+ 
>>> agnostic_cast(df_pd)
+   foo  bar
+0  1.0    6
+1  2.0    7
+2  3.0    8
+>>> agnostic_cast(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ f32 ┆ u8  │
+╞═════╪═════╡
+│ 1.0 ┆ 6   │
+│ 2.0 ┆ 7   │
+│ 3.0 ┆ 8   │
+└─────┴─────┘
+>>> agnostic_cast(df_pa)
+pyarrow.Table
+foo: float
+bar: uint8
+----
+foo: [[1,2,3]]
+bar: [[6,7,8]]
+
+ +
+ +
+ +
+ + +

+ count() + +

+ + +
+ +

Returns the number of non-null elements in the column.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3], "b": [None, 4, 4]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_count(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.all().count()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_count:

+ 
>>> agnostic_count(df_pd)
+   a  b
+0  3  2
+
+ 
>>> agnostic_count(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ u32 ┆ u32 │
+╞═════╪═════╡
+│ 3   ┆ 2   │
+└─────┴─────┘
+
+ 
>>> agnostic_count(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[3]]
+b: [[2]]
+
+ +
+ +
+ +
+ + +

+ cum_count(*, reverse=False) + +

+ + +
+ +

Return the cumulative count of the non-null values in the column.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ reverse + + bool + +
+

reverse the operation

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": ["x", "k", None, "d"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_cum_count(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("a").cum_count().alias("cum_count"),
+...         nw.col("a").cum_count(reverse=True).alias("cum_count_reverse"),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_cum_count:

+ 
>>> agnostic_cum_count(df_pd)
+      a  cum_count  cum_count_reverse
+0     x          1                  3
+1     k          2                  2
+2  None          2                  1
+3     d          3                  1
+
+ 
>>> agnostic_cum_count(df_pl)
+shape: (4, 3)
+┌──────┬───────────┬───────────────────┐
+│ a    ┆ cum_count ┆ cum_count_reverse │
+│ ---  ┆ ---       ┆ ---               │
+│ str  ┆ u32       ┆ u32               │
+╞══════╪═══════════╪═══════════════════╡
+│ x    ┆ 1         ┆ 3                 │
+│ k    ┆ 2         ┆ 2                 │
+│ null ┆ 2         ┆ 1                 │
+│ d    ┆ 3         ┆ 1                 │
+└──────┴───────────┴───────────────────┘
+
+ 
>>> agnostic_cum_count(df_pa)
+pyarrow.Table
+a: string
+cum_count: uint32
+cum_count_reverse: uint32
+----
+a: [["x","k",null,"d"]]
+cum_count: [[1,2,2,3]]
+cum_count_reverse: [[3,2,1,1]]
+
+ +
+ +
+ +
+ + +

+ cum_max(*, reverse=False) + +

+ + +
+ +

Return the cumulative max of the non-null values in the column.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ reverse + + bool + +
+

reverse the operation

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 3, None, 2]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_cum_max(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("a").cum_max().alias("cum_max"),
+...         nw.col("a").cum_max(reverse=True).alias("cum_max_reverse"),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_:

+ 
>>> agnostic_cum_max(df_pd)
+     a  cum_max  cum_max_reverse
+0  1.0      1.0              3.0
+1  3.0      3.0              3.0
+2  NaN      NaN              NaN
+3  2.0      3.0              2.0
+
+ 
>>> agnostic_cum_max(df_pl)
+shape: (4, 3)
+┌──────┬─────────┬─────────────────┐
+│ a    ┆ cum_max ┆ cum_max_reverse │
+│ ---  ┆ ---     ┆ ---             │
+│ i64  ┆ i64     ┆ i64             │
+╞══════╪═════════╪═════════════════╡
+│ 1    ┆ 1       ┆ 3               │
+│ 3    ┆ 3       ┆ 3               │
+│ null ┆ null    ┆ null            │
+│ 2    ┆ 3       ┆ 2               │
+└──────┴─────────┴─────────────────┘
+
+ 
>>> agnostic_cum_max(df_pa)
+pyarrow.Table
+a: int64
+cum_max: int64
+cum_max_reverse: int64
+----
+a: [[1,3,null,2]]
+cum_max: [[1,3,null,3]]
+cum_max_reverse: [[3,3,null,2]]
+
+ +
+ +
+ +
+ + +

+ cum_min(*, reverse=False) + +

+ + +
+ +

Return the cumulative min of the non-null values in the column.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ reverse + + bool + +
+

reverse the operation

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [3, 1, None, 2]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_cum_min(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("a").cum_min().alias("cum_min"),
+...         nw.col("a").cum_min(reverse=True).alias("cum_min_reverse"),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_cum_min:

+ 
>>> agnostic_cum_min(df_pd)
+     a  cum_min  cum_min_reverse
+0  3.0      3.0              1.0
+1  1.0      1.0              1.0
+2  NaN      NaN              NaN
+3  2.0      1.0              2.0
+
+ 
>>> agnostic_cum_min(df_pl)
+shape: (4, 3)
+┌──────┬─────────┬─────────────────┐
+│ a    ┆ cum_min ┆ cum_min_reverse │
+│ ---  ┆ ---     ┆ ---             │
+│ i64  ┆ i64     ┆ i64             │
+╞══════╪═════════╪═════════════════╡
+│ 3    ┆ 3       ┆ 1               │
+│ 1    ┆ 1       ┆ 1               │
+│ null ┆ null    ┆ null            │
+│ 2    ┆ 1       ┆ 2               │
+└──────┴─────────┴─────────────────┘
+
+ 
>>> agnostic_cum_min(df_pa)
+pyarrow.Table
+a: int64
+cum_min: int64
+cum_min_reverse: int64
+----
+a: [[3,1,null,2]]
+cum_min: [[3,1,null,1]]
+cum_min_reverse: [[1,1,null,2]]
+
+ +
+ +
+ +
+ + +

+ cum_prod(*, reverse=False) + +

+ + +
+ +

Return the cumulative product of the non-null values in the column.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ reverse + + bool + +
+

reverse the operation

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 3, None, 2]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_cum_prod(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("a").cum_prod().alias("cum_prod"),
+...         nw.col("a").cum_prod(reverse=True).alias("cum_prod_reverse"),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_cum_prod:

+ 
>>> agnostic_cum_prod(df_pd)
+     a  cum_prod  cum_prod_reverse
+0  1.0       1.0               6.0
+1  3.0       3.0               6.0
+2  NaN       NaN               NaN
+3  2.0       6.0               2.0
+
+ 
>>> agnostic_cum_prod(df_pl)
+shape: (4, 3)
+┌──────┬──────────┬──────────────────┐
+│ a    ┆ cum_prod ┆ cum_prod_reverse │
+│ ---  ┆ ---      ┆ ---              │
+│ i64  ┆ i64      ┆ i64              │
+╞══════╪══════════╪══════════════════╡
+│ 1    ┆ 1        ┆ 6                │
+│ 3    ┆ 3        ┆ 6                │
+│ null ┆ null     ┆ null             │
+│ 2    ┆ 6        ┆ 2                │
+└──────┴──────────┴──────────────────┘
+
+ 
>>> agnostic_cum_prod(df_pa)
+pyarrow.Table
+a: int64
+cum_prod: int64
+cum_prod_reverse: int64
+----
+a: [[1,3,null,2]]
+cum_prod: [[1,3,null,6]]
+cum_prod_reverse: [[6,6,null,2]]
+
+ +
+ +
+ +
+ + +

+ cum_sum(*, reverse=False) + +

+ + +
+ +

Return cumulative sum.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ reverse + + bool + +
+

reverse the operation

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 1, 3, 5, 5], "b": [2, 4, 4, 6, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_cum_sum(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a", "b").cum_sum()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_cum_sum:

+ 
>>> agnostic_cum_sum(df_pd)
+    a   b
+0   1   2
+1   2   6
+2   5  10
+3  10  16
+4  15  22
+>>> agnostic_cum_sum(df_pl)
+shape: (5, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 2   │
+│ 2   ┆ 6   │
+│ 5   ┆ 10  │
+│ 10  ┆ 16  │
+│ 15  ┆ 22  │
+└─────┴─────┘
+>>> agnostic_cum_sum(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[1,2,5,10,15]]
+b: [[2,6,10,16,22]]
+
+ +
+ +
+ +
+ + +

+ diff() + +

+ + +
+ +

Returns the difference between each element and the previous one.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +
+ Notes +

pandas may change the dtype here, for example when introducing missing +values in an integer column. To ensure, that the dtype doesn't change, +you may want to use fill_null and cast. For example, to calculate +the diff and fill missing values with 0 in a Int64 column, you could +do:

+
nw.col("a").diff().fill_null(0).cast(nw.Int64)
+
+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 1, 3, 5, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_diff(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(a_diff=nw.col("a").diff()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_diff:

+ 
>>> agnostic_diff(df_pd)
+   a_diff
+0     NaN
+1     0.0
+2     2.0
+3     2.0
+4     0.0
+
+ 
>>> agnostic_diff(df_pl)
+shape: (5, 1)
+┌────────┐
+│ a_diff │
+│ ---    │
+│ i64    │
+╞════════╡
+│ null   │
+│ 0      │
+│ 2      │
+│ 2      │
+│ 0      │
+└────────┘
+
+ 
>>> agnostic_diff(df_pa)
+pyarrow.Table
+a_diff: int64
+----
+a_diff: [[null,0,2,2,0]]
+
+ +
+ +
+ +
+ + +

+ drop_nulls() + +

+ + +
+ +

Drop null values.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +
+ Notes +

pandas handles null values differently from Polars and PyArrow. +See null_handling +for reference.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> df_pd = pd.DataFrame({"a": [2.0, 4.0, float("nan"), 3.0, None, 5.0]})
+>>> df_pl = pl.DataFrame({"a": [2.0, 4.0, None, 3.0, None, 5.0]})
+>>> df_pa = pa.table({"a": [2.0, 4.0, None, 3.0, None, 5.0]})
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_drop_nulls(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").drop_nulls()).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or +PyArrow to agnostic_drop_nulls:

+ 
>>> agnostic_drop_nulls(df_pd)
+     a
+0  2.0
+1  4.0
+3  3.0
+5  5.0
+
+ 
>>> agnostic_drop_nulls(df_pl)
+shape: (4, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ f64 │
+╞═════╡
+│ 2.0 │
+│ 4.0 │
+│ 3.0 │
+│ 5.0 │
+└─────┘
+
+ 
>>> agnostic_drop_nulls(df_pa)
+pyarrow.Table
+a: double
+----
+a: [[2,4,3,5]]
+
+ +
+ +
+ +
+ + +

+ ewm_mean(*, com=None, span=None, half_life=None, alpha=None, adjust=True, min_periods=1, ignore_nulls=False) + +

+ + +
+ +

Compute exponentially-weighted moving average.

+
+

Warning

+

This functionality is considered unstable. It may be changed at any point +without it being considered a breaking change.

+
+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ com + + float | None + +
+

Specify decay in terms of center of mass, \(\gamma\), with
\(\alpha = \frac{1}{1+\gamma}\forall\gamma\geq0\)

+
+ 		+ None +
+ span + + float | None + +
+

Specify decay in terms of span, \(\theta\), with
\(\alpha = \frac{2}{\theta + 1} \forall \theta \geq 1\)

+
+ 		+ None +
+ half_life + + float | None + +
+

Specify decay in terms of half-life, \(\tau\), with
\(\alpha = 1 - \exp \left\{ \frac{ -\ln(2) }{ \tau } \right\} \forall \tau > 0\)

+
+ 		+ None +
+ alpha + + float | None + +
+

Specify smoothing factor alpha directly, \(0 < \alpha \leq 1\).

+
+ 		+ None +
+ adjust + + bool + +
+

Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings

+
    +
  • When adjust=True (the default) the EW function is calculated + using weights \(w_i = (1 - \alpha)^i\)
    • +
  • When adjust=False the EW function is calculated recursively by + $$ + y_0=x_0 + $$ + $$ + y_t = (1 - \alpha)y_{t - 1} + \alpha x_t + $$
    • +
+
+ 		+ True +
+ min_periods + + int + +
+

Minimum number of observations in window required to have a value, (otherwise result is null).

+
+ 		+ 1 +
+ ignore_nulls + + bool + +
+

Ignore missing values when calculating weights.

+
    +
  • When ignore_nulls=False (default), weights are based on absolute + positions. + For example, the weights of \(x_0\) and \(x_2\) used in + calculating the final weighted average of \([x_0, None, x_2]\) are + \((1-\alpha)^2\) and \(1\) if adjust=True, and + \((1-\alpha)^2\) and \(\alpha\) if adjust=False.
    • +
  • When ignore_nulls=True, weights are based + on relative positions. For example, the weights of + \(x_0\) and \(x_2\) used in calculating the final weighted + average of \([x_0, None, x_2]\) are + \(1-\alpha\) and \(1\) if adjust=True, + and \(1-\alpha\) and \(\alpha\) if adjust=False.
    • +
+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

Expr

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_ewm_mean(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.col("a").ewm_mean(com=1, ignore_nulls=False)
+...     ).to_native()
+
+

We can then pass either pandas or Polars to agnostic_ewm_mean:

+ 
>>> agnostic_ewm_mean(df_pd)
+          a
+0  1.000000
+1  1.666667
+2  2.428571
+
+ 
>>> agnostic_ewm_mean(df_pl)
+shape: (3, 1)
+┌──────────┐
+│ a        │
+│ ---      │
+│ f64      │
+╞══════════╡
+│ 1.0      │
+│ 1.666667 │
+│ 2.428571 │
+└──────────┘
+
+ +
+ +
+ +
+ + +

+ fill_null(value=None, strategy=None, limit=None) + +

+ + +
+ +

Fill null values with given value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ value + + Any | None + +
+

Value used to fill null values.

+
+ 		+ None +
+ strategy + + Literal['forward', 'backward'] | None + +
+

Strategy used to fill null values.

+
+ 		+ None +
+ limit + + int | None + +
+

Number of consecutive null values to fill when using the 'forward' or 'backward' strategy.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +
+ Notes +

pandas handles null values differently from Polars and PyArrow. +See null_handling +for reference.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> df_pd = pd.DataFrame(
+...     {
+...         "a": [2, 4, None, None, 3, 5],
+...         "b": [2.0, 4.0, float("nan"), float("nan"), 3.0, 5.0],
+...     }
+... )
+>>> data = {
+...     "a": [2, 4, None, None, 3, 5],
+...     "b": [2.0, 4.0, None, None, 3.0, 5.0],
+... }
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_fill_null(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(nw.col("a", "b").fill_null(0)).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or +PyArrow to agnostic_fill_null:

+ 
>>> agnostic_fill_null(df_pd)
+     a    b
+0  2.0  2.0
+1  4.0  4.0
+2  0.0  0.0
+3  0.0  0.0
+4  3.0  3.0
+5  5.0  5.0
+
+ 
>>> agnostic_fill_null(df_pl)
+shape: (6, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ f64 │
+╞═════╪═════╡
+│ 2   ┆ 2.0 │
+│ 4   ┆ 4.0 │
+│ 0   ┆ 0.0 │
+│ 0   ┆ 0.0 │
+│ 3   ┆ 3.0 │
+│ 5   ┆ 5.0 │
+└─────┴─────┘
+
+ 
>>> agnostic_fill_null(df_pa)
+pyarrow.Table
+a: int64
+b: double
+----
+a: [[2,4,0,0,3,5]]
+b: [[2,4,0,0,3,5]]
+
+

Using a strategy:

+ 
>>> def agnostic_fill_null_with_strategy(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("a", "b")
+...         .fill_null(strategy="forward", limit=1)
+...         .name.suffix("_filled")
+...     ).to_native()
+
+ 
>>> agnostic_fill_null_with_strategy(df_pd)
+     a    b  a_filled  b_filled
+0  2.0  2.0       2.0       2.0
+1  4.0  4.0       4.0       4.0
+2  NaN  NaN       4.0       4.0
+3  NaN  NaN       NaN       NaN
+4  3.0  3.0       3.0       3.0
+5  5.0  5.0       5.0       5.0
+
+ 
>>> agnostic_fill_null_with_strategy(df_pl)
+shape: (6, 4)
+┌──────┬──────┬──────────┬──────────┐
+│ a    ┆ b    ┆ a_filled ┆ b_filled │
+│ ---  ┆ ---  ┆ ---      ┆ ---      │
+│ i64  ┆ f64  ┆ i64      ┆ f64      │
+╞══════╪══════╪══════════╪══════════╡
+│ 2    ┆ 2.0  ┆ 2        ┆ 2.0      │
+│ 4    ┆ 4.0  ┆ 4        ┆ 4.0      │
+│ null ┆ null ┆ 4        ┆ 4.0      │
+│ null ┆ null ┆ null     ┆ null     │
+│ 3    ┆ 3.0  ┆ 3        ┆ 3.0      │
+│ 5    ┆ 5.0  ┆ 5        ┆ 5.0      │
+└──────┴──────┴──────────┴──────────┘
+
+ 
>>> agnostic_fill_null_with_strategy(df_pa)
+pyarrow.Table
+a: int64
+b: double
+a_filled: int64
+b_filled: double
+----
+a: [[2,4,null,null,3,5]]
+b: [[2,4,null,null,3,5]]
+a_filled: [[2,4,4,null,3,5]]
+b_filled: [[2,4,4,null,3,5]]
+
+ +
+ +
+ +
+ + +

+ filter(*predicates) + +

+ + +
+ +

Filters elements based on a condition, returning a new expression.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ predicates + + Any + +
+

Conditions to filter by (which get ANDed together).

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [2, 3, 4, 5, 6, 7], "b": [10, 11, 12, 13, 14, 15]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_filter(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.col("a").filter(nw.col("a") > 4),
+...         nw.col("b").filter(nw.col("b") < 13),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_filter:

+ 
>>> agnostic_filter(df_pd)
+   a   b
+3  5  10
+4  6  11
+5  7  12
+
+ 
>>> agnostic_filter(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 5   ┆ 10  │
+│ 6   ┆ 11  │
+│ 7   ┆ 12  │
+└─────┴─────┘
+
+ 
>>> agnostic_filter(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[5,6,7]]
+b: [[10,11,12]]
+
+ +
+ +
+ +
+ + +

+ gather_every(n, offset=0) + +

+ + +
+ +

Take every nth value in the Series and return as new Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Gather every n-th row.

+
+ 		+ required +
+ offset + + int + +
+

Starting index.

+
+ 		+ 0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function in which gather every 2 rows, +starting from a offset of 1:

+ 
>>> def agnostic_gather_every(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").gather_every(n=2, offset=1)).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_gather_every:

+ 
>>> agnostic_gather_every(df_pd)
+   a
+1  2
+3  4
+
+ 
>>> agnostic_gather_every(df_pl)
+shape: (2, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 2   │
+│ 4   │
+└─────┘
+
+ 
>>> agnostic_gather_every(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[2,4]]
+
+ +
+ +
+ +
+ + +

+ head(n=10) + +

+ + +
+ +

Get the first n rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of rows to return.

+
+ 		+ 10 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": list(range(10))}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function that returns the first 3 rows:

+ 
>>> def agnostic_head(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").head(3)).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_head:

+ 
>>> agnostic_head(df_pd)
+   a
+0  0
+1  1
+2  2
+
+ 
>>> agnostic_head(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 0   │
+│ 1   │
+│ 2   │
+└─────┘
+
+ 
>>> agnostic_head(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[0,1,2]]
+
+ +
+ +
+ +
+ + +

+ clip(lower_bound=None, upper_bound=None) + +

+ + +
+ +

Clip values in the Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ lower_bound + + IntoExpr | Any | None + +
+

Lower bound value.

+
+ 		+ None +
+ upper_bound + + IntoExpr | Any | None + +
+

Upper bound value.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_clip_lower(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").clip(2)).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or +PyArrow to agnostic_clip_lower:

+ 
>>> agnostic_clip_lower(df_pd)
+   a
+0  2
+1  2
+2  3
+
+ 
>>> agnostic_clip_lower(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 2   │
+│ 2   │
+│ 3   │
+└─────┘
+
+ 
>>> agnostic_clip_lower(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[2,2,3]]
+
+

We define another library agnostic function:

+ 
>>> def agnostic_clip_upper(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").clip(upper_bound=2)).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or +PyArrow to agnostic_clip_upper:

+ 
>>> agnostic_clip_upper(df_pd)
+   a
+0  1
+1  2
+2  2
+
+ 
>>> agnostic_clip_upper(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 2   │
+│ 2   │
+└─────┘
+
+ 
>>> agnostic_clip_upper(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[1,2,2]]
+
+

We can have both at the same time

+ 
>>> data = {"a": [-1, 1, -3, 3, -5, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_clip(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").clip(-1, 3)).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or +PyArrow to agnostic_clip:

+ 
>>> agnostic_clip(df_pd)
+   a
+0 -1
+1  1
+2 -1
+3  3
+4 -1
+5  3
+
+ 
>>> agnostic_clip(df_pl)
+shape: (6, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ -1  │
+│ 1   │
+│ -1  │
+│ 3   │
+│ -1  │
+│ 3   │
+└─────┘
+
+ 
>>> agnostic_clip(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[-1,1,-1,3,-1,3]]
+
+ +
+ +
+ +
+ + +

+ is_between(lower_bound, upper_bound, closed='both') + +

+ + +
+ +

Check if this expression is between the given lower and upper bounds.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ lower_bound + + Any | IntoExpr + +
+

Lower bound value.

+
+ 		+ required +
+ upper_bound + + Any | IntoExpr + +
+

Upper bound value.

+
+ 		+ required +
+ closed + + Literal['left', 'right', 'none', 'both'] + +
+

Define which sides of the interval are closed (inclusive).

+
+ 		+ 'both' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3, 4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_between(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").is_between(2, 4, "right")).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_between:

+ 
>>> agnostic_is_between(df_pd)
+       a
+0  False
+1  False
+2   True
+3   True
+4  False
+
+ 
>>> agnostic_is_between(df_pl)
+shape: (5, 1)
+┌───────┐
+│ a     │
+│ ---   │
+│ bool  │
+╞═══════╡
+│ false │
+│ false │
+│ true  │
+│ true  │
+│ false │
+└───────┘
+
+ 
>>> agnostic_is_between(df_pa)
+pyarrow.Table
+a: bool
+----
+a: [[false,false,true,true,false]]
+
+ +
+ +
+ +
+ + +

+ is_duplicated() + +

+ + +
+ +

Return a boolean mask indicating duplicated values.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3, 1], "b": ["a", "a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_duplicated(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.all().is_duplicated()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_duplicated:

+ 
>>> agnostic_is_duplicated(df_pd)
+       a      b
+0   True   True
+1  False   True
+2  False  False
+3   True  False
+
+ 
>>> agnostic_is_duplicated(df_pl)
+shape: (4, 2)
+┌───────┬───────┐
+│ a     ┆ b     │
+│ ---   ┆ ---   │
+│ bool  ┆ bool  │
+╞═══════╪═══════╡
+│ true  ┆ true  │
+│ false ┆ true  │
+│ false ┆ false │
+│ true  ┆ false │
+└───────┴───────┘
+
+ 
>>> agnostic_is_duplicated(df_pa)
+pyarrow.Table
+a: bool
+b: bool
+----
+a: [[true,false,false,true]]
+b: [[true,true,false,false]]
+
+ +
+ +
+ +
+ + +

+ is_finite() + +

+ + +
+ +

Returns boolean values indicating which original values are finite.

+ + +
+ Warning +

Different backend handle null values differently. is_finite will return +False for NaN and Null's in the Dask and pandas non-nullable backend, while +for Polars, PyArrow and pandas nullable backends null values are kept as such.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

Expression of Boolean data type.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [float("nan"), float("inf"), 2.0, None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_is_finite(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").is_finite()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_finite:

+ 
>>> agnostic_is_finite(df_pd)
+       a
+0  False
+1  False
+2   True
+3  False
+
+ 
>>> agnostic_is_finite(df_pl)
+shape: (4, 1)
+┌───────┐
+│ a     │
+│ ---   │
+│ bool  │
+╞═══════╡
+│ false │
+│ false │
+│ true  │
+│ null  │
+└───────┘
+
+ 
>>> agnostic_is_finite(df_pa)
+pyarrow.Table
+a: bool
+----
+a: [[false,false,true,null]]
+
+ +
+ +
+ +
+ + +

+ is_first_distinct() + +

+ + +
+ +

Return a boolean mask indicating the first occurrence of each distinct value.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3, 1], "b": ["a", "a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_first_distinct(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.all().is_first_distinct()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_first_distinct:

+ 
>>> agnostic_is_first_distinct(df_pd)
+       a      b
+0   True   True
+1   True  False
+2   True   True
+3  False   True
+
+ 
>>> agnostic_is_first_distinct(df_pl)
+shape: (4, 2)
+┌───────┬───────┐
+│ a     ┆ b     │
+│ ---   ┆ ---   │
+│ bool  ┆ bool  │
+╞═══════╪═══════╡
+│ true  ┆ true  │
+│ true  ┆ false │
+│ true  ┆ true  │
+│ false ┆ true  │
+└───────┴───────┘
+
+ 
>>> agnostic_is_first_distinct(df_pa)
+pyarrow.Table
+a: bool
+b: bool
+----
+a: [[true,true,true,false]]
+b: [[true,false,true,true]]
+
+ +
+ +
+ +
+ + +

+ is_in(other) + +

+ + +
+ +

Check if elements of this expression are present in the other iterable.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ other + + Any + +
+

iterable

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 9, 10]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_in(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(b=nw.col("a").is_in([1, 2])).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_in:

+ 
>>> agnostic_is_in(df_pd)
+    a      b
+0   1   True
+1   2   True
+2   9  False
+3  10  False
+
+ 
>>> agnostic_is_in(df_pl)
+shape: (4, 2)
+┌─────┬───────┐
+│ a   ┆ b     │
+│ --- ┆ ---   │
+│ i64 ┆ bool  │
+╞═════╪═══════╡
+│ 1   ┆ true  │
+│ 2   ┆ true  │
+│ 9   ┆ false │
+│ 10  ┆ false │
+└─────┴───────┘
+
+ 
>>> agnostic_is_in(df_pa)
+pyarrow.Table
+a: int64
+b: bool
+----
+a: [[1,2,9,10]]
+b: [[true,true,false,false]]
+
+ +
+ +
+ +
+ + +

+ is_last_distinct() + +

+ + +
+ +

Return a boolean mask indicating the last occurrence of each distinct value.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3, 1], "b": ["a", "a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_last_distinct(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.all().is_last_distinct()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_last_distinct:

+ 
>>> agnostic_is_last_distinct(df_pd)
+       a      b
+0  False  False
+1   True   True
+2   True   True
+3   True   True
+
+ 
>>> agnostic_is_last_distinct(df_pl)
+shape: (4, 2)
+┌───────┬───────┐
+│ a     ┆ b     │
+│ ---   ┆ ---   │
+│ bool  ┆ bool  │
+╞═══════╪═══════╡
+│ false ┆ false │
+│ true  ┆ true  │
+│ true  ┆ true  │
+│ true  ┆ true  │
+└───────┴───────┘
+
+ 
>>> agnostic_is_last_distinct(df_pa)
+pyarrow.Table
+a: bool
+b: bool
+----
+a: [[false,true,true,true]]
+b: [[false,true,true,true]]
+
+ +
+ +
+ +
+ + +

+ is_nan() + +

+ + +
+ +

Indicate which values are NaN.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +
+ Notes +

pandas handles null values differently from Polars and PyArrow. +See null_handling +for reference.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"orig": [0.0, None, 2.0]}
+>>> df_pd = pd.DataFrame(data).astype({"orig": "Float64"})
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_self_div_is_nan(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         divided=nw.col("orig") / nw.col("orig"),
+...         divided_is_nan=(nw.col("orig") / nw.col("orig")).is_nan(),
+...     ).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or +PyArrow to agnostic_self_div_is_nan:

+ 
>>> print(agnostic_self_div_is_nan(df_pd))
+   orig  divided  divided_is_nan
+0   0.0      NaN            True
+1  <NA>     <NA>            <NA>
+2   2.0      1.0           False
+
+ 
>>> print(agnostic_self_div_is_nan(df_pl))
+shape: (3, 3)
+┌──────┬─────────┬────────────────┐
+│ orig ┆ divided ┆ divided_is_nan │
+│ ---  ┆ ---     ┆ ---            │
+│ f64  ┆ f64     ┆ bool           │
+╞══════╪═════════╪════════════════╡
+│ 0.0  ┆ NaN     ┆ true           │
+│ null ┆ null    ┆ null           │
+│ 2.0  ┆ 1.0     ┆ false          │
+└──────┴─────────┴────────────────┘
+
+ 
>>> print(agnostic_self_div_is_nan(df_pa))
+pyarrow.Table
+orig: double
+divided: double
+divided_is_nan: bool
+----
+orig: [[0,null,2]]
+divided: [[nan,null,1]]
+divided_is_nan: [[true,null,false]]
+
+ +
+ +
+ +
+ + +

+ is_null() + +

+ + +
+ +

Returns a boolean Series indicating which values are null.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +
+ Notes +

pandas handles null values differently from Polars and PyArrow. +See null_handling +for reference.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> df_pd = pd.DataFrame(
+...     {
+...         "a": [2, 4, None, 3, 5],
+...         "b": [2.0, 4.0, float("nan"), 3.0, 5.0],
+...     }
+... )
+>>> data = {
+...     "a": [2, 4, None, 3, 5],
+...     "b": [2.0, 4.0, None, 3.0, 5.0],
+... }
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_null(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         a_is_null=nw.col("a").is_null(), b_is_null=nw.col("b").is_null()
+...     ).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or +PyArrow to agnostic_is_null:

+ 
>>> agnostic_is_null(df_pd)
+     a    b  a_is_null  b_is_null
+0  2.0  2.0      False      False
+1  4.0  4.0      False      False
+2  NaN  NaN       True       True
+3  3.0  3.0      False      False
+4  5.0  5.0      False      False
+
+ 
>>> agnostic_is_null(df_pl)
+shape: (5, 4)
+┌──────┬──────┬───────────┬───────────┐
+│ a    ┆ b    ┆ a_is_null ┆ b_is_null │
+│ ---  ┆ ---  ┆ ---       ┆ ---       │
+│ i64  ┆ f64  ┆ bool      ┆ bool      │
+╞══════╪══════╪═══════════╪═══════════╡
+│ 2    ┆ 2.0  ┆ false     ┆ false     │
+│ 4    ┆ 4.0  ┆ false     ┆ false     │
+│ null ┆ null ┆ true      ┆ true      │
+│ 3    ┆ 3.0  ┆ false     ┆ false     │
+│ 5    ┆ 5.0  ┆ false     ┆ false     │
+└──────┴──────┴───────────┴───────────┘
+
+ 
>>> agnostic_is_null(df_pa)
+pyarrow.Table
+a: int64
+b: double
+a_is_null: bool
+b_is_null: bool
+----
+a: [[2,4,null,3,5]]
+b: [[2,4,null,3,5]]
+a_is_null: [[false,false,true,false,false]]
+b_is_null: [[false,false,true,false,false]]
+
+ +
+ +
+ +
+ + +

+ is_unique() + +

+ + +
+ +

Return a boolean mask indicating unique values.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3, 1], "b": ["a", "a", "b", "c"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_unique(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.all().is_unique()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_unique:

+ 
>>> agnostic_is_unique(df_pd)
+       a      b
+0  False  False
+1   True  False
+2   True   True
+3  False   True
+
+ 
>>> agnostic_is_unique(df_pl)
+shape: (4, 2)
+┌───────┬───────┐
+│ a     ┆ b     │
+│ ---   ┆ ---   │
+│ bool  ┆ bool  │
+╞═══════╪═══════╡
+│ false ┆ false │
+│ true  ┆ false │
+│ true  ┆ true  │
+│ false ┆ true  │
+└───────┴───────┘
+
+ 
>>> agnostic_is_unique(df_pa)
+pyarrow.Table
+a: bool
+b: bool
+----
+a: [[false,true,true,false]]
+b: [[false,false,true,true]]
+
+ +
+ +
+ +
+ + +

+ len() + +

+ + +
+ +

Return the number of elements in the column.

+

Null values count towards the total.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": ["x", "y", "z"], "b": [1, 2, 1]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function that computes the len over +different values of "b" column:

+ 
>>> def agnostic_len(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.col("a").filter(nw.col("b") == 1).len().alias("a1"),
+...         nw.col("a").filter(nw.col("b") == 2).len().alias("a2"),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_len:

+ 
>>> agnostic_len(df_pd)
+   a1  a2
+0   2   1
+
+ 
>>> agnostic_len(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a1  ┆ a2  │
+│ --- ┆ --- │
+│ u32 ┆ u32 │
+╞═════╪═════╡
+│ 2   ┆ 1   │
+└─────┴─────┘
+
+ 
>>> agnostic_len(df_pa)
+pyarrow.Table
+a1: int64
+a2: int64
+----
+a1: [[2]]
+a2: [[1]]
+
+ +
+ +
+ +
+ + +

+ map_batches(function, return_dtype=None) + +

+ + +
+ +

Apply a custom python function to a whole Series or sequence of Series.

+

The output of this custom function is presumed to be either a Series, +or a NumPy array (in which case it will be automatically converted into +a Series).

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ function + + Callable[[Any], Self] + +
+

Function to apply to Series.

+
+ 		+ required +
+ return_dtype + + DType | None + +
+

Dtype of the output Series. +If not set, the dtype will be inferred based on the first non-null value +that is returned by the function.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_map_batches(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.col("a", "b").map_batches(
+...             lambda s: s.to_numpy() + 1, return_dtype=nw.Float64
+...         )
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_map_batches:

+ 
>>> agnostic_map_batches(df_pd)
+     a    b
+0  2.0  5.0
+1  3.0  6.0
+2  4.0  7.0
+>>> agnostic_map_batches(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ f64 ┆ f64 │
+╞═════╪═════╡
+│ 2.0 ┆ 5.0 │
+│ 3.0 ┆ 6.0 │
+│ 4.0 ┆ 7.0 │
+└─────┴─────┘
+>>> agnostic_map_batches(df_pa)
+pyarrow.Table
+a: double
+b: double
+----
+a: [[2,3,4]]
+b: [[5,6,7]]
+
+ +
+ +
+ +
+ + +

+ max() + +

+ + +
+ +

Returns the maximum value(s) from a column(s).

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [10, 20], "b": [50, 100]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_max(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.max("a", "b")).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_max:

+ 
>>> agnostic_max(df_pd)
+    a    b
+0  20  100
+
+ 
>>> agnostic_max(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 20  ┆ 100 │
+└─────┴─────┘
+
+ 
>>> agnostic_max(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[20]]
+b: [[100]]
+
+ +
+ +
+ +
+ + +

+ mean() + +

+ + +
+ +

Get mean value.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [-1, 0, 1], "b": [2, 4, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_mean(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a", "b").mean()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_mean:

+ 
>>> agnostic_mean(df_pd)
+     a    b
+0  0.0  4.0
+
+ 
>>> agnostic_mean(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ f64 ┆ f64 │
+╞═════╪═════╡
+│ 0.0 ┆ 4.0 │
+└─────┴─────┘
+
+ 
>>> agnostic_mean(df_pa)
+pyarrow.Table
+a: double
+b: double
+----
+a: [[0]]
+b: [[4]]
+
+ +
+ +
+ +
+ + +

+ median() + +

+ + +
+ +

Get median value.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +
+ Notes +

Results might slightly differ across backends due to differences in the underlying algorithms used to compute the median.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 8, 3], "b": [4, 5, 2]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_median(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a", "b").median()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_median:

+ 
>>> agnostic_median(df_pd)
+     a    b
+0  3.0  4.0
+
+ 
>>> agnostic_median(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ f64 ┆ f64 │
+╞═════╪═════╡
+│ 3.0 ┆ 4.0 │
+└─────┴─────┘
+
+ 
>>> agnostic_median(df_pa)
+pyarrow.Table
+a: double
+b: double
+----
+a: [[3]]
+b: [[4]]
+
+ +
+ +
+ +
+ + +

+ min() + +

+ + +
+ +

Returns the minimum value(s) from a column(s).

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2], "b": [4, 3]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_min(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.min("a", "b")).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_min:

+ 
>>> agnostic_min(df_pd)
+   a  b
+0  1  3
+
+ 
>>> agnostic_min(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 3   │
+└─────┴─────┘
+
+ 
>>> agnostic_min(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[1]]
+b: [[3]]
+
+ +
+ +
+ +
+ + +

+ mode() + +

+ + +
+ +

Compute the most occurring value(s).

+

Can return multiple values.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [1, 1, 2, 3],
+...     "b": [1, 1, 2, 2],
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_mode(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").mode()).sort("a").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_mode:

+ 
>>> agnostic_mode(df_pd)
+   a
+0  1
+
+ 
>>> agnostic_mode(df_pl)
+shape: (1, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+└─────┘
+
+ 
>>> agnostic_mode(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[1]]
+
+ +
+ +
+ +
+ + +

+ null_count() + +

+ + +
+ +

Count null values.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +
+ Notes +

pandas handles null values differently from Polars and PyArrow. +See null_handling +for reference.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, None, 1], "b": ["a", None, "b", None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_null_count(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.all().null_count()).to_native()
+
+

We can then pass any supported library such as Pandas, Polars, or +PyArrow to agnostic_null_count:

+ 
>>> agnostic_null_count(df_pd)
+   a  b
+0  1  2
+
+ 
>>> agnostic_null_count(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ u32 ┆ u32 │
+╞═════╪═════╡
+│ 1   ┆ 2   │
+└─────┴─────┘
+
+ 
>>> agnostic_null_count(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[1]]
+b: [[2]]
+
+ +
+ +
+ +
+ + +

+ n_unique() + +

+ + +
+ +

Returns count of unique values.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3, 4, 5], "b": [1, 1, 3, 3, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_n_unique(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a", "b").n_unique()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_n_unique:

+ 
>>> agnostic_n_unique(df_pd)
+   a  b
+0  5  3
+>>> agnostic_n_unique(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ u32 ┆ u32 │
+╞═════╪═════╡
+│ 5   ┆ 3   │
+└─────┴─────┘
+>>> agnostic_n_unique(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[5]]
+b: [[3]]
+
+ +
+ +
+ +
+ + +

+ over(*keys) + +

+ + +
+ +

Compute expressions over the given groups.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ keys + + str | Iterable[str] + +
+

Names of columns to compute window expression over. + Must be names of columns, as opposed to expressions - + so, this is a bit less flexible than Polars' Expr.over.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3], "b": [1, 1, 2]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_min_over_b(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         a_min_per_group=nw.col("a").min().over("b")
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_min_over_b:

+ 
>>> agnostic_min_over_b(df_pd)
+   a  b  a_min_per_group
+0  1  1                1
+1  2  1                1
+2  3  2                3
+
+ 
>>> agnostic_min_over_b(df_pl)
+shape: (3, 3)
+┌─────┬─────┬─────────────────┐
+│ a   ┆ b   ┆ a_min_per_group │
+│ --- ┆ --- ┆ ---             │
+│ i64 ┆ i64 ┆ i64             │
+╞═════╪═════╪═════════════════╡
+│ 1   ┆ 1   ┆ 1               │
+│ 2   ┆ 1   ┆ 1               │
+│ 3   ┆ 2   ┆ 3               │
+└─────┴─────┴─────────────────┘
+
+ 
>>> agnostic_min_over_b(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+a_min_per_group: int64
+----
+a: [[1,2,3]]
+b: [[1,1,2]]
+a_min_per_group: [[1,1,3]]
+
+

Cumulative operations are also supported, but (currently) only for +pandas and Polars:

+ 
>>> def agnostic_cum_sum(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(c=nw.col("a").cum_sum().over("b")).to_native()
+
+ 
>>> agnostic_cum_sum(df_pd)
+   a  b  c
+0  1  1  1
+1  2  1  3
+2  3  2  3
+
+ 
>>> agnostic_cum_sum(df_pl)
+shape: (3, 3)
+┌─────┬─────┬─────┐
+│ a   ┆ b   ┆ c   │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ i64 │
+╞═════╪═════╪═════╡
+│ 1   ┆ 1   ┆ 1   │
+│ 2   ┆ 1   ┆ 3   │
+│ 3   ┆ 2   ┆ 3   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ pipe(function, *args, **kwargs) + +

+ + +
+ +

Pipe function call.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ function + + Callable[[Any], Self] + +
+

Function to apply.

+
+ 		+ required +
+ args + + Any + +
+

Positional arguments to pass to function.

+
+ 		+ () +
+ kwargs + + Any + +
+

Keyword arguments to pass to function.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3, 4]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Lets define a library-agnostic function:

+ 
>>> def agnostic_pipe(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").pipe(lambda x: x + 1)).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_pipe:

+ 
>>> agnostic_pipe(df_pd)
+   a
+0  2
+1  3
+2  4
+3  5
+
+ 
>>> agnostic_pipe(df_pl)
+shape: (4, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 2   │
+│ 3   │
+│ 4   │
+│ 5   │
+└─────┘
+
+ 
>>> agnostic_pipe(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[2,3,4,5]]
+
+ +
+ +
+ +
+ + +

+ quantile(quantile, interpolation) + +

+ + +
+ +

Get quantile value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ quantile + + float + +
+

Quantile between 0.0 and 1.0.

+
+ 		+ required +
+ interpolation + + Literal['nearest', 'higher', 'lower', 'midpoint', 'linear'] + +
+

Interpolation method.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +
+ Note +
    +
  • pandas and Polars may have implementation differences for a given interpolation method.
    • +
  • dask has + its own method to approximate quantile and it doesn't implement 'nearest', 'higher', + 'lower', 'midpoint' as interpolation method - use 'linear' which is closest to the + native 'dask' - method.
    • +
+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": list(range(50)), "b": list(range(50, 100))}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_quantile(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.col("a", "b").quantile(0.5, interpolation="linear")
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_quantile:

+ 
>>> agnostic_quantile(df_pd)
+      a     b
+0  24.5  74.5
+
+ 
>>> agnostic_quantile(df_pl)
+shape: (1, 2)
+┌──────┬──────┐
+│ a    ┆ b    │
+│ ---  ┆ ---  │
+│ f64  ┆ f64  │
+╞══════╪══════╡
+│ 24.5 ┆ 74.5 │
+└──────┴──────┘
+
+ 
>>> agnostic_quantile(df_pa)
+pyarrow.Table
+a: double
+b: double
+----
+a: [[24.5]]
+b: [[74.5]]
+
+ +
+ +
+ +
+ + +

+ rank(method='average', *, descending=False) + +

+ + +
+ +

Assign ranks to data, dealing with ties appropriately.

+ + +
+ Notes +

The resulting dtype may differ between backends.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ method + + Literal['average', 'min', 'max', 'dense', 'ordinal'] + +
+

The method used to assign ranks to tied elements. +The following methods are available (default is 'average'):

+
    +
  • 'average' : The average of the ranks that would have been assigned to + all the tied values is assigned to each value.
    • +
  • 'min' : The minimum of the ranks that would have been assigned to all + the tied values is assigned to each value. (This is also referred to + as "competition" ranking.)
    • +
  • 'max' : The maximum of the ranks that would have been assigned to all + the tied values is assigned to each value.
    • +
  • 'dense' : Like 'min', but the rank of the next highest element is + assigned the rank immediately after those assigned to the tied + elements.
    • +
  • 'ordinal' : All values are given a distinct rank, corresponding to the + order that the values occur in the Series.
    • +
+
+ 		+ 'average' +
+ descending + + bool + +
+

Rank in descending order.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression with rank data.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [3, 6, 1, 1, 6]}
+
+

We define a dataframe-agnostic function that computes the dense rank for +the data:

+ 
>>> def agnostic_dense_rank(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     result = df.with_columns(rnk=nw.col("a").rank(method="dense"))
+...     return result.to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dense_rank:

+ 
>>> agnostic_dense_rank(pd.DataFrame(data))
+   a  rnk
+0  3  2.0
+1  6  3.0
+2  1  1.0
+3  1  1.0
+4  6  3.0
+
+ 
>>> agnostic_dense_rank(pl.DataFrame(data))
+shape: (5, 2)
+┌─────┬─────┐
+│ a   ┆ rnk │
+│ --- ┆ --- │
+│ i64 ┆ u32 │
+╞═════╪═════╡
+│ 3   ┆ 2   │
+│ 6   ┆ 3   │
+│ 1   ┆ 1   │
+│ 1   ┆ 1   │
+│ 6   ┆ 3   │
+└─────┴─────┘
+
+ 
>>> agnostic_dense_rank(pa.table(data))
+pyarrow.Table
+a: int64
+rnk: uint64
+----
+a: [[3,6,1,1,6]]
+rnk: [[2,3,1,1,3]]
+
+ +
+ +
+ +
+ + +

+ replace_strict(old, new=None, *, return_dtype=None) + +

+ + +
+ +

Replace all values by different values.

+

This function must replace all non-null input values (else it raises an error).

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ old + + Sequence[Any] | Mapping[Any, Any] + +
+

Sequence of values to replace. It also accepts a mapping of values to +their replacement as syntactic sugar for +replace_all(old=list(mapping.keys()), new=list(mapping.values())).

+
+ 		+ required +
+ new + + Sequence[Any] | None + +
+

Sequence of values to replace by. Length must match the length of old.

+
+ 		+ None +
+ return_dtype + + DType | type[DType] | None + +
+

The data type of the resulting expression. If set to None +(default), the data type is determined automatically based on the other +inputs.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [3, 0, 1, 2]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define dataframe-agnostic functions:

+ 
>>> def agnostic_replace_strict(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         b=nw.col("a").replace_strict(
+...             [0, 1, 2, 3],
+...             ["zero", "one", "two", "three"],
+...             return_dtype=nw.String,
+...         )
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_replace_strict:

+ 
>>> agnostic_replace_strict(df_pd)
+   a      b
+0  3  three
+1  0   zero
+2  1    one
+3  2    two
+
+ 
>>> agnostic_replace_strict(df_pl)
+shape: (4, 2)
+┌─────┬───────┐
+│ a   ┆ b     │
+│ --- ┆ ---   │
+│ i64 ┆ str   │
+╞═════╪═══════╡
+│ 3   ┆ three │
+│ 0   ┆ zero  │
+│ 1   ┆ one   │
+│ 2   ┆ two   │
+└─────┴───────┘
+
+ 
>>> agnostic_replace_strict(df_pa)
+pyarrow.Table
+a: int64
+b: string
+----
+a: [[3,0,1,2]]
+b: [["three","zero","one","two"]]
+
+ +
+ +
+ +
+ + +

+ rolling_mean(window_size, *, min_periods=None, center=False) + +

+ + +
+ +

Apply a rolling mean (moving mean) over the values.

+
+

Warning

+

This functionality is considered unstable. It may be changed at any point +without it being considered a breaking change.

+
+

A window of length window_size will traverse the values. The resulting values +will be aggregated to their mean.

+

The window at a given row will include the row itself and the window_size - 1 +elements before it.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ window_size + + int + +
+

The length of the window in number of elements. It must be a +strictly positive integer.

+
+ 		+ required +
+ min_periods + + int | None + +
+

The number of values in the window that should be non-null before +computing a result. If set to None (default), it will be set equal to +window_size. If provided, it must be a strictly positive integer, and +less than or equal to window_size

+
+ 		+ None +
+ center + + bool + +
+

Set the labels at the center of the window.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1.0, 2.0, None, 4.0]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_rolling_mean(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         b=nw.col("a").rolling_mean(window_size=3, min_periods=1)
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_rolling_mean:

+ 
>>> agnostic_rolling_mean(df_pd)
+     a    b
+0  1.0  1.0
+1  2.0  1.5
+2  NaN  1.5
+3  4.0  3.0
+
+ 
>>> agnostic_rolling_mean(df_pl)
+shape: (4, 2)
+┌──────┬─────┐
+│ a    ┆ b   │
+│ ---  ┆ --- │
+│ f64  ┆ f64 │
+╞══════╪═════╡
+│ 1.0  ┆ 1.0 │
+│ 2.0  ┆ 1.5 │
+│ null ┆ 1.5 │
+│ 4.0  ┆ 3.0 │
+└──────┴─────┘
+
+ 
>>> agnostic_rolling_mean(df_pa)
+pyarrow.Table
+a: double
+b: double
+----
+a: [[1,2,null,4]]
+b: [[1,1.5,1.5,3]]
+
+ +
+ +
+ +
+ + +

+ rolling_std(window_size, *, min_periods=None, center=False, ddof=1) + +

+ + +
+ +

Apply a rolling standard deviation (moving standard deviation) over the values.

+
+

Warning

+

This functionality is considered unstable. It may be changed at any point +without it being considered a breaking change.

+
+

A window of length window_size will traverse the values. The resulting values +will be aggregated to their standard deviation.

+

The window at a given row will include the row itself and the window_size - 1 +elements before it.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ window_size + + int + +
+

The length of the window in number of elements. It must be a +strictly positive integer.

+
+ 		+ required +
+ min_periods + + int | None + +
+

The number of values in the window that should be non-null before +computing a result. If set to None (default), it will be set equal to +window_size. If provided, it must be a strictly positive integer, and +less than or equal to window_size.

+
+ 		+ None +
+ center + + bool + +
+

Set the labels at the center of the window.

+
+ 		+ False +
+ ddof + + int + +
+

Delta Degrees of Freedom; the divisor for a length N window is N - ddof.

+
+ 		+ 1 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1.0, 2.0, None, 4.0]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_rolling_std(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         b=nw.col("a").rolling_std(window_size=3, min_periods=1)
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_rolling_std:

+ 
>>> agnostic_rolling_std(df_pd)
+     a         b
+0  1.0       NaN
+1  2.0  0.707107
+2  NaN  0.707107
+3  4.0  1.414214
+
+ 
>>> agnostic_rolling_std(df_pl)
+shape: (4, 2)
+┌──────┬──────────┐
+│ a    ┆ b        │
+│ ---  ┆ ---      │
+│ f64  ┆ f64      │
+╞══════╪══════════╡
+│ 1.0  ┆ null     │
+│ 2.0  ┆ 0.707107 │
+│ null ┆ 0.707107 │
+│ 4.0  ┆ 1.414214 │
+└──────┴──────────┘
+
+ 
>>> agnostic_rolling_std(df_pa)
+pyarrow.Table
+a: double
+b: double
+----
+a: [[1,2,null,4]]
+b: [[nan,0.7071067811865476,0.7071067811865476,1.4142135623730951]]
+
+ +
+ +
+ +
+ + +

+ rolling_sum(window_size, *, min_periods=None, center=False) + +

+ + +
+ +

Apply a rolling sum (moving sum) over the values.

+
+

Warning

+

This functionality is considered unstable. It may be changed at any point +without it being considered a breaking change.

+
+

A window of length window_size will traverse the values. The resulting values +will be aggregated to their sum.

+

The window at a given row will include the row itself and the window_size - 1 +elements before it.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ window_size + + int + +
+

The length of the window in number of elements. It must be a +strictly positive integer.

+
+ 		+ required +
+ min_periods + + int | None + +
+

The number of values in the window that should be non-null before +computing a result. If set to None (default), it will be set equal to +window_size. If provided, it must be a strictly positive integer, and +less than or equal to window_size

+
+ 		+ None +
+ center + + bool + +
+

Set the labels at the center of the window.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1.0, 2.0, None, 4.0]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_rolling_sum(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         b=nw.col("a").rolling_sum(window_size=3, min_periods=1)
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_rolling_sum:

+ 
>>> agnostic_rolling_sum(df_pd)
+     a    b
+0  1.0  1.0
+1  2.0  3.0
+2  NaN  3.0
+3  4.0  6.0
+
+ 
>>> agnostic_rolling_sum(df_pl)
+shape: (4, 2)
+┌──────┬─────┐
+│ a    ┆ b   │
+│ ---  ┆ --- │
+│ f64  ┆ f64 │
+╞══════╪═════╡
+│ 1.0  ┆ 1.0 │
+│ 2.0  ┆ 3.0 │
+│ null ┆ 3.0 │
+│ 4.0  ┆ 6.0 │
+└──────┴─────┘
+
+ 
>>> agnostic_rolling_sum(df_pa)
+pyarrow.Table
+a: double
+b: double
+----
+a: [[1,2,null,4]]
+b: [[1,3,3,6]]
+
+ +
+ +
+ +
+ + +

+ rolling_var(window_size, *, min_periods=None, center=False, ddof=1) + +

+ + +
+ +

Apply a rolling variance (moving variance) over the values.

+
+

Warning

+

This functionality is considered unstable. It may be changed at any point +without it being considered a breaking change.

+
+

A window of length window_size will traverse the values. The resulting values +will be aggregated to their variance.

+

The window at a given row will include the row itself and the window_size - 1 +elements before it.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ window_size + + int + +
+

The length of the window in number of elements. It must be a +strictly positive integer.

+
+ 		+ required +
+ min_periods + + int | None + +
+

The number of values in the window that should be non-null before +computing a result. If set to None (default), it will be set equal to +window_size. If provided, it must be a strictly positive integer, and +less than or equal to window_size.

+
+ 		+ None +
+ center + + bool + +
+

Set the labels at the center of the window.

+
+ 		+ False +
+ ddof + + int + +
+

Delta Degrees of Freedom; the divisor for a length N window is N - ddof.

+
+ 		+ 1 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1.0, 2.0, None, 4.0]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_rolling_var(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         b=nw.col("a").rolling_var(window_size=3, min_periods=1)
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_rolling_var:

+ 
>>> agnostic_rolling_var(df_pd)
+     a    b
+0  1.0  NaN
+1  2.0  0.5
+2  NaN  0.5
+3  4.0  2.0
+
+ 
>>> agnostic_rolling_var(df_pl)
+shape: (4, 2)
+┌──────┬──────┐
+│ a    ┆ b    │
+│ ---  ┆ ---  │
+│ f64  ┆ f64  │
+╞══════╪══════╡
+│ 1.0  ┆ null │
+│ 2.0  ┆ 0.5  │
+│ null ┆ 0.5  │
+│ 4.0  ┆ 2.0  │
+└──────┴──────┘
+
+ 
>>> agnostic_rolling_var(df_pa)
+pyarrow.Table
+a: double
+b: double
+----
+a: [[1,2,null,4]]
+b: [[nan,0.5,0.5,2]]
+
+ +
+ +
+ +
+ + +

+ round(decimals=0) + +

+ + +
+ +

Round underlying floating point data by decimals digits.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ decimals + + int + +
+

Number of decimals to round by.

+
+ 		+ 0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +
+ Notes +

For values exactly halfway between rounded decimal values pandas behaves differently than Polars and Arrow.

+

pandas rounds to the nearest even value (e.g. -0.5 and 0.5 round to 0.0, 1.5 and 2.5 round to 2.0, 3.5 and +4.5 to 4.0, etc..).

+

Polars and Arrow round away from 0 (e.g. -0.5 to -1.0, 0.5 to 1.0, 1.5 to 2.0, 2.5 to 3.0, etc..).

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1.12345, 2.56789, 3.901234]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function that rounds to the first decimal:

+ 
>>> def agnostic_round(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").round(1)).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_round:

+ 
>>> agnostic_round(df_pd)
+     a
+0  1.1
+1  2.6
+2  3.9
+
+ 
>>> agnostic_round(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ f64 │
+╞═════╡
+│ 1.1 │
+│ 2.6 │
+│ 3.9 │
+└─────┘
+
+ 
>>> agnostic_round(df_pa)
+pyarrow.Table
+a: double
+----
+a: [[1.1,2.6,3.9]]
+
+ +
+ +
+ +
+ + +

+ sample(n=None, *, fraction=None, with_replacement=False, seed=None) + +

+ + +
+ +

Sample randomly from this expression.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int | None + +
+

Number of items to return. Cannot be used with fraction.

+
+ 		+ None +
+ fraction + + float | None + +
+

Fraction of items to return. Cannot be used with n.

+
+ 		+ None +
+ with_replacement + + bool + +
+

Allow values to be sampled more than once.

+
+ 		+ False +
+ seed + + int | None + +
+

Seed for the random number generator. If set to None (default), a random +seed is generated for each sample operation.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_sample(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.col("a").sample(fraction=1.0, with_replacement=True)
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_sample:

+ 
>>> agnostic_sample(df_pd)
+   a
+2  3
+0  1
+2  3
+
+ 
>>> agnostic_sample(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ f64 │
+╞═════╡
+│ 2   │
+│ 3   │
+│ 3   │
+└─────┘
+
+ 
>>> agnostic_sample(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[1,3,3]]
+
+ +
+ +
+ +
+ + +

+ shift(n) + +

+ + +
+ +

Shift values by n positions.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of positions to shift values by.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +
+ Notes +

pandas may change the dtype here, for example when introducing missing +values in an integer column. To ensure, that the dtype doesn't change, +you may want to use fill_null and cast. For example, to shift +and fill missing values with 0 in a Int64 column, you could +do:

+
nw.col("a").shift(1).fill_null(0).cast(nw.Int64)
+
+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 1, 3, 5, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_shift(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(a_shift=nw.col("a").shift(n=1)).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_shift:

+ 
>>> agnostic_shift(df_pd)
+   a_shift
+0      NaN
+1      1.0
+2      1.0
+3      3.0
+4      5.0
+
+ 
>>> agnostic_shift(df_pl)
+shape: (5, 1)
+┌─────────┐
+│ a_shift │
+│ ---     │
+│ i64     │
+╞═════════╡
+│ null    │
+│ 1       │
+│ 1       │
+│ 3       │
+│ 5       │
+└─────────┘
+
+ 
>>> agnostic_shift(df_pa)
+pyarrow.Table
+a_shift: int64
+----
+a_shift: [[null,1,1,3,5]]
+
+ +
+ +
+ +
+ + +

+ sort(*, descending=False, nulls_last=False) + +

+ + +
+ +

Sort this column. Place null values first.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ descending + + bool + +
+

Sort in descending order.

+
+ 		+ False +
+ nulls_last + + bool + +
+

Place null values last instead of first.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [5, None, 1, 2]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define dataframe-agnostic functions:

+ 
>>> def agnostic_sort(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").sort()).to_native()
+
+ 
>>> def agnostic_sort_descending(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").sort(descending=True)).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_sort and agnostic_sort_descending:

+ 
>>> agnostic_sort(df_pd)
+     a
+1  NaN
+2  1.0
+3  2.0
+0  5.0
+
+ 
>>> agnostic_sort(df_pl)
+shape: (4, 1)
+┌──────┐
+│ a    │
+│ ---  │
+│ i64  │
+╞══════╡
+│ null │
+│ 1    │
+│ 2    │
+│ 5    │
+└──────┘
+
+ 
>>> agnostic_sort(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[null,1,2,5]]
+
+ 
>>> agnostic_sort_descending(df_pd)
+     a
+1  NaN
+0  5.0
+3  2.0
+2  1.0
+
+ 
>>> agnostic_sort_descending(df_pl)
+shape: (4, 1)
+┌──────┐
+│ a    │
+│ ---  │
+│ i64  │
+╞══════╡
+│ null │
+│ 5    │
+│ 2    │
+│ 1    │
+└──────┘
+
+ 
>>> agnostic_sort_descending(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[null,5,2,1]]
+
+ +
+ +
+ +
+ + +

+ skew() + +

+ + +
+ +

Calculate the sample skewness of a column.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

An expression representing the sample skewness of the column.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3, 4, 5], "b": [1, 1, 2, 10, 100]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_skew(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a", "b").skew()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_skew:

+ 
>>> agnostic_skew(df_pd)
+     a         b
+0  0.0  1.472427
+
+ 
>>> agnostic_skew(df_pl)
+shape: (1, 2)
+┌─────┬──────────┐
+│ a   ┆ b        │
+│ --- ┆ ---      │
+│ f64 ┆ f64      │
+╞═════╪══════════╡
+│ 0.0 ┆ 1.472427 │
+└─────┴──────────┘
+
+ 
>>> agnostic_skew(df_pa)
+pyarrow.Table
+a: double
+b: double
+----
+a: [[0]]
+b: [[1.4724267269058975]]
+
+ +
+ +
+ +
+ + +

+ std(*, ddof=1) + +

+ + +
+ +

Get standard deviation.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ ddof + + int + +
+

"Delta Degrees of Freedom": the divisor used in the calculation is N - ddof, +where N represents the number of elements. By default ddof is 1.

+
+ 		+ 1 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [20, 25, 60], "b": [1.5, 1, -1.4]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_std(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a", "b").std(ddof=0)).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_std:

+ 
>>> agnostic_std(df_pd)
+          a         b
+0  17.79513  1.265789
+>>> agnostic_std(df_pl)
+shape: (1, 2)
+┌──────────┬──────────┐
+│ a        ┆ b        │
+│ ---      ┆ ---      │
+│ f64      ┆ f64      │
+╞══════════╪══════════╡
+│ 17.79513 ┆ 1.265789 │
+└──────────┴──────────┘
+>>> agnostic_std(df_pa)
+pyarrow.Table
+a: double
+b: double
+----
+a: [[17.795130420052185]]
+b: [[1.2657891697365016]]
+
+ +
+ +
+ +
+ + +

+ sum() + +

+ + +
+ +

Return the sum value.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [5, 10], "b": [50, 100]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_sum(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a", "b").sum()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_sum:

+ 
>>> agnostic_sum(df_pd)
+    a    b
+0  15  150
+>>> agnostic_sum(df_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 15  ┆ 150 │
+└─────┴─────┘
+>>> agnostic_sum(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[15]]
+b: [[150]]
+
+ +
+ +
+ +
+ + +

+ tail(n=10) + +

+ + +
+ +

Get the last n rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of rows to return.

+
+ 		+ 10 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": list(range(10))}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function that returns the last 3 rows:

+ 
>>> def agnostic_tail(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").tail(3)).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_tail:

+ 
>>> agnostic_tail(df_pd)
+   a
+7  7
+8  8
+9  9
+
+ 
>>> agnostic_tail(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 7   │
+│ 8   │
+│ 9   │
+└─────┘
+
+ 
>>> agnostic_tail(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[7,8,9]]
+
+ +
+ +
+ +
+ + +

+ unique(*, maintain_order=False) + +

+ + +
+ +

Return unique values of this expression.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ maintain_order + + bool + +
+

Keep the same order as the original expression. This may be more +expensive to compute. Settings this to True blocks the possibility +to run on the streaming engine for Polars.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 1, 3, 5, 5], "b": [2, 4, 4, 6, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_unique(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a", "b").unique(maintain_order=True)).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_unique:

+ 
>>> agnostic_unique(df_pd)
+   a  b
+0  1  2
+1  3  4
+2  5  6
+
+ 
>>> agnostic_unique(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 2   │
+│ 3   ┆ 4   │
+│ 5   ┆ 6   │
+└─────┴─────┘
+
+ 
>>> agnostic_unique(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[1,3,5]]
+b: [[2,4,6]]
+
+ +
+ +
+ +
+ + +

+ var(*, ddof=1) + +

+ + +
+ +

Get variance.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ ddof + + int + +
+

"Delta Degrees of Freedom": the divisor used in the calculation is N - ddof, + where N represents the number of elements. By default ddof is 1.

+
+ 		+ 1 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [20, 25, 60], "b": [1.5, 1, -1.4]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_var(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a", "b").var(ddof=0)).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_var:

+ 
>>> agnostic_var(df_pd)
+            a         b
+0  316.666667  1.602222
+
+ 
>>> agnostic_var(df_pl)
+shape: (1, 2)
+┌────────────┬──────────┐
+│ a          ┆ b        │
+│ ---        ┆ ---      │
+│ f64        ┆ f64      │
+╞════════════╪══════════╡
+│ 316.666667 ┆ 1.602222 │
+└────────────┴──────────┘
+
+ 
>>> agnostic_var(df_pa)
+pyarrow.Table
+a: double
+b: double
+----
+a: [[316.6666666666667]]
+b: [[1.6022222222222222]]
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
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+ + + +
+ + + +
+
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+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/expr_cat/index.html b/api-reference/expr_cat/index.html new file mode 100644 index 000000000..b6d917b0d --- /dev/null +++ b/api-reference/expr_cat/index.html @@ -0,0 +1,2034 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Expr.cat - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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narwhals.Expr.cat

+ + +
+ + + + +
+ + + + + + + + + + +
+ + + + + + + + + +
+ + +

+ get_categories() + +

+ + +
+ +

Get unique categories from column.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+

Let's create some dataframes:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"fruits": ["apple", "mango", "mango"]}
+>>> df_pd = pd.DataFrame(data, dtype="category")
+>>> df_pl = pl.DataFrame(data, schema={"fruits": pl.Categorical})
+
+

We define a dataframe-agnostic function to get unique categories +from column 'fruits':

+ 
>>> def agnostic_cat_get_categories(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("fruits").cat.get_categories()).to_native()
+
+

We can then pass any supported library such as pandas or Polars to + agnostic_cat_get_categories:

+
>>> agnostic_cat_get_categories(df_pd)
+  fruits
+0  apple
+1  mango
+
+>>> agnostic_cat_get_categories(df_pl)
+shape: (2, 1)
+┌────────┐
+│ fruits │
+│ ---    │
+│ str    │
+╞════════╡
+│ apple  │
+│ mango  │
+└────────┘
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/expr_dt/index.html b/api-reference/expr_dt/index.html new file mode 100644 index 000000000..75b120e6d --- /dev/null +++ b/api-reference/expr_dt/index.html @@ -0,0 +1,4470 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Expr.dt - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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narwhals.Expr.dt

+ + +
+ + + + +
+ + + + + + + + + + +
+ + + + + + + + + +
+ + +

+ convert_time_zone(time_zone) + +

+ + +
+ +

Convert to a new time zone.

+

If converting from a time-zone-naive column, then conversion happens +as if converting from UTC.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ time_zone + + str + +
+

Target time zone.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime, timezone
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [
+...         datetime(2024, 1, 1, tzinfo=timezone.utc),
+...         datetime(2024, 1, 2, tzinfo=timezone.utc),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_convert_time_zone(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.col("a").dt.convert_time_zone("Asia/Kathmandu")
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_convert_time_zone:

+ 
>>> agnostic_dt_convert_time_zone(df_pd)
+                          a
+0 2024-01-01 05:45:00+05:45
+1 2024-01-02 05:45:00+05:45
+
+ 
>>> agnostic_dt_convert_time_zone(df_pl)
+shape: (2, 1)
+┌──────────────────────────────┐
+│ a                            │
+│ ---                          │
+│ datetime[μs, Asia/Kathmandu] │
+╞══════════════════════════════╡
+│ 2024-01-01 05:45:00 +0545    │
+│ 2024-01-02 05:45:00 +0545    │
+└──────────────────────────────┘
+
+ 
>>> agnostic_dt_convert_time_zone(df_pa)
+pyarrow.Table
+a: timestamp[us, tz=Asia/Kathmandu]
+----
+a: [[2024-01-01 00:00:00.000000Z,2024-01-02 00:00:00.000000Z]]
+
+ +
+ +
+ +
+ + +

+ date() + +

+ + +
+ +

Extract the date from underlying DateTime representation.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ NotImplementedError + +
+

If pandas default backend is being used.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [datetime(2012, 1, 7, 10, 20), datetime(2023, 3, 10, 11, 32)]}
+>>> df_pd = pd.DataFrame(data).convert_dtypes(dtype_backend="pyarrow")
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_dt_date(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a").dt.date()).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_date:

+ 
>>> agnostic_dt_date(df_pd)
+            a
+0  2012-01-07
+1  2023-03-10
+
+ 
>>> agnostic_dt_date(df_pl)
+shape: (2, 1)
+┌────────────┐
+│ a          │
+│ ---        │
+│ date       │
+╞════════════╡
+│ 2012-01-07 │
+│ 2023-03-10 │
+└────────────┘
+
+ 
>>> agnostic_dt_date(df_pa)
+pyarrow.Table
+a: date32[day]
+----
+a: [[2012-01-07,2023-03-10]]
+
+ +
+ +
+ +
+ + +

+ day() + +

+ + +
+ +

Extract day from underlying DateTime representation.

+

Returns the day of month starting from 1. The return value ranges from 1 to 31. (The last day of month differs by months.)

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 6, 1),
+...         datetime(2024, 12, 13),
+...         datetime(2065, 1, 1),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_day(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("datetime").dt.day().alias("day"),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_day:

+ 
>>> agnostic_dt_day(df_pd)
+    datetime  day
+0 1978-06-01    1
+1 2024-12-13   13
+2 2065-01-01    1
+
+ 
>>> agnostic_dt_day(df_pl)
+shape: (3, 2)
+┌─────────────────────┬─────┐
+│ datetime            ┆ day │
+│ ---                 ┆ --- │
+│ datetime[μs]        ┆ i8  │
+╞═════════════════════╪═════╡
+│ 1978-06-01 00:00:00 ┆ 1   │
+│ 2024-12-13 00:00:00 ┆ 13  │
+│ 2065-01-01 00:00:00 ┆ 1   │
+└─────────────────────┴─────┘
+
+ 
>>> agnostic_dt_day(df_pa)
+pyarrow.Table
+datetime: timestamp[us]
+day: int64
+----
+datetime: [[1978-06-01 00:00:00.000000,2024-12-13 00:00:00.000000,2065-01-01 00:00:00.000000]]
+day: [[1,13,1]]
+
+ +
+ +
+ +
+ + +

+ hour() + +

+ + +
+ +

Extract hour from underlying DateTime representation.

+

Returns the hour number from 0 to 23.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 1, 1, 1),
+...         datetime(2024, 10, 13, 5),
+...         datetime(2065, 1, 1, 10),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_hour(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("datetime").dt.hour().alias("hour")
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_hour:

+ 
>>> agnostic_dt_hour(df_pd)
+             datetime  hour
+0 1978-01-01 01:00:00     1
+1 2024-10-13 05:00:00     5
+2 2065-01-01 10:00:00    10
+
+ 
>>> agnostic_dt_hour(df_pl)
+shape: (3, 2)
+┌─────────────────────┬──────┐
+│ datetime            ┆ hour │
+│ ---                 ┆ ---  │
+│ datetime[μs]        ┆ i8   │
+╞═════════════════════╪══════╡
+│ 1978-01-01 01:00:00 ┆ 1    │
+│ 2024-10-13 05:00:00 ┆ 5    │
+│ 2065-01-01 10:00:00 ┆ 10   │
+└─────────────────────┴──────┘
+
+ 
>>> agnostic_dt_hour(df_pa)
+pyarrow.Table
+datetime: timestamp[us]
+hour: int64
+----
+datetime: [[1978-01-01 01:00:00.000000,2024-10-13 05:00:00.000000,2065-01-01 10:00:00.000000]]
+hour: [[1,5,10]]
+
+ +
+ +
+ +
+ + +

+ microsecond() + +

+ + +
+ +

Extract microseconds from underlying DateTime representation.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 1, 1, 1, 1, 1, 0),
+...         datetime(2024, 10, 13, 5, 30, 14, 505000),
+...         datetime(2065, 1, 1, 10, 20, 30, 67000),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_microsecond(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("datetime").dt.microsecond().alias("microsecond"),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_microsecond:

+ 
>>> agnostic_dt_microsecond(df_pd)
+                 datetime  microsecond
+0 1978-01-01 01:01:01.000            0
+1 2024-10-13 05:30:14.505       505000
+2 2065-01-01 10:20:30.067        67000
+
+ 
>>> agnostic_dt_microsecond(df_pl)
+shape: (3, 2)
+┌─────────────────────────┬─────────────┐
+│ datetime                ┆ microsecond │
+│ ---                     ┆ ---         │
+│ datetime[μs]            ┆ i32         │
+╞═════════════════════════╪═════════════╡
+│ 1978-01-01 01:01:01     ┆ 0           │
+│ 2024-10-13 05:30:14.505 ┆ 505000      │
+│ 2065-01-01 10:20:30.067 ┆ 67000       │
+└─────────────────────────┴─────────────┘
+
+ 
>>> agnostic_dt_microsecond(df_pa)
+pyarrow.Table
+datetime: timestamp[us]
+microsecond: int64
+----
+datetime: [[1978-01-01 01:01:01.000000,2024-10-13 05:30:14.505000,2065-01-01 10:20:30.067000]]
+microsecond: [[0,505000,67000]]
+
+ +
+ +
+ +
+ + +

+ millisecond() + +

+ + +
+ +

Extract milliseconds from underlying DateTime representation.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 1, 1, 1, 1, 1, 0),
+...         datetime(2024, 10, 13, 5, 30, 14, 505000),
+...         datetime(2065, 1, 1, 10, 20, 30, 67000),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_millisecond(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("datetime").dt.millisecond().alias("millisecond"),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_millisecond:

+ 
>>> agnostic_dt_millisecond(df_pd)
+                 datetime  millisecond
+0 1978-01-01 01:01:01.000            0
+1 2024-10-13 05:30:14.505          505
+2 2065-01-01 10:20:30.067           67
+
+ 
>>> agnostic_dt_millisecond(df_pl)
+shape: (3, 2)
+┌─────────────────────────┬─────────────┐
+│ datetime                ┆ millisecond │
+│ ---                     ┆ ---         │
+│ datetime[μs]            ┆ i32         │
+╞═════════════════════════╪═════════════╡
+│ 1978-01-01 01:01:01     ┆ 0           │
+│ 2024-10-13 05:30:14.505 ┆ 505         │
+│ 2065-01-01 10:20:30.067 ┆ 67          │
+└─────────────────────────┴─────────────┘
+
+ 
>>> agnostic_dt_millisecond(df_pa)
+pyarrow.Table
+datetime: timestamp[us]
+millisecond: int64
+----
+datetime: [[1978-01-01 01:01:01.000000,2024-10-13 05:30:14.505000,2065-01-01 10:20:30.067000]]
+millisecond: [[0,505,67]]
+
+ +
+ +
+ +
+ + +

+ minute() + +

+ + +
+ +

Extract minutes from underlying DateTime representation.

+

Returns the minute number from 0 to 59.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 1, 1, 1, 1),
+...         datetime(2024, 10, 13, 5, 30),
+...         datetime(2065, 1, 1, 10, 20),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_minute(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("datetime").dt.minute().alias("minute"),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_minute:

+ 
>>> agnostic_dt_minute(df_pd)
+             datetime  minute
+0 1978-01-01 01:01:00       1
+1 2024-10-13 05:30:00      30
+2 2065-01-01 10:20:00      20
+
+ 
>>> agnostic_dt_minute(df_pl)
+shape: (3, 2)
+┌─────────────────────┬────────┐
+│ datetime            ┆ minute │
+│ ---                 ┆ ---    │
+│ datetime[μs]        ┆ i8     │
+╞═════════════════════╪════════╡
+│ 1978-01-01 01:01:00 ┆ 1      │
+│ 2024-10-13 05:30:00 ┆ 30     │
+│ 2065-01-01 10:20:00 ┆ 20     │
+└─────────────────────┴────────┘
+
+ 
>>> agnostic_dt_minute(df_pa)
+pyarrow.Table
+datetime: timestamp[us]
+minute: int64
+----
+datetime: [[1978-01-01 01:01:00.000000,2024-10-13 05:30:00.000000,2065-01-01 10:20:00.000000]]
+minute: [[1,30,20]]
+
+ +
+ +
+ +
+ + +

+ month() + +

+ + +
+ +

Extract month from underlying DateTime representation.

+

Returns the month number starting from 1. The return value ranges from 1 to 12.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 6, 1),
+...         datetime(2024, 12, 13),
+...         datetime(2065, 1, 1),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_month(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("datetime").dt.month().alias("month"),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_month:

+ 
>>> agnostic_dt_month(df_pd)
+    datetime  month
+0 1978-06-01      6
+1 2024-12-13     12
+2 2065-01-01      1
+
+ 
>>> agnostic_dt_month(df_pl)
+shape: (3, 2)
+┌─────────────────────┬───────┐
+│ datetime            ┆ month │
+│ ---                 ┆ ---   │
+│ datetime[μs]        ┆ i8    │
+╞═════════════════════╪═══════╡
+│ 1978-06-01 00:00:00 ┆ 6     │
+│ 2024-12-13 00:00:00 ┆ 12    │
+│ 2065-01-01 00:00:00 ┆ 1     │
+└─────────────────────┴───────┘
+
+ 
>>> agnostic_dt_month(df_pa)
+pyarrow.Table
+datetime: timestamp[us]
+month: int64
+----
+datetime: [[1978-06-01 00:00:00.000000,2024-12-13 00:00:00.000000,2065-01-01 00:00:00.000000]]
+month: [[6,12,1]]
+
+ +
+ +
+ +
+ + +

+ nanosecond() + +

+ + +
+ +

Extract Nanoseconds from underlying DateTime representation.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 1, 1, 1, 1, 1, 0),
+...         datetime(2024, 10, 13, 5, 30, 14, 500000),
+...         datetime(2065, 1, 1, 10, 20, 30, 60000),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_nanosecond(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("datetime").dt.nanosecond().alias("nanosecond"),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_nanosecond:

+ 
>>> agnostic_dt_nanosecond(df_pd)
+                 datetime  nanosecond
+0 1978-01-01 01:01:01.000           0
+1 2024-10-13 05:30:14.500   500000000
+2 2065-01-01 10:20:30.060    60000000
+
+ 
>>> agnostic_dt_nanosecond(df_pl)
+shape: (3, 2)
+┌─────────────────────────┬────────────┐
+│ datetime                ┆ nanosecond │
+│ ---                     ┆ ---        │
+│ datetime[μs]            ┆ i32        │
+╞═════════════════════════╪════════════╡
+│ 1978-01-01 01:01:01     ┆ 0          │
+│ 2024-10-13 05:30:14.500 ┆ 500000000  │
+│ 2065-01-01 10:20:30.060 ┆ 60000000   │
+└─────────────────────────┴────────────┘
+
+ 
>>> agnostic_dt_nanosecond(df_pa)
+pyarrow.Table
+datetime: timestamp[us]
+nanosecond: int64
+----
+datetime: [[1978-01-01 01:01:01.000000,2024-10-13 05:30:14.500000,2065-01-01 10:20:30.060000]]
+nanosecond: [[0,500000000,60000000]]
+
+ +
+ +
+ +
+ + +

+ ordinal_day() + +

+ + +
+ +

Get ordinal day.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [datetime(2020, 1, 1), datetime(2020, 8, 3)]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_ordinal_day(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         a_ordinal_day=nw.col("a").dt.ordinal_day()
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_ordinal_day:

+ 
>>> agnostic_dt_ordinal_day(df_pd)
+           a  a_ordinal_day
+0 2020-01-01              1
+1 2020-08-03            216
+
+ 
>>> agnostic_dt_ordinal_day(df_pl)
+shape: (2, 2)
+┌─────────────────────┬───────────────┐
+│ a                   ┆ a_ordinal_day │
+│ ---                 ┆ ---           │
+│ datetime[μs]        ┆ i16           │
+╞═════════════════════╪═══════════════╡
+│ 2020-01-01 00:00:00 ┆ 1             │
+│ 2020-08-03 00:00:00 ┆ 216           │
+└─────────────────────┴───────────────┘
+
+ 
>>> agnostic_dt_ordinal_day(df_pa)
+pyarrow.Table
+a: timestamp[us]
+a_ordinal_day: int64
+----
+a: [[2020-01-01 00:00:00.000000,2020-08-03 00:00:00.000000]]
+a_ordinal_day: [[1,216]]
+
+ +
+ +
+ +
+ + +

+ replace_time_zone(time_zone) + +

+ + +
+ +

Replace time zone.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ time_zone + + str | None + +
+

Target time zone.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime, timezone
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [
+...         datetime(2024, 1, 1, tzinfo=timezone.utc),
+...         datetime(2024, 1, 2, tzinfo=timezone.utc),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_replace_time_zone(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.col("a").dt.replace_time_zone("Asia/Kathmandu")
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_replace_time_zone:

+ 
>>> agnostic_dt_replace_time_zone(df_pd)
+                          a
+0 2024-01-01 00:00:00+05:45
+1 2024-01-02 00:00:00+05:45
+
+ 
>>> agnostic_dt_replace_time_zone(df_pl)
+shape: (2, 1)
+┌──────────────────────────────┐
+│ a                            │
+│ ---                          │
+│ datetime[μs, Asia/Kathmandu] │
+╞══════════════════════════════╡
+│ 2024-01-01 00:00:00 +0545    │
+│ 2024-01-02 00:00:00 +0545    │
+└──────────────────────────────┘
+
+ 
>>> agnostic_dt_replace_time_zone(df_pa)
+pyarrow.Table
+a: timestamp[us, tz=Asia/Kathmandu]
+----
+a: [[2023-12-31 18:15:00.000000Z,2024-01-01 18:15:00.000000Z]]
+
+ +
+ +
+ +
+ + +

+ second() + +

+ + +
+ +

Extract seconds from underlying DateTime representation.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 1, 1, 1, 1, 1),
+...         datetime(2024, 10, 13, 5, 30, 14),
+...         datetime(2065, 1, 1, 10, 20, 30),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_second(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("datetime").dt.second().alias("second"),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_second:

+ 
>>> agnostic_dt_second(df_pd)
+             datetime  second
+0 1978-01-01 01:01:01       1
+1 2024-10-13 05:30:14      14
+2 2065-01-01 10:20:30      30
+
+ 
>>> agnostic_dt_second(df_pl)
+shape: (3, 2)
+┌─────────────────────┬────────┐
+│ datetime            ┆ second │
+│ ---                 ┆ ---    │
+│ datetime[μs]        ┆ i8     │
+╞═════════════════════╪════════╡
+│ 1978-01-01 01:01:01 ┆ 1      │
+│ 2024-10-13 05:30:14 ┆ 14     │
+│ 2065-01-01 10:20:30 ┆ 30     │
+└─────────────────────┴────────┘
+
+ 
>>> agnostic_dt_second(df_pa)
+pyarrow.Table
+datetime: timestamp[us]
+second: int64
+----
+datetime: [[1978-01-01 01:01:01.000000,2024-10-13 05:30:14.000000,2065-01-01 10:20:30.000000]]
+second: [[1,14,30]]
+
+ +
+ +
+ +
+ + +

+ timestamp(time_unit='us') + +

+ + +
+ +

Return a timestamp in the given time unit.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ time_unit + + Literal['ns', 'us', 'ms'] + +
+

{'ns', 'us', 'ms'} +Time unit.

+
+ 		+ 'us' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from datetime import date
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"date": [date(2001, 1, 1), None, date(2001, 1, 3)]}
+>>> df_pd = pd.DataFrame(data, dtype="datetime64[ns]")
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_timestamp(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("date").dt.timestamp().alias("timestamp_us"),
+...         nw.col("date").dt.timestamp("ms").alias("timestamp_ms"),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_timestamp:

+ 
>>> agnostic_dt_timestamp(df_pd)
+        date  timestamp_us  timestamp_ms
+0 2001-01-01  9.783072e+14  9.783072e+11
+1        NaT           NaN           NaN
+2 2001-01-03  9.784800e+14  9.784800e+11
+
+ 
>>> agnostic_dt_timestamp(df_pl)
+shape: (3, 3)
+┌────────────┬─────────────────┬──────────────┐
+│ date       ┆ timestamp_us    ┆ timestamp_ms │
+│ ---        ┆ ---             ┆ ---          │
+│ date       ┆ i64             ┆ i64          │
+╞════════════╪═════════════════╪══════════════╡
+│ 2001-01-01 ┆ 978307200000000 ┆ 978307200000 │
+│ null       ┆ null            ┆ null         │
+│ 2001-01-03 ┆ 978480000000000 ┆ 978480000000 │
+└────────────┴─────────────────┴──────────────┘
+
+ 
>>> agnostic_dt_timestamp(df_pa)
+pyarrow.Table
+date: date32[day]
+timestamp_us: int64
+timestamp_ms: int64
+----
+date: [[2001-01-01,null,2001-01-03]]
+timestamp_us: [[978307200000000,null,978480000000000]]
+timestamp_ms: [[978307200000,null,978480000000]]
+
+ +
+ +
+ +
+ + +

+ total_microseconds() + +

+ + +
+ +

Get total microseconds.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

The function outputs the total microseconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() and cast in this case.

+
+ +

Examples:

+ 
>>> from datetime import timedelta
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [
+...         timedelta(microseconds=10),
+...         timedelta(milliseconds=1, microseconds=200),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_total_microseconds(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         a_total_microseconds=nw.col("a").dt.total_microseconds()
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_total_microseconds:

+ 
>>> agnostic_dt_total_microseconds(df_pd)
+                       a  a_total_microseconds
+0 0 days 00:00:00.000010                    10
+1 0 days 00:00:00.001200                  1200
+
+ 
>>> agnostic_dt_total_microseconds(df_pl)
+shape: (2, 2)
+┌──────────────┬──────────────────────┐
+│ a            ┆ a_total_microseconds │
+│ ---          ┆ ---                  │
+│ duration[μs] ┆ i64                  │
+╞══════════════╪══════════════════════╡
+│ 10µs         ┆ 10                   │
+│ 1200µs       ┆ 1200                 │
+└──────────────┴──────────────────────┘
+
+ 
>>> agnostic_dt_total_microseconds(df_pa)
+pyarrow.Table
+a: duration[us]
+a_total_microseconds: int64
+----
+a: [[10,1200]]
+a_total_microseconds: [[10,1200]]
+
+ +
+ +
+ +
+ + +

+ total_milliseconds() + +

+ + +
+ +

Get total milliseconds.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

The function outputs the total milliseconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() and cast in this case.

+
+ +

Examples:

+ 
>>> from datetime import timedelta
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [
+...         timedelta(milliseconds=10),
+...         timedelta(milliseconds=20, microseconds=40),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_total_milliseconds(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         a_total_milliseconds=nw.col("a").dt.total_milliseconds()
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_total_milliseconds:

+ 
>>> agnostic_dt_total_milliseconds(df_pd)
+                       a  a_total_milliseconds
+0 0 days 00:00:00.010000                    10
+1 0 days 00:00:00.020040                    20
+
+ 
>>> agnostic_dt_total_milliseconds(df_pl)
+shape: (2, 2)
+┌──────────────┬──────────────────────┐
+│ a            ┆ a_total_milliseconds │
+│ ---          ┆ ---                  │
+│ duration[μs] ┆ i64                  │
+╞══════════════╪══════════════════════╡
+│ 10ms         ┆ 10                   │
+│ 20040µs      ┆ 20                   │
+└──────────────┴──────────────────────┘
+
+ 
>>> agnostic_dt_total_milliseconds(df_pa)
+pyarrow.Table
+a: duration[us]
+a_total_milliseconds: int64
+----
+a: [[10000,20040]]
+a_total_milliseconds: [[10,20]]
+
+ +
+ +
+ +
+ + +

+ total_minutes() + +

+ + +
+ +

Get total minutes.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

The function outputs the total minutes in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() and cast in this case.

+
+ +

Examples:

+ 
>>> from datetime import timedelta
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [timedelta(minutes=10), timedelta(minutes=20, seconds=40)]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_total_minutes(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         a_total_minutes=nw.col("a").dt.total_minutes()
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_total_minutes:

+ 
>>> agnostic_dt_total_minutes(df_pd)
+                a  a_total_minutes
+0 0 days 00:10:00               10
+1 0 days 00:20:40               20
+
+ 
>>> agnostic_dt_total_minutes(df_pl)
+shape: (2, 2)
+┌──────────────┬─────────────────┐
+│ a            ┆ a_total_minutes │
+│ ---          ┆ ---             │
+│ duration[μs] ┆ i64             │
+╞══════════════╪═════════════════╡
+│ 10m          ┆ 10              │
+│ 20m 40s      ┆ 20              │
+└──────────────┴─────────────────┘
+
+ 
>>> agnostic_dt_total_minutes(df_pa)
+pyarrow.Table
+a: duration[us]
+a_total_minutes: int64
+----
+a: [[600000000,1240000000]]
+a_total_minutes: [[10,20]]
+
+ +
+ +
+ +
+ + +

+ total_nanoseconds() + +

+ + +
+ +

Get total nanoseconds.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

The function outputs the total nanoseconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() and cast in this case.

+
+ +

Examples:

+ 
>>> from datetime import timedelta
+>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = ["2024-01-01 00:00:00.000000001", "2024-01-01 00:00:00.000000002"]
+>>> df_pd = pd.DataFrame({"a": pd.to_datetime(data)})
+>>> df_pl = pl.DataFrame({"a": data}).with_columns(
+...     pl.col("a").str.to_datetime(time_unit="ns")
+... )
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_total_nanoseconds(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         a_diff_total_nanoseconds=nw.col("a").diff().dt.total_nanoseconds()
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_total_nanoseconds:

+ 
>>> agnostic_dt_total_nanoseconds(df_pd)
+                              a  a_diff_total_nanoseconds
+0 2024-01-01 00:00:00.000000001                       NaN
+1 2024-01-01 00:00:00.000000002                       1.0
+
+ 
>>> agnostic_dt_total_nanoseconds(df_pl)
+shape: (2, 2)
+┌───────────────────────────────┬──────────────────────────┐
+│ a                             ┆ a_diff_total_nanoseconds │
+│ ---                           ┆ ---                      │
+│ datetime[ns]                  ┆ i64                      │
+╞═══════════════════════════════╪══════════════════════════╡
+│ 2024-01-01 00:00:00.000000001 ┆ null                     │
+│ 2024-01-01 00:00:00.000000002 ┆ 1                        │
+└───────────────────────────────┴──────────────────────────┘
+
+ +
+ +
+ +
+ + +

+ total_seconds() + +

+ + +
+ +

Get total seconds.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

The function outputs the total seconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() and cast in this case.

+
+ +

Examples:

+ 
>>> from datetime import timedelta
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [timedelta(seconds=10), timedelta(seconds=20, milliseconds=40)]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_total_seconds(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         a_total_seconds=nw.col("a").dt.total_seconds()
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_total_seconds:

+ 
>>> agnostic_dt_total_seconds(df_pd)
+                       a  a_total_seconds
+0        0 days 00:00:10               10
+1 0 days 00:00:20.040000               20
+
+ 
>>> agnostic_dt_total_seconds(df_pl)
+shape: (2, 2)
+┌──────────────┬─────────────────┐
+│ a            ┆ a_total_seconds │
+│ ---          ┆ ---             │
+│ duration[μs] ┆ i64             │
+╞══════════════╪═════════════════╡
+│ 10s          ┆ 10              │
+│ 20s 40ms     ┆ 20              │
+└──────────────┴─────────────────┘
+
+ 
>>> agnostic_dt_total_seconds(df_pa)
+pyarrow.Table
+a: duration[us]
+a_total_seconds: int64
+----
+a: [[10000000,20040000]]
+a_total_seconds: [[10,20]]
+
+ +
+ +
+ +
+ + +

+ to_string(format) + +

+ + +
+ +

Convert a Date/Time/Datetime column into a String column with the given format.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ format + + str + +
+

Format to format temporal column with.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

Unfortunately, different libraries interpret format directives a bit +differently.

+
    +
  • Chrono, the library used by Polars, uses "%.f" for fractional seconds, + whereas pandas and Python stdlib use ".%f".
    • +
  • PyArrow interprets "%S" as "seconds, including fractional seconds" + whereas most other tools interpret it as "just seconds, as 2 digits".
    • +
+

Therefore, we make the following adjustments:

+
    +
  • for pandas-like libraries, we replace "%S.%f" with "%S%.f".
    • +
  • for PyArrow, we replace "%S.%f" with "%S".
    • +
+

Workarounds like these don't make us happy, and we try to avoid them as +much as possible, but here we feel like it's the best compromise.

+

If you just want to format a date/datetime Series as a local datetime +string, and have it work as consistently as possible across libraries, +we suggest using:

+
    +
  • "%Y-%m-%dT%H:%M:%S%.f" for datetimes
    • +
  • "%Y-%m-%d" for dates
    • +
+

though note that, even then, different tools may return a different number +of trailing zeros. Nonetheless, this is probably consistent enough for +most applications.

+

If you have an application where this is not enough, please open an issue +and let us know.

+
+ +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [
+...         datetime(2020, 3, 1),
+...         datetime(2020, 4, 1),
+...         datetime(2020, 5, 1),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_to_string(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.col("a").dt.to_string("%Y/%m/%d %H:%M:%S")
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_to_string:

+ 
>>> agnostic_dt_to_string(df_pd)
+                     a
+0  2020/03/01 00:00:00
+1  2020/04/01 00:00:00
+2  2020/05/01 00:00:00
+
+ 
>>> agnostic_dt_to_string(df_pl)
+shape: (3, 1)
+┌─────────────────────┐
+│ a                   │
+│ ---                 │
+│ str                 │
+╞═════════════════════╡
+│ 2020/03/01 00:00:00 │
+│ 2020/04/01 00:00:00 │
+│ 2020/05/01 00:00:00 │
+└─────────────────────┘
+
+ 
>>> agnostic_dt_to_string(df_pa)
+pyarrow.Table
+a: string
+----
+a: [["2020/03/01 00:00:00.000000","2020/04/01 00:00:00.000000","2020/05/01 00:00:00.000000"]]
+
+ +
+ +
+ +
+ + +

+ weekday() + +

+ + +
+ +

Extract the week day from the underlying Date representation.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

Returns the ISO weekday number where monday = 1 and sunday = 7

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [datetime(2020, 1, 1), datetime(2020, 8, 3)]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_weekday(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(a_weekday=nw.col("a").dt.weekday()).to_native()
+
+

We can then pass either pandas, Polars, PyArrow, and other supported libraries to +agnostic_dt_weekday:

+ 
>>> agnostic_dt_weekday(df_pd)
+           a  a_weekday
+0 2020-01-01          3
+1 2020-08-03          1
+
+ 
>>> agnostic_dt_weekday(df_pl)
+shape: (2, 2)
+┌─────────────────────┬───────────┐
+│ a                   ┆ a_weekday │
+│ ---                 ┆ ---       │
+│ datetime[μs]        ┆ i8        │
+╞═════════════════════╪═══════════╡
+│ 2020-01-01 00:00:00 ┆ 3         │
+│ 2020-08-03 00:00:00 ┆ 1         │
+└─────────────────────┴───────────┘
+
+ 
>>> agnostic_dt_weekday(df_pa)
+pyarrow.Table
+a: timestamp[us]
+a_weekday: int64
+----
+a: [[2020-01-01 00:00:00.000000,2020-08-03 00:00:00.000000]]
+a_weekday: [[3,1]]
+
+ +
+ +
+ +
+ + +

+ year() + +

+ + +
+ +

Extract year from underlying DateTime representation.

+

Returns the year number in the calendar date.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "datetime": [
+...         datetime(1978, 6, 1),
+...         datetime(2024, 12, 13),
+...         datetime(2065, 1, 1),
+...     ]
+... }
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_dt_year(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.col("datetime").dt.year().alias("year")
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dt_year:

+ 
>>> agnostic_dt_year(df_pd)
+    datetime  year
+0 1978-06-01  1978
+1 2024-12-13  2024
+2 2065-01-01  2065
+
+ 
>>> agnostic_dt_year(df_pl)
+shape: (3, 2)
+┌─────────────────────┬──────┐
+│ datetime            ┆ year │
+│ ---                 ┆ ---  │
+│ datetime[μs]        ┆ i32  │
+╞═════════════════════╪══════╡
+│ 1978-06-01 00:00:00 ┆ 1978 │
+│ 2024-12-13 00:00:00 ┆ 2024 │
+│ 2065-01-01 00:00:00 ┆ 2065 │
+└─────────────────────┴──────┘
+
+ 
>>> agnostic_dt_year(df_pa)
+pyarrow.Table
+datetime: timestamp[us]
+year: int64
+----
+datetime: [[1978-06-01 00:00:00.000000,2024-12-13 00:00:00.000000,2065-01-01 00:00:00.000000]]
+year: [[1978,2024,2065]]
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/expr_list/index.html b/api-reference/expr_list/index.html new file mode 100644 index 000000000..f81d2154b --- /dev/null +++ b/api-reference/expr_list/index.html @@ -0,0 +1,2045 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Expr.list - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Expr.list

+ + +
+ + + + +
+ + + + + + + + + + +
+ + + + + + + + + +
+ + +

+ len() + +

+ + +
+ +

Return the number of elements in each list.

+

Null values count towards the total.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [[1, 2], [3, 4, None], None, []]}
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_list_len(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(a_len=nw.col("a").list.len()).to_native()
+
+

We can then pass pandas / PyArrow / Polars / any other supported library:

+ 
>>> agnostic_list_len(
+...     pd.DataFrame(data).astype({"a": pd.ArrowDtype(pa.list_(pa.int64()))})
+... )
+               a  a_len
+0        [1. 2.]      2
+1  [ 3.  4. nan]      3
+2           <NA>   <NA>
+3             []      0
+
+ 
>>> agnostic_list_len(pl.DataFrame(data))
+shape: (4, 2)
+┌──────────────┬───────┐
+│ a            ┆ a_len │
+│ ---          ┆ ---   │
+│ list[i64]    ┆ u32   │
+╞══════════════╪═══════╡
+│ [1, 2]       ┆ 2     │
+│ [3, 4, null] ┆ 3     │
+│ null         ┆ null  │
+│ []           ┆ 0     │
+└──────────────┴───────┘
+
+ 
>>> agnostic_list_len(pa.table(data))
+pyarrow.Table
+a: list<item: int64>
+  child 0, item: int64
+a_len: uint32
+----
+a: [[[1,2],[3,4,null],null,[]]]
+a_len: [[2,3,null,0]]
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/expr_name/index.html b/api-reference/expr_name/index.html new file mode 100644 index 000000000..d80ddc880 --- /dev/null +++ b/api-reference/expr_name/index.html @@ -0,0 +1,2602 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Expr.name - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Expr.name

+ + +
+ + + + +
+ + + + + + + + + + +
+ + + + + + + + + +
+ + +

+ keep() + +

+ + +
+ +

Keep the original root name of the expression.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

This will undo any previous renaming operations on the expression. +Due to implementation constraints, this method can only be called as the last +expression in a chain. Only one name operation per expression will work.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrame
+>>>
+>>> data = {"foo": [1, 2], "BAR": [4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_name_keep(df_native: IntoFrame) -> list[str]:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("foo").alias("alias_for_foo").name.keep()).columns
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_name_keep:

+ 
>>> agnostic_name_keep(df_pd)
+['foo']
+
+ 
>>> agnostic_name_keep(df_pl)
+['foo']
+
+ 
>>> agnostic_name_keep(df_pa)
+['foo']
+
+ +
+ +
+ +
+ + +

+ map(function) + +

+ + +
+ +

Rename the output of an expression by mapping a function over the root name.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ function + + Callable[[str], str] + +
+

Function that maps a root name to a new name.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

This will undo any previous renaming operations on the expression. +Due to implementation constraints, this method can only be called as the last +expression in a chain. Only one name operation per expression will work.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrame
+>>>
+>>> data = {"foo": [1, 2], "BAR": [4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> renaming_func = lambda s: s[::-1]  # reverse column name
+>>> def agnostic_name_map(df_native: IntoFrame) -> list[str]:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("foo", "BAR").name.map(renaming_func)).columns
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_name_map:

+ 
>>> agnostic_name_map(df_pd)
+['oof', 'RAB']
+
+ 
>>> agnostic_name_map(df_pl)
+['oof', 'RAB']
+
+ 
>>> agnostic_name_map(df_pa)
+['oof', 'RAB']
+
+ +
+ +
+ +
+ + +

+ prefix(prefix) + +

+ + +
+ +

Add a prefix to the root column name of the expression.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ prefix + + str + +
+

Prefix to add to the root column name.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

This will undo any previous renaming operations on the expression. +Due to implementation constraints, this method can only be called as the last +expression in a chain. Only one name operation per expression will work.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrame
+>>>
+>>> data = {"foo": [1, 2], "BAR": [4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_name_prefix(df_native: IntoFrame, prefix: str) -> list[str]:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("foo", "BAR").name.prefix(prefix)).columns
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_name_prefix:

+ 
>>> agnostic_name_prefix(df_pd, "with_prefix_")
+['with_prefix_foo', 'with_prefix_BAR']
+
+ 
>>> agnostic_name_prefix(df_pl, "with_prefix_")
+['with_prefix_foo', 'with_prefix_BAR']
+
+ 
>>> agnostic_name_prefix(df_pa, "with_prefix_")
+['with_prefix_foo', 'with_prefix_BAR']
+
+ +
+ +
+ +
+ + +

+ suffix(suffix) + +

+ + +
+ +

Add a suffix to the root column name of the expression.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ suffix + + str + +
+

Suffix to add to the root column name.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

This will undo any previous renaming operations on the expression. +Due to implementation constraints, this method can only be called as the last +expression in a chain. Only one name operation per expression will work.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrame
+>>>
+>>> data = {"foo": [1, 2], "BAR": [4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_name_suffix(df_native: IntoFrame, suffix: str) -> list[str]:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("foo", "BAR").name.suffix(suffix)).columns
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_name_suffix:

+ 
>>> agnostic_name_suffix(df_pd, "_with_suffix")
+['foo_with_suffix', 'BAR_with_suffix']
+
+ 
>>> agnostic_name_suffix(df_pl, "_with_suffix")
+['foo_with_suffix', 'BAR_with_suffix']
+
+ 
>>> agnostic_name_suffix(df_pa, "_with_suffix")
+['foo_with_suffix', 'BAR_with_suffix']
+
+ +
+ +
+ +
+ + +

+ to_lowercase() + +

+ + +
+ +

Make the root column name lowercase.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

This will undo any previous renaming operations on the expression. +Due to implementation constraints, this method can only be called as the last +expression in a chain. Only one name operation per expression will work.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrame
+>>>
+>>> data = {"foo": [1, 2], "BAR": [4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_name_to_lowercase(df_native: IntoFrame) -> list[str]:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("foo", "BAR").name.to_lowercase()).columns
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_name_to_lowercase:

+ 
>>> agnostic_name_to_lowercase(df_pd)
+['foo', 'bar']
+
+ 
>>> agnostic_name_to_lowercase(df_pl)
+['foo', 'bar']
+
+ 
>>> agnostic_name_to_lowercase(df_pa)
+['foo', 'bar']
+
+ +
+ +
+ +
+ + +

+ to_uppercase() + +

+ + +
+ +

Make the root column name uppercase.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

This will undo any previous renaming operations on the expression. +Due to implementation constraints, this method can only be called as the last +expression in a chain. Only one name operation per expression will work.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrame
+>>>
+>>> data = {"foo": [1, 2], "BAR": [4, 5]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_name_to_uppercase(df_native: IntoFrame) -> list[str]:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("foo", "BAR").name.to_uppercase()).columns
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_name_to_uppercase:

+ 
>>> agnostic_name_to_uppercase(df_pd)
+['FOO', 'BAR']
+
+ 
>>> agnostic_name_to_uppercase(df_pl)
+['FOO', 'BAR']
+
+ 
>>> agnostic_name_to_uppercase(df_pa)
+['FOO', 'BAR']
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/expr_str/index.html b/api-reference/expr_str/index.html new file mode 100644 index 000000000..ea21fe54e --- /dev/null +++ b/api-reference/expr_str/index.html @@ -0,0 +1,3816 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Expr.str - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Expr.str

+ + +
+ + + + +
+ + + + + + + + + + +
+ + + + + + + + + +
+ + +

+ contains(pattern, *, literal=False) + +

+ + +
+ +

Check if string contains a substring that matches a pattern.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ pattern + + str + +
+

A Character sequence or valid regular expression pattern.

+
+ 		+ required +
+ literal + + bool + +
+

If True, treats the pattern as a literal string. + If False, assumes the pattern is a regular expression.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"pets": ["cat", "dog", "rabbit and parrot", "dove", None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_str_contains(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         default_match=nw.col("pets").str.contains("parrot|Dove"),
+...         case_insensitive_match=nw.col("pets").str.contains("(?i)parrot|Dove"),
+...         literal_match=nw.col("pets").str.contains(
+...             "parrot|Dove", literal=True
+...         ),
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_str_contains:

+ 
>>> agnostic_str_contains(df_pd)
+                pets default_match case_insensitive_match literal_match
+0                cat         False                  False         False
+1                dog         False                  False         False
+2  rabbit and parrot          True                   True         False
+3               dove         False                   True         False
+4               None          None                   None          None
+
+ 
>>> agnostic_str_contains(df_pl)
+shape: (5, 4)
+┌───────────────────┬───────────────┬────────────────────────┬───────────────┐
+│ pets              ┆ default_match ┆ case_insensitive_match ┆ literal_match │
+│ ---               ┆ ---           ┆ ---                    ┆ ---           │
+│ str               ┆ bool          ┆ bool                   ┆ bool          │
+╞═══════════════════╪═══════════════╪════════════════════════╪═══════════════╡
+│ cat               ┆ false         ┆ false                  ┆ false         │
+│ dog               ┆ false         ┆ false                  ┆ false         │
+│ rabbit and parrot ┆ true          ┆ true                   ┆ false         │
+│ dove              ┆ false         ┆ true                   ┆ false         │
+│ null              ┆ null          ┆ null                   ┆ null          │
+└───────────────────┴───────────────┴────────────────────────┴───────────────┘
+
+ 
>>> agnostic_str_contains(df_pa)
+pyarrow.Table
+pets: string
+default_match: bool
+case_insensitive_match: bool
+literal_match: bool
+----
+pets: [["cat","dog","rabbit and parrot","dove",null]]
+default_match: [[false,false,true,false,null]]
+case_insensitive_match: [[false,false,true,true,null]]
+literal_match: [[false,false,false,false,null]]
+
+ +
+ +
+ +
+ + +

+ ends_with(suffix) + +

+ + +
+ +

Check if string values end with a substring.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ suffix + + str + +
+

suffix substring

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"fruits": ["apple", "mango", None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_str_ends_with(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         has_suffix=nw.col("fruits").str.ends_with("ngo")
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_str_ends_with:

+ 
>>> agnostic_str_ends_with(df_pd)
+  fruits has_suffix
+0  apple      False
+1  mango       True
+2   None       None
+
+ 
>>> agnostic_str_ends_with(df_pl)
+shape: (3, 2)
+┌────────┬────────────┐
+│ fruits ┆ has_suffix │
+│ ---    ┆ ---        │
+│ str    ┆ bool       │
+╞════════╪════════════╡
+│ apple  ┆ false      │
+│ mango  ┆ true       │
+│ null   ┆ null       │
+└────────┴────────────┘
+
+ 
>>> agnostic_str_ends_with(df_pa)
+pyarrow.Table
+fruits: string
+has_suffix: bool
+----
+fruits: [["apple","mango",null]]
+has_suffix: [[false,true,null]]
+
+ +
+ +
+ +
+ + +

+ head(n=5) + +

+ + +
+ +

Take the first n elements of each string.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of elements to take. Negative indexing is not supported.

+
+ 		+ 5 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

If the length of the string has fewer than n characters, the full string is returned.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"lyrics": ["Atatata", "taata", "taatatata", "zukkyun"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_str_head(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         lyrics_head=nw.col("lyrics").str.head()
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_str_head:

+ 
>>> agnostic_str_head(df_pd)
+      lyrics lyrics_head
+0    Atatata       Atata
+1      taata       taata
+2  taatatata       taata
+3    zukkyun       zukky
+
+ 
>>> agnostic_str_head(df_pl)
+shape: (4, 2)
+┌───────────┬─────────────┐
+│ lyrics    ┆ lyrics_head │
+│ ---       ┆ ---         │
+│ str       ┆ str         │
+╞═══════════╪═════════════╡
+│ Atatata   ┆ Atata       │
+│ taata     ┆ taata       │
+│ taatatata ┆ taata       │
+│ zukkyun   ┆ zukky       │
+└───────────┴─────────────┘
+
+ 
>>> agnostic_str_head(df_pa)
+pyarrow.Table
+lyrics: string
+lyrics_head: string
+----
+lyrics: [["Atatata","taata","taatatata","zukkyun"]]
+lyrics_head: [["Atata","taata","taata","zukky"]]
+
+ +
+ +
+ +
+ + +

+ len_chars() + +

+ + +
+ +

Return the length of each string as the number of characters.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"words": ["foo", "Café", "345", "東京", None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_str_len_chars(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         words_len=nw.col("words").str.len_chars()
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_str_len_chars:

+ 
>>> agnostic_str_len_chars(df_pd)
+  words  words_len
+0   foo        3.0
+1  Café        4.0
+2   345        3.0
+3    東京        2.0
+4  None        NaN
+
+ 
>>> agnostic_str_len_chars(df_pl)
+shape: (5, 2)
+┌───────┬───────────┐
+│ words ┆ words_len │
+│ ---   ┆ ---       │
+│ str   ┆ u32       │
+╞═══════╪═══════════╡
+│ foo   ┆ 3         │
+│ Café  ┆ 4         │
+│ 345   ┆ 3         │
+│ 東京  ┆ 2         │
+│ null  ┆ null      │
+└───────┴───────────┘
+
+ 
>>> agnostic_str_len_chars(df_pa)
+pyarrow.Table
+words: string
+words_len: int32
+----
+words: [["foo","Café","345","東京",null]]
+words_len: [[3,4,3,2,null]]
+
+ +
+ +
+ +
+ + +

+ replace(pattern, value, *, literal=False, n=1) + +

+ + +
+ +

Replace first matching regex/literal substring with a new string value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ pattern + + str + +
+

A valid regular expression pattern.

+
+ 		+ required +
+ value + + str + +
+

String that will replace the matched substring.

+
+ 		+ required +
+ literal + + bool + +
+

Treat pattern as a literal string.

+
+ 		+ False +
+ n + + int + +
+

Number of matches to replace.

+
+ 		+ 1 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"foo": ["123abc", "abc abc123"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_str_replace(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     df = df.with_columns(replaced=nw.col("foo").str.replace("abc", ""))
+...     return df.to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_str_replace:

+ 
>>> agnostic_str_replace(df_pd)
+          foo replaced
+0      123abc      123
+1  abc abc123   abc123
+
+ 
>>> agnostic_str_replace(df_pl)
+shape: (2, 2)
+┌────────────┬──────────┐
+│ foo        ┆ replaced │
+│ ---        ┆ ---      │
+│ str        ┆ str      │
+╞════════════╪══════════╡
+│ 123abc     ┆ 123      │
+│ abc abc123 ┆  abc123  │
+└────────────┴──────────┘
+
+ 
>>> agnostic_str_replace(df_pa)
+pyarrow.Table
+foo: string
+replaced: string
+----
+foo: [["123abc","abc abc123"]]
+replaced: [["123"," abc123"]]
+
+ +
+ +
+ +
+ + +

+ replace_all(pattern, value, *, literal=False) + +

+ + +
+ +

Replace all matching regex/literal substring with a new string value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ pattern + + str + +
+

A valid regular expression pattern.

+
+ 		+ required +
+ value + + str + +
+

String that will replace the matched substring.

+
+ 		+ required +
+ literal + + bool + +
+

Treat pattern as a literal string.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"foo": ["123abc", "abc abc123"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_str_replace_all(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     df = df.with_columns(replaced=nw.col("foo").str.replace_all("abc", ""))
+...     return df.to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_str_replace_all:

+ 
>>> agnostic_str_replace_all(df_pd)
+          foo replaced
+0      123abc      123
+1  abc abc123      123
+
+ 
>>> agnostic_str_replace_all(df_pl)
+shape: (2, 2)
+┌────────────┬──────────┐
+│ foo        ┆ replaced │
+│ ---        ┆ ---      │
+│ str        ┆ str      │
+╞════════════╪══════════╡
+│ 123abc     ┆ 123      │
+│ abc abc123 ┆  123     │
+└────────────┴──────────┘
+
+ 
>>> agnostic_str_replace_all(df_pa)
+pyarrow.Table
+foo: string
+replaced: string
+----
+foo: [["123abc","abc abc123"]]
+replaced: [["123"," 123"]]
+
+ +
+ +
+ +
+ + +

+ slice(offset, length=None) + +

+ + +
+ +

Create subslices of the string values of an expression.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ offset + + int + +
+

Start index. Negative indexing is supported.

+
+ 		+ required +
+ length + + int | None + +
+

Length of the slice. If set to None (default), the slice is taken to the +end of the string.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"s": ["pear", None, "papaya", "dragonfruit"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_str_slice(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         s_sliced=nw.col("s").str.slice(4, length=3)
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_str_slice:

+ 
>>> agnostic_str_slice(df_pd)
+             s s_sliced
+0         pear
+1         None     None
+2       papaya       ya
+3  dragonfruit      onf
+
+ 
>>> agnostic_str_slice(df_pl)
+shape: (4, 2)
+┌─────────────┬──────────┐
+│ s           ┆ s_sliced │
+│ ---         ┆ ---      │
+│ str         ┆ str      │
+╞═════════════╪══════════╡
+│ pear        ┆          │
+│ null        ┆ null     │
+│ papaya      ┆ ya       │
+│ dragonfruit ┆ onf      │
+└─────────────┴──────────┘
+
+ 
>>> agnostic_str_slice(df_pa)
+pyarrow.Table
+s: string
+s_sliced: string
+----
+s: [["pear",null,"papaya","dragonfruit"]]
+s_sliced: [["",null,"ya","onf"]]
+
+

Using negative indexes:

+ 
>>> def agnostic_str_slice_negative(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(s_sliced=nw.col("s").str.slice(-3)).to_native()
+
+ 
>>> agnostic_str_slice_negative(df_pd)
+             s s_sliced
+0         pear      ear
+1         None     None
+2       papaya      aya
+3  dragonfruit      uit
+
+ 
>>> agnostic_str_slice_negative(df_pl)
+shape: (4, 2)
+┌─────────────┬──────────┐
+│ s           ┆ s_sliced │
+│ ---         ┆ ---      │
+│ str         ┆ str      │
+╞═════════════╪══════════╡
+│ pear        ┆ ear      │
+│ null        ┆ null     │
+│ papaya      ┆ aya      │
+│ dragonfruit ┆ uit      │
+└─────────────┴──────────┘
+
+ 
>>> agnostic_str_slice_negative(df_pa)
+pyarrow.Table
+s: string
+s_sliced: string
+----
+s: [["pear",null,"papaya","dragonfruit"]]
+s_sliced: [["ear",null,"aya","uit"]]
+
+ +
+ +
+ +
+ + +

+ starts_with(prefix) + +

+ + +
+ +

Check if string values start with a substring.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ prefix + + str + +
+

prefix substring

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"fruits": ["apple", "mango", None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_str_starts_with(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         has_prefix=nw.col("fruits").str.starts_with("app")
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_str_starts_with:

+ 
>>> agnostic_str_starts_with(df_pd)
+  fruits has_prefix
+0  apple       True
+1  mango      False
+2   None       None
+
+ 
>>> agnostic_str_starts_with(df_pl)
+shape: (3, 2)
+┌────────┬────────────┐
+│ fruits ┆ has_prefix │
+│ ---    ┆ ---        │
+│ str    ┆ bool       │
+╞════════╪════════════╡
+│ apple  ┆ true       │
+│ mango  ┆ false      │
+│ null   ┆ null       │
+└────────┴────────────┘
+
+ 
>>> agnostic_str_starts_with(df_pa)
+pyarrow.Table
+fruits: string
+has_prefix: bool
+----
+fruits: [["apple","mango",null]]
+has_prefix: [[true,false,null]]
+
+ +
+ +
+ +
+ + +

+ strip_chars(characters=None) + +

+ + +
+ +

Remove leading and trailing characters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ characters + + str | None + +
+

The set of characters to be removed. All combinations of this +set of characters will be stripped from the start and end of the string. +If set to None (default), all leading and trailing whitespace is removed +instead.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> from typing import Any
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrame
+>>>
+>>> data = {"fruits": ["apple", "\nmango"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_str_strip_chars(df_native: IntoFrame) -> dict[str, Any]:
+...     df = nw.from_native(df_native)
+...     df = df.with_columns(stripped=nw.col("fruits").str.strip_chars())
+...     return df.to_dict(as_series=False)
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_str_strip_chars:

+ 
>>> agnostic_str_strip_chars(df_pd)
+{'fruits': ['apple', '\nmango'], 'stripped': ['apple', 'mango']}
+
+ 
>>> agnostic_str_strip_chars(df_pl)
+{'fruits': ['apple', '\nmango'], 'stripped': ['apple', 'mango']}
+
+ 
>>> agnostic_str_strip_chars(df_pa)
+{'fruits': ['apple', '\nmango'], 'stripped': ['apple', 'mango']}
+
+ +
+ +
+ +
+ + +

+ tail(n=5) + +

+ + +
+ +

Take the last n elements of each string.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of elements to take. Negative indexing is not supported.

+
+ 		+ 5 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

If the length of the string has fewer than n characters, the full string is returned.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"lyrics": ["Atatata", "taata", "taatatata", "zukkyun"]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_str_tail(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         lyrics_tail=nw.col("lyrics").str.tail()
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_str_tail:

+ 
>>> agnostic_str_tail(df_pd)
+      lyrics lyrics_tail
+0    Atatata       atata
+1      taata       taata
+2  taatatata       atata
+3    zukkyun       kkyun
+
+ 
>>> agnostic_str_tail(df_pl)
+shape: (4, 2)
+┌───────────┬─────────────┐
+│ lyrics    ┆ lyrics_tail │
+│ ---       ┆ ---         │
+│ str       ┆ str         │
+╞═══════════╪═════════════╡
+│ Atatata   ┆ atata       │
+│ taata     ┆ taata       │
+│ taatatata ┆ atata       │
+│ zukkyun   ┆ kkyun       │
+└───────────┴─────────────┘
+
+ 
>>> agnostic_str_tail(df_pa)
+pyarrow.Table
+lyrics: string
+lyrics_tail: string
+----
+lyrics: [["Atatata","taata","taatatata","zukkyun"]]
+lyrics_tail: [["atata","taata","atata","kkyun"]]
+
+ +
+ +
+ +
+ + +

+ to_datetime(format=None) + +

+ + +
+ +

Convert to Datetime dtype.

+ + +
+ Warning +

As different backends auto-infer format in different ways, if format=None +there is no guarantee that the result will be equal.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ format + + str | None + +
+

Format to use for conversion. If set to None (default), the format is +inferred from the data.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

pandas defaults to nanosecond time unit, Polars to microsecond. +Prior to pandas 2.0, nanoseconds were the only time unit supported +in pandas, with no ability to set any other one. The ability to +set the time unit in pandas, if the version permits, will arrive.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = ["2020-01-01", "2020-01-02"]
+>>> df_pd = pd.DataFrame({"a": data})
+>>> df_pl = pl.DataFrame({"a": data})
+>>> df_pa = pa.table({"a": data})
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_str_to_datetime(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.col("a").str.to_datetime(format="%Y-%m-%d")
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_str_to_datetime:

+ 
>>> agnostic_str_to_datetime(df_pd)
+           a
+0 2020-01-01
+1 2020-01-02
+
+ 
>>> agnostic_str_to_datetime(df_pl)
+shape: (2, 1)
+┌─────────────────────┐
+│ a                   │
+│ ---                 │
+│ datetime[μs]        │
+╞═════════════════════╡
+│ 2020-01-01 00:00:00 │
+│ 2020-01-02 00:00:00 │
+└─────────────────────┘
+
+ 
>>> agnostic_str_to_datetime(df_pa)
+pyarrow.Table
+a: timestamp[us]
+----
+a: [[2020-01-01 00:00:00.000000,2020-01-02 00:00:00.000000]]
+
+ +
+ +
+ +
+ + +

+ to_lowercase() + +

+ + +
+ +

Transform string to lowercase variant.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"fruits": ["APPLE", "MANGO", None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_str_to_lowercase(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         lower_col=nw.col("fruits").str.to_lowercase()
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_str_to_lowercase:

+ 
>>> agnostic_str_to_lowercase(df_pd)
+  fruits lower_col
+0  APPLE     apple
+1  MANGO     mango
+2   None      None
+
+ 
>>> agnostic_str_to_lowercase(df_pl)
+shape: (3, 2)
+┌────────┬───────────┐
+│ fruits ┆ lower_col │
+│ ---    ┆ ---       │
+│ str    ┆ str       │
+╞════════╪═══════════╡
+│ APPLE  ┆ apple     │
+│ MANGO  ┆ mango     │
+│ null   ┆ null      │
+└────────┴───────────┘
+
+ 
>>> agnostic_str_to_lowercase(df_pa)
+pyarrow.Table
+fruits: string
+lower_col: string
+----
+fruits: [["APPLE","MANGO",null]]
+lower_col: [["apple","mango",null]]
+
+ +
+ +
+ +
+ + +

+ to_uppercase() + +

+ + +
+ +

Transform string to uppercase variant.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ExprT + +
+

A new expression.

+
+
+ + +
+ Notes +

The PyArrow backend will convert 'ß' to 'ẞ' instead of 'SS'. +For more info see the related issue. +There may be other unicode-edge-case-related variations across implementations.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"fruits": ["apple", "mango", None]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_str_to_uppercase(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         upper_col=nw.col("fruits").str.to_uppercase()
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_str_to_uppercase:

+ 
>>> agnostic_str_to_uppercase(df_pd)
+  fruits upper_col
+0  apple     APPLE
+1  mango     MANGO
+2   None      None
+
+ 
>>> agnostic_str_to_uppercase(df_pl)
+shape: (3, 2)
+┌────────┬───────────┐
+│ fruits ┆ upper_col │
+│ ---    ┆ ---       │
+│ str    ┆ str       │
+╞════════╪═══════════╡
+│ apple  ┆ APPLE     │
+│ mango  ┆ MANGO     │
+│ null   ┆ null      │
+└────────┴───────────┘
+
+ 
>>> agnostic_str_to_uppercase(df_pa)
+pyarrow.Table
+fruits: string
+upper_col: string
+----
+fruits: [["apple","mango",null]]
+upper_col: [["APPLE","MANGO",null]]
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/group_by/index.html b/api-reference/group_by/index.html new file mode 100644 index 000000000..a55f516bc --- /dev/null +++ b/api-reference/group_by/index.html @@ -0,0 +1,2112 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.GroupBy - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
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+ + + +
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+ + + +
+
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+ + + +
+
+ + + + + + + +

narwhals.GroupBy

+ + +
+ + + + +
+ + + + + + + + + + +
+ + + + + + + + + +
+ + +

+ agg(*aggs, **named_aggs) + +

+ + +
+ +

Compute aggregations for each group of a group by operation.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ aggs + + IntoExpr | Iterable[IntoExpr] + +
+

Aggregations to compute for each group of the group by operation, +specified as positional arguments.

+
+ 		+ () +
+ named_aggs + + IntoExpr + +
+

Additional aggregations, specified as keyword arguments.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DataFrameT + +
+

A new Dataframe.

+
+
+ + +

Examples:

+

Group by one column or by multiple columns and call agg to compute +the grouped sum of another column.

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame(
+...     {
+...         "a": ["a", "b", "a", "b", "c"],
+...         "b": [1, 2, 1, 3, 3],
+...         "c": [5, 4, 3, 2, 1],
+...     }
+... )
+>>> df_pl = pl.DataFrame(
+...     {
+...         "a": ["a", "b", "a", "b", "c"],
+...         "b": [1, 2, 1, 3, 3],
+...         "c": [5, 4, 3, 2, 1],
+...     }
+... )
+
+

We define library agnostic functions:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.group_by("a").agg(nw.col("b").sum()).sort("a")
+
+ 
>>> @nw.narwhalify
+... def func_mult_col(df):
+...     return df.group_by("a", "b").agg(nw.sum("c")).sort("a", "b")
+
+

We can then pass either pandas or Polars to func and func_mult_col:

+ 
>>> func(df_pd)
+   a  b
+0  a  2
+1  b  5
+2  c  3
+>>> func(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ str ┆ i64 │
+╞═════╪═════╡
+│ a   ┆ 2   │
+│ b   ┆ 5   │
+│ c   ┆ 3   │
+└─────┴─────┘
+>>> func_mult_col(df_pd)
+   a  b  c
+0  a  1  8
+1  b  2  4
+2  b  3  2
+3  c  3  1
+>>> func_mult_col(df_pl)
+shape: (4, 3)
+┌─────┬─────┬─────┐
+│ a   ┆ b   ┆ c   │
+│ --- ┆ --- ┆ --- │
+│ str ┆ i64 ┆ i64 │
+╞═════╪═════╪═════╡
+│ a   ┆ 1   ┆ 8   │
+│ b   ┆ 2   ┆ 4   │
+│ b   ┆ 3   ┆ 2   │
+│ c   ┆ 3   ┆ 1   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/index.html b/api-reference/index.html new file mode 100644 index 000000000..209c92b44 --- /dev/null +++ b/api-reference/index.html @@ -0,0 +1,1861 @@ + + + + + + + + + + + + + + + + + + + + + + + API Reference - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
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+ + + +
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+ + + +
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+ + + +
+ +
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/lazy_group_by/index.html b/api-reference/lazy_group_by/index.html new file mode 100644 index 000000000..4b86bdf60 --- /dev/null +++ b/api-reference/lazy_group_by/index.html @@ -0,0 +1,2095 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.LazyGroupBy - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
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+
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+ + + + + +
+
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+ + + +
+
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+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.LazyGroupBy

+ + +
+ + + + +
+ + + + + + + + + + +
+ + + + + + + + + +
+ + +

+ agg(*aggs, **named_aggs) + +

+ + +
+ +

Compute aggregations for each group of a group by operation.

+

If a library does not support lazy execution, then this is a no-op.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ aggs + + IntoExpr | Iterable[IntoExpr] + +
+

Aggregations to compute for each group of the group by operation, +specified as positional arguments.

+
+ 		+ () +
+ named_aggs + + IntoExpr + +
+

Additional aggregations, specified as keyword arguments.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ LazyFrameT + +
+

A new LazyFrame.

+
+
+ + +

Examples:

+

Group by one column or by multiple columns and call agg to compute +the grouped sum of another column.

+ 
>>> import polars as pl
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> lf_pl = pl.LazyFrame(
+...     {
+...         "a": ["a", "b", "a", "b", "c"],
+...         "b": [1, 2, 1, 3, 3],
+...         "c": [5, 4, 3, 2, 1],
+...     }
+... )
+
+

We define library agnostic functions:

+ 
>>> def agnostic_func_one_col(lf_native: IntoFrameT) -> IntoFrameT:
+...     lf = nw.from_native(lf_native)
+...     return nw.to_native(lf.group_by("a").agg(nw.col("b").sum()).sort("a"))
+
+ 
>>> def agnostic_func_mult_col(lf_native: IntoFrameT) -> IntoFrameT:
+...     lf = nw.from_native(lf_native)
+...     return nw.to_native(lf.group_by("a", "b").agg(nw.sum("c")).sort("a", "b"))
+
+

We can then pass a lazy frame and materialise it with collect:

+ 
>>> agnostic_func_one_col(lf_pl).collect()
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ str ┆ i64 │
+╞═════╪═════╡
+│ a   ┆ 2   │
+│ b   ┆ 5   │
+│ c   ┆ 3   │
+└─────┴─────┘
+>>> agnostic_func_mult_col(lf_pl).collect()
+shape: (4, 3)
+┌─────┬─────┬─────┐
+│ a   ┆ b   ┆ c   │
+│ --- ┆ --- ┆ --- │
+│ str ┆ i64 ┆ i64 │
+╞═════╪═════╪═════╡
+│ a   ┆ 1   ┆ 8   │
+│ b   ┆ 2   ┆ 4   │
+│ b   ┆ 3   ┆ 2   │
+│ c   ┆ 3   ┆ 1   │
+└─────┴─────┴─────┘
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/lazyframe/index.html b/api-reference/lazyframe/index.html new file mode 100644 index 000000000..f23c22446 --- /dev/null +++ b/api-reference/lazyframe/index.html @@ -0,0 +1,5845 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.LazyFrame - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ + + + + +
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+ + + +
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+ + + +
+
+ + + + + + + +

narwhals.LazyFrame

+ + +
+ + + + +
+ + +

Narwhals LazyFrame, backed by a native lazyframe.

+
+

Warning

+

This class is not meant to be instantiated directly - instead use +narwhals.from_native with a native +object that is a lazy dataframe from one of the supported +backend (e.g. polars.LazyFrame, dask_expr._collection.DataFrame): +

narwhals.from_native(native_lazyframe)
+

+
+ + + + + + + + + +
+ + + + + + + +
+ + + +

+ columns + + + property + + +

+ + +
+ +

Get column names.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ list[str] + +
+

The column names stored in a list.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrame
+>>>
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_columns(df_native: IntoFrame) -> list[str]:
+...     df = nw.from_native(df_native)
+...     return df.columns
+
+

We can then pass any supported library such as Polars or Dask to agnostic_columns:

+ 
>>> agnostic_columns(lf_pl)
+['foo', 'bar', 'ham']
+>>> agnostic_columns(lf_dask)
+['foo', 'bar', 'ham']
+
+
+ +
+ +
+ + + +

+ implementation + + + property + + +

+ + +
+ +

Return implementation of native frame.

+

This can be useful when you need to use special-casing for features outside of +Narwhals' scope - for example, when dealing with pandas' Period Dtype.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Implementation + +
+

Implementation.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> lf_pl = pl.LazyFrame({"a": [1, 2, 3]})
+>>> lf_dask = dd.from_dict({"a": [1, 2, 3]}, npartitions=2)
+
+ 
>>> lf = nw.from_native(lf_pl)
+>>> lf.implementation
+<Implementation.POLARS: 6>
+>>> lf.implementation.is_pandas()
+False
+>>> lf.implementation.is_polars()
+True
+
+ 
>>> lf = nw.from_native(lf_dask)
+>>> lf.implementation
+<Implementation.DASK: 7>
+>>> lf.implementation.is_dask()
+True
+
+
+ +
+ +
+ + + +

+ schema + + + property + + +

+ + +
+ +

Get an ordered mapping of column names to their data type.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Schema + +
+

A Narwhals Schema object that displays the mapping of column names.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6.0, 7.0, 8.0],
+...     "ham": ["a", "b", "c"],
+... }
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+ 
>>> lf = nw.from_native(lf_pl)
+>>> lf.schema
+Schema({'foo': Int64, 'bar': Float64, 'ham': String})
+
+ 
>>> lf = nw.from_native(lf_dask)
+>>> lf.schema
+Schema({'foo': Int64, 'bar': Float64, 'ham': String})
+
+
+ +
+ + + +
+ + +

+ clone() + +

+ + +
+ +

Create a copy of this DataFrame.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

An identical copy of the original LazyFrame.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import polars as pl
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2], "b": [3, 4]}
+>>> lf_pl = pl.LazyFrame(data)
+
+

Let's define a dataframe-agnostic function in which we copy the DataFrame:

+ 
>>> def agnostic_clone(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.clone().collect().to_native()
+
+

We can then pass any supported library such as Polars to agnostic_clone:

+ 
>>> agnostic_clone(lf_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 3   │
+│ 2   ┆ 4   │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ collect() + +

+ + +
+ +

Materialize this LazyFrame into a DataFrame.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DataFrame[Any] + +
+

DataFrame

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> data = {
+...     "a": ["a", "b", "a", "b", "b", "c"],
+...     "b": [1, 2, 3, 4, 5, 6],
+...     "c": [6, 5, 4, 3, 2, 1],
+... }
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+ 
>>> lf = nw.from_native(lf_pl)
+>>> lf
+┌─────────────────────────────┐
+|     Narwhals LazyFrame      |
+|-----------------------------|
+|<LazyFrame at ...
+└─────────────────────────────┘
+>>> df = lf.group_by("a").agg(nw.all().sum()).collect()
+>>> df.to_native().sort("a")
+shape: (3, 3)
+┌─────┬─────┬─────┐
+│ a   ┆ b   ┆ c   │
+│ --- ┆ --- ┆ --- │
+│ str ┆ i64 ┆ i64 │
+╞═════╪═════╪═════╡
+│ a   ┆ 4   ┆ 10  │
+│ b   ┆ 11  ┆ 10  │
+│ c   ┆ 6   ┆ 1   │
+└─────┴─────┴─────┘
+
+ 
>>> lf = nw.from_native(lf_dask)
+>>> lf
+┌───────────────────────────────────┐
+|        Narwhals LazyFrame         |
+|-----------------------------------|
+|Dask DataFrame Structure:          |
+|                    a      b      c|
+|npartitions=2                      |
+|0              string  int64  int64|
+|3                 ...    ...    ...|
+|5                 ...    ...    ...|
+|Dask Name: frompandas, 1 expression|
+|Expr=df                            |
+└───────────────────────────────────┘
+>>> df = lf.group_by("a").agg(nw.col("b", "c").sum()).collect()
+>>> df.to_native()
+   a   b   c
+0  a   4  10
+1  b  11  10
+2  c   6   1
+
+ +
+ +
+ +
+ + +

+ collect_schema() + +

+ + +
+ +

Get an ordered mapping of column names to their data type.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Schema + +
+

A Narwhals Schema object that displays the mapping of column names.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6.0, 7.0, 8.0],
+...     "ham": ["a", "b", "c"],
+... }
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+ 
>>> lf = nw.from_native(lf_pl)
+>>> lf.collect_schema()
+Schema({'foo': Int64, 'bar': Float64, 'ham': String})
+
+ 
>>> lf = nw.from_native(lf_dask)
+>>> lf.collect_schema()
+Schema({'foo': Int64, 'bar': Float64, 'ham': String})
+
+ +
+ +
+ +
+ + +

+ drop(*columns, strict=True) + +

+ + +
+ +

Remove columns from the LazyFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ *columns + + str | Iterable[str] + +
+

Names of the columns that should be removed from the dataframe.

+
+ 		+ () +
+ strict + + bool + +
+

Validate that all column names exist in the schema and throw an +exception if a column name does not exist in the schema.

+
+ 		+ True +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The LazyFrame with the specified columns removed.

+
+
+ + +
+ Warning +

strict argument is ignored for polars<1.0.0.

+

Please consider upgrading to a newer version or pass to eager mode.

+
+ +

Examples:

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_drop(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.drop("ham").collect().to_native()
+
+

We can then pass any supported library such as Polars or Dask to agnostic_drop:

+ 
>>> agnostic_drop(lf_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ f64 │
+╞═════╪═════╡
+│ 1   ┆ 6.0 │
+│ 2   ┆ 7.0 │
+│ 3   ┆ 8.0 │
+└─────┴─────┘
+>>> agnostic_drop(lf_dask)
+   foo  bar
+0    1  6.0
+1    2  7.0
+2    3  8.0
+
+

Use positional arguments to drop multiple columns.

+ 
>>> def agnostic_drop(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.drop("foo", "ham").collect().to_native()
+
+ 
>>> agnostic_drop(lf_pl)
+shape: (3, 1)
+┌─────┐
+│ bar │
+│ --- │
+│ f64 │
+╞═════╡
+│ 6.0 │
+│ 7.0 │
+│ 8.0 │
+└─────┘
+>>> agnostic_drop(lf_dask)
+   bar
+0  6.0
+1  7.0
+2  8.0
+
+ +
+ +
+ +
+ + +

+ drop_nulls(subset=None) + +

+ + +
+ +

Drop rows that contain null values.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ subset + + str | list[str] | None + +
+

Column name(s) for which null values are considered. If set to None +(default), use all columns.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The original object with the rows removed that contained the null values.

+
+
+ + +
+ Notes +

pandas handles null values differently from Polars and PyArrow. +See null_handling +for reference.

+
+ +

Examples:

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1.0, 2.0, None], "ba": [1.0, None, 2.0]}
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_drop_nulls(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.drop_nulls().collect().to_native()
+
+

We can then pass any supported library such as Polars or Dask to agnostic_drop_nulls:

+ 
>>> agnostic_drop_nulls(lf_pl)
+shape: (1, 2)
+┌─────┬─────┐
+│ a   ┆ ba  │
+│ --- ┆ --- │
+│ f64 ┆ f64 │
+╞═════╪═════╡
+│ 1.0 ┆ 1.0 │
+└─────┴─────┘
+>>> agnostic_drop_nulls(lf_dask)
+     a   ba
+0  1.0  1.0
+
+ +
+ +
+ +
+ + +

+ explode(columns, *more_columns) + +

+ + +
+ +

Explode the dataframe to long format by exploding the given columns.

+ + +
+ Notes +

It is possible to explode multiple columns only if these columns have +matching element counts.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ columns + + str | Sequence[str] + +
+

Column names. The underlying columns being exploded must be of the List data type.

+
+ 		+ required +
+ *more_columns + + str + +
+

Additional names of columns to explode, specified as positional arguments.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

New LazyFrame

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> import polars as pl
+>>> data = {
+...     "a": ["x", "y", "z", "w"],
+...     "lst1": [[1, 2], None, [None], []],
+...     "lst2": [[3, None], None, [42], []],
+... }
+
+

We define a library agnostic function:

+ 
>>> def agnostic_explode(df_native: IntoFrameT) -> IntoFrameT:
+...     return (
+...         nw.from_native(df_native)
+...         .with_columns(nw.col("lst1", "lst2").cast(nw.List(nw.Int32())))
+...         .explode("lst1", "lst2")
+...         .collect()
+...         .to_native()
+...     )
+
+

We can then pass any supported library such as Polars to agnostic_explode:

+ 
>>> agnostic_explode(pl.LazyFrame(data))
+shape: (5, 3)
+┌─────┬──────┬──────┐
+│ a   ┆ lst1 ┆ lst2 │
+│ --- ┆ ---  ┆ ---  │
+│ str ┆ i32  ┆ i32  │
+╞═════╪══════╪══════╡
+│ x   ┆ 1    ┆ 3    │
+│ x   ┆ 2    ┆ null │
+│ y   ┆ null ┆ null │
+│ z   ┆ null ┆ 42   │
+│ w   ┆ null ┆ null │
+└─────┴──────┴──────┘
+
+ +
+ +
+ +
+ + +

+ filter(*predicates, **constraints) + +

+ + +
+ +

Filter the rows in the LazyFrame based on a predicate expression.

+

The original order of the remaining rows is preserved.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ *predicates + + IntoExpr | Iterable[IntoExpr] | list[bool] + +
+

Expression that evaluates to a boolean Series. Can +also be a (single!) boolean list.

+
+ 		+ () +
+ **constraints + + Any + +
+

Column filters; use name = value to filter columns by the supplied value. +Each constraint will behave the same as nw.col(name).eq(value), and will be implicitly +joined with the other filter conditions using &.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The filtered LazyFrame.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6, 7, 8],
+...     "ham": ["a", "b", "c"],
+... }
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

Let's define a dataframe-agnostic function in which we filter on +one condition.

+ 
>>> def agnostic_filter(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.filter(nw.col("foo") > 1).collect().to_native()
+
+

We can then pass any supported library such as Polars or Dask to agnostic_filter:

+ 
>>> agnostic_filter(lf_pl)
+shape: (2, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 2   ┆ 7   ┆ b   │
+│ 3   ┆ 8   ┆ c   │
+└─────┴─────┴─────┘
+>>> agnostic_filter(lf_dask)
+   foo  bar ham
+1    2    7   b
+2    3    8   c
+
+

Filter on multiple conditions:

+ 
>>> def agnostic_filter(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return (
+...         df.filter((nw.col("foo") < 3) & (nw.col("ham") == "a"))
+...         .collect()
+...         .to_native()
+...     )
+
+ 
>>> agnostic_filter(lf_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+└─────┴─────┴─────┘
+>>> agnostic_filter(lf_dask)
+   foo  bar ham
+0    1    6   a
+
+

Provide multiple filters using *args syntax:

+ 
>>> def agnostic_filter(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return (
+...         df.filter(
+...             nw.col("foo") == 1,
+...             nw.col("ham") == "a",
+...         )
+...         .collect()
+...         .to_native()
+...     )
+
+ 
>>> agnostic_filter(lf_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+└─────┴─────┴─────┘
+>>> agnostic_filter(lf_dask)
+   foo  bar ham
+0    1    6   a
+
+

Filter on an OR condition:

+ 
>>> def agnostic_filter(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return (
+...         df.filter((nw.col("foo") == 1) | (nw.col("ham") == "c"))
+...         .collect()
+...         .to_native()
+...     )
+
+ 
>>> agnostic_filter(lf_pl)
+shape: (2, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6   ┆ a   │
+│ 3   ┆ 8   ┆ c   │
+└─────┴─────┴─────┘
+>>> agnostic_filter(lf_dask)
+   foo  bar ham
+0    1    6   a
+2    3    8   c
+
+

Provide multiple filters using **kwargs syntax:

+ 
>>> def agnostic_filter(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.filter(foo=2, ham="b").collect().to_native()
+
+ 
>>> agnostic_filter(lf_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ str │
+╞═════╪═════╪═════╡
+│ 2   ┆ 7   ┆ b   │
+└─────┴─────┴─────┘
+>>> agnostic_filter(lf_dask)
+   foo  bar ham
+1    2    7   b
+
+ +
+ +
+ +
+ + +

+ gather_every(n, offset=0) + +

+ + +
+ +

Take every nth row in the DataFrame and return as a new DataFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Gather every n-th row.

+
+ 		+ required +
+ offset + + int + +
+

Starting index.

+
+ 		+ 0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The LazyFrame containing only the selected rows.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]}
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

Let's define a dataframe-agnostic function in which we gather every 2 rows, +starting from a offset of 1:

+ 
>>> def agnostic_gather_every(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.gather_every(n=2, offset=1).collect().to_native()
+
+

We can then pass any supported library such as Polars or Dask to agnostic_gather_every:

+ 
>>> agnostic_gather_every(lf_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 2   ┆ 6   │
+│ 4   ┆ 8   │
+└─────┴─────┘
+>>> agnostic_gather_every(lf_dask)
+   a  b
+1  2  6
+3  4  8
+
+ +
+ +
+ +
+ + +

+ group_by(*keys, drop_null_keys=False) + +

+ + +
+ +

Start a group by operation.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ *keys + + str | Iterable[str] + +
+

Column(s) to group by. Accepts expression input. Strings are +parsed as column names.

+
+ 		+ () +
+ drop_null_keys + + bool + +
+

if True, then groups where any key is null won't be +included in the result.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ LazyGroupBy[Self] + +
+

Object which can be used to perform aggregations.

+
+
+ + +

Examples:

+

Group by one column and call agg to compute the grouped sum of +another column.

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": ["a", "b", "a", "b", "c"],
+...     "b": [1, 2, 1, 3, 3],
+...     "c": [5, 4, 3, 2, 1],
+... }
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

Let's define a dataframe-agnostic function in which we group by one column +and call agg to compute the grouped sum of another column.

+ 
>>> def agnostic_group_by_agg(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return (
+...         df.group_by("a")
+...         .agg(nw.col("b").sum())
+...         .sort("a")
+...         .collect()
+...         .to_native()
+...     )
+
+

We can then pass any supported library such as Polars or Dask to agnostic_group_by_agg:

+ 
>>> agnostic_group_by_agg(lf_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ str ┆ i64 │
+╞═════╪═════╡
+│ a   ┆ 2   │
+│ b   ┆ 5   │
+│ c   ┆ 3   │
+└─────┴─────┘
+>>> agnostic_group_by_agg(lf_dask)
+   a  b
+0  a  2
+1  b  5
+2  c  3
+
+

Group by multiple columns by passing a list of column names.

+ 
>>> def agnostic_group_by_agg(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return (
+...         df.group_by(["a", "b"])
+...         .agg(nw.max("c"))
+...         .sort(["a", "b"])
+...         .collect()
+...         .to_native()
+...     )
+
+ 
>>> agnostic_group_by_agg(lf_pl)
+shape: (4, 3)
+┌─────┬─────┬─────┐
+│ a   ┆ b   ┆ c   │
+│ --- ┆ --- ┆ --- │
+│ str ┆ i64 ┆ i64 │
+╞═════╪═════╪═════╡
+│ a   ┆ 1   ┆ 5   │
+│ b   ┆ 2   ┆ 4   │
+│ b   ┆ 3   ┆ 2   │
+│ c   ┆ 3   ┆ 1   │
+└─────┴─────┴─────┘
+>>> agnostic_group_by_agg(lf_dask)
+   a  b  c
+0  a  1  5
+1  b  2  4
+2  b  3  2
+3  c  3  1
+
+ +
+ +
+ +
+ + +

+ head(n=5) + +

+ + +
+ +

Get the first n rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of rows to return.

+
+ 		+ 5 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A subset of the LazyFrame of shape (n, n_columns).

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [1, 2, 3, 4, 5, 6],
+...     "b": [7, 8, 9, 10, 11, 12],
+... }
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

Let's define a dataframe-agnostic function that gets the first 3 rows.

+ 
>>> def agnostic_head(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.head(3).collect().to_native()
+
+

We can then pass any supported library such as Polars or Dask to agnostic_head:

+ 
>>> agnostic_head(lf_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 7   │
+│ 2   ┆ 8   │
+│ 3   ┆ 9   │
+└─────┴─────┘
+>>> agnostic_head(lf_dask)
+   a  b
+0  1  7
+1  2  8
+2  3  9
+
+ +
+ +
+ +
+ + +

+ join(other, on=None, how='inner', *, left_on=None, right_on=None, suffix='_right') + +

+ + +
+ +

Add a join operation to the Logical Plan.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ other + + Self + +
+

Lazy DataFrame to join with.

+
+ 		+ required +
+ on + + str | list[str] | None + +
+

Name(s) of the join columns in both DataFrames. If set, left_on and +right_on should be None.

+
+ 		+ None +
+ how + + Literal['inner', 'left', 'cross', 'semi', 'anti'] + +
+

Join strategy.

+
    +
  • inner: Returns rows that have matching values in both tables.
    • +
  • left: Returns all rows from the left table, and the matched rows from the right table.
    • +
  • cross: Returns the Cartesian product of rows from both tables.
    • +
  • semi: Filter rows that have a match in the right table.
    • +
  • anti: Filter rows that do not have a match in the right table.
    • +
+
+ 		+ 'inner' +
+ left_on + + str | list[str] | None + +
+

Join column of the left DataFrame.

+
+ 		+ None +
+ right_on + + str | list[str] | None + +
+

Join column of the right DataFrame.

+
+ 		+ None +
+ suffix + + str + +
+

Suffix to append to columns with a duplicate name.

+
+ 		+ '_right' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new joined LazyFrame.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6.0, 7.0, 8.0],
+...     "ham": ["a", "b", "c"],
+... }
+>>> data_other = {
+...     "apple": ["x", "y", "z"],
+...     "ham": ["a", "b", "d"],
+... }
+
+ 
>>> lf_pl = pl.LazyFrame(data)
+>>> other_pl = pl.LazyFrame(data_other)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+>>> other_dask = dd.from_dict(data_other, npartitions=2)
+
+

Let's define a dataframe-agnostic function in which we join over "ham" column:

+ 
>>> def agnostic_join_on_ham(
+...     df_native: IntoFrameT,
+...     other_native: IntoFrameT,
+... ) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     other = nw.from_native(other_native)
+...     return (
+...         df.join(other, left_on="ham", right_on="ham")
+...         .sort("ham")
+...         .collect()
+...         .to_native()
+...     )
+
+

We can then pass any supported library such as Polars or Dask to agnostic_join_on_ham:

+ 
>>> agnostic_join_on_ham(lf_pl, other_pl)
+shape: (2, 4)
+┌─────┬─────┬─────┬───────┐
+│ foo ┆ bar ┆ ham ┆ apple │
+│ --- ┆ --- ┆ --- ┆ ---   │
+│ i64 ┆ f64 ┆ str ┆ str   │
+╞═════╪═════╪═════╪═══════╡
+│ 1   ┆ 6.0 ┆ a   ┆ x     │
+│ 2   ┆ 7.0 ┆ b   ┆ y     │
+└─────┴─────┴─────┴───────┘
+>>> agnostic_join_on_ham(lf_dask, other_dask)
+   foo  bar ham apple
+0    1  6.0   a     x
+0    2  7.0   b     y
+
+ +
+ +
+ +
+ + +

+ join_asof(other, *, left_on=None, right_on=None, on=None, by_left=None, by_right=None, by=None, strategy='backward') + +

+ + +
+ +

Perform an asof join.

+

This is similar to a left-join except that we match on nearest key rather than equal keys.

+

Both DataFrames must be sorted by the asof_join key.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ other + + Self + +
+

DataFrame to join with.

+
+ 		+ required +
+ left_on + + str | None + +
+

Name(s) of the left join column(s).

+
+ 		+ None +
+ right_on + + str | None + +
+

Name(s) of the right join column(s).

+
+ 		+ None +
+ on + + str | None + +
+

Join column of both DataFrames. If set, left_on and right_on should be None.

+
+ 		+ None +
+ by_left + + str | list[str] | None + +
+

join on these columns before doing asof join

+
+ 		+ None +
+ by_right + + str | list[str] | None + +
+

join on these columns before doing asof join

+
+ 		+ None +
+ by + + str | list[str] | None + +
+

join on these columns before doing asof join

+
+ 		+ None +
+ strategy + + Literal['backward', 'forward', 'nearest'] + +
+

Join strategy. The default is "backward".

+
    +
  • backward: selects the last row in the right DataFrame whose "on" key is less than or equal to the left's key.
    • +
  • forward: selects the first row in the right DataFrame whose "on" key is greater than or equal to the left's key.
    • +
  • nearest: search selects the last row in the right DataFrame whose value is nearest to the left's key.
    • +
+
+ 		+ 'backward' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new joined LazyFrame.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import narwhals as nw
+>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> from typing import Literal
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data_gdp = {
+...     "datetime": [
+...         datetime(2016, 1, 1),
+...         datetime(2017, 1, 1),
+...         datetime(2018, 1, 1),
+...         datetime(2019, 1, 1),
+...         datetime(2020, 1, 1),
+...     ],
+...     "gdp": [4164, 4411, 4566, 4696, 4827],
+... }
+>>> data_population = {
+...     "datetime": [
+...         datetime(2016, 3, 1),
+...         datetime(2018, 8, 1),
+...         datetime(2019, 1, 1),
+...     ],
+...     "population": [82.19, 82.66, 83.12],
+... }
+>>> gdp_pl = pl.LazyFrame(data_gdp)
+>>> population_pl = pl.LazyFrame(data_population)
+>>> gdp_dask = dd.from_dict(data_gdp, npartitions=2)
+>>> population_dask = dd.from_dict(data_population, npartitions=2)
+
+

Let's define a dataframe-agnostic function in which we join over "datetime" column:

+ 
>>> def agnostic_join_asof_datetime(
+...     df_native: IntoFrameT,
+...     other_native: IntoFrameT,
+...     strategy: Literal["backward", "forward", "nearest"],
+... ) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     other = nw.from_native(other_native)
+...     return (
+...         df.sort("datetime")
+...         .join_asof(other, on="datetime", strategy=strategy)
+...         .collect()
+...         .to_native()
+...     )
+
+

We can then pass any supported library such as Polars or Dask to agnostic_join_asof_datetime:

+ 
>>> agnostic_join_asof_datetime(population_pl, gdp_pl, strategy="backward")
+shape: (3, 3)
+┌─────────────────────┬────────────┬──────┐
+│ datetime            ┆ population ┆ gdp  │
+│ ---                 ┆ ---        ┆ ---  │
+│ datetime[μs]        ┆ f64        ┆ i64  │
+╞═════════════════════╪════════════╪══════╡
+│ 2016-03-01 00:00:00 ┆ 82.19      ┆ 4164 │
+│ 2018-08-01 00:00:00 ┆ 82.66      ┆ 4566 │
+│ 2019-01-01 00:00:00 ┆ 83.12      ┆ 4696 │
+└─────────────────────┴────────────┴──────┘
+>>> agnostic_join_asof_datetime(population_dask, gdp_dask, strategy="backward")
+    datetime  population   gdp
+0 2016-03-01       82.19  4164
+1 2018-08-01       82.66  4566
+0 2019-01-01       83.12  4696
+
+

Here is a real-world times-series example that uses by argument.

+ 
>>> from datetime import datetime
+>>> import narwhals as nw
+>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data_quotes = {
+...     "datetime": [
+...         datetime(2016, 5, 25, 13, 30, 0, 23),
+...         datetime(2016, 5, 25, 13, 30, 0, 23),
+...         datetime(2016, 5, 25, 13, 30, 0, 30),
+...         datetime(2016, 5, 25, 13, 30, 0, 41),
+...         datetime(2016, 5, 25, 13, 30, 0, 48),
+...         datetime(2016, 5, 25, 13, 30, 0, 49),
+...         datetime(2016, 5, 25, 13, 30, 0, 72),
+...         datetime(2016, 5, 25, 13, 30, 0, 75),
+...     ],
+...     "ticker": [
+...         "GOOG",
+...         "MSFT",
+...         "MSFT",
+...         "MSFT",
+...         "GOOG",
+...         "AAPL",
+...         "GOOG",
+...         "MSFT",
+...     ],
+...     "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
+...     "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03],
+... }
+>>> data_trades = {
+...     "datetime": [
+...         datetime(2016, 5, 25, 13, 30, 0, 23),
+...         datetime(2016, 5, 25, 13, 30, 0, 38),
+...         datetime(2016, 5, 25, 13, 30, 0, 48),
+...         datetime(2016, 5, 25, 13, 30, 0, 49),
+...         datetime(2016, 5, 25, 13, 30, 0, 48),
+...     ],
+...     "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
+...     "price": [51.95, 51.95, 720.77, 720.92, 98.0],
+...     "quantity": [75, 155, 100, 100, 100],
+... }
+>>> quotes_pl = pl.LazyFrame(data_quotes)
+>>> trades_pl = pl.LazyFrame(data_trades)
+>>> quotes_dask = dd.from_dict(data_quotes, npartitions=2)
+>>> trades_dask = dd.from_dict(data_trades, npartitions=2)
+
+

Let's define a dataframe-agnostic function in which we join over "datetime" and by "ticker" columns:

+ 
>>> def agnostic_join_asof_datetime_by_ticker(
+...     df_native: IntoFrameT,
+...     other_native: IntoFrameT,
+... ) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     other = nw.from_native(other_native)
+...     return (
+...         df.sort("datetime", "ticker")
+...         .join_asof(other, on="datetime", by="ticker")
+...         .sort("datetime", "ticker")
+...         .collect()
+...         .to_native()
+...     )
+
+

We can then pass any supported library such as Polars or Dask to agnostic_join_asof_datetime_by_ticker:

+ 
>>> agnostic_join_asof_datetime_by_ticker(trades_pl, quotes_pl)
+shape: (5, 6)
+┌────────────────────────────┬────────┬────────┬──────────┬───────┬────────┐
+│ datetime                   ┆ ticker ┆ price  ┆ quantity ┆ bid   ┆ ask    │
+│ ---                        ┆ ---    ┆ ---    ┆ ---      ┆ ---   ┆ ---    │
+│ datetime[μs]               ┆ str    ┆ f64    ┆ i64      ┆ f64   ┆ f64    │
+╞════════════════════════════╪════════╪════════╪══════════╪═══════╪════════╡
+│ 2016-05-25 13:30:00.000023 ┆ MSFT   ┆ 51.95  ┆ 75       ┆ 51.95 ┆ 51.96  │
+│ 2016-05-25 13:30:00.000038 ┆ MSFT   ┆ 51.95  ┆ 155      ┆ 51.97 ┆ 51.98  │
+│ 2016-05-25 13:30:00.000048 ┆ AAPL   ┆ 98.0   ┆ 100      ┆ null  ┆ null   │
+│ 2016-05-25 13:30:00.000048 ┆ GOOG   ┆ 720.77 ┆ 100      ┆ 720.5 ┆ 720.93 │
+│ 2016-05-25 13:30:00.000049 ┆ GOOG   ┆ 720.92 ┆ 100      ┆ 720.5 ┆ 720.93 │
+└────────────────────────────┴────────┴────────┴──────────┴───────┴────────┘
+>>> agnostic_join_asof_datetime_by_ticker(trades_dask, quotes_dask)
+                    datetime ticker   price  quantity     bid     ask
+0 2016-05-25 13:30:00.000023   MSFT   51.95        75   51.95   51.96
+0 2016-05-25 13:30:00.000038   MSFT   51.95       155   51.97   51.98
+1 2016-05-25 13:30:00.000048   AAPL   98.00       100     NaN     NaN
+2 2016-05-25 13:30:00.000048   GOOG  720.77       100  720.50  720.93
+3 2016-05-25 13:30:00.000049   GOOG  720.92       100  720.50  720.93
+
+ +
+ +
+ +
+ + +

+ lazy() + +

+ + +
+ +

Lazify the DataFrame (if possible).

+

If a library does not support lazy execution, then this is a no-op.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A LazyFrame.

+
+
+ + +

Examples:

+

Construct pandas and Polars objects:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> df = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> df_pd = pd.DataFrame(df)
+>>> lf_pl = pl.LazyFrame(df)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_lazy(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.lazy().to_native()
+
+

Note that then, pandas dataframe stay eager, and the Polars LazyFrame stays lazy:

+ 
>>> agnostic_lazy(df_pd)
+   foo  bar ham
+0    1  6.0   a
+1    2  7.0   b
+2    3  8.0   c
+>>> agnostic_lazy(lf_pl)
+<LazyFrame ...>
+
+ +
+ +
+ +
+ + +

+ pipe(function, *args, **kwargs) + +

+ + +
+ +

Pipe function call.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ function + + Callable[[Any], Self] + +
+

Function to apply.

+
+ 		+ required +
+ args + + Any + +
+

Positional arguments to pass to function.

+
+ 		+ () +
+ kwargs + + Any + +
+

Keyword arguments to pass to function.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The original object with the function applied.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3], "ba": [4, 5, 6]}
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_pipe(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.pipe(lambda _df: _df.select("a")).collect().to_native()
+
+

We can then pass any supported library such as Polars or Dask to agnostic_pipe:

+ 
>>> agnostic_pipe(lf_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 2   │
+│ 3   │
+└─────┘
+>>> agnostic_pipe(lf_dask)
+   a
+0  1
+1  2
+2  3
+
+ +
+ +
+ +
+ + +

+ rename(mapping) + +

+ + +
+ +

Rename column names.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ mapping + + dict[str, str] + +
+

Key value pairs that map from old name to new name, or a + function that takes the old name as input and returns the + new name.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The LazyFrame with the specified columns renamed.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"foo": [1, 2, 3], "bar": [6, 7, 8], "ham": ["a", "b", "c"]}
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_rename(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.rename({"foo": "apple"}).collect().to_native()
+
+

We can then pass any supported library such as Polars or Dask to agnostic_rename:

+ 
>>> agnostic_rename(lf_pl)
+shape: (3, 3)
+┌───────┬─────┬─────┐
+│ apple ┆ bar ┆ ham │
+│ ---   ┆ --- ┆ --- │
+│ i64   ┆ i64 ┆ str │
+╞═══════╪═════╪═════╡
+│ 1     ┆ 6   ┆ a   │
+│ 2     ┆ 7   ┆ b   │
+│ 3     ┆ 8   ┆ c   │
+└───────┴─────┴─────┘
+>>> agnostic_rename(lf_dask)
+   apple  bar ham
+0      1    6   a
+1      2    7   b
+2      3    8   c
+
+ +
+ +
+ +
+ + +

+ select(*exprs, **named_exprs) + +

+ + +
+ +

Select columns from this LazyFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ *exprs + + IntoExpr | Iterable[IntoExpr] + +
+

Column(s) to select, specified as positional arguments. +Accepts expression input. Strings are parsed as column names.

+
+ 		+ () +
+ **named_exprs + + IntoExpr + +
+

Additional columns to select, specified as keyword arguments. +The columns will be renamed to the keyword used.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The LazyFrame containing only the selected columns.

+
+
+ + +
+ Notes +

If you'd like to select a column whose name isn't a string (for example, +if you're working with pandas) then you should explicitly use nw.col instead +of just passing the column name. For example, to select a column named +0 use df.select(nw.col(0)), not df.select(0).

+
+ +

Examples:

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "foo": [1, 2, 3],
+...     "bar": [6, 7, 8],
+...     "ham": ["a", "b", "c"],
+... }
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

Let's define a dataframe-agnostic function in which we pass the name of a +column to select that column.

+ 
>>> def agnostic_select(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select("foo").collect().to_native()
+
+

We can then pass any supported library such as Polars or Dask to agnostic_select:

+ 
>>> agnostic_select(lf_pl)
+shape: (3, 1)
+┌─────┐
+│ foo │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 2   │
+│ 3   │
+└─────┘
+>>> agnostic_select(lf_dask)
+   foo
+0    1
+1    2
+2    3
+
+

Multiple columns can be selected by passing a list of column names.

+ 
>>> def agnostic_select(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(["foo", "bar"]).collect().to_native()
+
+ 
>>> agnostic_select(lf_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 6   │
+│ 2   ┆ 7   │
+│ 3   ┆ 8   │
+└─────┴─────┘
+>>> agnostic_select(lf_dask)
+   foo  bar
+0    1    6
+1    2    7
+2    3    8
+
+

Multiple columns can also be selected using positional arguments instead of a +list. Expressions are also accepted.

+ 
>>> def agnostic_select(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("foo"), nw.col("bar") + 1).collect().to_native()
+
+ 
>>> agnostic_select(lf_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ foo ┆ bar │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 7   │
+│ 2   ┆ 8   │
+│ 3   ┆ 9   │
+└─────┴─────┘
+>>> agnostic_select(lf_dask)
+   foo  bar
+0    1    7
+1    2    8
+2    3    9
+
+

Use keyword arguments to easily name your expression inputs.

+ 
>>> def agnostic_select(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(threshold=nw.col("foo") * 2).collect().to_native()
+
+ 
>>> agnostic_select(lf_pl)
+shape: (3, 1)
+┌───────────┐
+│ threshold │
+│ ---       │
+│ i64       │
+╞═══════════╡
+│ 2         │
+│ 4         │
+│ 6         │
+└───────────┘
+>>> agnostic_select(lf_dask)
+   threshold
+0          2
+1          4
+2          6
+
+ +
+ +
+ +
+ + +

+ sort(by, *more_by, descending=False, nulls_last=False) + +

+ + +
+ +

Sort the LazyFrame by the given columns.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ by + + str | Iterable[str] + +
+

Column(s) names to sort by.

+
+ 		+ required +
+ *more_by + + str + +
+

Additional columns to sort by, specified as positional arguments.

+
+ 		+ () +
+ descending + + bool | Sequence[bool] + +
+

Sort in descending order. When sorting by multiple columns, can be +specified per column by passing a sequence of booleans.

+
+ 		+ False +
+ nulls_last + + bool + +
+

Place null values last; can specify a single boolean applying to +all columns or a sequence of booleans for per-column control.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The sorted LazyFrame.

+
+
+ + +
+ Warning +

Unlike Polars, it is not possible to specify a sequence of booleans for +nulls_last in order to control per-column behaviour. Instead a single +boolean is applied for all by columns.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [1, 2, None],
+...     "b": [6.0, 5.0, 4.0],
+...     "c": ["a", "c", "b"],
+... }
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

Let's define a dataframe-agnostic function in which we sort by multiple +columns in different orders

+ 
>>> def agnostic_sort(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.sort("c", "a", descending=[False, True]).collect().to_native()
+
+

We can then pass any supported library such as Polars or Dask to agnostic_sort:

+ 
>>> agnostic_sort(lf_pl)
+shape: (3, 3)
+┌──────┬─────┬─────┐
+│ a    ┆ b   ┆ c   │
+│ ---  ┆ --- ┆ --- │
+│ i64  ┆ f64 ┆ str │
+╞══════╪═════╪═════╡
+│ 1    ┆ 6.0 ┆ a   │
+│ null ┆ 4.0 ┆ b   │
+│ 2    ┆ 5.0 ┆ c   │
+└──────┴─────┴─────┘
+>>> agnostic_sort(lf_dask)
+     a    b  c
+0  1.0  6.0  a
+2  NaN  4.0  b
+1  2.0  5.0  c
+
+ +
+ +
+ +
+ + +

+ tail(n=5) + +

+ + +
+ +

Get the last n rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of rows to return.

+
+ 		+ 5 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A subset of the LazyFrame of shape (n, n_columns).

+
+
+ + +
+ Notes +

LazyFrame.tail is not supported for the Dask backend with multiple +partitions.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [1, 2, 3, 4, 5, 6],
+...     "b": [7, 8, 9, 10, 11, 12],
+... }
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=1)
+
+

Let's define a dataframe-agnostic function that gets the last 3 rows.

+ 
>>> def agnostic_tail(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.tail(3).collect().to_native()
+
+

We can then pass any supported library such as Polars or Dask to agnostic_tail:

+ 
>>> agnostic_tail(lf_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 4   ┆ 10  │
+│ 5   ┆ 11  │
+│ 6   ┆ 12  │
+└─────┴─────┘
+>>> agnostic_tail(lf_dask)
+   a   b
+3  4  10
+4  5  11
+5  6  12
+
+ +
+ +
+ +
+ + +

+ to_native() + +

+ + +
+ +

Convert Narwhals LazyFrame to native one.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ FrameT + +
+

Object of class that user started with.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>>
+>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0], "ham": ["a", "b", "c"]}
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

Calling to_native on a Narwhals LazyFrame returns the native object:

+ 
>>> nw.from_native(lf_pl).to_native().collect()
+shape: (3, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ f64 ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ 6.0 ┆ a   │
+│ 2   ┆ 7.0 ┆ b   │
+│ 3   ┆ 8.0 ┆ c   │
+└─────┴─────┴─────┘
+>>> nw.from_native(lf_dask).to_native().compute()
+   foo  bar ham
+0    1  6.0   a
+1    2  7.0   b
+2    3  8.0   c
+
+ +
+ +
+ +
+ + +

+ unique(subset=None, *, keep='any', maintain_order=None) + +

+ + +
+ +

Drop duplicate rows from this LazyFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ subset + + str | list[str] | None + +
+

Column name(s) to consider when identifying duplicate rows. + If set to None, use all columns.

+
+ 		+ None +
+ keep + + Literal['any', 'none'] + +
+

{'first', 'none'} +Which of the duplicate rows to keep.

+
    +
  • 'any': Does not give any guarantee of which row is kept. + This allows more optimizations.
    • +
  • 'none': Don't keep duplicate rows.
    • +
+
+ 		+ 'any' +
+ maintain_order + + bool | None + +
+

Has no effect and is kept around only for backwards-compatibility.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The LazyFrame with unique rows.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "foo": [1, 2, 3, 1],
+...     "bar": ["a", "a", "a", "a"],
+...     "ham": ["b", "b", "b", "b"],
+... }
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_unique(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.unique(["bar", "ham"]).collect().to_native()
+
+

We can then pass any supported library such as Polars or Dask to agnostic_unique:

+ 
>>> agnostic_unique(lf_pl)
+shape: (1, 3)
+┌─────┬─────┬─────┐
+│ foo ┆ bar ┆ ham │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ str ┆ str │
+╞═════╪═════╪═════╡
+│ 1   ┆ a   ┆ b   │
+└─────┴─────┴─────┘
+>>> agnostic_unique(lf_dask)
+   foo bar ham
+0    1   a   b
+
+ +
+ +
+ +
+ + +

+ unpivot(on=None, *, index=None, variable_name=None, value_name=None) + +

+ + +
+ +

Unpivot a DataFrame from wide to long format.

+

Optionally leaves identifiers set.

+

This function is useful to massage a DataFrame into a format where one or more +columns are identifier variables (index) while all other columns, considered +measured variables (on), are "unpivoted" to the row axis leaving just +two non-identifier columns, 'variable' and 'value'.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ on + + str | list[str] | None + +
+

Column(s) to use as values variables; if on is empty all columns that +are not in index will be used.

+
+ 		+ None +
+ index + + str | list[str] | None + +
+

Column(s) to use as identifier variables.

+
+ 		+ None +
+ variable_name + + str | None + +
+

Name to give to the variable column. Defaults to "variable".

+
+ 		+ None +
+ value_name + + str | None + +
+

Name to give to the value column. Defaults to "value".

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The unpivoted LazyFrame.

+
+
+ + +
+ Notes +

If you're coming from pandas, this is similar to pandas.DataFrame.melt, +but with index replacing id_vars and on replacing value_vars. +In other frameworks, you might know this operation as pivot_longer.

+
+ +

Examples:

+ 
>>> import narwhals as nw
+>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": ["x", "y", "z"],
+...     "b": [1, 3, 5],
+...     "c": [2, 4, 6],
+... }
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_unpivot(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return (
+...         (df.unpivot(on=["b", "c"], index="a").sort(["variable", "a"]))
+...         .collect()
+...         .to_native()
+...     )
+
+

We can then pass any supported library such as Polars or Dask to agnostic_unpivot:

+ 
>>> agnostic_unpivot(lf_pl)
+shape: (6, 3)
+┌─────┬──────────┬───────┐
+│ a   ┆ variable ┆ value │
+│ --- ┆ ---      ┆ ---   │
+│ str ┆ str      ┆ i64   │
+╞═════╪══════════╪═══════╡
+│ x   ┆ b        ┆ 1     │
+│ y   ┆ b        ┆ 3     │
+│ z   ┆ b        ┆ 5     │
+│ x   ┆ c        ┆ 2     │
+│ y   ┆ c        ┆ 4     │
+│ z   ┆ c        ┆ 6     │
+└─────┴──────────┴───────┘
+>>> agnostic_unpivot(lf_dask)
+   a variable  value
+0  x        b      1
+1  y        b      3
+0  z        b      5
+2  x        c      2
+3  y        c      4
+1  z        c      6
+
+ +
+ +
+ +
+ + +

+ with_columns(*exprs, **named_exprs) + +

+ + +
+ +

Add columns to this LazyFrame.

+

Added columns will replace existing columns with the same name.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ *exprs + + IntoExpr | Iterable[IntoExpr] + +
+

Column(s) to add, specified as positional arguments. + Accepts expression input. Strings are parsed as column names, other + non-expression inputs are parsed as literals.

+
+ 		+ () +
+ **named_exprs + + IntoExpr + +
+

Additional columns to add, specified as keyword arguments. + The columns will be renamed to the keyword used.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
Name TypeDescription
LazyFrame + Self + +
+

A new LazyFrame with the columns added.

+
+
+ + +
+ Note +

Creating a new LazyFrame using this method does not create a new copy of +existing data.

+
+ +

Examples:

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [1, 2, 3, 4],
+...     "b": [0.5, 4, 10, 13],
+...     "c": [True, True, False, True],
+... }
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

Let's define a dataframe-agnostic function in which we pass an expression +to add it as a new column:

+ 
>>> def agnostic_with_columns(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return (
+...         df.with_columns((nw.col("a") * 2).alias("2a")).collect().to_native()
+...     )
+
+

We can then pass any supported library such as Polars or Dask to agnostic_with_columns:

+ 
>>> agnostic_with_columns(lf_pl)
+shape: (4, 4)
+┌─────┬──────┬───────┬─────┐
+│ a   ┆ b    ┆ c     ┆ 2a  │
+│ --- ┆ ---  ┆ ---   ┆ --- │
+│ i64 ┆ f64  ┆ bool  ┆ i64 │
+╞═════╪══════╪═══════╪═════╡
+│ 1   ┆ 0.5  ┆ true  ┆ 2   │
+│ 2   ┆ 4.0  ┆ true  ┆ 4   │
+│ 3   ┆ 10.0 ┆ false ┆ 6   │
+│ 4   ┆ 13.0 ┆ true  ┆ 8   │
+└─────┴──────┴───────┴─────┘
+>>> agnostic_with_columns(lf_dask)
+   a     b      c  2a
+0  1   0.5   True   2
+1  2   4.0   True   4
+2  3  10.0  False   6
+3  4  13.0   True   8
+
+ +
+ +
+ +
+ + +

+ with_row_index(name='index') + +

+ + +
+ +

Insert column which enumerates rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ name + + str + +
+

The name of the column as a string. The default is "index".

+
+ 		+ 'index' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

The original object with the column added.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import dask.dataframe as dd
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+>>> lf_pl = pl.LazyFrame(data)
+>>> lf_dask = dd.from_dict(data, npartitions=2)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_with_row_index(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_row_index().collect().to_native()
+
+

We can then pass any supported library such as Polars or Dask to agnostic_with_row_index:

+ 
>>> agnostic_with_row_index(lf_pl)
+shape: (3, 3)
+┌───────┬─────┬─────┐
+│ index ┆ a   ┆ b   │
+│ ---   ┆ --- ┆ --- │
+│ u32   ┆ i64 ┆ i64 │
+╞═══════╪═════╪═════╡
+│ 0     ┆ 1   ┆ 4   │
+│ 1     ┆ 2   ┆ 5   │
+│ 2     ┆ 3   ┆ 6   │
+└───────┴─────┴─────┘
+>>> agnostic_with_row_index(lf_dask)
+   index  a  b
+0      0  1  4
+1      1  2  5
+2      2  3  6
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/narwhals/index.html b/api-reference/narwhals/index.html new file mode 100644 index 000000000..947be41e2 --- /dev/null +++ b/api-reference/narwhals/index.html @@ -0,0 +1,7972 @@ + + + + + + + + + + + + + + + + + + + + + + + Top-level functions - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Top-level functions

+

Here are the top-level functions available in Narwhals.

+ + +
+ + + + +
+ + + + + + + + +
+ + + + + + + + + +
+ + +

+ all() + +

+ + +
+ +

Instantiate an expression representing all columns.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_all(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.all() * 2).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_all:

+ 
>>> agnostic_all(df_pd)
+   a   b
+0  2   8
+1  4  10
+2  6  12
+
+ 
>>> agnostic_all(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 2   ┆ 8   │
+│ 4   ┆ 10  │
+│ 6   ┆ 12  │
+└─────┴─────┘
+
+ 
>>> agnostic_all(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[2,4,6]]
+b: [[8,10,12]]
+
+ +
+ +
+ +
+ + +

+ all_horizontal(*exprs) + +

+ + +
+ +

Compute the bitwise AND horizontally across columns.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ exprs + + IntoExpr | Iterable[IntoExpr] + +
+

Name(s) of the columns to use in the aggregation function. Accepts +expression input.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [False, False, True, True, False, None],
+...     "b": [False, True, True, None, None, None],
+... }
+>>> df_pl = pl.DataFrame(data)
+>>> df_pd = pd.DataFrame(data).convert_dtypes(dtype_backend="pyarrow")
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_all_horizontal(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select("a", "b", all=nw.all_horizontal("a", "b")).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_all_horizontal:

+ 
>>> agnostic_all_horizontal(df_pd)
+       a      b    all
+0  False  False  False
+1  False   True  False
+2   True   True   True
+3   True   <NA>   <NA>
+4  False   <NA>  False
+5   <NA>   <NA>   <NA>
+
+ 
>>> agnostic_all_horizontal(df_pl)
+shape: (6, 3)
+┌───────┬───────┬───────┐
+│ a     ┆ b     ┆ all   │
+│ ---   ┆ ---   ┆ ---   │
+│ bool  ┆ bool  ┆ bool  │
+╞═══════╪═══════╪═══════╡
+│ false ┆ false ┆ false │
+│ false ┆ true  ┆ false │
+│ true  ┆ true  ┆ true  │
+│ true  ┆ null  ┆ null  │
+│ false ┆ null  ┆ false │
+│ null  ┆ null  ┆ null  │
+└───────┴───────┴───────┘
+
+ 
>>> agnostic_all_horizontal(df_pa)
+pyarrow.Table
+a: bool
+b: bool
+all: bool
+----
+a: [[false,false,true,true,false,null]]
+b: [[false,true,true,null,null,null]]
+all: [[false,false,true,null,false,null]]
+
+ +
+ +
+ +
+ + +

+ any_horizontal(*exprs) + +

+ + +
+ +

Compute the bitwise OR horizontally across columns.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ exprs + + IntoExpr | Iterable[IntoExpr] + +
+

Name(s) of the columns to use in the aggregation function. Accepts +expression input.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [False, False, True, True, False, None],
+...     "b": [False, True, True, None, None, None],
+... }
+>>> df_pl = pl.DataFrame(data)
+>>> df_pd = pd.DataFrame(data).convert_dtypes(dtype_backend="pyarrow")
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_any_horizontal(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select("a", "b", any=nw.any_horizontal("a", "b")).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_any_horizontal:

+ 
>>> agnostic_any_horizontal(df_pd)
+       a      b    any
+0  False  False  False
+1  False   True   True
+2   True   True   True
+3   True   <NA>   True
+4  False   <NA>   <NA>
+5   <NA>   <NA>   <NA>
+
+ 
>>> agnostic_any_horizontal(df_pl)
+shape: (6, 3)
+┌───────┬───────┬───────┐
+│ a     ┆ b     ┆ any   │
+│ ---   ┆ ---   ┆ ---   │
+│ bool  ┆ bool  ┆ bool  │
+╞═══════╪═══════╪═══════╡
+│ false ┆ false ┆ false │
+│ false ┆ true  ┆ true  │
+│ true  ┆ true  ┆ true  │
+│ true  ┆ null  ┆ true  │
+│ false ┆ null  ┆ null  │
+│ null  ┆ null  ┆ null  │
+└───────┴───────┴───────┘
+
+ 
>>> agnostic_any_horizontal(df_pa)
+pyarrow.Table
+a: bool
+b: bool
+any: bool
+----
+a: [[false,false,true,true,false,null]]
+b: [[false,true,true,null,null,null]]
+any: [[false,true,true,true,null,null]]
+
+ +
+ +
+ +
+ + +

+ col(*names) + +

+ + +
+ +

Creates an expression that references one or more columns by their name(s).

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ names + + str | Iterable[str] + +
+

Name(s) of the columns to use.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2], "b": [3, 4]}
+>>> df_pl = pl.DataFrame(data)
+>>> df_pd = pd.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_col(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.col("a") * nw.col("b")).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_col:

+ 
>>> agnostic_col(df_pd)
+   a
+0  3
+1  8
+
+ 
>>> agnostic_col(df_pl)
+shape: (2, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 3   │
+│ 8   │
+└─────┘
+
+ 
>>> agnostic_col(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[3,8]]
+
+ +
+ +
+ +
+ + +

+ concat(items, *, how='vertical') + +

+ + +
+ +

Concatenate multiple DataFrames, LazyFrames into a single entity.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ items + + Iterable[FrameT] + +
+

DataFrames, LazyFrames to concatenate.

+
+ 		+ required +
+ how + + Literal['horizontal', 'vertical', 'diagonal'] + +
+

concatenating strategy:

+
    +
  • vertical: Concatenate vertically. Column names must match.
    • +
  • horizontal: Concatenate horizontally. If lengths don't match, then + missing rows are filled with null values.
    • +
  • diagonal: Finds a union between the column schemas and fills missing column + values with null.
    • +
+
+ 		+ 'vertical' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ FrameT + +
+

A new DataFrame, Lazyframe resulting from the concatenation.

+
+
+ + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ TypeError + +
+

The items to concatenate should either all be eager, or all lazy

+
+
+ + +

Examples:

+

Let's take an example of vertical concatenation:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data_1 = {"a": [1, 2, 3], "b": [4, 5, 6]}
+>>> data_2 = {"a": [5, 2], "b": [1, 4]}
+
+ 
>>> df_pd_1 = pd.DataFrame(data_1)
+>>> df_pd_2 = pd.DataFrame(data_2)
+>>> df_pl_1 = pl.DataFrame(data_1)
+>>> df_pl_2 = pl.DataFrame(data_2)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def agnostic_vertical_concat(df1, df2):
+...     return nw.concat([df1, df2], how="vertical")
+
+ 
>>> agnostic_vertical_concat(df_pd_1, df_pd_2)
+   a  b
+0  1  4
+1  2  5
+2  3  6
+0  5  1
+1  2  4
+>>> agnostic_vertical_concat(df_pl_1, df_pl_2)
+shape: (5, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 4   │
+│ 2   ┆ 5   │
+│ 3   ┆ 6   │
+│ 5   ┆ 1   │
+│ 2   ┆ 4   │
+└─────┴─────┘
+
+

Let's look at case a for horizontal concatenation:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data_1 = {"a": [1, 2, 3], "b": [4, 5, 6]}
+>>> data_2 = {"c": [5, 2], "d": [1, 4]}
+
+ 
>>> df_pd_1 = pd.DataFrame(data_1)
+>>> df_pd_2 = pd.DataFrame(data_2)
+>>> df_pl_1 = pl.DataFrame(data_1)
+>>> df_pl_2 = pl.DataFrame(data_2)
+
+

Defining a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def agnostic_horizontal_concat(df1, df2):
+...     return nw.concat([df1, df2], how="horizontal")
+
+ 
>>> agnostic_horizontal_concat(df_pd_1, df_pd_2)
+   a  b    c    d
+0  1  4  5.0  1.0
+1  2  5  2.0  4.0
+2  3  6  NaN  NaN
+
+ 
>>> agnostic_horizontal_concat(df_pl_1, df_pl_2)
+shape: (3, 4)
+┌─────┬─────┬──────┬──────┐
+│ a   ┆ b   ┆ c    ┆ d    │
+│ --- ┆ --- ┆ ---  ┆ ---  │
+│ i64 ┆ i64 ┆ i64  ┆ i64  │
+╞═════╪═════╪══════╪══════╡
+│ 1   ┆ 4   ┆ 5    ┆ 1    │
+│ 2   ┆ 5   ┆ 2    ┆ 4    │
+│ 3   ┆ 6   ┆ null ┆ null │
+└─────┴─────┴──────┴──────┘
+
+

Let's look at case a for diagonal concatenation:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> data_1 = {"a": [1, 2], "b": [3.5, 4.5]}
+>>> data_2 = {"a": [3, 4], "z": ["x", "y"]}
+
+ 
>>> df_pd_1 = pd.DataFrame(data_1)
+>>> df_pd_2 = pd.DataFrame(data_2)
+>>> df_pl_1 = pl.DataFrame(data_1)
+>>> df_pl_2 = pl.DataFrame(data_2)
+
+

Defining a dataframe-agnostic function:

+ 
>>> @nw.narwhalify
+... def agnostic_diagonal_concat(df1, df2):
+...     return nw.concat([df1, df2], how="diagonal")
+
+ 
>>> agnostic_diagonal_concat(df_pd_1, df_pd_2)
+   a    b    z
+0  1  3.5  NaN
+1  2  4.5  NaN
+0  3  NaN    x
+1  4  NaN    y
+
+ 
>>> agnostic_diagonal_concat(df_pl_1, df_pl_2)
+shape: (4, 3)
+┌─────┬──────┬──────┐
+│ a   ┆ b    ┆ z    │
+│ --- ┆ ---  ┆ ---  │
+│ i64 ┆ f64  ┆ str  │
+╞═════╪══════╪══════╡
+│ 1   ┆ 3.5  ┆ null │
+│ 2   ┆ 4.5  ┆ null │
+│ 3   ┆ null ┆ x    │
+│ 4   ┆ null ┆ y    │
+└─────┴──────┴──────┘
+
+ +
+ +
+ +
+ + +

+ concat_str(exprs, *more_exprs, separator='', ignore_nulls=False) + +

+ + +
+ +

Horizontally concatenate columns into a single string column.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ exprs + + IntoExpr | Iterable[IntoExpr] + +
+

Columns to concatenate into a single string column. Accepts expression +input. Strings are parsed as column names, other non-expression inputs are +parsed as literals. Non-String columns are cast to String.

+
+ 		+ required +
+ *more_exprs + + IntoExpr + +
+

Additional columns to concatenate into a single string column, +specified as positional arguments.

+
+ 		+ () +
+ separator + + str + +
+

String that will be used to separate the values of each column.

+
+ 		+ '' +
+ ignore_nulls + + bool + +
+

Ignore null values (default is False). +If set to False, null values will be propagated and if the row contains any +null values, the output is null.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [1, 2, 3],
+...     "b": ["dogs", "cats", None],
+...     "c": ["play", "swim", "walk"],
+... }
+
+

We define a dataframe-agnostic function that computes the horizontal string +concatenation of different columns

+ 
>>> def agnostic_concat_str(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(
+...         nw.concat_str(
+...             [
+...                 nw.col("a") * 2,
+...                 nw.col("b"),
+...                 nw.col("c"),
+...             ],
+...             separator=" ",
+...         ).alias("full_sentence")
+...     ).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow +to agnostic_concat_str:

+ 
>>> agnostic_concat_str(pd.DataFrame(data))
+  full_sentence
+0   2 dogs play
+1   4 cats swim
+2          None
+
+ 
>>> agnostic_concat_str(pl.DataFrame(data))
+shape: (3, 1)
+┌───────────────┐
+│ full_sentence │
+│ ---           │
+│ str           │
+╞═══════════════╡
+│ 2 dogs play   │
+│ 4 cats swim   │
+│ null          │
+└───────────────┘
+
+ 
>>> agnostic_concat_str(pa.table(data))
+pyarrow.Table
+full_sentence: string
+----
+full_sentence: [["2 dogs play","4 cats swim",null]]
+
+ +
+ +
+ +
+ + +

+ from_arrow(native_frame, *, native_namespace) + +

+ + +
+ +

Construct a DataFrame from an object which supports the PyCapsule Interface.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ native_frame + + ArrowStreamExportable + +
+

Object which implements __arrow_c_stream__.

+
+ 		+ required +
+ native_namespace + + ModuleType + +
+

The native library to use for DataFrame creation.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DataFrame[Any] + +
+

A new DataFrame.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+
+

Let's define a dataframe-agnostic function which creates a PyArrow +Table.

+ 
>>> def agnostic_to_arrow(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return nw.from_arrow(df, native_namespace=pa).to_native()
+
+

Let's see what happens when passing pandas / Polars input:

+ 
>>> agnostic_to_arrow(pd.DataFrame(data))
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[1,2,3]]
+b: [[4,5,6]]
+>>> agnostic_to_arrow(pl.DataFrame(data))
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[1,2,3]]
+b: [[4,5,6]]
+
+ +
+ +
+ +
+ + +

+ from_dict(data, schema=None, *, native_namespace=None) + +

+ + +
+ +

Instantiate DataFrame from dictionary.

+

Indexes (if present, for pandas-like backends) are aligned following +the left-hand-rule.

+ + +
+ Notes +

For pandas-like dataframes, conversion to schema is applied after dataframe +creation.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ data + + dict[str, Any] + +
+

Dictionary to create DataFrame from.

+
+ 		+ required +
+ schema + + dict[str, DType] | Schema | None + +
+

The DataFrame schema as Schema or dict of {name: type}.

+
+ 		+ None +
+ native_namespace + + ModuleType | None + +
+

The native library to use for DataFrame creation. Only +necessary if inputs are not Narwhals Series.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DataFrame[Any] + +
+

A new DataFrame.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+
+

Let's create a new dataframe of the same class as the dataframe we started with, from a dict of new data:

+ 
>>> def agnostic_from_dict(df_native: IntoFrameT) -> IntoFrameT:
+...     new_data = {"c": [5, 2], "d": [1, 4]}
+...     native_namespace = nw.get_native_namespace(df_native)
+...     return nw.from_dict(new_data, native_namespace=native_namespace).to_native()
+
+

Let's see what happens when passing pandas, Polars or PyArrow input:

+ 
>>> agnostic_from_dict(pd.DataFrame(data))
+   c  d
+0  5  1
+1  2  4
+>>> agnostic_from_dict(pl.DataFrame(data))
+shape: (2, 2)
+┌─────┬─────┐
+│ c   ┆ d   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 5   ┆ 1   │
+│ 2   ┆ 4   │
+└─────┴─────┘
+>>> agnostic_from_dict(pa.table(data))
+pyarrow.Table
+c: int64
+d: int64
+----
+c: [[5,2]]
+d: [[1,4]]
+
+ +
+ +
+ +
+ + +

+ from_native(native_object, *, strict=None, pass_through=None, eager_only=False, eager_or_interchange_only=False, series_only=False, allow_series=None) + +

+ + +
+ +

Convert native_object to Narwhals Dataframe, Lazyframe, or Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ native_object + + IntoFrameT | IntoSeries | T + +
+

Raw object from user. +Depending on the other arguments, input object can be:

+
    +
  • a Dataframe / Lazyframe / Series supported by Narwhals (pandas, Polars, PyArrow, ...)
    • +
  • an object which implements __narwhals_dataframe__, __narwhals_lazyframe__, + or __narwhals_series__
    • +
+
+ 		+ required +
+ strict + + bool | None + +
+

Determine what happens if the object can't be converted to Narwhals:

+
    +
  • True or None (default): raise an error
    • +
  • False: pass object through as-is
    • +
+

Deprecated (v1.13.0): + Please use pass_through instead. Note that strict is still available + (and won't emit a deprecation warning) if you use narwhals.stable.v1, + see perfect backwards compatibility policy.

+
+ 		+ None +
+ pass_through + + bool | None + +
+

Determine what happens if the object can't be converted to Narwhals:

+
    +
  • False or None (default): raise an error
    • +
  • True: pass object through as-is
    • +
+
+ 		+ None +
+ eager_only + + bool + +
+

Whether to only allow eager objects:

+
    +
  • False (default): don't require native_object to be eager
    • +
  • True: only convert to Narwhals if native_object is eager
    • +
+
+ 		+ False +
+ eager_or_interchange_only + + bool + +
+

Whether to only allow eager objects or objects which +have interchange-level support in Narwhals:

+
    +
  • False (default): don't require native_object to either be eager or to + have interchange-level support in Narwhals
    • +
  • True: only convert to Narwhals if native_object is eager or has + interchange-level support in Narwhals
    • +
+

See interchange-only support +for more details.

+
+ 		+ False +
+ series_only + + bool + +
+

Whether to only allow Series:

+
    +
  • False (default): don't require native_object to be a Series
    • +
  • True: only convert to Narwhals if native_object is a Series
    • +
+
+ 		+ False +
+ allow_series + + bool | None + +
+

Whether to allow Series (default is only Dataframe / Lazyframe):

+
    +
  • False or None (default): don't convert to Narwhals if native_object is a Series
    • +
  • True: allow native_object to be a Series
    • +
+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ LazyFrame[IntoFrameT] | DataFrame[IntoFrameT] | Series[IntoSeriesT] | T + +
+

DataFrame, LazyFrame, Series, or original object, depending +on which combination of parameters was passed.

+
+
+ +
+ +
+ +
+ + +

+ from_numpy(data, schema=None, *, native_namespace) + +

+ + +
+ +

Construct a DataFrame from a NumPy ndarray.

+ + +
+ Notes +

Only row orientation is currently supported.

+

For pandas-like dataframes, conversion to schema is applied after dataframe +creation.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ data + + ndarray + +
+

Two-dimensional data represented as a NumPy ndarray.

+
+ 		+ required +
+ schema + + dict[str, DType] | Schema | list[str] | None + +
+

The DataFrame schema as Schema, dict of {name: type}, or a list of str.

+
+ 		+ None +
+ native_namespace + + ModuleType + +
+

The native library to use for DataFrame creation.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DataFrame[Any] + +
+

A new DataFrame.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> import numpy as np
+>>> from narwhals.typing import IntoFrameT
+>>> data = {"a": [1, 2], "b": [3, 4]}
+
+

Let's create a new dataframe of the same class as the dataframe we started with, from a NumPy ndarray of new data:

+ 
>>> def agnostic_from_numpy(df_native: IntoFrameT) -> IntoFrameT:
+...     new_data = np.array([[5, 2, 1], [1, 4, 3]])
+...     df = nw.from_native(df_native)
+...     native_namespace = nw.get_native_namespace(df)
+...     return nw.from_numpy(new_data, native_namespace=native_namespace).to_native()
+
+

Let's see what happens when passing pandas, Polars or PyArrow input:

+ 
>>> agnostic_from_numpy(pd.DataFrame(data))
+   column_0  column_1  column_2
+0         5         2         1
+1         1         4         3
+>>> agnostic_from_numpy(pl.DataFrame(data))
+shape: (2, 3)
+┌──────────┬──────────┬──────────┐
+│ column_0 ┆ column_1 ┆ column_2 │
+│ ---      ┆ ---      ┆ ---      │
+│ i64      ┆ i64      ┆ i64      │
+╞══════════╪══════════╪══════════╡
+│ 5        ┆ 2        ┆ 1        │
+│ 1        ┆ 4        ┆ 3        │
+└──────────┴──────────┴──────────┘
+>>> agnostic_from_numpy(pa.table(data))
+pyarrow.Table
+column_0: int64
+column_1: int64
+column_2: int64
+----
+column_0: [[5,1]]
+column_1: [[2,4]]
+column_2: [[1,3]]
+
+

Let's specify the column names:

+ 
>>> def agnostic_from_numpy(df_native: IntoFrameT) -> IntoFrameT:
+...     new_data = np.array([[5, 2, 1], [1, 4, 3]])
+...     schema = ["c", "d", "e"]
+...     df = nw.from_native(df_native)
+...     native_namespace = nw.get_native_namespace(df)
+...     return nw.from_numpy(
+...         new_data, native_namespace=native_namespace, schema=schema
+...     ).to_native()
+
+

Let's see the modified outputs:

+ 
>>> agnostic_from_numpy(pd.DataFrame(data))
+   c  d  e
+0  5  2  1
+1  1  4  3
+>>> agnostic_from_numpy(pl.DataFrame(data))
+shape: (2, 3)
+┌─────┬─────┬─────┐
+│ c   ┆ d   ┆ e   │
+│ --- ┆ --- ┆ --- │
+│ i64 ┆ i64 ┆ i64 │
+╞═════╪═════╪═════╡
+│ 5   ┆ 2   ┆ 1   │
+│ 1   ┆ 4   ┆ 3   │
+└─────┴─────┴─────┘
+>>> agnostic_from_numpy(pa.table(data))
+pyarrow.Table
+c: int64
+d: int64
+e: int64
+----
+c: [[5,1]]
+d: [[2,4]]
+e: [[1,3]]
+
+

Let's modify the function so that it specifies the schema:

+ 
>>> def agnostic_from_numpy(df_native: IntoFrameT) -> IntoFrameT:
+...     new_data = np.array([[5, 2, 1], [1, 4, 3]])
+...     schema = {"c": nw.Int16(), "d": nw.Float32(), "e": nw.Int8()}
+...     df = nw.from_native(df_native)
+...     native_namespace = nw.get_native_namespace(df)
+...     return nw.from_numpy(
+...         new_data, native_namespace=native_namespace, schema=schema
+...     ).to_native()
+
+

Let's see the outputs:

+ 
>>> agnostic_from_numpy(pd.DataFrame(data))
+   c    d  e
+0  5  2.0  1
+1  1  4.0  3
+>>> agnostic_from_numpy(pl.DataFrame(data))
+shape: (2, 3)
+┌─────┬─────┬─────┐
+│ c   ┆ d   ┆ e   │
+│ --- ┆ --- ┆ --- │
+│ i16 ┆ f32 ┆ i8  │
+╞═════╪═════╪═════╡
+│ 5   ┆ 2.0 ┆ 1   │
+│ 1   ┆ 4.0 ┆ 3   │
+└─────┴─────┴─────┘
+>>> agnostic_from_numpy(pa.table(data))
+pyarrow.Table
+c: int16
+d: float
+e: int8
+----
+c: [[5,1]]
+d: [[2,4]]
+e: [[1,3]]
+
+ +
+ +
+ +
+ + +

+ generate_temporary_column_name(n_bytes, columns) + +

+ + +
+ +

Generates a unique column name that is not present in the given list of columns.

+

It relies on python secrets token_hex +function to return a string nbytes random bytes.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n_bytes + + int + +
+

The number of bytes to generate for the token.

+
+ 		+ required +
+ columns + + list[str] + +
+

The list of columns to check for uniqueness.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ str + +
+

A unique token that is not present in the given list of columns.

+
+
+ + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ AssertionError + +
+

If a unique token cannot be generated after 100 attempts.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> columns = ["abc", "xyz"]
+>>> nw.generate_temporary_column_name(n_bytes=8, columns=columns) not in columns
+True
+
+ +
+ +
+ +
+ + +

+ get_level(obj) + +

+ + +
+ +

Level of support Narwhals has for current object.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ obj + + DataFrame[Any] | LazyFrame[Any] | Series[IntoSeriesT] + +
+

Dataframe or Series.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Literal['full', 'lazy', 'interchange'] + +
+

This can be one of:

+
    +
  • 'full': full Narwhals API support
    • +
  • 'lazy': only lazy operations are supported. This excludes anything + which involves iterating over rows in Python.
    • +
  • 'interchange': only metadata operations are supported (df.schema)
    • +
+
+
+ +
+ +
+ +
+ + +

+ get_native_namespace(obj) + +

+ + +
+ +

Get native namespace from object.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ obj + + DataFrame[Any] | LazyFrame[Any] | Series[Any] | DataFrame | Series | DataFrame | LazyFrame | Series | Table | ChunkedArray + +
+

Dataframe, Lazyframe, or Series.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

Native module.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import narwhals as nw
+>>> df = nw.from_native(pd.DataFrame({"a": [1, 2, 3]}))
+>>> nw.get_native_namespace(df)
+<module 'pandas'...>
+>>> df = nw.from_native(pl.DataFrame({"a": [1, 2, 3]}))
+>>> nw.get_native_namespace(df)
+<module 'polars'...>
+
+ +
+ +
+ +
+ + +

+ is_ordered_categorical(series) + +

+ + +
+ +

Return whether indices of categories are semantically meaningful.

+

This is a convenience function to accessing what would otherwise be +the is_ordered property from the DataFrame Interchange Protocol, +see https://data-apis.org/dataframe-protocol/latest/API.html.

+
    +
  • For Polars:
    • +
  • Enums are always ordered.
    • +
  • Categoricals are ordered if dtype.ordering == "physical".
    • +
  • For pandas-like APIs:
    • +
  • Categoricals are ordered if dtype.cat.ordered == True.
    • +
  • For PyArrow table:
    • +
  • Categoricals are ordered if dtype.type.ordered == True.
    • +
+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ series + + Series[Any] + +
+

Input Series.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ bool + +
+

Whether the Series is an ordered categorical.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> import polars as pl
+>>> data = ["x", "y"]
+>>> s_pd = pd.Series(data, dtype=pd.CategoricalDtype(ordered=True))
+>>> s_pl = pl.Series(data, dtype=pl.Categorical(ordering="physical"))
+
+

Let's define a library-agnostic function:

+ 
>>> @nw.narwhalify
+... def func(s):
+...     return nw.is_ordered_categorical(s)
+
+

Then, we can pass any supported library to func:

+ 
>>> func(s_pd)
+True
+>>> func(s_pl)
+True
+
+ +
+ +
+ +
+ + +

+ len() + +

+ + +
+ +

Return the number of rows.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2], "b": [5, 10]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_len(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.len()).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_len:

+ 
>>> agnostic_len(df_pd)
+   len
+0    2
+>>> agnostic_len(df_pl)
+shape: (1, 1)
+┌─────┐
+│ len │
+│ --- │
+│ u32 │
+╞═════╡
+│ 2   │
+└─────┘
+>>> agnostic_len(df_pa)
+pyarrow.Table
+len: int64
+----
+len: [[2]]
+
+ +
+ +
+ +
+ + +

+ lit(value, dtype=None) + +

+ + +
+ +

Return an expression representing a literal value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ value + + Any + +
+

The value to use as literal.

+
+ 		+ required +
+ dtype + + DType | type[DType] | None + +
+

The data type of the literal value. If not provided, the data type will +be inferred.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2]}
+>>> df_pl = pl.DataFrame(data)
+>>> df_pd = pd.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_lit(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(nw.lit(3)).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_lit:

+ 
>>> agnostic_lit(df_pd)
+   a  literal
+0  1        3
+1  2        3
+
+ 
>>> agnostic_lit(df_pl)
+shape: (2, 2)
+┌─────┬─────────┐
+│ a   ┆ literal │
+│ --- ┆ ---     │
+│ i64 ┆ i32     │
+╞═════╪═════════╡
+│ 1   ┆ 3       │
+│ 2   ┆ 3       │
+└─────┴─────────┘
+
+ 
>>> agnostic_lit(df_pa)
+pyarrow.Table
+a: int64
+literal: int64
+----
+a: [[1,2]]
+literal: [[3,3]]
+
+ +
+ +
+ +
+ + +

+ max(*columns) + +

+ + +
+ +

Return the maximum value.

+ + +
+ Note +

Syntactic sugar for nw.col(columns).max().

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ columns + + str + +
+

Name(s) of the columns to use in the aggregation function.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2], "b": [5, 10]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_max(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.max("a")).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_max:

+ 
>>> agnostic_max(df_pd)
+   a
+0  2
+
+ 
>>> agnostic_max(df_pl)
+shape: (1, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 2   │
+└─────┘
+
+ 
>>> agnostic_max(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[2]]
+
+ +
+ +
+ +
+ + +

+ max_horizontal(*exprs) + +

+ + +
+ +

Get the maximum value horizontally across columns.

+ + +
+ Notes +

We support max_horizontal over numeric columns only.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ exprs + + IntoExpr | Iterable[IntoExpr] + +
+

Name(s) of the columns to use in the aggregation function. Accepts +expression input.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [1, 8, 3],
+...     "b": [4, 5, None],
+...     "c": ["x", "y", "z"],
+... }
+
+

We define a dataframe-agnostic function that computes the horizontal max of "a" +and "b" columns:

+ 
>>> def agnostic_max_horizontal(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.max_horizontal("a", "b")).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_max_horizontal:

+ 
>>> agnostic_max_horizontal(pd.DataFrame(data))
+     a
+0  4.0
+1  8.0
+2  3.0
+
+ 
>>> agnostic_max_horizontal(pl.DataFrame(data))
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 4   │
+│ 8   │
+│ 3   │
+└─────┘
+
+ 
>>> agnostic_max_horizontal(pa.table(data))
+pyarrow.Table
+a: int64
+----
+a: [[4,8,3]]
+
+ +
+ +
+ +
+ + +

+ maybe_align_index(lhs, rhs) + +

+ + +
+ +

Align lhs to the Index of rhs, if they're both pandas-like.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ lhs + + FrameOrSeriesT + +
+

Dataframe or Series.

+
+ 		+ required +
+ rhs + + Series[Any] | DataFrame[Any] | LazyFrame[Any] + +
+

Dataframe or Series to align with.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ FrameOrSeriesT + +
+

Same type as input.

+
+
+ + +
+ Notes +

This is only really intended for backwards-compatibility purposes, +for example if your library already aligns indices for users. +If you're designing a new library, we highly encourage you to not +rely on the Index. +For non-pandas-like inputs, this only checks that lhs and rhs +are the same length.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2]}, index=[3, 4])
+>>> s_pd = pd.Series([6, 7], index=[4, 3])
+>>> df = nw.from_native(df_pd)
+>>> s = nw.from_native(s_pd, series_only=True)
+>>> nw.to_native(nw.maybe_align_index(df, s))
+   a
+4  2
+3  1
+
+ +
+ +
+ +
+ + +

+ maybe_convert_dtypes(obj, *args, **kwargs) + +

+ + +
+ +

Convert columns or series to the best possible dtypes using dtypes supporting pd.NA, if df is pandas-like.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ obj + + FrameOrSeriesT + +
+

DataFrame or Series.

+
+ 		+ required +
+ *args + + bool + +
+

Additional arguments which gets passed through.

+
+ 		+ () +
+ **kwargs + + bool | str + +
+

Additional arguments which gets passed through.

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ FrameOrSeriesT + +
+

Same type as input.

+
+
+ + +
+ Notes +

For non-pandas-like inputs, this is a no-op. +Also, args and kwargs just get passed down to the underlying library as-is.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> import numpy as np
+>>> df_pd = pd.DataFrame(
+...     {
+...         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
+...         "b": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
+...     }
+... )
+>>> df = nw.from_native(df_pd)
+>>> nw.to_native(nw.maybe_convert_dtypes(df)).dtypes
+a             Int32
+b           boolean
+dtype: object
+
+ +
+ +
+ +
+ + +

+ maybe_get_index(obj) + +

+ + +
+ +

Get the index of a DataFrame or a Series, if it's pandas-like.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ obj + + DataFrame[Any] | LazyFrame[Any] | Series[Any] + +
+

Dataframe or Series.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any | None + +
+

Same type as input.

+
+
+ + +
+ Notes +

This is only really intended for backwards-compatibility purposes, +for example if your library already aligns indices for users. +If you're designing a new library, we highly encourage you to not +rely on the Index. +For non-pandas-like inputs, this returns None.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [4, 5]})
+>>> df = nw.from_native(df_pd)
+>>> nw.maybe_get_index(df)
+RangeIndex(start=0, stop=2, step=1)
+>>> series_pd = pd.Series([1, 2])
+>>> series = nw.from_native(series_pd, series_only=True)
+>>> nw.maybe_get_index(series)
+RangeIndex(start=0, stop=2, step=1)
+
+ +
+ +
+ +
+ + +

+ maybe_reset_index(obj) + +

+ + +
+ +

Reset the index to the default integer index of a DataFrame or a Series, if it's pandas-like.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ obj + + FrameOrSeriesT + +
+

Dataframe or Series.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ FrameOrSeriesT + +
+

Same type as input.

+
+
+ + +
+ Notes +

This is only really intended for backwards-compatibility purposes, +for example if your library already resets the index for users. +If you're designing a new library, we highly encourage you to not +rely on the Index. +For non-pandas-like inputs, this is a no-op.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [4, 5]}, index=([6, 7]))
+>>> df = nw.from_native(df_pd)
+>>> nw.to_native(nw.maybe_reset_index(df))
+   a  b
+0  1  4
+1  2  5
+>>> series_pd = pd.Series([1, 2])
+>>> series = nw.from_native(series_pd, series_only=True)
+>>> nw.maybe_get_index(series)
+RangeIndex(start=0, stop=2, step=1)
+
+ +
+ +
+ +
+ + +

+ maybe_set_index(obj, column_names=None, *, index=None) + +

+ + +
+ +

Set the index of a DataFrame or a Series, if it's pandas-like.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ obj + + FrameOrSeriesT + +
+

object for which maybe set the index (can be either a Narwhals DataFrame +or Series).

+
+ 		+ required +
+ column_names + + str | list[str] | None + +
+

name or list of names of the columns to set as index. +For dataframes, only one of column_names and index can be specified but +not both. If column_names is passed and df is a Series, then a +ValueError is raised.

+
+ 		+ None +
+ index + + Series[IntoSeriesT] | list[Series[IntoSeriesT]] | None + +
+

series or list of series to set as index.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ FrameOrSeriesT + +
+

Same type as input.

+
+
+ + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ ValueError + +
+

If one of the following condition happens:

+
    +
  • none of column_names and index are provided
    • +
  • both column_names and index are provided
    • +
  • column_names is provided and df is a Series
    • +
+
+
+ + +
+ Notes +

This is only really intended for backwards-compatibility purposes, for example if +your library already aligns indices for users. +If you're designing a new library, we highly encourage you to not +rely on the Index.

+

For non-pandas-like inputs, this is a no-op.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [4, 5]})
+>>> df = nw.from_native(df_pd)
+>>> nw.to_native(nw.maybe_set_index(df, "b"))
+   a
+b
+4  1
+5  2
+
+ +
+ +
+ +
+ + +

+ mean(*columns) + +

+ + +
+ +

Get the mean value.

+ + +
+ Note +

Syntactic sugar for nw.col(columns).mean()

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ columns + + str + +
+

Name(s) of the columns to use in the aggregation function

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 8, 3]}
+>>> df_pl = pl.DataFrame(data)
+>>> df_pd = pd.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe agnostic function:

+ 
>>> def agnostic_mean(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.mean("a")).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_mean:

+ 
>>> agnostic_mean(df_pd)
+     a
+0  4.0
+
+ 
>>> agnostic_mean(df_pl)
+shape: (1, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ f64 │
+╞═════╡
+│ 4.0 │
+└─────┘
+
+ 
>>> agnostic_mean(df_pa)
+pyarrow.Table
+a: double
+----
+a: [[4]]
+
+ +
+ +
+ +
+ + +

+ mean_horizontal(*exprs) + +

+ + +
+ +

Compute the mean of all values horizontally across columns.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ exprs + + IntoExpr | Iterable[IntoExpr] + +
+

Name(s) of the columns to use in the aggregation function. Accepts +expression input.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [1, 8, 3],
+...     "b": [4, 5, None],
+...     "c": ["x", "y", "z"],
+... }
+>>> df_pl = pl.DataFrame(data)
+>>> df_pd = pd.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function that computes the horizontal mean of "a" +and "b" columns:

+ 
>>> def agnostic_mean_horizontal(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.mean_horizontal("a", "b")).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_mean_horizontal:

+ 
>>> agnostic_mean_horizontal(df_pd)
+     a
+0  2.5
+1  6.5
+2  3.0
+
+ 
>>> agnostic_mean_horizontal(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ f64 │
+╞═════╡
+│ 2.5 │
+│ 6.5 │
+│ 3.0 │
+└─────┘
+
+ 
>>> agnostic_mean_horizontal(df_pa)
+pyarrow.Table
+a: double
+----
+a: [[2.5,6.5,3]]
+
+ +
+ +
+ +
+ + +

+ median(*columns) + +

+ + +
+ +

Get the median value.

+ + +
+ Notes +
    +
  • Syntactic sugar for nw.col(columns).median()
    • +
  • Results might slightly differ across backends due to differences in the + underlying algorithms used to compute the median.
    • +
+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ columns + + str + +
+

Name(s) of the columns to use in the aggregation function

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [4, 5, 2]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe agnostic function:

+ 
>>> def agnostic_median(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.median("a")).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_median:

+ 
>>> agnostic_median(df_pd)
+     a
+0  4.0
+
+ 
>>> agnostic_median(df_pl)
+shape: (1, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ f64 │
+╞═════╡
+│ 4.0 │
+└─────┘
+
+ 
>>> agnostic_median(df_pa)
+pyarrow.Table
+a: double
+----
+a: [[4]]
+
+ +
+ +
+ +
+ + +

+ min(*columns) + +

+ + +
+ +

Return the minimum value.

+ + +
+ Note +

Syntactic sugar for nw.col(columns).min().

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ columns + + str + +
+

Name(s) of the columns to use in the aggregation function.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2], "b": [5, 10]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_min(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.min("b")).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_min:

+ 
>>> agnostic_min(df_pd)
+   b
+0  5
+
+ 
>>> agnostic_min(df_pl)
+shape: (1, 1)
+┌─────┐
+│ b   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 5   │
+└─────┘
+
+ 
>>> agnostic_min(df_pa)
+pyarrow.Table
+b: int64
+----
+b: [[5]]
+
+ +
+ +
+ +
+ + +

+ min_horizontal(*exprs) + +

+ + +
+ +

Get the minimum value horizontally across columns.

+ + +
+ Notes +

We support min_horizontal over numeric columns only.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ exprs + + IntoExpr | Iterable[IntoExpr] + +
+

Name(s) of the columns to use in the aggregation function. Accepts +expression input.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {
+...     "a": [1, 8, 3],
+...     "b": [4, 5, None],
+...     "c": ["x", "y", "z"],
+... }
+
+

We define a dataframe-agnostic function that computes the horizontal min of "a" +and "b" columns:

+ 
>>> def agnostic_min_horizontal(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.min_horizontal("a", "b")).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_min_horizontal:

+ 
>>> agnostic_min_horizontal(pd.DataFrame(data))
+     a
+0  1.0
+1  5.0
+2  3.0
+
+ 
>>> agnostic_min_horizontal(pl.DataFrame(data))
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 5   │
+│ 3   │
+└─────┘
+
+ 
>>> agnostic_min_horizontal(pa.table(data))
+pyarrow.Table
+a: int64
+----
+a: [[1,5,3]]
+
+ +
+ +
+ +
+ + +

+ narwhalify(func=None, *, strict=None, pass_through=None, eager_only=False, eager_or_interchange_only=False, series_only=False, allow_series=True) + +

+ + +
+ +

Decorate function so it becomes dataframe-agnostic.

+

This will try to convert any dataframe/series-like object into the Narwhals +respective DataFrame/Series, while leaving the other parameters as they are. +Similarly, if the output of the function is a Narwhals DataFrame or Series, it will be +converted back to the original dataframe/series type, while if the output is another +type it will be left as is. +By setting pass_through=False, then every input and every output will be required to be a +dataframe/series-like object.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ func + + Callable[..., Any] | None + +
+

Function to wrap in a from_native-to_native block.

+
+ 		+ None +
+ strict + + bool | None + +
+

Deprecated (v1.13.0): +Please use pass_through instead. Note that strict is still available +(and won't emit a deprecation warning) if you use narwhals.stable.v1, +see perfect backwards compatibility policy.

+

Determine what happens if the object can't be converted to Narwhals:

+
    +
  • True or None (default): raise an error
    • +
  • False: pass object through as-is
    • +
+
+ 		+ None +
+ pass_through + + bool | None + +
+

Determine what happens if the object can't be converted to Narwhals:

+
    +
  • False or None (default): raise an error
    • +
  • True: pass object through as-is
    • +
+
+ 		+ None +
+ eager_only + + bool + +
+

Whether to only allow eager objects:

+
    +
  • False (default): don't require native_object to be eager
    • +
  • True: only convert to Narwhals if native_object is eager
    • +
+
+ 		+ False +
+ eager_or_interchange_only + + bool + +
+

Whether to only allow eager objects or objects which +have interchange-level support in Narwhals:

+
    +
  • False (default): don't require native_object to either be eager or to + have interchange-level support in Narwhals
    • +
  • True: only convert to Narwhals if native_object is eager or has + interchange-level support in Narwhals
    • +
+

See interchange-only support +for more details.

+
+ 		+ False +
+ series_only + + bool + +
+

Whether to only allow Series:

+
    +
  • False (default): don't require native_object to be a Series
    • +
  • True: only convert to Narwhals if native_object is a Series
    • +
+
+ 		+ False +
+ allow_series + + bool | None + +
+

Whether to allow Series (default is only Dataframe / Lazyframe):

+
    +
  • False or None: don't convert to Narwhals if native_object is a Series
    • +
  • True (default): allow native_object to be a Series
    • +
+
+ 		+ True +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Callable[..., Any] + +
+

Decorated function.

+
+
+ + +

Examples:

+

Instead of writing

+ 
>>> import narwhals as nw
+>>> def agnostic_group_by_sum(df):
+...     df = nw.from_native(df, pass_through=True)
+...     df = df.group_by("a").agg(nw.col("b").sum())
+...     return nw.to_native(df)
+
+

you can just write

+ 
>>> @nw.narwhalify
+... def agnostic_group_by_sum(df):
+...     return df.group_by("a").agg(nw.col("b").sum())
+
+ +
+ +
+ +
+ + +

+ new_series(name, values, dtype=None, *, native_namespace) + +

+ + +
+ +

Instantiate Narwhals Series from iterable (e.g. list or array).

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ name + + str + +
+

Name of resulting Series.

+
+ 		+ required +
+ values + + Any + +
+

Values of make Series from.

+
+ 		+ required +
+ dtype + + DType | type[DType] | None + +
+

(Narwhals) dtype. If not provided, the native library +may auto-infer it from values.

+
+ 		+ None +
+ native_namespace + + ModuleType + +
+

The native library to use for DataFrame creation.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Series[Any] + +
+

A new Series

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT, IntoSeriesT
+>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_new_series(df_native: IntoFrameT) -> IntoSeriesT:
+...     values = [4, 1, 2, 3]
+...     native_namespace = nw.get_native_namespace(df_native)
+...     return nw.new_series(
+...         name="a",
+...         values=values,
+...         dtype=nw.Int32,
+...         native_namespace=native_namespace,
+...     ).to_native()
+
+

We can then pass any supported eager library, such as pandas / Polars / PyArrow:

+ 
>>> agnostic_new_series(pd.DataFrame(data))
+0    4
+1    1
+2    2
+3    3
+Name: a, dtype: int32
+>>> agnostic_new_series(pl.DataFrame(data))
+shape: (4,)
+Series: 'a' [i32]
+[
+   4
+   1
+   2
+   3
+]
+>>> agnostic_new_series(pa.table(data))
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    4,
+    1,
+    2,
+    3
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ nth(*indices) + +

+ + +
+ +

Creates an expression that references one or more columns by their index(es).

+ + +
+ Notes +

nth is not supported for Polars version<1.0.0. Please use +narwhals.col instead.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ indices + + int | Sequence[int] + +
+

One or more indices representing the columns to retrieve.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2], "b": [3, 4]}
+>>> df_pl = pl.DataFrame(data)
+>>> df_pd = pd.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_nth(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.nth(0) * 2).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to agnostic_nth:

+ 
>>> agnostic_nth(df_pd)
+   a
+0  2
+1  4
+
+ 
>>> agnostic_nth(df_pl)
+shape: (2, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 2   │
+│ 4   │
+└─────┘
+
+ 
>>> agnostic_nth(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[2,4]]
+
+ +
+ +
+ +
+ + +

+ read_csv(source, *, native_namespace, **kwargs) + +

+ + +
+ +

Read a CSV file into a DataFrame.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ source + + str + +
+

Path to a file.

+
+ 		+ required +
+ native_namespace + + ModuleType + +
+

The native library to use for DataFrame creation.

+
+ 		+ required +
+ kwargs + + Any + +
+

Extra keyword arguments which are passed to the native CSV reader. +For example, you could use +nw.read_csv('file.csv', native_namespace=pd, engine='pyarrow').

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DataFrame[Any] + +
+

DataFrame.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> from types import ModuleType
+
+

Let's create an agnostic function that reads a csv file with a specified native namespace:

+ 
>>> def agnostic_read_csv(native_namespace: ModuleType) -> IntoDataFrame:
+...     return nw.read_csv("file.csv", native_namespace=native_namespace).to_native()
+
+

Then we can read the file by passing pandas, Polars or PyArrow namespaces:

+ 
>>> agnostic_read_csv(native_namespace=pd)
+   a  b
+0  1  4
+1  2  5
+2  3  6
+>>> agnostic_read_csv(native_namespace=pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 4   │
+│ 2   ┆ 5   │
+│ 3   ┆ 6   │
+└─────┴─────┘
+>>> agnostic_read_csv(native_namespace=pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[1,2,3]]
+b: [[4,5,6]]
+
+ +
+ +
+ +
+ + +

+ read_parquet(source, *, native_namespace, **kwargs) + +

+ + +
+ +

Read into a DataFrame from a parquet file.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ source + + str + +
+

Path to a file.

+
+ 		+ required +
+ native_namespace + + ModuleType + +
+

The native library to use for DataFrame creation.

+
+ 		+ required +
+ kwargs + + Any + +
+

Extra keyword arguments which are passed to the native parquet reader. +For example, you could use +nw.read_parquet('file.parquet', native_namespace=pd, engine='pyarrow').

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DataFrame[Any] + +
+

DataFrame.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> from types import ModuleType
+
+

Let's create an agnostic function that reads a parquet file with a specified native namespace:

+ 
>>> def agnostic_read_parquet(native_namespace: ModuleType) -> IntoDataFrame:
+...     return nw.read_parquet(
+...         "file.parquet", native_namespace=native_namespace
+...     ).to_native()
+
+

Then we can read the file by passing pandas, Polars or PyArrow namespaces:

+ 
>>> agnostic_read_parquet(native_namespace=pd)
+   a  b
+0  1  4
+1  2  5
+2  3  6
+>>> agnostic_read_parquet(native_namespace=pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 4   │
+│ 2   ┆ 5   │
+│ 3   ┆ 6   │
+└─────┴─────┘
+>>> agnostic_read_parquet(native_namespace=pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[1,2,3]]
+b: [[4,5,6]]
+
+ +
+ +
+ +
+ + +

+ scan_csv(source, *, native_namespace, **kwargs) + +

+ + +
+ +

Lazily read from a CSV file.

+

For the libraries that do not support lazy dataframes, the function reads +a csv file eagerly and then converts the resulting dataframe to a lazyframe.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ source + + str + +
+

Path to a file.

+
+ 		+ required +
+ native_namespace + + ModuleType + +
+

The native library to use for DataFrame creation.

+
+ 		+ required +
+ kwargs + + Any + +
+

Extra keyword arguments which are passed to the native CSV reader. +For example, you could use +nw.scan_csv('file.csv', native_namespace=pd, engine='pyarrow').

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ LazyFrame[Any] + +
+

LazyFrame.

+
+
+ + +

Examples:

+ 
>>> import dask.dataframe as dd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrame
+>>> from types import ModuleType
+
+

Let's create an agnostic function that lazily reads a csv file with a specified native namespace:

+ 
>>> def agnostic_scan_csv(native_namespace: ModuleType) -> IntoFrame:
+...     return nw.scan_csv("file.csv", native_namespace=native_namespace).to_native()
+
+

Then we can read the file by passing, for example, Polars or Dask namespaces:

+ 
>>> agnostic_scan_csv(native_namespace=pl).collect()
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 4   │
+│ 2   ┆ 5   │
+│ 3   ┆ 6   │
+└─────┴─────┘
+>>> agnostic_scan_csv(native_namespace=dd).compute()
+   a  b
+0  1  4
+1  2  5
+2  3  6
+
+ +
+ +
+ +
+ + +

+ scan_parquet(source, *, native_namespace, **kwargs) + +

+ + +
+ +

Lazily read from a parquet file.

+

For the libraries that do not support lazy dataframes, the function reads +a parquet file eagerly and then converts the resulting dataframe to a lazyframe.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ source + + str + +
+

Path to a file.

+
+ 		+ required +
+ native_namespace + + ModuleType + +
+

The native library to use for DataFrame creation.

+
+ 		+ required +
+ kwargs + + Any + +
+

Extra keyword arguments which are passed to the native parquet reader. +For example, you could use +nw.scan_parquet('file.parquet', native_namespace=pd, engine='pyarrow').

+
+ 		+ {} +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ LazyFrame[Any] + +
+

LazyFrame.

+
+
+ + +

Examples:

+ 
>>> import dask.dataframe as dd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrame
+>>> from types import ModuleType
+
+

Let's create an agnostic function that lazily reads a parquet file with a specified native namespace:

+ 
>>> def agnostic_scan_parquet(native_namespace: ModuleType) -> IntoFrame:
+...     return nw.scan_parquet(
+...         "file.parquet", native_namespace=native_namespace
+...     ).to_native()
+
+

Then we can read the file by passing, for example, Polars or Dask namespaces:

+ 
>>> agnostic_scan_parquet(native_namespace=pl).collect()
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 1   ┆ 4   │
+│ 2   ┆ 5   │
+│ 3   ┆ 6   │
+└─────┴─────┘
+>>> agnostic_scan_parquet(native_namespace=dd).compute()
+   a  b
+0  1  4
+1  2  5
+2  3  6
+
+ +
+ +
+ +
+ + +

+ sum(*columns) + +

+ + +
+ +

Sum all values.

+ + +
+ Note +

Syntactic sugar for nw.col(columns).sum()

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ columns + + str + +
+

Name(s) of the columns to use in the aggregation function

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2]}
+>>> df_pl = pl.DataFrame(data)
+>>> df_pd = pd.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_sum(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.sum("a")).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_sum:

+ 
>>> agnostic_sum(df_pd)
+   a
+0  3
+
+ 
>>> agnostic_sum(df_pl)
+shape: (1, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 3   │
+└─────┘
+
+ 
>>> agnostic_sum(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[3]]
+
+ +
+ +
+ +
+ + +

+ sum_horizontal(*exprs) + +

+ + +
+ +

Sum all values horizontally across columns.

+ + +
+ Warning +

Unlike Polars, we support horizontal sum over numeric columns only.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ exprs + + IntoExpr | Iterable[IntoExpr] + +
+

Name(s) of the columns to use in the aggregation function. Accepts +expression input.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3], "b": [5, 10, None]}
+>>> df_pl = pl.DataFrame(data)
+>>> df_pd = pd.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_sum_horizontal(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.select(nw.sum_horizontal("a", "b")).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to agnostic_sum_horizontal:

+ 
>>> agnostic_sum_horizontal(df_pd)
+      a
+0   6.0
+1  12.0
+2   3.0
+
+ 
>>> agnostic_sum_horizontal(df_pl)
+shape: (3, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 6   │
+│ 12  │
+│ 3   │
+└─────┘
+
+ 
>>> agnostic_sum_horizontal(df_pa)
+pyarrow.Table
+a: int64
+----
+a: [[6,12,3]]
+
+ +
+ +
+ +
+ + +

+ show_versions() + +

+ + +
+ +

Print useful debugging information.

+ + +

Examples:

+ 
>>> from narwhals import show_versions
+>>> show_versions()
+
+ +
+ +
+ +
+ + +

+ to_native(narwhals_object, *, strict=None, pass_through=None) + +

+ + +
+ +

Convert Narwhals object to native one.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ narwhals_object + + DataFrame[IntoFrameT] | LazyFrame[IntoFrameT] | Series[IntoSeriesT] + +
+

Narwhals object.

+
+ 		+ required +
+ strict + + bool | None + +
+

Determine what happens if narwhals_object isn't a Narwhals class:

+
    +
  • True (default): raise an error
    • +
  • False: pass object through as-is
    • +
+

Deprecated (v1.13.0): + Please use pass_through instead. Note that strict is still available + (and won't emit a deprecation warning) if you use narwhals.stable.v1, + see perfect backwards compatibility policy.

+
+ 		+ None +
+ pass_through + + bool | None + +
+

Determine what happens if narwhals_object isn't a Narwhals class:

+
    +
  • False (default): raise an error
    • +
  • True: pass object through as-is
    • +
+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ IntoFrameT | Any + +
+

Object of class that user started with.

+
+
+ +
+ +
+ +
+ + +

+ to_py_scalar(scalar_like) + +

+ + +
+ +

If a scalar is not Python native, converts it to Python native.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ scalar_like + + Any + +
+

Scalar-like value.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

Python scalar.

+
+
+ + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ ValueError + +
+

If the object is not convertible to a scalar.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+>>> df = nw.from_native(pd.DataFrame({"a": [1, 2, 3]}))
+>>> nw.to_py_scalar(df["a"].item(0))
+1
+>>> import pyarrow as pa
+>>> df = nw.from_native(pa.table({"a": [1, 2, 3]}))
+>>> nw.to_py_scalar(df["a"].item(0))
+1
+>>> nw.to_py_scalar(1)
+1
+
+ +
+ +
+ +
+ + +

+ when(*predicates) + +

+ + +
+ +

Start a when-then-otherwise expression.

+

Expression similar to an if-else statement in Python. Always initiated by a +pl.when(<condition>).then(<value if condition>), and optionally followed by +chaining one or more .when(<condition>).then(<value>) statements. +Chained when-then operations should be read as Python if, elif, ... elif +blocks, not as if, if, ... if, i.e. the first condition that evaluates to +True will be picked. +If none of the conditions are True, an optional +.otherwise(<value if all statements are false>) can be appended at the end. +If not appended, and none of the conditions are True, None will be returned.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ predicates + + IntoExpr | Iterable[IntoExpr] + +
+

Condition(s) that must be met in order to apply the subsequent +statement. Accepts one or more boolean expressions, which are implicitly +combined with &. String input is parsed as a column name.

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ When + +
+

A "when" object, which .then can be called on.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>>
+>>> data = {"a": [1, 2, 3], "b": [5, 10, 15]}
+>>> df_pl = pl.DataFrame(data)
+>>> df_pd = pd.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_when_then_otherwise(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(
+...         nw.when(nw.col("a") < 3).then(5).otherwise(6).alias("a_when")
+...     ).to_native()
+
+

We can pass any supported library such as Pandas, Polars, or PyArrow to +agnostic_when_then_otherwise:

+ 
>>> agnostic_when_then_otherwise(df_pd)
+   a   b  a_when
+0  1   5       5
+1  2  10       5
+2  3  15       6
+
+ 
>>> agnostic_when_then_otherwise(df_pl)
+shape: (3, 3)
+┌─────┬─────┬────────┐
+│ a   ┆ b   ┆ a_when │
+│ --- ┆ --- ┆ ---    │
+│ i64 ┆ i64 ┆ i32    │
+╞═════╪═════╪════════╡
+│ 1   ┆ 5   ┆ 5      │
+│ 2   ┆ 10  ┆ 5      │
+│ 3   ┆ 15  ┆ 6      │
+└─────┴─────┴────────┘
+
+ 
>>> agnostic_when_then_otherwise(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+a_when: int64
+----
+a: [[1,2,3]]
+b: [[5,10,15]]
+a_when: [[5,5,6]]
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/schema/index.html b/api-reference/schema/index.html new file mode 100644 index 000000000..535d014cb --- /dev/null +++ b/api-reference/schema/index.html @@ -0,0 +1,2168 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Schema - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Schema

+ + +
+ + + + +
+ + +

Ordered mapping of column names to their data type.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ schema + + Mapping[str, DType] | Iterable[tuple[str, DType]] | None + +
+

Mapping[str, DType] | Iterable[tuple[str, DType]] | None +The schema definition given by column names and their associated. +instantiated Narwhals data type. Accepts a mapping or an iterable of tuples.

+
+ 		+ None +
+ + +

Examples:

+

Define a schema by passing instantiated data types.

+ 
>>> import narwhals as nw
+>>> schema = nw.Schema({"foo": nw.Int8(), "bar": nw.String()})
+>>> schema
+Schema({'foo': Int8, 'bar': String})
+
+

Access the data type associated with a specific column name.

+ 
>>> schema["foo"]
+Int8
+
+

Access various schema properties using the names, dtypes, and len methods.

+ 
>>> schema.names()
+['foo', 'bar']
+>>> schema.dtypes()
+[Int8, String]
+>>> schema.len()
+2
+
+ + + + + + + + + +
+ + + + + + + + + +
+ + +

+ names() + +

+ + +
+ +

Get the column names of the schema.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ list[str] + +
+

Column names.

+
+
+ +
+ +
+ +
+ + +

+ dtypes() + +

+ + +
+ +

Get the data types of the schema.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ list[DType] + +
+

Data types of schema.

+
+
+ +
+ +
+ +
+ + +

+ len() + +

+ + +
+ +

Get the number of columns in the schema.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ int + +
+

Number of columns.

+
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/selectors/index.html b/api-reference/selectors/index.html new file mode 100644 index 000000000..6ddf599ee --- /dev/null +++ b/api-reference/selectors/index.html @@ -0,0 +1,2435 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.selectors - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.selectors

+

The following selectors are all supported. In addition, just like in Polars, the following +set operations are supported:

+
    +
  • set intersection: &
    • +
  • set union: |
    • +
  • set difference: -
    • +
  • complement: ~
    • +
+ + +
+ + + + +
+ + + + + + + + +
+ + + + + + + + + +
+ + +

+ boolean() + +

+ + +
+ +

Select boolean columns.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import narwhals.selectors as ncs
+>>> import pandas as pd
+>>> import polars as pl
+>>>
+>>> data = {"a": [1, 2], "b": ["x", "y"], "c": [False, True]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function to select boolean +dtypes:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(ncs.boolean())
+
+

We can then pass either pandas or Polars dataframes:

+ 
>>> func(df_pd)
+       c
+0  False
+1   True
+>>> func(df_pl)
+shape: (2, 1)
+┌───────┐
+│ c     │
+│ ---   │
+│ bool  │
+╞═══════╡
+│ false │
+│ true  │
+└───────┘
+
+ +
+ +
+ +
+ + +

+ by_dtype(*dtypes) + +

+ + +
+ +

Select columns based on their dtype.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ dtypes + + Any + +
+

one or data types to select

+
+ 		+ () +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import narwhals.selectors as ncs
+>>> import pandas as pd
+>>> import polars as pl
+>>>
+>>> data = {"a": [1, 2], "b": ["x", "y"], "c": [4.1, 2.3]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function to select int64 and float64 +dtypes and multiplies each value by 2:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(ncs.by_dtype(nw.Int64, nw.Float64) * 2)
+
+

We can then pass either pandas or Polars dataframes:

+ 
>>> func(df_pd)
+   a    c
+0  2  8.2
+1  4  4.6
+>>> func(df_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ c   │
+│ --- ┆ --- │
+│ i64 ┆ f64 │
+╞═════╪═════╡
+│ 2   ┆ 8.2 │
+│ 4   ┆ 4.6 │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ categorical() + +

+ + +
+ +

Select categorical columns.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import narwhals.selectors as ncs
+>>> import pandas as pd
+>>> import polars as pl
+>>>
+>>> data = {"a": [1, 2], "b": ["x", "y"], "c": [False, True]}
+>>> df_pd = pd.DataFrame(data).astype({"b": "category"})
+>>> df_pl = pl.DataFrame(data, schema_overrides={"b": pl.Categorical})
+
+

Let's define a dataframe-agnostic function to select string +dtypes:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(ncs.categorical())
+
+

We can then pass either pandas or Polars dataframes:

+ 
>>> func(df_pd)
+   b
+0  x
+1  y
+>>> func(df_pl)
+shape: (2, 1)
+┌─────┐
+│ b   │
+│ --- │
+│ cat │
+╞═════╡
+│ x   │
+│ y   │
+└─────┘
+
+ +
+ +
+ +
+ + +

+ numeric() + +

+ + +
+ +

Select numeric columns.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import narwhals.selectors as ncs
+>>> import pandas as pd
+>>> import polars as pl
+>>>
+>>> data = {"a": [1, 2], "b": ["x", "y"], "c": [4.1, 2.3]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function to select numeric +dtypes and multiplies each value by 2:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(ncs.numeric() * 2)
+
+

We can then pass either pandas or Polars dataframes:

+ 
>>> func(df_pd)
+   a    c
+0  2  8.2
+1  4  4.6
+>>> func(df_pl)
+shape: (2, 2)
+┌─────┬─────┐
+│ a   ┆ c   │
+│ --- ┆ --- │
+│ i64 ┆ f64 │
+╞═════╪═════╡
+│ 2   ┆ 8.2 │
+│ 4   ┆ 4.6 │
+└─────┴─────┘
+
+ +
+ +
+ +
+ + +

+ string() + +

+ + +
+ +

Select string columns.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Expr + +
+

A new expression.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import narwhals.selectors as ncs
+>>> import pandas as pd
+>>> import polars as pl
+>>>
+>>> data = {"a": [1, 2], "b": ["x", "y"], "c": [False, True]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+
+

Let's define a dataframe-agnostic function to select string +dtypes:

+ 
>>> @nw.narwhalify
+... def func(df):
+...     return df.select(ncs.string())
+
+

We can then pass either pandas or Polars dataframes:

+ 
>>> func(df_pd)
+   b
+0  x
+1  y
+>>> func(df_pl)
+shape: (2, 1)
+┌─────┐
+│ b   │
+│ --- │
+│ str │
+╞═════╡
+│ x   │
+│ y   │
+└─────┘
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/series/index.html b/api-reference/series/index.html new file mode 100644 index 000000000..9065b9ed6 --- /dev/null +++ b/api-reference/series/index.html @@ -0,0 +1,11725 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Series - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Series

+ + +
+ + + + +
+ + +

Narwhals Series, backed by a native series.

+
+

Warning

+

This class is not meant to be instantiated directly - instead:

+
    +
  • +

    If the native object is a series from one of the supported backend (e.g. + pandas.Series, polars.Series, pyarrow.ChunkedArray), you can use + narwhals.from_native: + 

    narwhals.from_native(native_series, allow_series=True)
+narwhals.from_native(native_series, series_only=True)
+

    +
    • +
  • +

    If the object is a generic sequence (e.g. a list or a tuple of values), you can + create a series via narwhals.new_series: + 

    narwhals.new_series(
+    name=name,
+    values=values,
+    native_namespace=narwhals.get_native_namespace(another_object),
+)
+

    +
    • +
+
+ + + + + + + + + +
+ + + + + + + +
+ + + +

+ dtype + + + property + + +

+ + +
+ +

Get the data type of the Series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DType + +
+

The data type of the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_dtype(s_native: IntoSeriesT) -> nw.dtypes.DType:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dtype
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dtype:

+ 
>>> agnostic_dtype(s_pd)
+Int64
+
+ 
>>> agnostic_dtype(s_pl)
+Int64
+
+ 
>>> agnostic_dtype(s_pa)
+Int64
+
+
+ +
+ +
+ + + +

+ implementation + + + property + + +

+ + +
+ +

Return implementation of native Series.

+

This can be useful when you need to use special-casing for features outside of +Narwhals' scope - for example, when dealing with pandas' Period Dtype.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Implementation + +
+

Implementation.

+
+
+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> import pandas as pd
+
+ 
>>> s_native = pd.Series([1, 2, 3])
+>>> s = nw.from_native(s_native, series_only=True)
+
+ 
>>> s.implementation
+<Implementation.PANDAS: 1>
+
+ 
>>> s.implementation.is_pandas()
+True
+
+ 
>>> s.implementation.is_pandas_like()
+True
+
+ 
>>> s.implementation.is_polars()
+False
+
+
+ +
+ +
+ + + +

+ name + + + property + + +

+ + +
+ +

Get the name of the Series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ str + +
+

The name of the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data, name="foo")
+>>> s_pl = pl.Series("foo", data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_name(s_native: IntoSeries) -> str:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.name
+
+

We can then pass any supported library such as pandas or Polars +to agnostic_name:

+ 
>>> agnostic_name(s_pd)
+'foo'
+
+ 
>>> agnostic_name(s_pl)
+'foo'
+
+
+ +
+ +
+ + + +

+ shape + + + property + + +

+ + +
+ +

Get the shape of the Series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ tuple[int] + +
+

A tuple containing the length of the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_shape(s_native: IntoSeries) -> tuple[int]:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.shape
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_shape:

+ 
>>> agnostic_shape(s_pd)
+(3,)
+
+ 
>>> agnostic_shape(s_pl)
+(3,)
+
+ 
>>> agnostic_shape(s_pa)
+(3,)
+
+
+ +
+ + + +
+ + +

+ __arrow_c_stream__(requested_schema=None) + +

+ + +
+ +

Export a Series via the Arrow PyCapsule Interface.

+

Narwhals doesn't implement anything itself here:

+
    +
  • if the underlying series implements the interface, it'll return that
    • +
  • else, it'll call to_arrow and then defer to PyArrow's implementation
    • +
+

See PyCapsule Interface +for more.

+ +
+ +
+ +
+ + +

+ __getitem__(idx) + +

+ + +
+ +

Retrieve elements from the object using integer indexing or slicing.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ idx + + int | slice | Sequence[int] + +
+

The index, slice, or sequence of indices to retrieve.

+
    +
  • If idx is an integer, a single element is returned.
    • +
  • If idx is a slice or a sequence of integers, + a subset of the Series is returned.
    • +
+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any | Self + +
+

A single element if idx is an integer, else a subset of the Series.

+
+
+ + +

Examples:

+ 
>>> from typing import Any
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_get_first_item(s_native: IntoSeriesT) -> Any:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s[0]
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_get_first_item:

+ 
>>> agnostic_get_first_item(s_pd)
+np.int64(1)
+
+ 
>>> agnostic_get_first_item(s_pl)
+1
+
+ 
>>> agnostic_get_first_item(s_pa)
+1
+
+

We can also make a function to slice the Series:

+ 
>>> def agnostic_slice(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s[:2].to_native()
+
+ 
>>> agnostic_slice(s_pd)
+0    1
+1    2
+dtype: int64
+
+ 
>>> agnostic_slice(s_pl)
+shape: (2,)
+Series: '' [i64]
+[
+    1
+    2
+]
+
+ 
>>> agnostic_slice(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    2
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ __iter__() + +

+ + +
+ +
+ +
+ +
+ + +

+ abs() + +

+ + +
+ +

Calculate the absolute value of each element.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with the absolute values of the original elements.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [2, -4, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_abs(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.abs().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_abs:

+ 
>>> agnostic_abs(s_pd)
+0    2
+1    4
+2    3
+dtype: int64
+
+ 
>>> agnostic_abs(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   2
+   4
+   3
+]
+
+ 
>>> agnostic_abs(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    2,
+    4,
+    3
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ alias(name) + +

+ + +
+ +

Rename the Series.

+ + +
+ Notes +

This method is very cheap, but does not guarantee that data +will be copied. For example:

+
s1: nw.Series
+s2 = s1.alias("foo")
+arr = s2.to_numpy()
+arr[0] = 999
+
+

may (depending on the backend, and on the version) result in +s1's data being modified. We recommend:

+
- if you need to alias an object and don't need the original
+  one around any more, just use `alias` without worrying about it.
+- if you were expecting `alias` to copy data, then explicily call
+  `.clone` before calling `alias`.
+
+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ name + + str + +
+

The new name.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with the updated name.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data, name="foo")
+>>> s_pl = pl.Series("foo", data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_alias(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.alias("bar").to_native()
+
+

We can then pass any supported library such as pandas or Polars, or +PyArrow to agnostic_alias:

+ 
>>> agnostic_alias(s_pd)
+0    1
+1    2
+2    3
+Name: bar, dtype: int64
+
+ 
>>> agnostic_alias(s_pl)
+shape: (3,)
+Series: 'bar' [i64]
+[
+   1
+   2
+   3
+]
+
+ 
>>> agnostic_alias(s_pa)
+<pyarrow.lib.ChunkedArray object at 0x...>
+[
+  [
+    1,
+    2,
+    3
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ all() + +

+ + +
+ +

Return whether all values in the Series are True.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

A boolean indicating if all values in the Series are True.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [False, True, False]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_all(s_native: IntoSeries) -> bool:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.all()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_all:

+ 
>>> agnostic_all(s_pd)
+np.False_
+
+ 
>>> agnostic_all(s_pl)
+False
+
+ 
>>> agnostic_all(s_pa)
+False
+
+ +
+ +
+ +
+ + +

+ any() + +

+ + +
+ +

Return whether any of the values in the Series are True.

+ + +
+ Notes +

Only works on Series of data type Boolean.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

A boolean indicating if any values in the Series are True.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [False, True, False]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_any(s_native: IntoSeries) -> bool:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.any()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_any:

+ 
>>> agnostic_any(s_pd)
+np.True_
+
+ 
>>> agnostic_any(s_pl)
+True
+
+ 
>>> agnostic_any(s_pa)
+True
+
+ +
+ +
+ +
+ + +

+ arg_max() + +

+ + +
+ +

Returns the index of the maximum value.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_arg_max(s_native: IntoSeries):
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.arg_max()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_arg_max:

+ 
>>> agnostic_arg_max(s_pd)
+np.int64(2)
+
+ 
>>> agnostic_arg_max(s_pl)
+2
+
+ 
>>> agnostic_arg_max(s_pa)
+2
+
+ +
+ +
+ +
+ + +

+ arg_min() + +

+ + +
+ +

Returns the index of the minimum value.

+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_arg_min(s_native: IntoSeries):
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.arg_min()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_arg_min:

+ 
>>> agnostic_arg_min(s_pd)
+np.int64(0)
+
+ 
>>> agnostic_arg_min(s_pl)
+0
+
+ 
>>> agnostic_arg_min(s_pa)
+0
+
+ +
+ +
+ +
+ + +

+ arg_true() + +

+ + +
+ +

Find elements where boolean Series is True.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with the indices of elements that are True.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, None, None, 2]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_arg_true(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.is_null().arg_true().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_arg_true:

+ 
>>> agnostic_arg_true(s_pd)
+1    1
+2    2
+dtype: int64
+
+ 
>>> agnostic_arg_true(s_pl)
+shape: (2,)
+Series: '' [u32]
+[
+   1
+   2
+]
+
+ 
>>> agnostic_arg_true(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    2
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ cast(dtype) + +

+ + +
+ +

Cast between data types.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ dtype + + DType | type[DType] + +
+

Data type that the object will be cast into.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with the specified data type.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [True, False, True]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_cast(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.cast(nw.Int64).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_cast:

+ 
>>> agnostic_cast(s_pd)
+0    1
+1    0
+2    1
+dtype: int64
+
+ 
>>> agnostic_cast(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   1
+   0
+   1
+]
+
+ 
>>> agnostic_cast(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    0,
+    1
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ clip(lower_bound=None, upper_bound=None) + +

+ + +
+ +

Clip values in the Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ lower_bound + + Self | Any | None + +
+

Lower bound value.

+
+ 		+ None +
+ upper_bound + + Self | Any | None + +
+

Upper bound value.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with values clipped to the specified bounds.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_clip_lower(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.clip(2).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_clip_lower:

+ 
>>> agnostic_clip_lower(s_pd)
+0    2
+1    2
+2    3
+dtype: int64
+
+ 
>>> agnostic_clip_lower(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   2
+   2
+   3
+]
+
+ 
>>> agnostic_clip_lower(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    2,
+    2,
+    3
+  ]
+]
+
+

We define another library agnostic function:

+ 
>>> def agnostic_clip_upper(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.clip(upper_bound=2).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or + PyArrow to agnostic_clip_upper:

+
>>> agnostic_clip_upper(s_pd)
+0    1
+1    2
+2    2
+dtype: int64
+
+>>> agnostic_clip_upper(s_pl)  # doctest: +NORMALIZE_WHITESPACE
+shape: (3,)
+Series: '' [i64]
+[
+   1
+   2
+   2
+]
+
+>>> agnostic_clip_upper(s_pa)  # doctest: +ELLIPSIS
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    2,
+    2
+  ]
+]
+
+We can have both at the same time
+
+>>> data = [-1, 1, -3, 3, -5, 5]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+We define a library agnostic function:
+
+>>> def agnostic_clip(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.clip(-1, 3).to_native()
+
+We can then pass any supported library such as pandas, Polars, or
+PyArrow to `agnostic_clip`:
+
+>>> agnostic_clip(s_pd)
+0   -1
+1    1
+2   -1
+3    3
+4   -1
+5    3
+dtype: int64
+
+>>> agnostic_clip(s_pl)  # doctest: +NORMALIZE_WHITESPACE
+shape: (6,)
+Series: '' [i64]
+[
+   -1
+    1
+   -1
+    3
+   -1
+    3
+]
+
+>>> agnostic_clip_upper(s_pa)  # doctest: +ELLIPSIS
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    -1,
+    1,
+    -3,
+    2,
+    -5,
+    2
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ count() + +

+ + +
+ +

Returns the number of non-null elements in the Series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

The number of non-null elements in the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_count(s_native: IntoSeries) -> int:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.count()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_count:

+ 
>>> agnostic_count(s_pd)
+np.int64(3)
+
+ 
>>> agnostic_count(s_pl)
+3
+
+ 
>>> agnostic_count(s_pa)
+3
+
+ +
+ +
+ +
+ + +

+ cum_count(*, reverse=False) + +

+ + +
+ +

Return the cumulative count of the non-null values in the series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ reverse + + bool + +
+

reverse the operation

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with the cumulative count of non-null values.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["x", "k", None, "d"]
+
+

We define a library agnostic function:

+ 
>>> def agnostic_cum_count(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.cum_count(reverse=True).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_cum_count:

+ 
>>> agnostic_cum_count(pd.Series(data))
+0    3
+1    2
+2    1
+3    1
+dtype: int64
+
+ 
>>> agnostic_cum_count(pl.Series(data))
+shape: (4,)
+Series: '' [u32]
+[
+    3
+    2
+    1
+    1
+]
+
+ 
>>> agnostic_cum_count(pa.chunked_array([data]))
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    3,
+    2,
+    1,
+    1
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ cum_max(*, reverse=False) + +

+ + +
+ +

Return the cumulative max of the non-null values in the series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ reverse + + bool + +
+

reverse the operation

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with the cumulative max of non-null values.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 3, None, 2]
+
+

We define a library agnostic function:

+ 
>>> def agnostic_cum_max(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.cum_max().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_cum_max:

+ 
>>> agnostic_cum_max(pd.Series(data))
+0    1.0
+1    3.0
+2    NaN
+3    3.0
+dtype: float64
+
+ 
>>> agnostic_cum_max(pl.Series(data))
+shape: (4,)
+Series: '' [i64]
+[
+   1
+   3
+   null
+   3
+]
+
+ 
>>> agnostic_cum_max(pa.chunked_array([data]))
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    3,
+    null,
+    3
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ cum_min(*, reverse=False) + +

+ + +
+ +

Return the cumulative min of the non-null values in the series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ reverse + + bool + +
+

reverse the operation

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with the cumulative min of non-null values.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [3, 1, None, 2]
+
+

We define a library agnostic function:

+ 
>>> def agnostic_cum_min(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.cum_min().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_cum_min:

+ 
>>> agnostic_cum_min(pd.Series(data))
+0    3.0
+1    1.0
+2    NaN
+3    1.0
+dtype: float64
+
+ 
>>> agnostic_cum_min(pl.Series(data))
+shape: (4,)
+Series: '' [i64]
+[
+   3
+   1
+   null
+   1
+]
+
+ 
>>> agnostic_cum_min(pa.chunked_array([data]))
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    3,
+    1,
+    null,
+    1
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ cum_prod(*, reverse=False) + +

+ + +
+ +

Return the cumulative product of the non-null values in the series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ reverse + + bool + +
+

reverse the operation

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with the cumulative product of non-null values.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 3, None, 2]
+
+

We define a library agnostic function:

+ 
>>> def agnostic_cum_prod(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.cum_prod().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_cum_prod:

+ 
>>> agnostic_cum_prod(pd.Series(data))
+0    1.0
+1    3.0
+2    NaN
+3    6.0
+dtype: float64
+
+ 
>>> agnostic_cum_prod(pl.Series(data))
+shape: (4,)
+Series: '' [i64]
+[
+   1
+   3
+   null
+   6
+]
+
+ 
>>> agnostic_cum_prod(pa.chunked_array([data]))
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    3,
+    null,
+    6
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ cum_sum(*, reverse=False) + +

+ + +
+ +

Calculate the cumulative sum.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ reverse + + bool + +
+

reverse the operation

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with the cumulative sum of non-null values.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [2, 4, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_cum_sum(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.cum_sum().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_cum_sum:

+ 
>>> agnostic_cum_sum(s_pd)
+0    2
+1    6
+2    9
+dtype: int64
+
+ 
>>> agnostic_cum_sum(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   2
+   6
+   9
+]
+
+ 
>>> agnostic_cum_sum(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    2,
+    6,
+    9
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ diff() + +

+ + +
+ +

Calculate the difference with the previous element, for each element.

+ + +
+ Notes +

pandas may change the dtype here, for example when introducing missing +values in an integer column. To ensure, that the dtype doesn't change, +you may want to use fill_null and cast. For example, to calculate +the diff and fill missing values with 0 in a Int64 column, you could +do:

+
s.diff().fill_null(0).cast(nw.Int64)
+
+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with the difference between each element and its predecessor.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [2, 4, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_diff(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.diff().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_diff:

+ 
>>> agnostic_diff(s_pd)
+0    NaN
+1    2.0
+2   -1.0
+dtype: float64
+
+ 
>>> agnostic_diff(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   null
+   2
+   -1
+]
+
+ 
>>> agnostic_diff(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    null,
+    2,
+    -1
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ drop_nulls() + +

+ + +
+ +

Drop null values.

+ + +
+ Notes +

pandas handles null values differently from Polars and PyArrow. +See null_handling +for reference.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with null values removed.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [2, 4, None, 3, 5]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_drop_nulls(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.drop_nulls().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_drop_nulls:

+ 
>>> agnostic_drop_nulls(s_pd)
+0    2.0
+1    4.0
+3    3.0
+4    5.0
+dtype: float64
+
+ 
>>> agnostic_drop_nulls(s_pl)
+shape: (4,)
+Series: '' [i64]
+[
+    2
+    4
+    3
+    5
+]
+
+ 
>>> agnostic_drop_nulls(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    2,
+    4,
+    3,
+    5
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ ewm_mean(*, com=None, span=None, half_life=None, alpha=None, adjust=True, min_periods=1, ignore_nulls=False) + +

+ + +
+ +

Compute exponentially-weighted moving average.

+
+

Warning

+

This functionality is considered unstable. It may be changed at any point +without it being considered a breaking change.

+
+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ com + + float | None + +
+

Specify decay in terms of center of mass, \(\gamma\), with
\(\alpha = \frac{1}{1+\gamma}\forall\gamma\geq0\)

+
+ 		+ None +
+ span + + float | None + +
+

Specify decay in terms of span, \(\theta\), with
\(\alpha = \frac{2}{\theta + 1} \forall \theta \geq 1\)

+
+ 		+ None +
+ half_life + + float | None + +
+

Specify decay in terms of half-life, \(\tau\), with
\(\alpha = 1 - \exp \left\{ \frac{ -\ln(2) }{ \tau } \right\} \forall \tau > 0\)

+
+ 		+ None +
+ alpha + + float | None + +
+

Specify smoothing factor alpha directly, \(0 < \alpha \leq 1\).

+
+ 		+ None +
+ adjust + + bool + +
+

Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings

+
    +
  • When adjust=True (the default) the EW function is calculated + using weights \(w_i = (1 - \alpha)^i\)
    • +
  • When adjust=False the EW function is calculated recursively by + $$ + y_0=x_0 + $$ + $$ + y_t = (1 - \alpha)y_{t - 1} + \alpha x_t + $$
    • +
+
+ 		+ True +
+ min_periods + + int + +
+

Minimum number of observations in window required to have a value (otherwise result is null).

+
+ 		+ 1 +
+ ignore_nulls + + bool + +
+

Ignore missing values when calculating weights.

+
    +
  • When ignore_nulls=False (default), weights are based on absolute + positions. + For example, the weights of \(x_0\) and \(x_2\) used in + calculating the final weighted average of \([x_0, None, x_2]\) are + \((1-\alpha)^2\) and \(1\) if adjust=True, and + \((1-\alpha)^2\) and \(\alpha\) if adjust=False.
    • +
  • When ignore_nulls=True, weights are based + on relative positions. For example, the weights of + \(x_0\) and \(x_2\) used in calculating the final weighted + average of \([x_0, None, x_2]\) are + \(1-\alpha\) and \(1\) if adjust=True, + and \(1-\alpha\) and \(\alpha\) if adjust=False.
    • +
+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

Series

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(name="a", data=data)
+>>> s_pl = pl.Series(name="a", values=data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_ewm_mean(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.ewm_mean(com=1, ignore_nulls=False).to_native()
+
+

We can then pass any supported library such as pandas or Polars +to agnostic_ewm_mean:

+ 
>>> agnostic_ewm_mean(s_pd)
+0    1.000000
+1    1.666667
+2    2.428571
+Name: a, dtype: float64
+
+ 
>>> agnostic_ewm_mean(s_pl)
+shape: (3,)
+Series: 'a' [f64]
+[
+   1.0
+   1.666667
+   2.428571
+]
+
+ +
+ +
+ +
+ + +

+ fill_null(value=None, strategy=None, limit=None) + +

+ + +
+ +

Fill null values using the specified value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ value + + Any | None + +
+

Value used to fill null values.

+
+ 		+ None +
+ strategy + + Literal['forward', 'backward'] | None + +
+

Strategy used to fill null values.

+
+ 		+ None +
+ limit + + int | None + +
+

Number of consecutive null values to fill when using the 'forward' or 'backward' strategy.

+
+ 		+ None +
+ + +
+ Notes +

pandas handles null values differently from Polars and PyArrow. +See null_handling +for reference.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with null values filled according to the specified value or strategy.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, None]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_fill_null(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.fill_null(5).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_fill_null:

+ 
>>> agnostic_fill_null(s_pd)
+0    1.0
+1    2.0
+2    5.0
+dtype: float64
+
+ 
>>> agnostic_fill_null(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   1
+   2
+   5
+]
+
+ 
>>> agnostic_fill_null(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    2,
+    5
+  ]
+]
+
+

Using a strategy:

+ 
>>> def agnostic_fill_null_with_strategy(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.fill_null(strategy="forward", limit=1).to_native()
+
+ 
>>> agnostic_fill_null_with_strategy(s_pd)
+0    1.0
+1    2.0
+2    2.0
+dtype: float64
+
+ 
>>> agnostic_fill_null_with_strategy(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   1
+   2
+   2
+]
+
+ 
>>> agnostic_fill_null_with_strategy(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    2,
+    2
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ filter(other) + +

+ + +
+ +

Filter elements in the Series based on a condition.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with elements that satisfy the condition.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [4, 10, 15, 34, 50]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_filter(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.filter(s > 10).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_filter:

+ 
>>> agnostic_filter(s_pd)
+2    15
+3    34
+4    50
+dtype: int64
+
+ 
>>> agnostic_filter(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   15
+   34
+   50
+]
+
+ 
>>> agnostic_filter(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    15,
+    34,
+    50
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ gather_every(n, offset=0) + +

+ + +
+ +

Take every nth value in the Series and return as new Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Gather every n-th row.

+
+ 		+ required +
+ offset + + int + +
+

Starting index.

+
+ 		+ 0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with every nth value starting from the offset.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3, 4]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function in which gather every 2 rows, +starting from a offset of 1:

+ 
>>> def agnostic_gather_every(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.gather_every(n=2, offset=1).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_gather_every:

+ 
>>> agnostic_gather_every(s_pd)
+1    2
+3    4
+dtype: int64
+
+ 
>>> agnostic_gather_every(s_pl)
+shape: (2,)
+Series: '' [i64]
+[
+   2
+   4
+]
+
+ 
>>> agnostic_gather_every(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    2,
+    4
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ head(n=10) + +

+ + +
+ +

Get the first n rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of rows to return.

+
+ 		+ 10 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series containing the first n characters of each string.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = list(range(10))
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function that returns the first 3 rows:

+ 
>>> def agnostic_head(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.head(3).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_head:

+ 
>>> agnostic_head(s_pd)
+0    0
+1    1
+2    2
+dtype: int64
+
+ 
>>> agnostic_head(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   0
+   1
+   2
+]
+
+ 
>>> agnostic_head(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    0,
+    1,
+    2
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ is_between(lower_bound, upper_bound, closed='both') + +

+ + +
+ +

Get a boolean mask of the values that are between the given lower/upper bounds.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ lower_bound + + Any | Self + +
+

Lower bound value.

+
+ 		+ required +
+ upper_bound + + Any | Self + +
+

Upper bound value.

+
+ 		+ required +
+ closed + + Literal['left', 'right', 'none', 'both'] + +
+

Define which sides of the interval are closed (inclusive).

+
+ 		+ 'both' +
+ + +
+ Notes +

If the value of the lower_bound is greater than that of the upper_bound, +then the values will be False, as no value can satisfy the condition.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A boolean Series indicating which values are between the given bounds.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3, 4, 5]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_is_between(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.is_between(2, 4, "right").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_between:

+ 
>>> agnostic_is_between(s_pd)
+0    False
+1    False
+2     True
+3     True
+4    False
+dtype: bool
+
+ 
>>> agnostic_is_between(s_pl)
+shape: (5,)
+Series: '' [bool]
+[
+   false
+   false
+   true
+   true
+   false
+]
+
+ 
>>> agnostic_is_between(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    false,
+    false,
+    true,
+    true,
+    false
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ is_duplicated() + +

+ + +
+ +

Get a mask of all duplicated rows in the Series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with boolean values indicating duplicated rows.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3, 1]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_duplicated(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.is_duplicated().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_duplicated:

+ 
>>> agnostic_is_duplicated(s_pd)
+0     True
+1    False
+2    False
+3     True
+dtype: bool
+
+ 
>>> agnostic_is_duplicated(s_pl)
+shape: (4,)
+Series: '' [bool]
+[
+    true
+    false
+    false
+    true
+]
+
+ 
>>> agnostic_is_duplicated(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    true,
+    false,
+    false,
+    true
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ is_empty() + +

+ + +
+ +

Check if the series is empty.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ bool + +
+

A boolean indicating if the series is empty.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+

Let's define a dataframe-agnostic function that filters rows in which "foo" +values are greater than 10, and then checks if the result is empty or not:

+ 
>>> def agnostic_is_empty(s_native: IntoSeries) -> bool:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.filter(s > 10).is_empty()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_empty:

+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+>>> agnostic_is_empty(s_pd), agnostic_is_empty(s_pl), agnostic_is_empty(s_pa)
+(True, True, True)
+
+ 
>>> data = [100, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+>>> agnostic_is_empty(s_pd), agnostic_is_empty(s_pl), agnostic_is_empty(s_pa)
+(False, False, False)
+
+ +
+ +
+ +
+ + +

+ is_finite() + +

+ + +
+ +

Returns a boolean Series indicating which values are finite.

+ + +
+ Warning +

Different backend handle null values differently. is_finite will return +False for NaN and Null's in the Dask and pandas non-nullable backend, while +for Polars, PyArrow and pandas nullable backends null values are kept as such.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

Expression of Boolean data type.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [float("nan"), float("inf"), 2.0, None]
+
+

We define a library agnostic function:

+ 
>>> def agnostic_is_finite(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.is_finite().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_finite:

+ 
>>> agnostic_is_finite(pd.Series(data))
+0    False
+1    False
+2     True
+3    False
+dtype: bool
+
+ 
>>> agnostic_is_finite(pl.Series(data))
+shape: (4,)
+Series: '' [bool]
+[
+   false
+   false
+   true
+   null
+]
+
+ 
>>> agnostic_is_finite(pa.chunked_array([data]))
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    false,
+    false,
+    true,
+    null
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ is_first_distinct() + +

+ + +
+ +

Return a boolean mask indicating the first occurrence of each distinct value.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with boolean values indicating the first occurrence of each distinct value.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 1, 2, 3, 2]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_first_distinct(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.is_first_distinct().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_first_distinct:

+ 
>>> agnostic_is_first_distinct(s_pd)
+0     True
+1    False
+2     True
+3     True
+4    False
+dtype: bool
+
+ 
>>> agnostic_is_first_distinct(s_pl)
+shape: (5,)
+Series: '' [bool]
+[
+    true
+    false
+    true
+    true
+    false
+]
+
+ 
>>> agnostic_is_first_distinct(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    true,
+    false,
+    true,
+    true,
+    false
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ is_in(other) + +

+ + +
+ +

Check if the elements of this Series are in the other sequence.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ other + + Any + +
+

Sequence of primitive type.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with boolean values indicating if the elements are in the other sequence.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_is_in(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.is_in([3, 2, 8]).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_in:

+ 
>>> agnostic_is_in(s_pd)
+0    False
+1     True
+2     True
+dtype: bool
+
+ 
>>> agnostic_is_in(s_pl)
+shape: (3,)
+Series: '' [bool]
+[
+   false
+   true
+   true
+]
+
+ 
>>> agnostic_is_in(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    false,
+    true,
+    true
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ is_last_distinct() + +

+ + +
+ +

Return a boolean mask indicating the last occurrence of each distinct value.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with boolean values indicating the last occurrence of each distinct value.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 1, 2, 3, 2]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_last_distinct(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.is_last_distinct().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_last_distinct:

+ 
>>> agnostic_is_last_distinct(s_pd)
+0    False
+1     True
+2    False
+3     True
+4     True
+dtype: bool
+
+ 
>>> agnostic_is_last_distinct(s_pl)
+shape: (5,)
+Series: '' [bool]
+[
+    false
+    true
+    false
+    true
+    true
+]
+
+ 
>>> agnostic_is_last_distinct(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    false,
+    true,
+    false,
+    true,
+    true
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ is_nan() + +

+ + +
+ +

Returns a boolean Series indicating which values are NaN.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A boolean Series indicating which values are NaN.

+
+
+ + +
+ Notes +

pandas handles null values differently from Polars and PyArrow. +See null_handling +for reference.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [0.0, None, 2.0]
+>>> s_pd = pd.Series(data, dtype="Float64")
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data], type=pa.float64())
+
+ 
>>> def agnostic_self_div_is_nan(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.is_nan().to_native()
+
+ 
>>> print(agnostic_self_div_is_nan(s_pd))
+0    False
+1     <NA>
+2    False
+dtype: boolean
+
+ 
>>> print(agnostic_self_div_is_nan(s_pl))
+shape: (3,)
+Series: '' [bool]
+[
+        false
+        null
+        false
+]
+
+ 
>>> print(agnostic_self_div_is_nan(s_pa))
+[
+  [
+    false,
+    null,
+    false
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ is_null() + +

+ + +
+ +

Returns a boolean Series indicating which values are null.

+ + +
+ Notes +

pandas handles null values differently from Polars and PyArrow. +See null_handling +for reference.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A boolean Series indicating which values are null.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, None]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_null(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.is_null().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_null:

+ 
>>> agnostic_is_null(s_pd)
+0    False
+1    False
+2     True
+dtype: bool
+
+ 
>>> agnostic_is_null(s_pl)
+shape: (3,)
+Series: '' [bool]
+[
+   false
+   false
+   true
+]
+
+ 
>>> agnostic_is_null(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    false,
+    false,
+    true
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ is_sorted(*, descending=False) + +

+ + +
+ +

Check if the Series is sorted.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ descending + + bool + +
+

Check if the Series is sorted in descending order.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ bool + +
+

A boolean indicating if the Series is sorted.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> unsorted_data = [1, 3, 2]
+>>> sorted_data = [3, 2, 1]
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_sorted(s_native: IntoSeries, descending: bool = False):
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.is_sorted(descending=descending)
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_sorted:

+ 
>>> agnostic_is_sorted(pd.Series(unsorted_data))
+False
+
+ 
>>> agnostic_is_sorted(pd.Series(sorted_data), descending=True)
+True
+
+ 
>>> agnostic_is_sorted(pl.Series(unsorted_data))
+False
+
+ 
>>> agnostic_is_sorted(pl.Series(sorted_data), descending=True)
+True
+
+ 
>>> agnostic_is_sorted(pa.chunked_array([unsorted_data]))
+False
+
+ 
>>> agnostic_is_sorted(pa.chunked_array([sorted_data]), descending=True)
+True
+
+ +
+ +
+ +
+ + +

+ is_unique() + +

+ + +
+ +

Get a mask of all unique rows in the Series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with boolean values indicating unique rows.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3, 1]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_is_unique(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.is_unique().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_is_unique:

+ 
>>> agnostic_is_unique(s_pd)
+0    False
+1     True
+2     True
+3    False
+dtype: bool
+
+ 
>>> agnostic_is_unique(s_pl)
+shape: (4,)
+Series: '' [bool]
+[
+    false
+     true
+     true
+    false
+]
+>>> agnostic_is_unique(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    false,
+    true,
+    true,
+    false
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ item(index=None) + +

+ + +
+ +

Return the Series as a scalar, or return the element at the given index.

+

If no index is provided, this is equivalent to s[0], with a check +that the shape is (1,). With an index, this is equivalent to s[index].

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

The scalar value of the Series or the element at the given index.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+

Let's define a dataframe-agnostic function that returns item at given index

+ 
>>> def agnostic_item(s_native: IntoSeries, index=None):
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.item(index)
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_item:

+ 
>>> (
+...     agnostic_item(pl.Series("a", [1]), None),
+...     agnostic_item(pd.Series([1]), None),
+...     agnostic_item(pa.chunked_array([[1]]), None),
+... )
+(1, np.int64(1), 1)
+
+ 
>>> (
+...     agnostic_item(pl.Series("a", [9, 8, 7]), -1),
+...     agnostic_item(pl.Series([9, 8, 7]), -2),
+...     agnostic_item(pa.chunked_array([[9, 8, 7]]), -3),
+... )
+(7, 8, 9)
+
+ +
+ +
+ +
+ + +

+ len() + +

+ + +
+ +

Return the number of elements in the Series.

+

Null values count towards the total.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ int + +
+

The number of elements in the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, None]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function that computes the len of the series:

+ 
>>> def agnostic_len(s_native: IntoSeries) -> int:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.len()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_len:

+ 
>>> agnostic_len(s_pd)
+3
+
+ 
>>> agnostic_len(s_pl)
+3
+
+ 
>>> agnostic_len(s_pa)
+3
+
+ +
+ +
+ +
+ + +

+ max() + +

+ + +
+ +

Get the maximum value in this Series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

The maximum value in the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_max(s_native: IntoSeries):
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.max()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_max:

+ 
>>> agnostic_max(s_pd)
+np.int64(3)
+
+ 
>>> agnostic_max(s_pl)
+3
+
+ 
>>> agnostic_max(s_pa)
+3
+
+ +
+ +
+ +
+ + +

+ mean() + +

+ + +
+ +

Reduce this Series to the mean value.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

The average of all elements in the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_mean(s_native: IntoSeries) -> float:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.mean()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_mean:

+ 
>>> agnostic_mean(s_pd)
+np.float64(2.0)
+
+ 
>>> agnostic_mean(s_pl)
+2.0
+
+ 
>>> agnostic_mean(s_pa)
+2.0
+
+ +
+ +
+ +
+ + +

+ median() + +

+ + +
+ +

Reduce this Series to the median value.

+ + +
+ Notes +

Results might slightly differ across backends due to differences in the underlying algorithms used to compute the median.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

The median value of all elements in the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [5, 3, 8]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a library agnostic function:

+ 
>>> def agnostic_median(s_native: IntoSeries) -> float:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.median()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_median:

+ 
>>> agnostic_median(s_pd)
+np.float64(5.0)
+
+ 
>>> agnostic_median(s_pl)
+5.0
+
+ 
>>> agnostic_median(s_pa)
+5.0
+
+ +
+ +
+ +
+ + +

+ min() + +

+ + +
+ +

Get the minimal value in this Series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

The minimum value in the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_min(s_native: IntoSeries):
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.min()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_min:

+ 
>>> agnostic_min(s_pd)
+np.int64(1)
+
+ 
>>> agnostic_min(s_pl)
+1
+
+ 
>>> agnostic_min(s_pa)
+1
+
+ +
+ +
+ +
+ + +

+ mode() + +

+ + +
+ +

Compute the most occurring value(s).

+

Can return multiple values.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series containing the mode(s) (values that appear most frequently).

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 1, 2, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_mode(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.mode().sort().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_mode:

+ 
>>> agnostic_mode(s_pd)
+0    1
+1    2
+dtype: int64
+
+ 
>>> agnostic_mode(s_pl)
+shape: (2,)
+Series: '' [i64]
+[
+   1
+   2
+]
+
+ 
>>> agnostic_mode(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    2
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ n_unique() + +

+ + +
+ +

Count the number of unique values.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ int + +
+

Number of unique values in the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_n_unique(s_native: IntoSeries) -> int:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.n_unique()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_n_unique:

+ 
>>> agnostic_n_unique(s_pd)
+3
+
+ 
>>> agnostic_n_unique(s_pl)
+3
+
+ 
>>> agnostic_n_unique(s_pa)
+3
+
+ +
+ +
+ +
+ + +

+ null_count() + +

+ + +
+ +

Create a new Series that shows the null counts per column.

+ + +
+ Notes +

pandas handles null values differently from Polars and PyArrow. +See null_handling +for reference.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ int + +
+

The number of null values in the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, None, None]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function that returns the null count of +the series:

+ 
>>> def agnostic_null_count(s_native: IntoSeries) -> int:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.null_count()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_null_count:

+ 
>>> agnostic_null_count(s_pd)
+np.int64(2)
+
+ 
>>> agnostic_null_count(s_pl)
+2
+
+ 
>>> agnostic_null_count(s_pa)
+2
+
+ +
+ +
+ +
+ + +

+ pipe(function, *args, **kwargs) + +

+ + +
+ +

Pipe function call.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with the results of the piped function applied.

+
+
+ + +

Examples:

+ 
>>> import polars as pl
+>>> import pandas as pd
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a function to pipe into:

+ 
>>> def agnostic_pipe(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.pipe(lambda x: x + 2).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_pipe:

+ 
>>> agnostic_pipe(s_pd)
+0    3
+1    4
+2    5
+dtype: int64
+
+ 
>>> agnostic_pipe(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   3
+   4
+   5
+]
+
+ 
>>> agnostic_pipe(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    3,
+    4,
+    5
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ quantile(quantile, interpolation) + +

+ + +
+ +

Get quantile value of the series.

+ + +
+ Note +

pandas and Polars may have implementation differences for a given interpolation method.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ quantile + + float + +
+

Quantile between 0.0 and 1.0.

+
+ 		+ required +
+ interpolation + + Literal['nearest', 'higher', 'lower', 'midpoint', 'linear'] + +
+

Interpolation method.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

The quantile value.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = list(range(50))
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_quantile(s_native: IntoSeries) -> list[float]:
+...     s = nw.from_native(s_native, series_only=True)
+...     return [
+...         s.quantile(quantile=q, interpolation="nearest")
+...         for q in (0.1, 0.25, 0.5, 0.75, 0.9)
+...     ]
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_quantile:

+ 
>>> agnostic_quantile(s_pd)
+[np.int64(5), np.int64(12), np.int64(24), np.int64(37), np.int64(44)]
+
+ 
>>> agnostic_quantile(s_pl)
+[5.0, 12.0, 25.0, 37.0, 44.0]
+
+ 
>>> agnostic_quantile(s_pa)
+[5, 12, 24, 37, 44]
+
+ +
+ +
+ +
+ + +

+ rank(method='average', *, descending=False) + +

+ + +
+ +

Assign ranks to data, dealing with ties appropriately.

+ + +
+ Notes +

The resulting dtype may differ between backends.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ method + + Literal['average', 'min', 'max', 'dense', 'ordinal'] + +
+

The method used to assign ranks to tied elements. +The following methods are available (default is 'average'):

+
    +
  • 'average' : The average of the ranks that would have been assigned to + all the tied values is assigned to each value.
    • +
  • 'min' : The minimum of the ranks that would have been assigned to all + the tied values is assigned to each value. (This is also referred to + as "competition" ranking.)
    • +
  • 'max' : The maximum of the ranks that would have been assigned to all + the tied values is assigned to each value.
    • +
  • 'dense' : Like 'min', but the rank of the next highest element is + assigned the rank immediately after those assigned to the tied + elements.
    • +
  • 'ordinal' : All values are given a distinct rank, corresponding to the + order that the values occur in the Series.
    • +
+
+ 		+ 'average' +
+ descending + + bool + +
+

Rank in descending order.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new series with rank data as values.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+>>>
+>>> data = [3, 6, 1, 1, 6]
+
+

We define a dataframe-agnostic function that computes the dense rank for +the data:

+ 
>>> def agnostic_dense_rank(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.rank(method="dense").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_dense_rank:

+ 
>>> agnostic_dense_rank(pd.Series(data))
+0    2.0
+1    3.0
+2    1.0
+3    1.0
+4    3.0
+dtype: float64
+
+ 
>>> agnostic_dense_rank(pl.Series(data))
+shape: (5,)
+Series: '' [u32]
+[
+   2
+   3
+   1
+   1
+   3
+]
+
+ 
>>> agnostic_dense_rank(pa.chunked_array([data]))
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    2,
+    3,
+    1,
+    1,
+    3
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ rename(name) + +

+ + +
+ +

Rename the Series.

+

Alias for Series.alias().

+ + +
+ Notes +

This method is very cheap, but does not guarantee that data +will be copied. For example:

+
s1: nw.Series
+s2 = s1.rename("foo")
+arr = s2.to_numpy()
+arr[0] = 999
+
+

may (depending on the backend, and on the version) result in +s1's data being modified. We recommend:

+
- if you need to rename an object and don't need the original
+  one around any more, just use `rename` without worrying about it.
+- if you were expecting `rename` to copy data, then explicily call
+  `.clone` before calling `rename`.
+
+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ name + + str + +
+

The new name.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with the updated name.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data, name="foo")
+>>> s_pl = pl.Series("foo", data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_rename(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.rename("bar").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_rename:

+ 
>>> agnostic_rename(s_pd)
+0    1
+1    2
+2    3
+Name: bar, dtype: int64
+
+ 
>>> agnostic_rename(s_pl)
+shape: (3,)
+Series: 'bar' [i64]
+[
+   1
+   2
+   3
+]
+
+ 
>>> agnostic_rename(s_pa)
+<pyarrow.lib.ChunkedArray object at 0x...>
+[
+  [
+    1,
+    2,
+    3
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ replace_strict(old, new=None, *, return_dtype=None) + +

+ + +
+ +

Replace all values by different values.

+

This function must replace all non-null input values (else it raises an error).

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ old + + Sequence[Any] | Mapping[Any, Any] + +
+

Sequence of values to replace. It also accepts a mapping of values to +their replacement as syntactic sugar for +replace_all(old=list(mapping.keys()), new=list(mapping.values())).

+
+ 		+ required +
+ new + + Sequence[Any] | None + +
+

Sequence of values to replace by. Length must match the length of old.

+
+ 		+ None +
+ return_dtype + + DType | type[DType] | None + +
+

The data type of the resulting expression. If set to None +(default), the data type is determined automatically based on the other +inputs.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with values replaced according to the mapping.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = {"a": [3, 0, 1, 2]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

Let's define dataframe-agnostic functions:

+ 
>>> def agnostic_replace_strict(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.replace_strict(
+...         [0, 1, 2, 3], ["zero", "one", "two", "three"], return_dtype=nw.String
+...     ).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_replace_strict:

+ 
>>> agnostic_replace_strict(df_pd["a"])
+0    three
+1     zero
+2      one
+3      two
+Name: a, dtype: object
+
+ 
>>> agnostic_replace_strict(df_pl["a"])
+shape: (4,)
+Series: 'a' [str]
+[
+    "three"
+    "zero"
+    "one"
+    "two"
+]
+
+ 
>>> agnostic_replace_strict(df_pa["a"])
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    "three",
+    "zero",
+    "one",
+    "two"
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ rolling_mean(window_size, *, min_periods=None, center=False) + +

+ + +
+ +

Apply a rolling mean (moving mean) over the values.

+
+

Warning

+

This functionality is considered unstable. It may be changed at any point +without it being considered a breaking change.

+
+

A window of length window_size will traverse the values. The resulting values +will be aggregated to their mean.

+

The window at a given row will include the row itself and the window_size - 1 +elements before it.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ window_size + + int + +
+

The length of the window in number of elements. It must be a +strictly positive integer.

+
+ 		+ required +
+ min_periods + + int | None + +
+

The number of values in the window that should be non-null before +computing a result. If set to None (default), it will be set equal to +window_size. If provided, it must be a strictly positive integer, and +less than or equal to window_size

+
+ 		+ None +
+ center + + bool + +
+

Set the labels at the center of the window.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1.0, 2.0, 3.0, 4.0]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_rolling_mean(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.rolling_mean(window_size=2).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_rolling_mean:

+ 
>>> agnostic_rolling_mean(s_pd)
+0    NaN
+1    1.5
+2    2.5
+3    3.5
+dtype: float64
+
+ 
>>> agnostic_rolling_mean(s_pl)
+shape: (4,)
+Series: '' [f64]
+[
+   null
+   1.5
+   2.5
+   3.5
+]
+
+ 
>>> agnostic_rolling_mean(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    null,
+    1.5,
+    2.5,
+    3.5
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ rolling_std(window_size, *, min_periods=None, center=False, ddof=1) + +

+ + +
+ +

Apply a rolling standard deviation (moving standard deviation) over the values.

+
+

Warning

+

This functionality is considered unstable. It may be changed at any point +without it being considered a breaking change.

+
+

A window of length window_size will traverse the values. The resulting values +will be aggregated to their standard deviation.

+

The window at a given row will include the row itself and the window_size - 1 +elements before it.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ window_size + + int + +
+

The length of the window in number of elements. It must be a +strictly positive integer.

+
+ 		+ required +
+ min_periods + + int | None + +
+

The number of values in the window that should be non-null before +computing a result. If set to None (default), it will be set equal to +window_size. If provided, it must be a strictly positive integer, and +less than or equal to window_size.

+
+ 		+ None +
+ center + + bool + +
+

Set the labels at the center of the window.

+
+ 		+ False +
+ ddof + + int + +
+

Delta Degrees of Freedom; the divisor for a length N window is N - ddof.

+
+ 		+ 1 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1.0, 3.0, 1.0, 4.0]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_rolling_std(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.rolling_std(window_size=2, min_periods=1).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_rolling_std:

+ 
>>> agnostic_rolling_std(s_pd)
+0         NaN
+1    1.414214
+2    1.414214
+3    2.121320
+dtype: float64
+
+ 
>>> agnostic_rolling_std(s_pl)
+shape: (4,)
+Series: '' [f64]
+[
+   null
+   1.414214
+   1.414214
+   2.12132
+]
+
+ 
>>> agnostic_rolling_std(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    nan,
+    1.4142135623730951,
+    1.4142135623730951,
+    2.1213203435596424
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ rolling_sum(window_size, *, min_periods=None, center=False) + +

+ + +
+ +

Apply a rolling sum (moving sum) over the values.

+
+

Warning

+

This functionality is considered unstable. It may be changed at any point +without it being considered a breaking change.

+
+

A window of length window_size will traverse the values. The resulting values +will be aggregated to their sum.

+

The window at a given row will include the row itself and the window_size - 1 +elements before it.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ window_size + + int + +
+

The length of the window in number of elements. It must be a +strictly positive integer.

+
+ 		+ required +
+ min_periods + + int | None + +
+

The number of values in the window that should be non-null before +computing a result. If set to None (default), it will be set equal to +window_size. If provided, it must be a strictly positive integer, and +less than or equal to window_size

+
+ 		+ None +
+ center + + bool + +
+

Set the labels at the center of the window.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1.0, 2.0, 3.0, 4.0]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_rolling_sum(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.rolling_sum(window_size=2).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_rolling_sum:

+ 
>>> agnostic_rolling_sum(s_pd)
+0    NaN
+1    3.0
+2    5.0
+3    7.0
+dtype: float64
+
+ 
>>> agnostic_rolling_sum(s_pl)
+shape: (4,)
+Series: '' [f64]
+[
+   null
+   3.0
+   5.0
+   7.0
+]
+
+ 
>>> agnostic_rolling_sum(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    null,
+    3,
+    5,
+    7
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ rolling_var(window_size, *, min_periods=None, center=False, ddof=1) + +

+ + +
+ +

Apply a rolling variance (moving variance) over the values.

+
+

Warning

+

This functionality is considered unstable. It may be changed at any point +without it being considered a breaking change.

+
+

A window of length window_size will traverse the values. The resulting values +will be aggregated to their variance.

+

The window at a given row will include the row itself and the window_size - 1 +elements before it.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ window_size + + int + +
+

The length of the window in number of elements. It must be a +strictly positive integer.

+
+ 		+ required +
+ min_periods + + int | None + +
+

The number of values in the window that should be non-null before +computing a result. If set to None (default), it will be set equal to +window_size. If provided, it must be a strictly positive integer, and +less than or equal to window_size.

+
+ 		+ None +
+ center + + bool + +
+

Set the labels at the center of the window.

+
+ 		+ False +
+ ddof + + int + +
+

Delta Degrees of Freedom; the divisor for a length N window is N - ddof.

+
+ 		+ 1 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1.0, 3.0, 1.0, 4.0]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_rolling_var(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.rolling_var(window_size=2, min_periods=1).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_rolling_var:

+ 
>>> agnostic_rolling_var(s_pd)
+0    NaN
+1    2.0
+2    2.0
+3    4.5
+dtype: float64
+
+ 
>>> agnostic_rolling_var(s_pl)
+shape: (4,)
+Series: '' [f64]
+[
+   null
+   2.0
+   2.0
+   4.5
+]
+
+ 
>>> agnostic_rolling_var(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    nan,
+    2,
+    2,
+    4.5
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ round(decimals=0) + +

+ + +
+ +

Round underlying floating point data by decimals digits.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ decimals + + int + +
+

Number of decimals to round by.

+
+ 		+ 0 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with rounded values.

+
+
+ + +
+ Notes +

For values exactly halfway between rounded decimal values pandas behaves differently than Polars and Arrow.

+

pandas rounds to the nearest even value (e.g. -0.5 and 0.5 round to 0.0, 1.5 and 2.5 round to 2.0, 3.5 and +4.5 to 4.0, etc..).

+

Polars and Arrow round away from 0 (e.g. -0.5 to -1.0, 0.5 to 1.0, 1.5 to 2.0, 2.5 to 3.0, etc..).

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1.12345, 2.56789, 3.901234]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function that rounds to the first decimal:

+ 
>>> def agnostic_round(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.round(1).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_round:

+ 
>>> agnostic_round(s_pd)
+0    1.1
+1    2.6
+2    3.9
+dtype: float64
+
+ 
>>> agnostic_round(s_pl)
+shape: (3,)
+Series: '' [f64]
+[
+   1.1
+   2.6
+   3.9
+]
+
+ 
>>> agnostic_round(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1.1,
+    2.6,
+    3.9
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ sample(n=None, *, fraction=None, with_replacement=False, seed=None) + +

+ + +
+ +

Sample randomly from this Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int | None + +
+

Number of items to return. Cannot be used with fraction.

+
+ 		+ None +
+ fraction + + float | None + +
+

Fraction of items to return. Cannot be used with n.

+
+ 		+ None +
+ with_replacement + + bool + +
+

Allow values to be sampled more than once.

+
+ 		+ False +
+ seed + + int | None + +
+

Seed for the random number generator. If set to None (default), a random +seed is generated for each sample operation.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series containing randomly sampled values from the original Series.

+
+
+ + +
+ Notes +

The sample method returns a Series with a specified number of +randomly selected items chosen from this Series. +The results are not consistent across libraries.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3, 4]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_sample(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.sample(fraction=1.0, with_replacement=True).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_sample:

+ 
>>> agnostic_sample(s_pd)
+   a
+2  3
+1  2
+3  4
+3  4
+
+ 
>>> agnostic_sample(s_pl)
+shape: (4,)
+Series: '' [i64]
+[
+   1
+   4
+   3
+   4
+]
+
+ 
>>> agnostic_sample(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    4,
+    3,
+    4
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ scatter(indices, values) + +

+ + +
+ +

Set value(s) at given position(s).

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ indices + + int | Sequence[int] + +
+

Position(s) to set items at.

+
+ 		+ required +
+ values + + Any + +
+

Values to set.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with values set at given positions.

+
+
+ + +
+ Note +

This method always returns a new Series, without modifying the original one. +Using this function in a for-loop is an anti-pattern, we recommend building +up your positions and values beforehand and doing an update in one go.

+

For example, instead of

+
for i in [1, 3, 2]:
+    value = some_function(i)
+    s = s.scatter(i, value)
+
+

prefer

+
positions = [1, 3, 2]
+values = [some_function(x) for x in positions]
+s = s.scatter(positions, values)
+
+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+
+ 
>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
+>>> df_pd = pd.DataFrame(data)
+>>> df_pl = pl.DataFrame(data)
+>>> df_pa = pa.table(data)
+
+

We define a library agnostic function:

+ 
>>> def agnostic_scatter(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(df["a"].scatter([0, 1], [999, 888])).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_scatter:

+ 
>>> agnostic_scatter(df_pd)
+     a  b
+0  999  4
+1  888  5
+2    3  6
+
+ 
>>> agnostic_scatter(df_pl)
+shape: (3, 2)
+┌─────┬─────┐
+│ a   ┆ b   │
+│ --- ┆ --- │
+│ i64 ┆ i64 │
+╞═════╪═════╡
+│ 999 ┆ 4   │
+│ 888 ┆ 5   │
+│ 3   ┆ 6   │
+└─────┴─────┘
+
+ 
>>> agnostic_scatter(df_pa)
+pyarrow.Table
+a: int64
+b: int64
+----
+a: [[999,888,3]]
+b: [[4,5,6]]
+
+ +
+ +
+ +
+ + +

+ shift(n) + +

+ + +
+ +

Shift values by n positions.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of indices to shift forward. If a negative value is passed, +values are shifted in the opposite direction instead.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with values shifted by n positions.

+
+
+ + +
+ Notes +

pandas may change the dtype here, for example when introducing missing +values in an integer column. To ensure, that the dtype doesn't change, +you may want to use fill_null and cast. For example, to shift +and fill missing values with 0 in a Int64 column, you could +do:

+
s.shift(1).fill_null(0).cast(nw.Int64)
+
+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [2, 4, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_shift(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.shift(1).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_shift:

+ 
>>> agnostic_shift(s_pd)
+0    NaN
+1    2.0
+2    4.0
+dtype: float64
+
+ 
>>> agnostic_shift(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   null
+   2
+   4
+]
+
+ 
>>> agnostic_shift(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    null,
+    2,
+    4
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ sort(*, descending=False, nulls_last=False) + +

+ + +
+ +

Sort this Series. Place null values first.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ descending + + bool + +
+

Sort in descending order.

+
+ 		+ False +
+ nulls_last + + bool + +
+

Place null values last instead of first.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new sorted Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [5, None, 1, 2]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define library agnostic functions:

+ 
>>> def agnostic_sort(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.sort().to_native()
+
+ 
>>> def agnostic_sort_descending(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.sort(descending=True).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_sort and agnostic_sort_descending:

+ 
>>> agnostic_sort(s_pd)
+1    NaN
+2    1.0
+3    2.0
+0    5.0
+dtype: float64
+
+ 
>>> agnostic_sort(s_pl)
+shape: (4,)
+Series: '' [i64]
+[
+   null
+   1
+   2
+   5
+]
+
+ 
>>> agnostic_sort(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    null,
+    1,
+    2,
+    5
+  ]
+]
+
+ 
>>> agnostic_sort_descending(s_pd)
+1    NaN
+0    5.0
+3    2.0
+2    1.0
+dtype: float64
+
+ 
>>> agnostic_sort_descending(s_pl)
+shape: (4,)
+Series: '' [i64]
+[
+   null
+   5
+   2
+   1
+]
+
+ 
>>> agnostic_sort_descending(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    null,
+    5,
+    2,
+    1
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ skew() + +

+ + +
+ +

Calculate the sample skewness of the Series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

The sample skewness of the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 1, 2, 10, 100]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_skew(s_native: IntoSeries) -> float:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.skew()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_skew:

+ 
>>> agnostic_skew(s_pd)
+np.float64(1.4724267269058975)
+
+ 
>>> agnostic_skew(s_pl)
+1.4724267269058975
+
+ 
>>> agnostic_skew(s_pa)
+1.4724267269058975
+
+ + +
+ Notes +

The skewness is a measure of the asymmetry of the probability distribution. +A perfectly symmetric distribution has a skewness of 0.

+
+
+ +
+ +
+ + +

+ std(*, ddof=1) + +

+ + +
+ +

Get the standard deviation of this Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ ddof + + int + +
+

"Delta Degrees of Freedom": the divisor used in the calculation is N - ddof, + where N represents the number of elements.

+
+ 		+ 1 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

The standard deviation of all elements in the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_std(s_native: IntoSeries) -> float:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.std()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_std:

+ 
>>> agnostic_std(s_pd)
+np.float64(1.0)
+
+ 
>>> agnostic_std(s_pl)
+1.0
+
+ 
>>> agnostic_std(s_pa)
+1.0
+
+ +
+ +
+ +
+ + +

+ sum() + +

+ + +
+ +

Reduce this Series to the sum value.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Any + +
+

The sum of all elements in the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_sum(s_native: IntoSeries):
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.sum()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_sum:

+ 
>>> agnostic_sum(s_pd)
+np.int64(6)
+
+ 
>>> agnostic_sum(s_pl)
+6
+
+ 
>>> agnostic_sum(s_pa)
+6
+
+ +
+ +
+ +
+ + +

+ tail(n=10) + +

+ + +
+ +

Get the last n rows.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of rows to return.

+
+ 		+ 10 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with the last n rows.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = list(range(10))
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function that returns the last 3 rows:

+ 
>>> def agnostic_tail(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.tail(3).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_tail:

+ 
>>> agnostic_tail(s_pd)
+7    7
+8    8
+9    9
+dtype: int64
+
+ 
>>> agnostic_tail(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   7
+   8
+   9
+]
+
+ 
>>> agnostic_tail(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    7,
+    8,
+    9
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ to_arrow() + +

+ + +
+ +

Convert to arrow.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Array + +
+

A PyArrow Array containing the data from the Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3, 4]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function that converts to arrow:

+ 
>>> def agnostic_to_arrow(s_native: IntoSeries) -> pa.Array:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.to_arrow()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_to_arrow:

+ 
>>> agnostic_to_arrow(s_pd)
+<pyarrow.lib.Int64Array object at ...>
+[
+    1,
+    2,
+    3,
+    4
+]
+
+ 
>>> agnostic_to_arrow(s_pl)
+<pyarrow.lib.Int64Array object at ...>
+[
+    1,
+    2,
+    3,
+    4
+]
+
+ 
>>> agnostic_to_arrow(s_pa)
+<pyarrow.lib.Int64Array object at ...>
+[
+    1,
+    2,
+    3,
+    4
+]
+
+ +
+ +
+ +
+ + +

+ to_dummies(*, separator='_', drop_first=False) + +

+ + +
+ +

Get dummy/indicator variables.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ separator + + str + +
+

Separator/delimiter used when generating column names.

+
+ 		+ '_' +
+ drop_first + + bool + +
+

Remove the first category from the variable being encoded.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DataFrame[Any] + +
+

A new DataFrame containing the dummy/indicator variables.

+
+
+ + +
+ Notes +

pandas and Polars handle null values differently. Polars distinguishes +between NaN and Null, whereas pandas doesn't.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data, name="a")
+>>> s_pl = pl.Series("a", data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_to_dummies(
+...     s_native: IntoSeries, drop_first: bool = False
+... ) -> IntoDataFrame:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.to_dummies(drop_first=drop_first).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_to_dummies:

+ 
>>> agnostic_to_dummies(s_pd)
+   a_1  a_2  a_3
+0    1    0    0
+1    0    1    0
+2    0    0    1
+
+ 
>>> agnostic_to_dummies(s_pd, drop_first=True)
+   a_2  a_3
+0    0    0
+1    1    0
+2    0    1
+
+ 
>>> agnostic_to_dummies(s_pl)
+shape: (3, 3)
+┌─────┬─────┬─────┐
+│ a_1 ┆ a_2 ┆ a_3 │
+│ --- ┆ --- ┆ --- │
+│ i8  ┆ i8  ┆ i8  │
+╞═════╪═════╪═════╡
+│ 1   ┆ 0   ┆ 0   │
+│ 0   ┆ 1   ┆ 0   │
+│ 0   ┆ 0   ┆ 1   │
+└─────┴─────┴─────┘
+
+ 
>>> agnostic_to_dummies(s_pl, drop_first=True)
+shape: (3, 2)
+┌─────┬─────┐
+│ a_2 ┆ a_3 │
+│ --- ┆ --- │
+│ i8  ┆ i8  │
+╞═════╪═════╡
+│ 0   ┆ 0   │
+│ 1   ┆ 0   │
+│ 0   ┆ 1   │
+└─────┴─────┘
+
+ 
>>> agnostic_to_dummies(s_pa)
+pyarrow.Table
+_1: int8
+_2: int8
+_3: int8
+----
+_1: [[1,0,0]]
+_2: [[0,1,0]]
+_3: [[0,0,1]]
+>>> agnostic_to_dummies(s_pa, drop_first=True)
+pyarrow.Table
+_2: int8
+_3: int8
+----
+_2: [[0,1,0]]
+_3: [[0,0,1]]
+
+ +
+ +
+ +
+ + +

+ to_frame() + +

+ + +
+ +

Convert to dataframe.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ DataFrame[Any] + +
+

A DataFrame containing this Series as a single column.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2]
+>>> s_pd = pd.Series(data, name="a")
+>>> s_pl = pl.Series("a", data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_to_frame(s_native: IntoSeries) -> IntoDataFrame:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.to_frame().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_to_frame:

+ 
>>> agnostic_to_frame(s_pd)
+   a
+0  1
+1  2
+
+ 
>>> agnostic_to_frame(s_pl)
+shape: (2, 1)
+┌─────┐
+│ a   │
+│ --- │
+│ i64 │
+╞═════╡
+│ 1   │
+│ 2   │
+└─────┘
+
+ 
>>> agnostic_to_frame(s_pa)
+pyarrow.Table
+: int64
+----
+: [[1,2]]
+
+ +
+ +
+ +
+ + +

+ to_list() + +

+ + +
+ +

Convert to list.

+ + +
+ Notes +

This function converts to Python scalars. It's typically +more efficient to keep your data in the format native to +your original dataframe, so we recommend only calling this +when you absolutely need to.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ list[Any] + +
+

A list of Python objects.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_to_list(s_native: IntoSeries):
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.to_list()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_to_list:

+ 
>>> agnostic_to_list(s_pd)
+[1, 2, 3]
+
+ 
>>> agnostic_to_list(s_pl)
+[1, 2, 3]
+
+ 
>>> agnostic_to_list(s_pa)
+[1, 2, 3]
+
+ +
+ +
+ +
+ + +

+ to_numpy() + +

+ + +
+ +

Convert to numpy.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ ndarray + +
+

NumPy ndarray representation of the Series.

+
+
+ + +

Examples:

+ 
>>> import numpy as np
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data, name="a")
+>>> s_pl = pl.Series("a", data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_to_numpy(s_native: IntoSeries) -> np.ndarray:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.to_numpy()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_to_numpy:

+ 
>>> agnostic_to_numpy(s_pd)
+array([1, 2, 3]...)
+
+ 
>>> agnostic_to_numpy(s_pl)
+array([1, 2, 3]...)
+
+ 
>>> agnostic_to_numpy(s_pa)
+array([1, 2, 3]...)
+
+ +
+ +
+ +
+ + +

+ to_pandas() + +

+ + +
+ +

Convert to pandas.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Series + +
+

A pandas Series containing the data from this Series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data, name="a")
+>>> s_pl = pl.Series("a", data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_to_pandas(s_native: IntoSeries) -> pd.Series:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.to_pandas()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_to_pandas:

+ 
>>> agnostic_to_pandas(s_pd)
+0    1
+1    2
+2    3
+Name: a, dtype: int64
+
+ 
>>> agnostic_to_pandas(s_pl)
+0    1
+1    2
+2    3
+Name: a, dtype: int64
+
+ 
>>> agnostic_to_pandas(s_pa)
+0    1
+1    2
+2    3
+Name: , dtype: int64
+
+ +
+ +
+ +
+ + +

+ to_native() + +

+ + +
+ +

Convert Narwhals series to native series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ IntoSeriesT + +
+

Series of class that user started with.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_to_native(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_to_native:

+ 
>>> agnostic_to_native(s_pd)
+0    1
+1    2
+2    3
+dtype: int64
+
+ 
>>> agnostic_to_native(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+    1
+    2
+    3
+]
+
+ 
>>> agnostic_to_native(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    2,
+    3
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ unique(*, maintain_order=False) + +

+ + +
+ +

Returns unique values of the series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ maintain_order + + bool + +
+

Keep the same order as the original series. This may be more +expensive to compute. Settings this to True blocks the possibility +to run on the streaming engine for Polars.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with duplicate values removed.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [2, 4, 4, 6]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_unique(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.unique(maintain_order=True).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_unique:

+ 
>>> agnostic_unique(s_pd)
+0    2
+1    4
+2    6
+dtype: int64
+
+ 
>>> agnostic_unique(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+   2
+   4
+   6
+]
+
+ 
>>> agnostic_unique(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    2,
+    4,
+    6
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ value_counts(*, sort=False, parallel=False, name=None, normalize=False) + +

+ + +
+ +

Count the occurrences of unique values.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ sort + + bool + +
+

Sort the output by count in descending order. If set to False (default), +the order of the output is random.

+
+ 		+ False +
+ parallel + + bool + +
+

Execute the computation in parallel. Used for Polars only.

+
+ 		+ False +
+ name + + str | None + +
+

Give the resulting count column a specific name; if normalize is True +defaults to "proportion", otherwise defaults to "count".

+
+ 		+ None +
+ normalize + + bool + +
+

If true gives relative frequencies of the unique values

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + + + + + + + + + +
TypeDescription
+ DataFrame[Any] + +
+

A DataFrame with two columns:

+
+
+ DataFrame[Any] + +
+
    +
  • The original values as first column
    • +
+
+
+ DataFrame[Any] + +
+
    +
  • Either count or proportion as second column, depending on normalize parameter.
    • +
+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 1, 2, 3, 2]
+>>> s_pd = pd.Series(data, name="s")
+>>> s_pl = pl.Series(values=data, name="s")
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_value_counts(s_native: IntoSeries) -> IntoDataFrame:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.value_counts(sort=True).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_value_counts:

+ 
>>> agnostic_value_counts(s_pd)
+   s  count
+0  1      2
+1  2      2
+2  3      1
+
+ 
>>> agnostic_value_counts(s_pl)
+shape: (3, 2)
+┌─────┬───────┐
+│ s   ┆ count │
+│ --- ┆ ---   │
+│ i64 ┆ u32   │
+╞═════╪═══════╡
+│ 1   ┆ 2     │
+│ 2   ┆ 2     │
+│ 3   ┆ 1     │
+└─────┴───────┘
+
+ 
>>> agnostic_value_counts(s_pa)
+pyarrow.Table
+: int64
+count: int64
+----
+: [[1,2,3]]
+count: [[2,2,1]]
+
+ +
+ +
+ +
+ + +

+ var(*, ddof=1) + +

+ + +
+ +

Get the variance of this Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ ddof + + int + +
+

"Delta Degrees of Freedom": the divisor used in the calculation is N - ddof, + where N represents the number of elements.

+
+ 		+ 1 +
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+
+ 
>>> data = [1, 2, 3]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_var(s_native: IntoSeries) -> float:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.var()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_var:

+ 
>>> agnostic_var(s_pd)
+np.float64(1.0)
+
+ 
>>> agnostic_var(s_pl)
+1.0
+
+ 
>>> agnostic_var(s_pa)
+1.0
+
+ +
+ +
+ +
+ + +

+ zip_with(mask, other) + +

+ + +
+ +

Take values from self or other based on the given mask.

+

Where mask evaluates true, take values from self. Where mask evaluates false, +take values from other.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ mask + + Self + +
+

Boolean Series

+
+ 		+ required +
+ other + + Self + +
+

Series of same type.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ Self + +
+

A new Series with values selected from self or other based on the mask.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [1, 2, 3, 4, 5]
+>>> other = [5, 4, 3, 2, 1]
+>>> mask = [True, False, True, False, True]
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_zip_with(
+...     s1_native: IntoSeriesT, mask_native: IntoSeriesT, s2_native: IntoSeriesT
+... ) -> IntoSeriesT:
+...     s1 = nw.from_native(s1_native, series_only=True)
+...     mask = nw.from_native(mask_native, series_only=True)
+...     s2 = nw.from_native(s2_native, series_only=True)
+...     return s1.zip_with(mask, s2).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_zip_with:

+ 
>>> agnostic_zip_with(
+...     s1_native=pl.Series(data),
+...     mask_native=pl.Series(mask),
+...     s2_native=pl.Series(other),
+... )
+shape: (5,)
+Series: '' [i64]
+[
+   1
+   4
+   3
+   2
+   5
+]
+
+ 
>>> agnostic_zip_with(
+...     s1_native=pd.Series(data),
+...     mask_native=pd.Series(mask),
+...     s2_native=pd.Series(other),
+... )
+0    1
+1    4
+2    3
+3    2
+4    5
+dtype: int64
+
+ 
>>> agnostic_zip_with(
+...     s1_native=pa.chunked_array([data]),
+...     mask_native=pa.chunked_array([mask]),
+...     s2_native=pa.chunked_array([other]),
+... )
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    4,
+    3,
+    2,
+    5
+  ]
+]
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/series_cat/index.html b/api-reference/series_cat/index.html new file mode 100644 index 000000000..40e9885bb --- /dev/null +++ b/api-reference/series_cat/index.html @@ -0,0 +1,2041 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Series.cat - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Series.cat

+ + +
+ + + + +
+ + + + + + + + + + +
+ + + + + + + + + +
+ + +

+ get_categories() + +

+ + +
+ +

Get unique categories from column.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the unique categories.

+
+
+ + +

Examples:

+

Let's create some series:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["apple", "mango", "mango"]
+>>> s_pd = pd.Series(data, dtype="category")
+>>> s_pl = pl.Series(data, dtype=pl.Categorical)
+>>> s_pa = pa.chunked_array([data]).dictionary_encode()
+
+

We define a dataframe-agnostic function to get unique categories +from column 'fruits':

+ 
>>> def agnostic_get_categories(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.cat.get_categories().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_get_categories:

+ 
>>> agnostic_get_categories(s_pd)
+0    apple
+1    mango
+dtype: object
+
+ 
>>> agnostic_get_categories(s_pl)
+shape: (2,)
+Series: '' [str]
+[
+   "apple"
+   "mango"
+]
+
+ 
>>> agnostic_get_categories(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    "apple",
+    "mango"
+  ]
+]
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/series_dt/index.html b/api-reference/series_dt/index.html new file mode 100644 index 000000000..041208c59 --- /dev/null +++ b/api-reference/series_dt/index.html @@ -0,0 +1,4347 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Series.dt - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Series.dt

+ + +
+ + + + +
+ + + + + + + + + + +
+ + + + + + + + + +
+ + +

+ convert_time_zone(time_zone) + +

+ + +
+ +

Convert time zone.

+

If converting from a time-zone-naive column, then conversion happens +as if converting from UTC.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ time_zone + + str + +
+

Target time zone.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with the specified time zone.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime, timezone
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [
+...     datetime(2024, 1, 1, tzinfo=timezone.utc),
+...     datetime(2024, 1, 2, tzinfo=timezone.utc),
+... ]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_convert_time_zone(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.convert_time_zone("Asia/Kathmandu").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_convert_time_zone:

+ 
>>> agnostic_convert_time_zone(s_pd)
+0   2024-01-01 05:45:00+05:45
+1   2024-01-02 05:45:00+05:45
+dtype: datetime64[ns, Asia/Kathmandu]
+
+ 
>>> agnostic_convert_time_zone(s_pl)
+shape: (2,)
+Series: '' [datetime[μs, Asia/Kathmandu]]
+[
+    2024-01-01 05:45:00 +0545
+    2024-01-02 05:45:00 +0545
+]
+
+ 
>>> agnostic_convert_time_zone(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    2024-01-01 00:00:00.000000Z,
+    2024-01-02 00:00:00.000000Z
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ date() + +

+ + +
+ +

Get the date in a datetime series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with the date portion of the datetime values.

+
+
+ + +

Raises:

+ + + + + + + + + + + + + +
TypeDescription
+ NotImplementedError + +
+

If pandas default backend is being used.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> dates = [datetime(2012, 1, 7, 10, 20), datetime(2023, 3, 10, 11, 32)]
+>>> s_pd = pd.Series(dates).convert_dtypes(dtype_backend="pyarrow")
+>>> s_pl = pl.Series(dates)
+>>> s_pa = pa.chunked_array([dates])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_date(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.date().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_date:

+ 
>>> agnostic_date(s_pd)
+0    2012-01-07
+1    2023-03-10
+dtype: date32[day][pyarrow]
+
+ 
>>> agnostic_date(s_pl)
+shape: (2,)
+Series: '' [date]
+[
+   2012-01-07
+   2023-03-10
+]
+
+ 
>>> agnostic_date(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    2012-01-07,
+    2023-03-10
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ day() + +

+ + +
+ +

Extracts the day in a datetime series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the day component of each datetime value.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> dates = [datetime(2022, 1, 1), datetime(2022, 1, 5)]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+>>> s_pa = pa.chunked_array([dates])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_day(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.day().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_day:

+ 
>>> agnostic_day(s_pd)
+0    1
+1    5
+dtype: int...
+
+ 
>>> agnostic_day(s_pl)
+shape: (2,)
+Series: '' [i8]
+[
+   1
+   5
+]
+
+ 
>>> agnostic_day(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    5
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ hour() + +

+ + +
+ +

Extracts the hour in a datetime series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the hour component of each datetime value.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> dates = [datetime(2022, 1, 1, 5, 3), datetime(2022, 1, 5, 9, 12)]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+>>> s_pa = pa.chunked_array([dates])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_hour(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.hour().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_hour:

+ 
>>> agnostic_hour(s_pd)
+0    5
+1    9
+dtype: int...
+
+ 
>>> agnostic_hour(s_pl)
+shape: (2,)
+Series: '' [i8]
+[
+   5
+   9
+]
+
+ 
>>> agnostic_hour(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    5,
+    9
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ microsecond() + +

+ + +
+ +

Extracts the microseconds in a datetime series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the microsecond component of each datetime value.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> dates = [
+...     datetime(2023, 5, 21, 12, 55, 10, 400000),
+...     datetime(2023, 5, 21, 12, 55, 10, 600000),
+...     datetime(2023, 5, 21, 12, 55, 10, 800000),
+...     datetime(2023, 5, 21, 12, 55, 11, 0),
+...     datetime(2023, 5, 21, 12, 55, 11, 200000),
+... ]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+>>> s_pa = pa.chunked_array([dates])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_microsecond(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.microsecond().alias("datetime").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_microsecond:

+ 
>>> agnostic_microsecond(s_pd)
+0    400000
+1    600000
+2    800000
+3         0
+4    200000
+Name: datetime, dtype: int...
+
+ 
>>> agnostic_microsecond(s_pl)
+shape: (5,)
+Series: 'datetime' [i32]
+[
+   400000
+   600000
+   800000
+   0
+   200000
+]
+
+ 
>>> agnostic_microsecond(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    400000,
+    600000,
+    800000,
+    0,
+    200000
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ millisecond() + +

+ + +
+ +

Extracts the milliseconds in a datetime series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the millisecond component of each datetime value.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> dates = [
+...     datetime(2023, 5, 21, 12, 55, 10, 400000),
+...     datetime(2023, 5, 21, 12, 55, 10, 600000),
+...     datetime(2023, 5, 21, 12, 55, 10, 800000),
+...     datetime(2023, 5, 21, 12, 55, 11, 0),
+...     datetime(2023, 5, 21, 12, 55, 11, 200000),
+... ]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+>>> s_pa = pa.chunked_array([dates])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_millisecond(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.millisecond().alias("datetime").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_millisecond:

+ 
>>> agnostic_millisecond(s_pd)
+0    400
+1    600
+2    800
+3      0
+4    200
+Name: datetime, dtype: int...
+
+ 
>>> agnostic_millisecond(s_pl)
+shape: (5,)
+Series: 'datetime' [i32]
+[
+    400
+    600
+    800
+    0
+    200
+]
+
+ 
>>> agnostic_millisecond(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    400,
+    600,
+    800,
+    0,
+    200
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ minute() + +

+ + +
+ +

Extracts the minute in a datetime series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the minute component of each datetime value.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> dates = [datetime(2022, 1, 1, 5, 3), datetime(2022, 1, 5, 9, 12)]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+>>> s_pa = pa.chunked_array([dates])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_minute(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.minute().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_minute:

+ 
>>> agnostic_minute(s_pd)
+0     3
+1    12
+dtype: int...
+
+ 
>>> agnostic_minute(s_pl)
+shape: (2,)
+Series: '' [i8]
+[
+   3
+   12
+]
+
+ 
>>> agnostic_minute(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    3,
+    12
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ month() + +

+ + +
+ +

Gets the month in a datetime series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the month component of each datetime value.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> dates = [datetime(2023, 2, 1), datetime(2023, 8, 3)]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+>>> s_pa = pa.chunked_array([dates])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_month(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.month().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_month:

+ 
>>> agnostic_month(s_pd)
+0    2
+1    8
+dtype: int...
+>>> agnostic_month(s_pl)
+shape: (2,)
+Series: '' [i8]
+[
+   2
+   8
+]
+
+ 
>>> agnostic_month(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    2,
+    8
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ nanosecond() + +

+ + +
+ +

Extract the nanoseconds in a date series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the nanosecond component of each datetime value.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> dates = [
+...     datetime(2022, 1, 1, 5, 3, 10, 500000),
+...     datetime(2022, 1, 5, 9, 12, 4, 60000),
+... ]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+>>> s_pa = pa.chunked_array([dates])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_nanosecond(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.nanosecond().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_nanosecond:

+ 
>>> agnostic_nanosecond(s_pd)
+0    500000000
+1     60000000
+dtype: int...
+
+ 
>>> agnostic_nanosecond(s_pl)
+shape: (2,)
+Series: '' [i32]
+[
+   500000000
+   60000000
+]
+
+ 
>>> agnostic_nanosecond(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    500000000,
+    60000000
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ ordinal_day() + +

+ + +
+ +

Get ordinal day.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the ordinal day (day of year) for each datetime value.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [datetime(2020, 1, 1), datetime(2020, 8, 3)]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_ordinal_day(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.ordinal_day().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_ordinal_day:

+ 
>>> agnostic_ordinal_day(s_pd)
+0      1
+1    216
+dtype: int32
+
+ 
>>> agnostic_ordinal_day(s_pl)
+shape: (2,)
+Series: '' [i16]
+[
+   1
+   216
+]
+
+ + 
>>> agnostic_ordinal_day(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    1,
+    216
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ replace_time_zone(time_zone) + +

+ + +
+ +

Replace time zone.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ time_zone + + str | None + +
+

Target time zone.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with the specified time zone.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime, timezone
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [
+...     datetime(2024, 1, 1, tzinfo=timezone.utc),
+...     datetime(2024, 1, 2, tzinfo=timezone.utc),
+... ]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_replace_time_zone(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.replace_time_zone("Asia/Kathmandu").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_replace_time_zone:

+ 
>>> agnostic_replace_time_zone(s_pd)
+0   2024-01-01 00:00:00+05:45
+1   2024-01-02 00:00:00+05:45
+dtype: datetime64[ns, Asia/Kathmandu]
+
+ 
>>> agnostic_replace_time_zone(s_pl)
+shape: (2,)
+Series: '' [datetime[μs, Asia/Kathmandu]]
+[
+    2024-01-01 00:00:00 +0545
+    2024-01-02 00:00:00 +0545
+]
+
+ 
>>> agnostic_replace_time_zone(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    2023-12-31 18:15:00.000000Z,
+    2024-01-01 18:15:00.000000Z
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ second() + +

+ + +
+ +

Extracts the seconds in a datetime series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the second component of each datetime value.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> dates = [datetime(2022, 1, 1, 5, 3, 10), datetime(2022, 1, 5, 9, 12, 4)]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+>>> s_pa = pa.chunked_array([dates])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_second(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.second().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_second:

+ 
>>> agnostic_second(s_pd)
+0    10
+1     4
+dtype: int...
+
+ 
>>> agnostic_second(s_pl)
+shape: (2,)
+Series: '' [i8]
+[
+   10
+    4
+]
+
+ 
>>> agnostic_second(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    10,
+    4
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ timestamp(time_unit='us') + +

+ + +
+ +

Return a timestamp in the given time unit.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ time_unit + + Literal['ns', 'us', 'ms'] + +
+

{'ns', 'us', 'ms'} +Time unit.

+
+ 		+ 'us' +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with timestamps in the specified time unit.

+
+
+ + +

Examples:

+ 
>>> from datetime import date
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [date(2001, 1, 1), None, date(2001, 1, 3)]
+>>> s_pd = pd.Series(data, dtype="datetime64[ns]")
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_timestamp(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.timestamp("ms").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_timestamp:

+ 
>>> agnostic_timestamp(s_pd)
+0    9.783072e+11
+1             NaN
+2    9.784800e+11
+dtype: float64
+
+ 
>>> agnostic_timestamp(s_pl)
+shape: (3,)
+Series: '' [i64]
+[
+        978307200000
+        null
+        978480000000
+]
+
+ 
>>> agnostic_timestamp(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    978307200000,
+    null,
+    978480000000
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ total_microseconds() + +

+ + +
+ +

Get total microseconds.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the total number of microseconds for each timedelta value.

+
+
+ + +
+ Notes +

The function outputs the total microseconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() in this case.

+
+ +

Examples:

+ 
>>> from datetime import timedelta
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [
+...     timedelta(microseconds=10),
+...     timedelta(milliseconds=1, microseconds=200),
+... ]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_total_microseconds(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.total_microseconds().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_total_microseconds:

+ 
>>> agnostic_total_microseconds(s_pd)
+0      10
+1    1200
+dtype: int...
+
+ 
>>> agnostic_total_microseconds(s_pl)
+shape: (2,)
+Series: '' [i64]
+[
+    10
+    1200
+]
+
+ 
>>> agnostic_total_microseconds(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    10,
+    1200
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ total_milliseconds() + +

+ + +
+ +

Get total milliseconds.

+ + +
+ Notes +

The function outputs the total milliseconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() in this case.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the total number of milliseconds for each timedelta value.

+
+
+ + +

Examples:

+ 
>>> from datetime import timedelta
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [
+...     timedelta(milliseconds=10),
+...     timedelta(milliseconds=20, microseconds=40),
+... ]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_total_milliseconds(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.total_milliseconds().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_total_milliseconds:

+ 
>>> agnostic_total_milliseconds(s_pd)
+0    10
+1    20
+dtype: int...
+
+ 
>>> agnostic_total_milliseconds(s_pl)
+shape: (2,)
+Series: '' [i64]
+[
+        10
+        20
+]
+
+ 
>>> agnostic_total_milliseconds(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    10,
+    20
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ total_minutes() + +

+ + +
+ +

Get total minutes.

+ + +
+ Notes +

The function outputs the total minutes in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() in this case.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the total number of minutes for each timedelta value.

+
+
+ + +

Examples:

+ 
>>> from datetime import timedelta
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [timedelta(minutes=10), timedelta(minutes=20, seconds=40)]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_total_minutes(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.total_minutes().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_total_minutes:

+ 
>>> agnostic_total_minutes(s_pd)
+0    10
+1    20
+dtype: int...
+
+ 
>>> agnostic_total_minutes(s_pl)
+shape: (2,)
+Series: '' [i64]
+[
+        10
+        20
+]
+
+ 
>>> agnostic_total_minutes(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    10,
+    20
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ total_nanoseconds() + +

+ + +
+ +

Get total nanoseconds.

+ + +
+ Notes +

The function outputs the total nanoseconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() in this case.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the total number of nanoseconds for each timedelta value.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["2024-01-01 00:00:00.000000001", "2024-01-01 00:00:00.000000002"]
+>>> s_pd = pd.to_datetime(pd.Series(data))
+>>> s_pl = pl.Series(data).str.to_datetime(time_unit="ns")
+
+

We define a library agnostic function:

+ 
>>> def agnostic_total_nanoseconds(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.diff().dt.total_nanoseconds().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_total_nanoseconds:

+ 
>>> agnostic_total_nanoseconds(s_pd)
+0    NaN
+1    1.0
+dtype: float64
+
+ 
>>> agnostic_total_nanoseconds(s_pl)
+shape: (2,)
+Series: '' [i64]
+[
+        null
+        1
+]
+
+ +
+ +
+ +
+ + +

+ total_seconds() + +

+ + +
+ +

Get total seconds.

+ + +
+ Notes +

The function outputs the total seconds in the int dtype by default, +however, pandas may change the dtype to float when there are missing values, +consider using fill_null() in this case.

+
+ +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the total number of seconds for each timedelta value.

+
+
+ + +

Examples:

+ 
>>> from datetime import timedelta
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [timedelta(seconds=10), timedelta(seconds=20, milliseconds=40)]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_total_seconds(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.total_seconds().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_total_seconds:

+ 
>>> agnostic_total_seconds(s_pd)
+0    10
+1    20
+dtype: int...
+
+ 
>>> agnostic_total_seconds(s_pl)
+shape: (2,)
+Series: '' [i64]
+[
+        10
+        20
+]
+
+ 
>>> agnostic_total_seconds(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    10,
+    20
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ to_string(format) + +

+ + +
+ +

Convert a Date/Time/Datetime series into a String series with the given format.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ format + + str + +
+

Format string for converting the datetime to string.

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with the datetime values formatted as strings according to the specified format.

+
+
+ + +
+ Notes +

Unfortunately, different libraries interpret format directives a bit +differently.

+
    +
  • Chrono, the library used by Polars, uses "%.f" for fractional seconds, + whereas pandas and Python stdlib use ".%f".
    • +
  • PyArrow interprets "%S" as "seconds, including fractional seconds" + whereas most other tools interpret it as "just seconds, as 2 digits".
    • +
+

Therefore, we make the following adjustments:

+
    +
  • for pandas-like libraries, we replace "%S.%f" with "%S%.f".
    • +
  • for PyArrow, we replace "%S.%f" with "%S".
    • +
+

Workarounds like these don't make us happy, and we try to avoid them as +much as possible, but here we feel like it's the best compromise.

+

If you just want to format a date/datetime Series as a local datetime +string, and have it work as consistently as possible across libraries, +we suggest using:

+
    +
  • "%Y-%m-%dT%H:%M:%S%.f" for datetimes
    • +
  • "%Y-%m-%d" for dates
    • +
+

though note that, even then, different tools may return a different number +of trailing zeros. Nonetheless, this is probably consistent enough for +most applications.

+

If you have an application where this is not enough, please open an issue +and let us know.

+
+ +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [
+...     datetime(2020, 3, 1),
+...     datetime(2020, 4, 1),
+...     datetime(2020, 5, 1),
+... ]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_to_string(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.to_string("%Y/%m/%d").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_to_string:

+ 
>>> agnostic_to_string(s_pd)
+0    2020/03/01
+1    2020/04/01
+2    2020/05/01
+dtype: object
+
+ 
>>> agnostic_to_string(s_pl)
+shape: (3,)
+Series: '' [str]
+[
+   "2020/03/01"
+   "2020/04/01"
+   "2020/05/01"
+]
+
+ 
>>> agnostic_to_string(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    "2020/03/01",
+    "2020/04/01",
+    "2020/05/01"
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ weekday() + +

+ + +
+ +

Extract the week day in a datetime series.

+ + +

Returns:

+ + + + + + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the week day for each datetime value.

+
+
+ SeriesT + +
+

Returns the ISO weekday number where monday = 1 and sunday = 7

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+>>> data = [datetime(2020, 1, 1), datetime(2020, 8, 3)]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_weekday(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.weekday().to_native()
+
+

We can then pass either pandas, Polars, PyArrow, and other supported libraries to agnostic_weekday:

+ 
>>> agnostic_weekday(s_pd)
+0    3
+1    1
+dtype: int32
+>>> agnostic_weekday(s_pl)
+shape: (2,)
+Series: '' [i8]
+[
+   3
+   1
+]
+>>> agnostic_weekday(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    3,
+    1
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ year() + +

+ + +
+ +

Get the year in a datetime series.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the year component of each datetime value.

+
+
+ + +

Examples:

+ 
>>> from datetime import datetime
+>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> dates = [datetime(2012, 1, 7), datetime(2023, 3, 10)]
+>>> s_pd = pd.Series(dates)
+>>> s_pl = pl.Series(dates)
+>>> s_pa = pa.chunked_array([dates])
+
+

We define a library agnostic function:

+ 
>>> def agnostic_year(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.dt.year().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_year:

+ 
>>> agnostic_year(s_pd)
+0    2012
+1    2023
+dtype: int...
+
+ 
>>> agnostic_year(s_pl)
+shape: (2,)
+Series: '' [i32]
+[
+   2012
+   2023
+]
+
+ 
>>> agnostic_year(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    2012,
+    2023
+  ]
+]
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/series_list/index.html b/api-reference/series_list/index.html new file mode 100644 index 000000000..4b8200f98 --- /dev/null +++ b/api-reference/series_list/index.html @@ -0,0 +1,2044 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Series.list - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Series.list

+ + +
+ + + + +
+ + + + + + + + + + +
+ + + + + + + + + +
+ + +

+ len() + +

+ + +
+ +

Return the number of elements in each list.

+

Null values count towards the total.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new series.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = [[1, 2], [3, 4, None], None, []]
+
+

Let's define a dataframe-agnostic function:

+ 
>>> def agnostic_list_len(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.list.len().to_native()
+
+

We can then pass pandas / PyArrow / Polars / any other supported library:

+ 
>>> agnostic_list_len(
+...     pd.Series(data, dtype=pd.ArrowDtype(pa.list_(pa.int64())))
+... )
+0       2
+1       3
+2    <NA>
+3       0
+dtype: int32[pyarrow]
+
+ 
>>> agnostic_list_len(pl.Series(data))
+shape: (4,)
+Series: '' [u32]
+[
+   2
+   3
+   null
+   0
+]
+
+ 
>>> agnostic_list_len(pa.chunked_array([data]))
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    2,
+    3,
+    null,
+    0
+  ]
+]
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/series_str/index.html b/api-reference/series_str/index.html new file mode 100644 index 000000000..afb60d013 --- /dev/null +++ b/api-reference/series_str/index.html @@ -0,0 +1,3798 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.Series.str - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.Series.str

+ + +
+ + + + +
+ + + + + + + + + + +
+ + + + + + + + + +
+ + +

+ contains(pattern, *, literal=False) + +

+ + +
+ +

Check if string contains a substring that matches a pattern.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ pattern + + str + +
+

A Character sequence or valid regular expression pattern.

+
+ 		+ required +
+ literal + + bool + +
+

If True, treats the pattern as a literal string. + If False, assumes the pattern is a regular expression.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with boolean values indicating if each string contains the pattern.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["cat", "dog", "rabbit and parrot", "dove", None]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_contains(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.str.contains("parrot|dove").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or PyArrow to agnostic_contains:

+ 
>>> agnostic_contains(s_pd)
+0    False
+1    False
+2     True
+3     True
+4     None
+dtype: object
+
+ 
>>> agnostic_contains(s_pl)
+shape: (5,)
+Series: '' [bool]
+[
+   false
+   false
+   true
+   true
+   null
+]
+
+ 
>>> agnostic_contains(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    false,
+    false,
+    true,
+    true,
+    null
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ ends_with(suffix) + +

+ + +
+ +

Check if string values end with a substring.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ suffix + + str + +
+

suffix substring

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with boolean values indicating if each string ends with the suffix.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["apple", "mango", None]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_ends_with(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.str.ends_with("ngo").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_ends_with:

+ 
>>> agnostic_ends_with(s_pd)
+0    False
+1     True
+2     None
+dtype: object
+
+ 
>>> agnostic_ends_with(s_pl)
+shape: (3,)
+Series: '' [bool]
+[
+   false
+   true
+   null
+]
+
+ 
>>> agnostic_ends_with(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    false,
+    true,
+    null
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ head(n=5) + +

+ + +
+ +

Take the first n elements of each string.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of elements to take. Negative indexing is supported (see note (1.))

+
+ 		+ 5 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the first n characters of each string.

+
+
+ + +
+ Notes +
    +
  1. When the n input is negative, head returns characters up to the n-th from the end of the string. + For example, if n = -3, then all characters except the last three are returned.
    2. +
  2. If the length of the string has fewer than n characters, the full string is returned.
    3. +
+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["Atatata", "taata", "taatatata", "zukkyun"]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_head(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.str.head().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_head:

+ 
>>> agnostic_head(s_pd)
+0    Atata
+1    taata
+2    taata
+3    zukky
+dtype: object
+
+ 
>>> agnostic_head(s_pl)
+shape: (4,)
+Series: '' [str]
+[
+   "Atata"
+   "taata"
+   "taata"
+   "zukky"
+]
+
+ 
>>> agnostic_head(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    "Atata",
+    "taata",
+    "taata",
+    "zukky"
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ len_chars() + +

+ + +
+ +

Return the length of each string as the number of characters.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the length of each string in characters.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["foo", "Café", "345", "東京", None]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_len_chars(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.str.len_chars().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_len_chars:

+ 
>>> agnostic_len_chars(s_pd)
+0    3.0
+1    4.0
+2    3.0
+3    2.0
+4    NaN
+dtype: float64
+
+ 
>>> agnostic_len_chars(s_pl)
+shape: (5,)
+Series: '' [u32]
+[
+   3
+   4
+   3
+   2
+   null
+]
+
+ 
>>> agnostic_len_chars(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    3,
+    4,
+    3,
+    2,
+    null
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ replace(pattern, value, *, literal=False, n=1) + +

+ + +
+ +

Replace first matching regex/literal substring with a new string value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ pattern + + str + +
+

A valid regular expression pattern.

+
+ 		+ required +
+ value + + str + +
+

String that will replace the matched substring.

+
+ 		+ required +
+ literal + + bool + +
+

Treat pattern as a literal string.

+
+ 		+ False +
+ n + + int + +
+

Number of matches to replace.

+
+ 		+ 1 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with the regex/literal pattern replaced with the specified value.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["123abc", "abc abc123"]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_replace(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     s = s.str.replace("abc", "")
+...     return s.to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_replace:

+ 
>>> agnostic_replace(s_pd)
+0        123
+1     abc123
+dtype: object
+
+ 
>>> agnostic_replace(s_pl)
+shape: (2,)
+Series: '' [str]
+[
+    "123"
+    " abc123"
+]
+
+ 
>>> agnostic_replace(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    "123",
+    " abc123"
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ replace_all(pattern, value, *, literal=False) + +

+ + +
+ +

Replace all matching regex/literal substring with a new string value.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ pattern + + str + +
+

A valid regular expression pattern.

+
+ 		+ required +
+ value + + str + +
+

String that will replace the matched substring.

+
+ 		+ required +
+ literal + + bool + +
+

Treat pattern as a literal string.

+
+ 		+ False +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with all occurrences of pattern replaced with the specified value.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["123abc", "abc abc123"]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_replace_all(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     s = s.str.replace_all("abc", "")
+...     return s.to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_replace_all:

+ 
>>> agnostic_replace_all(s_pd)
+0     123
+1     123
+dtype: object
+
+ 
>>> agnostic_replace_all(s_pl)
+shape: (2,)
+Series: '' [str]
+[
+    "123"
+    " 123"
+]
+
+ 
>>> agnostic_replace_all(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    "123",
+    " 123"
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ slice(offset, length=None) + +

+ + +
+ +

Create subslices of the string values of a Series.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ offset + + int + +
+

Start index. Negative indexing is supported.

+
+ 		+ required +
+ length + + int | None + +
+

Length of the slice. If set to None (default), the slice is taken to the +end of the string.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing subslices of each string.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["pear", None, "papaya", "dragonfruit"]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_slice(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.str.slice(4, length=3).to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_slice:

+ 
>>> agnostic_slice(s_pd)
+0
+1    None
+2      ya
+3     onf
+dtype: object
+
+ 
>>> agnostic_slice(s_pl)
+shape: (4,)
+Series: '' [str]
+[
+   ""
+   null
+   "ya"
+   "onf"
+]
+
+ 
>>> agnostic_slice(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    "",
+    null,
+    "ya",
+    "onf"
+  ]
+]
+
+

Using negative indexes:

+ 
>>> def agnostic_slice(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.str.slice(-3).to_native()
+
+ 
>>> agnostic_slice(s_pd)
+0     ear
+1    None
+2     aya
+3     uit
+dtype: object
+
+ 
>>> agnostic_slice(s_pl)
+shape: (4,)
+Series: '' [str]
+[
+    "ear"
+    null
+    "aya"
+    "uit"
+]
+
+ 
>>> agnostic_slice(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    "ear",
+    null,
+    "aya",
+    "uit"
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ starts_with(prefix) + +

+ + +
+ +

Check if string values start with a substring.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ prefix + + str + +
+

prefix substring

+
+ 		+ required +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with boolean values indicating if each string starts with the prefix.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["apple", "mango", None]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_starts_with(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.str.starts_with("app").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_starts_with:

+ 
>>> agnostic_starts_with(s_pd)
+0     True
+1    False
+2     None
+dtype: object
+
+ 
>>> agnostic_starts_with(s_pl)
+shape: (3,)
+Series: '' [bool]
+[
+   true
+   false
+   null
+]
+
+ 
>>> agnostic_starts_with(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    true,
+    false,
+    null
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ strip_chars(characters=None) + +

+ + +
+ +

Remove leading and trailing characters.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ characters + + str | None + +
+

The set of characters to be removed. All combinations of this set of characters will be stripped from the start and end of the string. If set to None (default), all leading and trailing whitespace is removed instead.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with leading and trailing characters removed.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["apple", "\nmango"]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_strip_chars(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     s = s.str.strip_chars()
+...     return s.to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_strip_chars:

+ 
>>> agnostic_strip_chars(s_pd)
+0    apple
+1    mango
+dtype: object
+
+ 
>>> agnostic_strip_chars(s_pl)
+shape: (2,)
+Series: '' [str]
+[
+    "apple"
+    "mango"
+]
+
+ 
>>> agnostic_strip_chars(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    "apple",
+    "mango"
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ tail(n=5) + +

+ + +
+ +

Take the last n elements of each string.

+ + +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ n + + int + +
+

Number of elements to take. Negative indexing is supported (see note (1.))

+
+ 		+ 5 +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series containing the last n characters of each string.

+
+
+ + +
+ Notes +
    +
  1. When the n input is negative, tail returns characters starting from the n-th from the beginning of + the string. For example, if n = -3, then all characters except the first three are returned.
    2. +
  2. If the length of the string has fewer than n characters, the full string is returned.
    3. +
+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["Atatata", "taata", "taatatata", "zukkyun"]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_tail(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.str.tail().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_tail:

+ 
>>> agnostic_tail(s_pd)
+0    atata
+1    taata
+2    atata
+3    kkyun
+dtype: object
+
+ 
>>> agnostic_tail(s_pl)
+shape: (4,)
+Series: '' [str]
+[
+   "atata"
+   "taata"
+   "atata"
+   "kkyun"
+]
+
+ 
>>> agnostic_tail(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    "atata",
+    "taata",
+    "atata",
+    "kkyun"
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ to_datetime(format=None) + +

+ + +
+ +

Parse Series with strings to a Series with Datetime dtype.

+ + +
+ Notes +

pandas defaults to nanosecond time unit, Polars to microsecond. +Prior to pandas 2.0, nanoseconds were the only time unit supported +in pandas, with no ability to set any other one. The ability to +set the time unit in pandas, if the version permits, will arrive.

+
+ +
+ Warning +

As different backends auto-infer format in different ways, if format=None +there is no guarantee that the result will be equal.

+
+ +

Parameters:

+ + + + + + + + + + + + + + + + + +
NameTypeDescriptionDefault
+ format + + str | None + +
+

Format to use for conversion. If set to None (default), the format is +inferred from the data.

+
+ 		+ None +
+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with datetime dtype.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["2020-01-01", "2020-01-02"]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_to_datetime(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.str.to_datetime(format="%Y-%m-%d").to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_to_datetime:

+ 
>>> agnostic_to_datetime(s_pd)
+0   2020-01-01
+1   2020-01-02
+dtype: datetime64[ns]
+
+ 
>>> agnostic_to_datetime(s_pl)
+shape: (2,)
+Series: '' [datetime[μs]]
+[
+   2020-01-01 00:00:00
+   2020-01-02 00:00:00
+]
+
+ 
>>> agnostic_to_datetime(s_pa)
+<pyarrow.lib.ChunkedArray object at 0x...>
+[
+  [
+    2020-01-01 00:00:00.000000,
+    2020-01-02 00:00:00.000000
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ to_lowercase() + +

+ + +
+ +

Transform string to lowercase variant.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with values converted to lowercase.

+
+
+ + +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["APPLE", "MANGO", None]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_to_lowercase(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.str.to_lowercase().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_to_lowercase:

+ 
>>> agnostic_to_lowercase(s_pd)
+0    apple
+1    mango
+2     None
+dtype: object
+
+ 
>>> agnostic_to_lowercase(s_pl)
+shape: (3,)
+Series: '' [str]
+[
+   "apple"
+   "mango"
+   null
+]
+
+ 
>>> agnostic_to_lowercase(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    "apple",
+    "mango",
+    null
+  ]
+]
+
+ +
+ +
+ +
+ + +

+ to_uppercase() + +

+ + +
+ +

Transform string to uppercase variant.

+ + +

Returns:

+ + + + + + + + + + + + + +
TypeDescription
+ SeriesT + +
+

A new Series with values converted to uppercase.

+
+
+ + +
+ Notes +

The PyArrow backend will convert 'ß' to 'ẞ' instead of 'SS'. +For more info see: https://github.com/apache/arrow/issues/34599 +There may be other unicode-edge-case-related variations across implementations.

+
+ +

Examples:

+ 
>>> import pandas as pd
+>>> import polars as pl
+>>> import pyarrow as pa
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+
+ 
>>> data = ["apple", "mango", None]
+>>> s_pd = pd.Series(data)
+>>> s_pl = pl.Series(data)
+>>> s_pa = pa.chunked_array([data])
+
+

We define a dataframe-agnostic function:

+ 
>>> def agnostic_to_uppercase(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.str.to_uppercase().to_native()
+
+

We can then pass any supported library such as pandas, Polars, or +PyArrow to agnostic_to_uppercase:

+ 
>>> agnostic_to_uppercase(s_pd)
+0    APPLE
+1    MANGO
+2     None
+dtype: object
+
+ 
>>> agnostic_to_uppercase(s_pl)
+shape: (3,)
+Series: '' [str]
+[
+   "APPLE"
+   "MANGO"
+   null
+]
+
+ 
>>> agnostic_to_uppercase(s_pa)
+<pyarrow.lib.ChunkedArray object at ...>
+[
+  [
+    "APPLE",
+    "MANGO",
+    null
+  ]
+]
+
+ +
+ +
+ + + +
+ +
+ +
+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/api-reference/typing/index.html b/api-reference/typing/index.html new file mode 100644 index 000000000..8b4a6f0ee --- /dev/null +++ b/api-reference/typing/index.html @@ -0,0 +1,2483 @@ + + + + + + + + + + + + + + + + + + + + + + + narwhals.typing - Narwhals + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

narwhals.typing

+

Narwhals comes fully statically typed. In addition to nw.DataFrame, nw.Expr, +nw.Series, nw.LazyFrame, we also provide the following type hints:

+ + +
+ + + + +
+ + + + + + + + +
+ + + + + + + +
+ + + +

+ DataFrameT = TypeVar('DataFrameT', bound='DataFrame[Any]') + + + module-attribute + + +

+ + +
+ +

TypeVar bound to Narwhals DataFrame.

+

Use this if your function can accept a Narwhals DataFrame and returns a Narwhals +DataFrame backed by the same backend.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> from narwhals.typing import DataFrameT
+>>> @nw.narwhalify
+>>> def func(df: DataFrameT) -> DataFrameT:
+...     return df.with_columns(c=df["a"] + 1)
+
+
+ +
+ +
+ + + +

+ Frame = Union['DataFrame[Any]', 'LazyFrame[Any]'] + + + module-attribute + + +

+ + +
+ +

Narwhals DataFrame or Narwhals LazyFrame.

+

Use this if your function can work with either and your function doesn't care +about its backend.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> from narwhals.typing import Frame
+>>> @nw.narwhalify
+... def agnostic_columns(df: Frame) -> list[str]:
+...     return df.columns
+
+
+ +
+ +
+ + + +

+ FrameT = TypeVar('FrameT', bound='Frame') + + + module-attribute + + +

+ + +
+ +

TypeVar bound to Narwhals DataFrame or Narwhals LazyFrame.

+

Use this if your function accepts either nw.DataFrame or nw.LazyFrame and returns +an object of the same kind.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> from narwhals.typing import FrameT
+>>> @nw.narwhalify
+... def agnostic_func(df: FrameT) -> FrameT:
+...     return df.with_columns(c=nw.col("a") + 1)
+
+
+ +
+ +
+ + + +

+ IntoDataFrame = Union['NativeFrame', 'DataFrame[Any]', 'DataFrameLike'] + + + module-attribute + + +

+ + +
+ +

Anything which can be converted to a Narwhals DataFrame.

+

Use this if your function accepts a narwhalifiable object but doesn't care about its backend.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrame
+>>> def agnostic_shape(df_native: IntoDataFrame) -> tuple[int, int]:
+...     df = nw.from_native(df_native, eager_only=True)
+...     return df.shape
+
+
+ +
+ +
+ + + +

+ IntoDataFrameT = TypeVar('IntoDataFrameT', bound='IntoDataFrame') + + + module-attribute + + +

+ + +
+ +

TypeVar bound to object convertible to Narwhals DataFrame.

+

Use this if your function accepts an object which can be converted to nw.DataFrame +and returns an object of the same class.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> from narwhals.typing import IntoDataFrameT
+>>> def agnostic_func(df_native: IntoDataFrameT) -> IntoDataFrameT:
+...     df = nw.from_native(df_native, eager_only=True)
+...     return df.with_columns(c=df["a"] + 1).to_native()
+
+
+ +
+ +
+ + + +

+ IntoExpr = Union['Expr', str, 'Series[Any]'] + + + module-attribute + + +

+ + +
+ +

Anything which can be converted to an expression.

+

Use this to mean "either a Narwhals expression, or something which can be converted +into one". For example, exprs in DataFrame.select is typed to accept IntoExpr, +as it can either accept a nw.Expr (e.g. df.select(nw.col('a'))) or a string +which will be interpreted as a nw.Expr, e.g. df.select('a').

+
+ +
+ +
+ + + +

+ IntoFrame = Union['NativeFrame', 'DataFrame[Any]', 'LazyFrame[Any]', 'DataFrameLike'] + + + module-attribute + + +

+ + +
+ +

Anything which can be converted to a Narwhals DataFrame or LazyFrame.

+

Use this if your function can accept an object which can be converted to either +nw.DataFrame or nw.LazyFrame and it doesn't care about its backend.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrame
+>>> def agnostic_columns(df_native: IntoFrame) -> list[str]:
+...     df = nw.from_native(df_native)
+...     return df.collect_schema().names()
+
+
+ +
+ +
+ + + +

+ IntoFrameT = TypeVar('IntoFrameT', bound='IntoFrame') + + + module-attribute + + +

+ + +
+ +

TypeVar bound to object convertible to Narwhals DataFrame or Narwhals LazyFrame.

+

Use this if your function accepts an object which is convertible to nw.DataFrame +or nw.LazyFrame and returns an object of the same type.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> from narwhals.typing import IntoFrameT
+>>> def agnostic_func(df_native: IntoFrameT) -> IntoFrameT:
+...     df = nw.from_native(df_native)
+...     return df.with_columns(c=nw.col("a") + 1).to_native()
+
+
+ +
+ +
+ + + +

+ IntoSeries = Union['Series[Any]', 'NativeSeries'] + + + module-attribute + + +

+ + +
+ +

Anything which can be converted to a Narwhals Series.

+

Use this if your function can accept an object which can be converted to nw.Series +and it doesn't care about its backend.

+ + +

Examples:

+ 
>>> from typing import Any
+>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeries
+>>> def agnostic_to_list(s_native: IntoSeries) -> list[Any]:
+...     s = nw.from_native(s_native)
+...     return s.to_list()
+
+
+ +
+ +
+ + + +

+ IntoSeriesT = TypeVar('IntoSeriesT', bound='IntoSeries') + + + module-attribute + + +

+ + +
+ +

TypeVar bound to object convertible to Narwhals Series.

+

Use this if your function accepts an object which can be converted to nw.Series +and returns an object of the same class.

+ + +

Examples:

+ 
>>> import narwhals as nw
+>>> from narwhals.typing import IntoSeriesT
+>>> def agnostic_abs(s_native: IntoSeriesT) -> IntoSeriesT:
+...     s = nw.from_native(s_native, series_only=True)
+...     return s.abs().to_native()
+
+
+ +
+ + + + + +
+ +
+ +

nw.narwhalify, or nw.from_native?

+

Although some people find the former more readable, the latter is better at preserving type hints.

+

Here's an example: +

import polars as pl
+import narwhals as nw
+from narwhals.typing import IntoDataFrameT, DataFrameT
+
+df = pl.DataFrame({"a": [1, 2, 3]})
+
+
+def func(df_native: IntoDataFrameT) -> IntoDataFrameT:
+    df = nw.from_native(df_native, eager_only=True)
+    return df.select(b=nw.col("a")).to_native()
+
+
+reveal_type(func(df))
+
+
+@nw.narwhalify(strict=True)
+def func_2(df: DataFrameT) -> DataFrameT:
+    return df.select(b=nw.col("a"))
+
+
+reveal_type(func_2(df))
+

+

Running mypy on it gives: +

$ mypy t.py 
+t.py:13: note: Revealed type is "polars.dataframe.frame.DataFrame"
+t.py:21: note: Revealed type is "Any"
+Success: no issues found in 1 source file
+

+

In the first case, mypy can infer that df is a polars.DataFrame. In the second case, it can't.

+

If you want to make the most out of type hints and preserve them as much as possible, we recommend +nw.from_native and nw.to_native. Type hints will still be respected +inside the function body if you type the arguments.

+ + + + + + + + + + + + + +
+
+ + + +
+ + + +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/assets/_mkdocstrings.css b/assets/_mkdocstrings.css new file mode 100644 index 000000000..b500381b5 --- /dev/null +++ b/assets/_mkdocstrings.css @@ -0,0 +1,143 @@ + +/* Avoid breaking parameter names, etc. in table cells. */ +.doc-contents td code { + word-break: normal !important; +} + +/* No line break before first paragraph of descriptions. */ +.doc-md-description, +.doc-md-description>p:first-child { + display: inline; +} + +/* Max width for docstring sections tables. */ +.doc .md-typeset__table, +.doc .md-typeset__table table { + display: table !important; + width: 100%; +} + +.doc .md-typeset__table tr { + display: table-row; +} + +/* Defaults in Spacy table style. */ +.doc-param-default { + float: right; +} + +/* Parameter headings must be inline, not blocks. */ +.doc-heading-parameter { + display: inline; +} + +/* Prefer space on the right, not the left of parameter permalinks. */ 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l=R(`label[for="${c.id}"]`);l.replaceChildren(x("a",{href:`#${l.htmlFor}`,tabIndex:-1},...Array.from(l.childNodes))),h(l.firstElementChild,"click").pipe(W(p),b(f=>!(f.metaKey||f.ctrlKey)),w(f=>{f.preventDefault(),f.stopPropagation()})).subscribe(()=>{history.replaceState({},"",`#${l.htmlFor}`),l.click()})}return B("content.tabs.link")&&s.pipe(Ce(1),re(t)).subscribe(([{active:c},{offset:l}])=>{let f=c.innerText.trim();if(c.hasAttribute("data-md-switching"))c.removeAttribute("data-md-switching");else{let u=e.offsetTop-l.y;for(let y of P("[data-tabs]"))for(let L of P(":scope > input",y)){let X=R(`label[for="${L.id}"]`);if(X!==c&&X.innerText.trim()===f){X.setAttribute("data-md-switching",""),L.click();break}}window.scrollTo({top:e.offsetTop-u});let d=__md_get("__tabs")||[];__md_set("__tabs",[...new Set([f,...d])])}}),s.pipe(W(p)).subscribe(()=>{for(let c of P("audio, video",e))c.pause()}),Ya(n).pipe(w(c=>s.next(c)),_(()=>s.complete()),m(c=>$({ref:e},c)))}).pipe(Ke(se))}function zn(e,{viewport$:t,target$:r,print$:o}){return O(...P(".annotate:not(.highlight)",e).map(n=>Pn(n,{target$:r,print$:o})),...P("pre:not(.mermaid) > code",e).map(n=>jn(n,{target$:r,print$:o})),...P("pre.mermaid",e).map(n=>Wn(n)),...P("table:not([class])",e).map(n=>Vn(n)),...P("details",e).map(n=>Fn(n,{target$:r,print$:o})),...P("[data-tabs]",e).map(n=>Nn(n,{viewport$:t,target$:r})),...P("[title]",e).filter(()=>B("content.tooltips")).map(n=>mt(n,{viewport$:t})))}function Ba(e,{alert$:t}){return t.pipe(v(r=>O(I(!0),I(!1).pipe(Ge(2e3))).pipe(m(o=>({message:r,active:o})))))}function qn(e,t){let r=R(".md-typeset",e);return C(()=>{let o=new g;return o.subscribe(({message:n,active:i})=>{e.classList.toggle("md-dialog--active",i),r.textContent=n}),Ba(e,t).pipe(w(n=>o.next(n)),_(()=>o.complete()),m(n=>$({ref:e},n)))})}var Ga=0;function Ja(e,t){document.body.append(e);let{width:r}=ce(e);e.style.setProperty("--md-tooltip-width",`${r}px`),e.remove();let o=cr(t),n=typeof 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Xa({viewport$:e}){if(!B("header.autohide"))return I(!1);let t=e.pipe(m(({offset:{y:n}})=>n),Be(2,1),m(([n,i])=>[nMath.abs(i-n.y)>100),m(([,[n]])=>n),K()),o=ze("search");return z([e,o]).pipe(m(([{offset:n},i])=>n.y>400&&!i),K(),v(n=>n?r:I(!1)),Q(!1))}function Kn(e,t){return C(()=>z([ge(e),Xa(t)])).pipe(m(([{height:r},o])=>({height:r,hidden:o})),K((r,o)=>r.height===o.height&&r.hidden===o.hidden),G(1))}function Yn(e,{header$:t,main$:r}){return C(()=>{let o=new g,n=o.pipe(Z(),ie(!0));o.pipe(ee("active"),He(t)).subscribe(([{active:a},{hidden:s}])=>{e.classList.toggle("md-header--shadow",a&&!s),e.hidden=s});let i=ue(P("[title]",e)).pipe(b(()=>B("content.tooltips")),ne(a=>Qn(a)));return r.subscribe(o),t.pipe(W(n),m(a=>$({ref:e},a)),Re(i.pipe(W(n))))})}function Za(e,{viewport$:t,header$:r}){return mr(e,{viewport$:t,header$:r}).pipe(m(({offset:{y:o}})=>{let{height:n}=ce(e);return{active:o>=n}}),ee("active"))}function Bn(e,t){return C(()=>{let r=new g;r.subscribe({next({active:n}){e.classList.toggle("md-header__title--active",n)},complete(){e.classList.remove("md-header__title--active")}});let o=fe(".md-content h1");return typeof o=="undefined"?S:Za(o,t).pipe(w(n=>r.next(n)),_(()=>r.complete()),m(n=>$({ref:e},n)))})}function Gn(e,{viewport$:t,header$:r}){let o=r.pipe(m(({height:i})=>i),K()),n=o.pipe(v(()=>ge(e).pipe(m(({height:i})=>({top:e.offsetTop,bottom:e.offsetTop+i})),ee("bottom"))));return z([o,n,t]).pipe(m(([i,{top:a,bottom:s},{offset:{y:p},size:{height:c}}])=>(c=Math.max(0,c-Math.max(0,a-p,i)-Math.max(0,c+p-s)),{offset:a-i,height:c,active:a-i<=p})),K((i,a)=>i.offset===a.offset&&i.height===a.height&&i.active===a.active))}function es(e){let t=__md_get("__palette")||{index:e.findIndex(o=>matchMedia(o.getAttribute("data-md-color-media")).matches)},r=Math.max(0,Math.min(t.index,e.length-1));return I(...e).pipe(ne(o=>h(o,"change").pipe(m(()=>o))),Q(e[r]),m(o=>({index:e.indexOf(o),color:{media:o.getAttribute("data-md-color-media"),scheme:o.getAttribute("data-md-color-scheme"),primary:o.getAttribute("data-md-color-primary"),accent:o.getAttribute("data-md-color-accent")}})),G(1))}function Jn(e){let t=P("input",e),r=x("meta",{name:"theme-color"});document.head.appendChild(r);let o=x("meta",{name:"color-scheme"});document.head.appendChild(o);let n=Pt("(prefers-color-scheme: light)");return C(()=>{let i=new g;return i.subscribe(a=>{if(document.body.setAttribute("data-md-color-switching",""),a.color.media==="(prefers-color-scheme)"){let s=matchMedia("(prefers-color-scheme: light)"),p=document.querySelector(s.matches?"[data-md-color-media='(prefers-color-scheme: light)']":"[data-md-color-media='(prefers-color-scheme: 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g,n=on(e.parentElement).pipe(b(Boolean)),i=e.parentElement,a=R(":scope > :first-child",e),s=R(":scope > :last-child",e);ze("search").subscribe(l=>s.setAttribute("role",l?"list":"presentation")),o.pipe(re(r),Wr(t.pipe(Ae(jt)))).subscribe(([{items:l},{value:f}])=>{switch(l.length){case 0:a.textContent=f.length?Ee("search.result.none"):Ee("search.result.placeholder");break;case 1:a.textContent=Ee("search.result.one");break;default:let u=sr(l.length);a.textContent=Ee("search.result.other",u)}});let p=o.pipe(w(()=>s.innerHTML=""),v(({items:l})=>O(I(...l.slice(0,10)),I(...l.slice(10)).pipe(Be(4),Vr(n),v(([f])=>f)))),m(Mn),pe());return p.subscribe(l=>s.appendChild(l)),p.pipe(ne(l=>{let f=fe("details",l);return typeof f=="undefined"?S:h(f,"toggle").pipe(W(o),m(()=>f))})).subscribe(l=>{l.open===!1&&l.offsetTop<=i.scrollTop&&i.scrollTo({top:l.offsetTop})}),t.pipe(b(dr),m(({data:l})=>l)).pipe(w(l=>o.next(l)),_(()=>o.complete()),m(l=>$({ref:e},l)))}function ms(e,{query$:t}){return t.pipe(m(({value:r})=>{let o=ye();return o.hash="",r=r.replace(/\s+/g,"+").replace(/&/g,"%26").replace(/=/g,"%3D"),o.search=`q=${r}`,{url:o}}))}function mi(e,t){let r=new g,o=r.pipe(Z(),ie(!0));return r.subscribe(({url:n})=>{e.setAttribute("data-clipboard-text",e.href),e.href=`${n}`}),h(e,"click").pipe(W(o)).subscribe(n=>n.preventDefault()),ms(e,t).pipe(w(n=>r.next(n)),_(()=>r.complete()),m(n=>$({ref:e},n)))}function fi(e,{worker$:t,keyboard$:r}){let o=new g,n=Se("search-query"),i=O(h(n,"keydown"),h(n,"focus")).pipe(ve(se),m(()=>n.value),K());return o.pipe(He(i),m(([{suggest:s},p])=>{let c=p.split(/([\s-]+)/);if(s!=null&&s.length&&c[c.length-1]){let l=s[s.length-1];l.startsWith(c[c.length-1])&&(c[c.length-1]=l)}else c.length=0;return c})).subscribe(s=>e.innerHTML=s.join("").replace(/\s/g," ")),r.pipe(b(({mode:s})=>s==="search")).subscribe(s=>{switch(s.type){case"ArrowRight":e.innerText.length&&n.selectionStart===n.value.length&&(n.value=e.innerText);break}}),t.pipe(b(dr),m(({data:s})=>s)).pipe(w(s=>o.next(s)),_(()=>o.complete()),m(()=>({ref:e})))}function ui(e,{index$:t,keyboard$:r}){let o=xe();try{let n=ai(o.search,t),i=Se("search-query",e),a=Se("search-result",e);h(e,"click").pipe(b(({target:p})=>p instanceof Element&&!!p.closest("a"))).subscribe(()=>Je("search",!1)),r.pipe(b(({mode:p})=>p==="search")).subscribe(p=>{let c=Ie();switch(p.type){case"Enter":if(c===i){let l=new Map;for(let f of P(":first-child [href]",a)){let u=f.firstElementChild;l.set(f,parseFloat(u.getAttribute("data-md-score")))}if(l.size){let[[f]]=[...l].sort(([,u],[,d])=>d-u);f.click()}p.claim()}break;case"Escape":case"Tab":Je("search",!1),i.blur();break;case"ArrowUp":case"ArrowDown":if(typeof c=="undefined")i.focus();else{let 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n){let{childNodes:c}=x("span",null,p);s.replaceWith(...Array.from(c))}return{ref:e,nodes:n}}))}function fs(e,{viewport$:t,main$:r}){let o=e.closest(".md-grid"),n=o.offsetTop-o.parentElement.offsetTop;return z([r,t]).pipe(m(([{offset:i,height:a},{offset:{y:s}}])=>(a=a+Math.min(n,Math.max(0,s-i))-n,{height:a,locked:s>=i+n})),K((i,a)=>i.height===a.height&&i.locked===a.locked))}function Zr(e,o){var n=o,{header$:t}=n,r=so(n,["header$"]);let i=R(".md-sidebar__scrollwrap",e),{y:a}=Ve(i);return C(()=>{let s=new g,p=s.pipe(Z(),ie(!0)),c=s.pipe(Me(0,me));return c.pipe(re(t)).subscribe({next([{height:l},{height:f}]){i.style.height=`${l-2*a}px`,e.style.top=`${f}px`},complete(){i.style.height="",e.style.top=""}}),c.pipe(Ae()).subscribe(()=>{for(let l of P(".md-nav__link--active[href]",e)){if(!l.clientHeight)continue;let f=l.closest(".md-sidebar__scrollwrap");if(typeof f!="undefined"){let u=l.offsetTop-f.offsetTop,{height:d}=ce(f);f.scrollTo({top:u-d/2})}}}),ue(P("label[tabindex]",e)).pipe(ne(l=>h(l,"click").pipe(ve(se),m(()=>l),W(p)))).subscribe(l=>{let f=R(`[id="${l.htmlFor}"]`);R(`[aria-labelledby="${l.id}"]`).setAttribute("aria-expanded",`${f.checked}`)}),fs(e,r).pipe(w(l=>s.next(l)),_(()=>s.complete()),m(l=>$({ref:e},l)))})}function hi(e,t){if(typeof t!="undefined"){let r=`https://api.github.com/repos/${e}/${t}`;return st(je(`${r}/releases/latest`).pipe(de(()=>S),m(o=>({version:o.tag_name})),De({})),je(r).pipe(de(()=>S),m(o=>({stars:o.stargazers_count,forks:o.forks_count})),De({}))).pipe(m(([o,n])=>$($({},o),n)))}else{let r=`https://api.github.com/users/${e}`;return je(r).pipe(m(o=>({repositories:o.public_repos})),De({}))}}function bi(e,t){let r=`https://${e}/api/v4/projects/${encodeURIComponent(t)}`;return st(je(`${r}/releases/permalink/latest`).pipe(de(()=>S),m(({tag_name:o})=>({version:o})),De({})),je(r).pipe(de(()=>S),m(({star_count:o,forks_count:n})=>({stars:o,forks:n})),De({}))).pipe(m(([o,n])=>$($({},o),n)))}function vi(e){let t=e.match(/^.+github\.com\/([^/]+)\/?([^/]+)?/i);if(t){let[,r,o]=t;return hi(r,o)}if(t=e.match(/^.+?([^/]*gitlab[^/]+)\/(.+?)\/?$/i),t){let[,r,o]=t;return bi(r,o)}return S}var us;function ds(e){return us||(us=C(()=>{let t=__md_get("__source",sessionStorage);if(t)return I(t);if(ae("consent").length){let o=__md_get("__consent");if(!(o&&o.github))return S}return vi(e.href).pipe(w(o=>__md_set("__source",o,sessionStorage)))}).pipe(de(()=>S),b(t=>Object.keys(t).length>0),m(t=>({facts:t})),G(1)))}function gi(e){let t=R(":scope > :last-child",e);return C(()=>{let r=new g;return r.subscribe(({facts:o})=>{t.appendChild(_n(o)),t.classList.add("md-source__repository--active")}),ds(e).pipe(w(o=>r.next(o)),_(()=>r.complete()),m(o=>$({ref:e},o)))})}function hs(e,{viewport$:t,header$:r}){return ge(document.body).pipe(v(()=>mr(e,{header$:r,viewport$:t})),m(({offset:{y:o}})=>({hidden:o>=10})),ee("hidden"))}function yi(e,t){return C(()=>{let r=new g;return r.subscribe({next({hidden:o}){e.hidden=o},complete(){e.hidden=!1}}),(B("navigation.tabs.sticky")?I({hidden:!1}):hs(e,t)).pipe(w(o=>r.next(o)),_(()=>r.complete()),m(o=>$({ref:e},o)))})}function bs(e,{viewport$:t,header$:r}){let o=new Map,n=P(".md-nav__link",e);for(let s of n){let p=decodeURIComponent(s.hash.substring(1)),c=fe(`[id="${p}"]`);typeof c!="undefined"&&o.set(s,c)}let i=r.pipe(ee("height"),m(({height:s})=>{let p=Se("main"),c=R(":scope > :first-child",p);return s+.8*(c.offsetTop-p.offsetTop)}),pe());return ge(document.body).pipe(ee("height"),v(s=>C(()=>{let p=[];return I([...o].reduce((c,[l,f])=>{for(;p.length&&o.get(p[p.length-1]).tagName>=f.tagName;)p.pop();let u=f.offsetTop;for(;!u&&f.parentElement;)f=f.parentElement,u=f.offsetTop;let d=f.offsetParent;for(;d;d=d.offsetParent)u+=d.offsetTop;return c.set([...p=[...p,l]].reverse(),u)},new Map))}).pipe(m(p=>new Map([...p].sort(([,c],[,l])=>c-l))),He(i),v(([p,c])=>t.pipe(Fr(([l,f],{offset:{y:u},size:d})=>{let y=u+d.height>=Math.floor(s.height);for(;f.length;){let[,L]=f[0];if(L-c=u&&!y)f=[l.pop(),...f];else break}return[l,f]},[[],[...p]]),K((l,f)=>l[0]===f[0]&&l[1]===f[1])))))).pipe(m(([s,p])=>({prev:s.map(([c])=>c),next:p.map(([c])=>c)})),Q({prev:[],next:[]}),Be(2,1),m(([s,p])=>s.prev.length{let i=new g,a=i.pipe(Z(),ie(!0));if(i.subscribe(({prev:s,next:p})=>{for(let[c]of p)c.classList.remove("md-nav__link--passed"),c.classList.remove("md-nav__link--active");for(let[c,[l]]of s.entries())l.classList.add("md-nav__link--passed"),l.classList.toggle("md-nav__link--active",c===s.length-1)}),B("toc.follow")){let s=O(t.pipe(_e(1),m(()=>{})),t.pipe(_e(250),m(()=>"smooth")));i.pipe(b(({prev:p})=>p.length>0),He(o.pipe(ve(se))),re(s)).subscribe(([[{prev:p}],c])=>{let[l]=p[p.length-1];if(l.offsetHeight){let f=cr(l);if(typeof f!="undefined"){let u=l.offsetTop-f.offsetTop,{height:d}=ce(f);f.scrollTo({top:u-d/2,behavior:c})}}})}return B("navigation.tracking")&&t.pipe(W(a),ee("offset"),_e(250),Ce(1),W(n.pipe(Ce(1))),ct({delay:250}),re(i)).subscribe(([,{prev:s}])=>{let p=ye(),c=s[s.length-1];if(c&&c.length){let[l]=c,{hash:f}=new URL(l.href);p.hash!==f&&(p.hash=f,history.replaceState({},"",`${p}`))}else p.hash="",history.replaceState({},"",`${p}`)}),bs(e,{viewport$:t,header$:r}).pipe(w(s=>i.next(s)),_(()=>i.complete()),m(s=>$({ref:e},s)))})}function vs(e,{viewport$:t,main$:r,target$:o}){let n=t.pipe(m(({offset:{y:a}})=>a),Be(2,1),m(([a,s])=>a>s&&s>0),K()),i=r.pipe(m(({active:a})=>a));return z([i,n]).pipe(m(([a,s])=>!(a&&s)),K(),W(o.pipe(Ce(1))),ie(!0),ct({delay:250}),m(a=>({hidden:a})))}function Ei(e,{viewport$:t,header$:r,main$:o,target$:n}){let i=new g,a=i.pipe(Z(),ie(!0));return i.subscribe({next({hidden:s}){e.hidden=s,s?(e.setAttribute("tabindex","-1"),e.blur()):e.removeAttribute("tabindex")},complete(){e.style.top="",e.hidden=!0,e.removeAttribute("tabindex")}}),r.pipe(W(a),ee("height")).subscribe(({height:s})=>{e.style.top=`${s+16}px`}),h(e,"click").subscribe(s=>{s.preventDefault(),window.scrollTo({top:0})}),vs(e,{viewport$:t,main$:o,target$:n}).pipe(w(s=>i.next(s)),_(()=>i.complete()),m(s=>$({ref:e},s)))}function wi({document$:e,viewport$:t}){e.pipe(v(()=>P(".md-ellipsis")),ne(r=>tt(r).pipe(W(e.pipe(Ce(1))),b(o=>o),m(()=>r),Te(1))),b(r=>r.offsetWidth{let o=r.innerText,n=r.closest("a")||r;return n.title=o,B("content.tooltips")?mt(n,{viewport$:t}).pipe(W(e.pipe(Ce(1))),_(()=>n.removeAttribute("title"))):S})).subscribe(),B("content.tooltips")&&e.pipe(v(()=>P(".md-status")),ne(r=>mt(r,{viewport$:t}))).subscribe()}function Ti({document$:e,tablet$:t}){e.pipe(v(()=>P(".md-toggle--indeterminate")),w(r=>{r.indeterminate=!0,r.checked=!1}),ne(r=>h(r,"change").pipe(Dr(()=>r.classList.contains("md-toggle--indeterminate")),m(()=>r))),re(t)).subscribe(([r,o])=>{r.classList.remove("md-toggle--indeterminate"),o&&(r.checked=!1)})}function gs(){return/(iPad|iPhone|iPod)/.test(navigator.userAgent)}function Si({document$:e}){e.pipe(v(()=>P("[data-md-scrollfix]")),w(t=>t.removeAttribute("data-md-scrollfix")),b(gs),ne(t=>h(t,"touchstart").pipe(m(()=>t)))).subscribe(t=>{let r=t.scrollTop;r===0?t.scrollTop=1:r+t.offsetHeight===t.scrollHeight&&(t.scrollTop=r-1)})}function Oi({viewport$:e,tablet$:t}){z([ze("search"),t]).pipe(m(([r,o])=>r&&!o),v(r=>I(r).pipe(Ge(r?400:100))),re(e)).subscribe(([r,{offset:{y:o}}])=>{if(r)document.body.setAttribute("data-md-scrolllock",""),document.body.style.top=`-${o}px`;else{let 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URL("search/search_index.js",eo.base)}`).pipe(m(()=>__index),G(1)):je(new URL("search/search_index.json",eo.base))}document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var ot=Go(),Ut=sn(),Lt=ln(Ut),to=an(),Oe=gn(),hr=Pt("(min-width: 960px)"),Mi=Pt("(min-width: 1220px)"),_i=mn(),eo=xe(),Ai=document.forms.namedItem("search")?ys():Ye,ro=new g;Zn({alert$:ro});var oo=new g;B("navigation.instant")&&oi({location$:Ut,viewport$:Oe,progress$:oo}).subscribe(ot);var Li;((Li=eo.version)==null?void 0:Li.provider)==="mike"&&ci({document$:ot});O(Ut,Lt).pipe(Ge(125)).subscribe(()=>{Je("drawer",!1),Je("search",!1)});to.pipe(b(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=fe("link[rel=prev]");typeof t!="undefined"&<(t);break;case"n":case".":let r=fe("link[rel=next]");typeof r!="undefined"&<(r);break;case"Enter":let o=Ie();o instanceof 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rt=Kn(Se("header"),{viewport$:Oe}),Ft=ot.pipe(m(()=>Se("main")),v(e=>Gn(e,{viewport$:Oe,header$:rt})),G(1)),xs=O(...ae("consent").map(e=>En(e,{target$:Lt})),...ae("dialog").map(e=>qn(e,{alert$:ro})),...ae("palette").map(e=>Jn(e)),...ae("progress").map(e=>Xn(e,{progress$:oo})),...ae("search").map(e=>ui(e,{index$:Ai,keyboard$:to})),...ae("source").map(e=>gi(e))),Es=C(()=>O(...ae("announce").map(e=>xn(e)),...ae("content").map(e=>zn(e,{viewport$:Oe,target$:Lt,print$:_i})),...ae("content").map(e=>B("search.highlight")?di(e,{index$:Ai,location$:Ut}):S),...ae("header").map(e=>Yn(e,{viewport$:Oe,header$:rt,main$:Ft})),...ae("header-title").map(e=>Bn(e,{viewport$:Oe,header$:rt})),...ae("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Nr(Mi,()=>Zr(e,{viewport$:Oe,header$:rt,main$:Ft})):Nr(hr,()=>Zr(e,{viewport$:Oe,header$:rt,main$:Ft}))),...ae("tabs").map(e=>yi(e,{viewport$:Oe,header$:rt})),...ae("toc").map(e=>xi(e,{viewport$:Oe,header$:rt,main$:Ft,target$:Lt})),...ae("top").map(e=>Ei(e,{viewport$:Oe,header$:rt,main$:Ft,target$:Lt})))),Ci=ot.pipe(v(()=>Es),Re(xs),G(1));Ci.subscribe();window.document$=ot;window.location$=Ut;window.target$=Lt;window.keyboard$=to;window.viewport$=Oe;window.tablet$=hr;window.screen$=Mi;window.print$=_i;window.alert$=ro;window.progress$=oo;window.component$=Ci;})(); +//# sourceMappingURL=bundle.88dd0f4e.min.js.map + diff --git a/assets/javascripts/bundle.88dd0f4e.min.js.map b/assets/javascripts/bundle.88dd0f4e.min.js.map new file mode 100644 index 000000000..dab2a8754 --- /dev/null +++ b/assets/javascripts/bundle.88dd0f4e.min.js.map @@ -0,0 +1,7 @@ +{ + "version": 3, + "sources": ["node_modules/focus-visible/dist/focus-visible.js", "node_modules/escape-html/index.js", "node_modules/clipboard/dist/clipboard.js", "src/templates/assets/javascripts/bundle.ts", "node_modules/tslib/tslib.es6.mjs", "node_modules/rxjs/src/internal/util/isFunction.ts", "node_modules/rxjs/src/internal/util/createErrorClass.ts", "node_modules/rxjs/src/internal/util/UnsubscriptionError.ts", "node_modules/rxjs/src/internal/util/arrRemove.ts", "node_modules/rxjs/src/internal/Subscription.ts", "node_modules/rxjs/src/internal/config.ts", "node_modules/rxjs/src/internal/scheduler/timeoutProvider.ts", "node_modules/rxjs/src/internal/util/reportUnhandledError.ts", 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"src/templates/assets/javascripts/components/main/index.ts", "src/templates/assets/javascripts/components/palette/index.ts", "src/templates/assets/javascripts/components/progress/index.ts", "src/templates/assets/javascripts/integrations/clipboard/index.ts", "src/templates/assets/javascripts/integrations/sitemap/index.ts", "src/templates/assets/javascripts/integrations/instant/index.ts", "src/templates/assets/javascripts/integrations/search/highlighter/index.ts", "src/templates/assets/javascripts/integrations/search/worker/message/index.ts", "src/templates/assets/javascripts/integrations/search/worker/_/index.ts", "src/templates/assets/javascripts/integrations/version/findurl/index.ts", "src/templates/assets/javascripts/integrations/version/index.ts", "src/templates/assets/javascripts/components/search/query/index.ts", "src/templates/assets/javascripts/components/search/result/index.ts", "src/templates/assets/javascripts/components/search/share/index.ts", "src/templates/assets/javascripts/components/search/suggest/index.ts", "src/templates/assets/javascripts/components/search/_/index.ts", "src/templates/assets/javascripts/components/search/highlight/index.ts", "src/templates/assets/javascripts/components/sidebar/index.ts", "src/templates/assets/javascripts/components/source/facts/github/index.ts", "src/templates/assets/javascripts/components/source/facts/gitlab/index.ts", "src/templates/assets/javascripts/components/source/facts/_/index.ts", "src/templates/assets/javascripts/components/source/_/index.ts", "src/templates/assets/javascripts/components/tabs/index.ts", "src/templates/assets/javascripts/components/toc/index.ts", "src/templates/assets/javascripts/components/top/index.ts", "src/templates/assets/javascripts/patches/ellipsis/index.ts", "src/templates/assets/javascripts/patches/indeterminate/index.ts", "src/templates/assets/javascripts/patches/scrollfix/index.ts", "src/templates/assets/javascripts/patches/scrolllock/index.ts", "src/templates/assets/javascripts/polyfills/index.ts"], + "sourcesContent": ["(function (global, factory) {\n typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n typeof define === 'function' && define.amd ? define(factory) :\n (factory());\n}(this, (function () { 'use strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n try {\n return document.execCommand(type);\n } catch (err) {\n return false;\n }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n var selectedText = select_default()(target);\n command('cut');\n return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n fakeElement.style.fontSize = '12pt'; // Reset box model\n\n fakeElement.style.border = '0';\n fakeElement.style.padding = '0';\n fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n fakeElement.style.position = 'absolute';\n fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n fakeElement.style.top = \"\".concat(yPosition, \"px\");\n fakeElement.setAttribute('readonly', '');\n fakeElement.value = value;\n return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n var fakeElement = createFakeElement(value);\n options.container.appendChild(fakeElement);\n var selectedText = select_default()(fakeElement);\n command('copy');\n fakeElement.remove();\n return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n var selectedText = '';\n\n if (typeof target === 'string') {\n selectedText = fakeCopyAction(target, options);\n } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n selectedText = fakeCopyAction(target.value, options);\n } else {\n selectedText = select_default()(target);\n command('copy');\n }\n\n return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n // Defines base properties passed from constructor.\n var _options$action = options.action,\n action = _options$action === void 0 ? 'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*\n * Copyright (c) 2016-2024 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"focus-visible\"\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getActiveElement,\n getOptionalElement,\n requestJSON,\n setLocation,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchScript,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountAnnounce,\n mountBackToTop,\n mountConsent,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountProgress,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantNavigation,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchEllipsis,\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable {\n if (location.protocol === \"file:\") {\n return watchScript(\n `${new URL(\"search/search_index.js\", config.base)}`\n )\n .pipe(\n // @ts-ignore - @todo fix typings\n map(() => __index),\n shareReplay(1)\n )\n } else {\n return requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? fetchSearchIndex()\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up progress indicator */\nconst progress$ = new Subject()\n\n/* Set up instant navigation, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantNavigation({ location$, viewport$, progress$ })\n .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"link[rel=prev]\")\n if (typeof prev !== \"undefined\")\n setLocation(prev)\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"link[rel=next]\")\n if (typeof next !== \"undefined\")\n setLocation(next)\n break\n\n /* Expand navigation, see https://bit.ly/3ZjG5io */\n case \"Enter\":\n const active = getActiveElement()\n if (active instanceof HTMLLabelElement)\n active.click()\n }\n })\n\n/* Set up patches */\npatchEllipsis({ viewport$, document$ })\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Consent */\n ...getComponentElements(\"consent\")\n .map(el => mountConsent(el, { target$ })),\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Progress bar */\n ...getComponentElements(\"progress\")\n .map(el => mountProgress(el, { progress$ })),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Announcement bar */\n ...getComponentElements(\"announce\")\n .map(el => mountAnnounce(el)),\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, {\n viewport$, header$, main$, target$\n })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.progress$ = progress$ /* Progress indicator subject */\nwindow.component$ = component$ /* Component observable */\n", "/******************************************************************************\nCopyright (c) Microsoft Corporation.\n\nPermission to use, copy, modify, and/or distribute this software for any\npurpose with or without fee is hereby granted.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\nPERFORMANCE OF THIS SOFTWARE.\n***************************************************************************** */\n/* global Reflect, Promise, SuppressedError, Symbol, Iterator */\n\nvar extendStatics = function(d, b) {\n extendStatics = Object.setPrototypeOf ||\n ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\n function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\n return extendStatics(d, b);\n};\n\nexport function __extends(d, b) {\n if (typeof b !== \"function\" && b !== null)\n throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\n extendStatics(d, b);\n function __() { this.constructor = d; }\n d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\n}\n\nexport var __assign = function() {\n __assign = Object.assign || function __assign(t) {\n for (var s, i = 1, n = arguments.length; i < n; i++) {\n s = arguments[i];\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\n }\n return t;\n }\n return __assign.apply(this, arguments);\n}\n\nexport function __rest(s, e) {\n var t = {};\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\n t[p] = s[p];\n if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\n for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\n if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\n t[p[i]] = s[p[i]];\n }\n return t;\n}\n\nexport function __decorate(decorators, target, key, desc) {\n var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\n if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\n else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\n return c > 3 && r && Object.defineProperty(target, key, r), r;\n}\n\nexport function __param(paramIndex, decorator) {\n return function (target, key) { decorator(target, key, paramIndex); }\n}\n\nexport function __esDecorate(ctor, descriptorIn, decorators, contextIn, initializers, extraInitializers) {\n function accept(f) { if (f !== void 0 && typeof f !== \"function\") throw new TypeError(\"Function expected\"); return f; }\n var kind = contextIn.kind, key = kind === \"getter\" ? \"get\" : kind === \"setter\" ? \"set\" : \"value\";\n var target = !descriptorIn && ctor ? contextIn[\"static\"] ? ctor : ctor.prototype : null;\n var descriptor = descriptorIn || (target ? Object.getOwnPropertyDescriptor(target, contextIn.name) : {});\n var _, done = false;\n for (var i = decorators.length - 1; i >= 0; i--) {\n var context = {};\n for (var p in contextIn) context[p] = p === \"access\" ? {} : contextIn[p];\n for (var p in contextIn.access) context.access[p] = contextIn.access[p];\n context.addInitializer = function (f) { if (done) throw new TypeError(\"Cannot add initializers after decoration has completed\"); extraInitializers.push(accept(f || null)); };\n var result = (0, decorators[i])(kind === \"accessor\" ? { get: descriptor.get, set: descriptor.set } : descriptor[key], context);\n if (kind === \"accessor\") {\n if (result === void 0) continue;\n if (result === null || typeof result !== \"object\") throw new TypeError(\"Object expected\");\n if (_ = accept(result.get)) descriptor.get = _;\n if (_ = accept(result.set)) descriptor.set = _;\n if (_ = accept(result.init)) initializers.unshift(_);\n }\n else if (_ = accept(result)) {\n if (kind === \"field\") initializers.unshift(_);\n else descriptor[key] = _;\n }\n }\n if (target) Object.defineProperty(target, contextIn.name, descriptor);\n done = true;\n};\n\nexport function __runInitializers(thisArg, initializers, value) {\n var useValue = arguments.length > 2;\n for (var i = 0; i < initializers.length; i++) {\n value = useValue ? initializers[i].call(thisArg, value) : initializers[i].call(thisArg);\n }\n return useValue ? value : void 0;\n};\n\nexport function __propKey(x) {\n return typeof x === \"symbol\" ? x : \"\".concat(x);\n};\n\nexport function __setFunctionName(f, name, prefix) {\n if (typeof name === \"symbol\") name = name.description ? \"[\".concat(name.description, \"]\") : \"\";\n return Object.defineProperty(f, \"name\", { configurable: true, value: prefix ? \"\".concat(prefix, \" \", name) : name });\n};\n\nexport function __metadata(metadataKey, metadataValue) {\n if (typeof Reflect === \"object\" && typeof Reflect.metadata === \"function\") return Reflect.metadata(metadataKey, metadataValue);\n}\n\nexport function __awaiter(thisArg, _arguments, P, generator) {\n function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }\n return new (P || (P = Promise))(function (resolve, reject) {\n function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }\n function rejected(value) { try { step(generator[\"throw\"](value)); } catch (e) { reject(e); } }\n function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }\n step((generator = generator.apply(thisArg, _arguments || [])).next());\n });\n}\n\nexport function __generator(thisArg, body) {\n var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g = Object.create((typeof Iterator === \"function\" ? Iterator : Object).prototype);\n return g.next = verb(0), g[\"throw\"] = verb(1), g[\"return\"] = verb(2), typeof Symbol === \"function\" && (g[Symbol.iterator] = function() { return this; }), g;\n function verb(n) { return function (v) { return step([n, v]); }; }\n function step(op) {\n if (f) throw new TypeError(\"Generator is already executing.\");\n while (g && (g = 0, op[0] && (_ = 0)), _) try {\n if (f = 1, y && (t = op[0] & 2 ? y[\"return\"] : op[0] ? y[\"throw\"] || ((t = y[\"return\"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;\n if (y = 0, t) op = [op[0] & 2, t.value];\n switch (op[0]) {\n case 0: case 1: t = op; break;\n case 4: _.label++; return { value: op[1], done: false };\n case 5: _.label++; y = op[1]; op = [0]; continue;\n case 7: op = _.ops.pop(); _.trys.pop(); continue;\n default:\n if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }\n if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }\n if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }\n if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }\n if (t[2]) _.ops.pop();\n _.trys.pop(); continue;\n }\n op = body.call(thisArg, _);\n } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }\n if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };\n }\n}\n\nexport var __createBinding = Object.create ? (function(o, m, k, k2) {\n if (k2 === undefined) k2 = k;\n var desc = Object.getOwnPropertyDescriptor(m, k);\n if (!desc || (\"get\" in desc ? !m.__esModule : desc.writable || desc.configurable)) {\n desc = { enumerable: true, get: function() { return m[k]; } };\n }\n Object.defineProperty(o, k2, desc);\n}) : (function(o, m, k, k2) {\n if (k2 === undefined) k2 = k;\n o[k2] = m[k];\n});\n\nexport function __exportStar(m, o) {\n for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\n}\n\nexport function __values(o) {\n var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\n if (m) return m.call(o);\n if (o && typeof o.length === \"number\") return {\n next: function () {\n if (o && i >= o.length) o = void 0;\n return { value: o && o[i++], done: !o };\n }\n };\n throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\n}\n\nexport function __read(o, n) {\n var m = typeof Symbol === \"function\" && o[Symbol.iterator];\n if (!m) return o;\n var i = m.call(o), r, ar = [], e;\n try {\n while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\n }\n catch (error) { e = { error: error }; }\n finally {\n try {\n if (r && !r.done && (m = i[\"return\"])) m.call(i);\n }\n finally { if (e) throw e.error; }\n }\n return ar;\n}\n\n/** @deprecated */\nexport function __spread() {\n for (var ar = [], i = 0; i < arguments.length; i++)\n ar = ar.concat(__read(arguments[i]));\n return ar;\n}\n\n/** @deprecated */\nexport function __spreadArrays() {\n for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\n for (var r = Array(s), k = 0, i = 0; i < il; i++)\n for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\n r[k] = a[j];\n return r;\n}\n\nexport function __spreadArray(to, from, pack) {\n if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\n if (ar || !(i in from)) {\n if (!ar) ar = Array.prototype.slice.call(from, 0, i);\n ar[i] = from[i];\n }\n }\n return to.concat(ar || Array.prototype.slice.call(from));\n}\n\nexport function __await(v) {\n return this instanceof __await ? (this.v = v, this) : new __await(v);\n}\n\nexport function __asyncGenerator(thisArg, _arguments, generator) {\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\n var g = generator.apply(thisArg, _arguments || []), i, q = [];\n return i = Object.create((typeof AsyncIterator === \"function\" ? AsyncIterator : Object).prototype), verb(\"next\"), verb(\"throw\"), verb(\"return\", awaitReturn), i[Symbol.asyncIterator] = function () { return this; }, i;\n function awaitReturn(f) { return function (v) { return Promise.resolve(v).then(f, reject); }; }\n function verb(n, f) { if (g[n]) { i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; if (f) i[n] = f(i[n]); } }\n function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\n function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\n function fulfill(value) { resume(\"next\", value); }\n function reject(value) { resume(\"throw\", value); }\n function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\n}\n\nexport function __asyncDelegator(o) {\n var i, p;\n return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\n function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: false } : f ? f(v) : v; } : f; }\n}\n\nexport function __asyncValues(o) {\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\n var m = o[Symbol.asyncIterator], i;\n return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\n function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\n function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\n}\n\nexport function __makeTemplateObject(cooked, raw) {\n if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\n return cooked;\n};\n\nvar __setModuleDefault = Object.create ? (function(o, v) {\n Object.defineProperty(o, \"default\", { enumerable: true, value: v });\n}) : function(o, v) {\n o[\"default\"] = v;\n};\n\nexport function __importStar(mod) {\n if (mod && mod.__esModule) return mod;\n var result = {};\n if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\n __setModuleDefault(result, mod);\n return result;\n}\n\nexport function __importDefault(mod) {\n return (mod && mod.__esModule) ? mod : { default: mod };\n}\n\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\n return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\n}\n\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\n if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\n return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\n}\n\nexport function __classPrivateFieldIn(state, receiver) {\n if (receiver === null || (typeof receiver !== \"object\" && typeof receiver !== \"function\")) throw new TypeError(\"Cannot use 'in' operator on non-object\");\n return typeof state === \"function\" ? receiver === state : state.has(receiver);\n}\n\nexport function __addDisposableResource(env, value, async) {\n if (value !== null && value !== void 0) {\n if (typeof value !== \"object\" && typeof value !== \"function\") throw new TypeError(\"Object expected.\");\n var dispose, inner;\n if (async) {\n if (!Symbol.asyncDispose) throw new TypeError(\"Symbol.asyncDispose is not defined.\");\n dispose = value[Symbol.asyncDispose];\n }\n if (dispose === void 0) {\n if (!Symbol.dispose) throw new TypeError(\"Symbol.dispose is not defined.\");\n dispose = value[Symbol.dispose];\n if (async) inner = dispose;\n }\n if (typeof dispose !== \"function\") throw new TypeError(\"Object not disposable.\");\n if (inner) dispose = function() { try { inner.call(this); } catch (e) { return Promise.reject(e); } };\n env.stack.push({ value: value, dispose: dispose, async: async });\n }\n else if (async) {\n env.stack.push({ async: true });\n }\n return value;\n}\n\nvar _SuppressedError = typeof SuppressedError === \"function\" ? SuppressedError : function (error, suppressed, message) {\n var e = new Error(message);\n return e.name = \"SuppressedError\", e.error = error, e.suppressed = suppressed, e;\n};\n\nexport function __disposeResources(env) {\n function fail(e) {\n env.error = env.hasError ? new _SuppressedError(e, env.error, \"An error was suppressed during disposal.\") : e;\n env.hasError = true;\n }\n var r, s = 0;\n function next() {\n while (r = env.stack.pop()) {\n try {\n if (!r.async && s === 1) return s = 0, env.stack.push(r), Promise.resolve().then(next);\n if (r.dispose) {\n var result = r.dispose.call(r.value);\n if (r.async) return s |= 2, Promise.resolve(result).then(next, function(e) { fail(e); return next(); });\n }\n else s |= 1;\n }\n catch (e) {\n fail(e);\n }\n }\n if (s === 1) return env.hasError ? Promise.reject(env.error) : Promise.resolve();\n if (env.hasError) throw env.error;\n }\n return next();\n}\n\nexport default {\n __extends,\n __assign,\n __rest,\n __decorate,\n __param,\n __metadata,\n __awaiter,\n __generator,\n __createBinding,\n __exportStar,\n __values,\n __read,\n __spread,\n __spreadArrays,\n __spreadArray,\n __await,\n __asyncGenerator,\n __asyncDelegator,\n __asyncValues,\n __makeTemplateObject,\n __importStar,\n __importDefault,\n __classPrivateFieldGet,\n __classPrivateFieldSet,\n __classPrivateFieldIn,\n __addDisposableResource,\n __disposeResources,\n};\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass(createImpl: (_super: any) => any): T {\n const _super = (instance: any) => {\n Error.call(instance);\n instance.stack = new Error().stack;\n };\n\n const ctorFunc = createImpl(_super);\n ctorFunc.prototype = Object.create(Error.prototype);\n ctorFunc.prototype.constructor = ctorFunc;\n return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n (_super) =>\n function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n _super(this);\n this.message = errors\n ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n ')}`\n : '';\n this.name = 'UnsubscriptionError';\n this.errors = errors;\n }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove(arr: T[] | undefined | null, item: T) {\n if (arr) {\n const index = arr.indexOf(item);\n 0 <= index && arr.splice(index, 1);\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n *\n * @class Subscription\n */\nexport class Subscription implements SubscriptionLike {\n /** @nocollapse */\n public static EMPTY = (() => {\n const empty = new Subscription();\n empty.closed = true;\n return empty;\n })();\n\n /**\n * A flag to indicate whether this Subscription has already been unsubscribed.\n */\n public closed = false;\n\n private _parentage: Subscription[] | Subscription | null = null;\n\n /**\n * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n * list occurs in the {@link #add} and {@link #remove} methods.\n */\n private _finalizers: Exclude[] | null = null;\n\n /**\n * @param initialTeardown A function executed first as part of the finalization\n * process that is kicked off when {@link #unsubscribe} is called.\n */\n constructor(private initialTeardown?: () => void) {}\n\n /**\n * Disposes the resources held by the subscription. May, for instance, cancel\n * an ongoing Observable execution or cancel any other type of work that\n * started when the Subscription was created.\n * @return {void}\n */\n unsubscribe(): void {\n let errors: any[] | undefined;\n\n if (!this.closed) {\n this.closed = true;\n\n // Remove this from it's parents.\n const { _parentage } = this;\n if (_parentage) {\n this._parentage = null;\n if (Array.isArray(_parentage)) {\n for (const parent of _parentage) {\n parent.remove(this);\n }\n } else {\n _parentage.remove(this);\n }\n }\n\n const { initialTeardown: initialFinalizer } = this;\n if (isFunction(initialFinalizer)) {\n try {\n initialFinalizer();\n } catch (e) {\n errors = e instanceof UnsubscriptionError ? e.errors : [e];\n }\n }\n\n const { _finalizers } = this;\n if (_finalizers) {\n this._finalizers = null;\n for (const finalizer of _finalizers) {\n try {\n execFinalizer(finalizer);\n } catch (err) {\n errors = errors ?? [];\n if (err instanceof UnsubscriptionError) {\n errors = [...errors, ...err.errors];\n } else {\n errors.push(err);\n }\n }\n }\n }\n\n if (errors) {\n throw new UnsubscriptionError(errors);\n }\n }\n }\n\n /**\n * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n * because it has already been unsubscribed, then whatever finalizer is passed to it\n * will automatically be executed (unless the finalizer itself is also a closed subscription).\n *\n * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n * subscription to a any subscription will result in no operation. (A noop).\n *\n * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n * operation at all. (A noop).\n *\n * `Subscription` instances that are added to this instance will automatically remove themselves\n * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n * will need to be removed manually with {@link #remove}\n *\n * @param teardown The finalization logic to add to this subscription.\n */\n add(teardown: TeardownLogic): void {\n // Only add the finalizer if it's not undefined\n // and don't add a subscription to itself.\n if (teardown && teardown !== this) {\n if (this.closed) {\n // If this subscription is already closed,\n // execute whatever finalizer is handed to it automatically.\n execFinalizer(teardown);\n } else {\n if (teardown instanceof Subscription) {\n // We don't add closed subscriptions, and we don't add the same subscription\n // twice. Subscription unsubscribe is idempotent.\n if (teardown.closed || teardown._hasParent(this)) {\n return;\n }\n teardown._addParent(this);\n }\n (this._finalizers = this._finalizers ?? []).push(teardown);\n }\n }\n }\n\n /**\n * Checks to see if a this subscription already has a particular parent.\n * This will signal that this subscription has already been added to the parent in question.\n * @param parent the parent to check for\n */\n private _hasParent(parent: Subscription) {\n const { _parentage } = this;\n return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n }\n\n /**\n * Adds a parent to this subscription so it can be removed from the parent if it\n * unsubscribes on it's own.\n *\n * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n * @param parent The parent subscription to add\n */\n private _addParent(parent: Subscription) {\n const { _parentage } = this;\n this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n }\n\n /**\n * Called on a child when it is removed via {@link #remove}.\n * @param parent The parent to remove\n */\n private _removeParent(parent: Subscription) {\n const { _parentage } = this;\n if (_parentage === parent) {\n this._parentage = null;\n } else if (Array.isArray(_parentage)) {\n arrRemove(_parentage, parent);\n }\n }\n\n /**\n * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n *\n * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n * from every other `Subscription` they have been added to. This means that using the `remove` method\n * is not a common thing and should be used thoughtfully.\n *\n * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n * more than once, you will need to call `remove` the same number of times to remove all instances.\n *\n * All finalizer instances are removed to free up memory upon unsubscription.\n *\n * @param teardown The finalizer to remove from this subscription\n */\n remove(teardown: Exclude): void {\n const { _finalizers } = this;\n _finalizers && arrRemove(_finalizers, teardown);\n\n if (teardown instanceof Subscription) {\n teardown._removeParent(this);\n }\n }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n return (\n value instanceof Subscription ||\n (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n if (isFunction(finalizer)) {\n finalizer();\n } else {\n finalizer.unsubscribe();\n }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n onUnhandledError: null,\n onStoppedNotification: null,\n Promise: undefined,\n useDeprecatedSynchronousErrorHandling: false,\n useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n /**\n * A registration point for unhandled errors from RxJS. These are errors that\n * cannot were not handled by consuming code in the usual subscription path. For\n * example, if you have this configured, and you subscribe to an observable without\n * providing an error handler, errors from that subscription will end up here. This\n * will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onUnhandledError: ((err: any) => void) | null;\n\n /**\n * A registration point for notifications that cannot be sent to subscribers because they\n * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n * might want a different behavior. For example, with sources that attempt to report errors\n * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n * This will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onStoppedNotification: ((notification: ObservableNotification, subscriber: Subscriber) => void) | null;\n\n /**\n * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n * methods.\n *\n * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n * Promise constructor. If you need a Promise implementation other than native promises,\n * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n */\n Promise?: PromiseConstructorLike;\n\n /**\n * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n * call in a try/catch block. It also enables producer interference, a nasty bug\n * where a multicast can be broken for all observers by a downstream consumer with\n * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n * FOR MIGRATION REASONS.\n *\n * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n * behaviors described above. Will be removed in v8.\n */\n useDeprecatedSynchronousErrorHandling: boolean;\n\n /**\n * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n * `unsubscribe()` via `this` context in `next` functions created in observers passed\n * to `subscribe`.\n *\n * This is being removed because the performance was severely problematic, and it could also cause\n * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n * their `this` context overwritten.\n *\n * @deprecated As of version 8, RxJS will no longer support altering the\n * context of next functions provided as part of an observer to Subscribe. Instead,\n * you will have access to a subscription or a signal or token that will allow you to do things like\n * unsubscribe and test closed status. Will be removed in v8.\n */\n useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n delegate:\n | {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n }\n | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setTimeout(handler: () => void, timeout?: number, ...args) {\n const { delegate } = timeoutProvider;\n if (delegate?.setTimeout) {\n return delegate.setTimeout(handler, timeout, ...args);\n }\n return setTimeout(handler, timeout, ...args);\n },\n clearTimeout(handle) {\n const { delegate } = timeoutProvider;\n return (delegate?.clearTimeout || clearTimeout)(handle as any);\n },\n delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n timeoutProvider.setTimeout(() => {\n const { onUnhandledError } = config;\n if (onUnhandledError) {\n // Execute the user-configured error handler.\n onUnhandledError(err);\n } else {\n // Throw so it is picked up by the runtime's uncaught error mechanism.\n throw err;\n }\n });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification(value: T) {\n return createNotification('N', value, undefined) as NextNotification;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n return {\n kind,\n value,\n error,\n };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n const isRoot = !context;\n if (isRoot) {\n context = { errorThrown: false, error: null };\n }\n cb();\n if (isRoot) {\n const { errorThrown, error } = context!;\n context = null;\n if (errorThrown) {\n throw error;\n }\n }\n } else {\n // This is the general non-deprecated path for everyone that\n // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n cb();\n }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n if (config.useDeprecatedSynchronousErrorHandling && context) {\n context.errorThrown = true;\n context.error = err;\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n *\n * @class Subscriber\n */\nexport class Subscriber extends Subscription implements Observer {\n /**\n * A static factory for a Subscriber, given a (potentially partial) definition\n * of an Observer.\n * @param next The `next` callback of an Observer.\n * @param error The `error` callback of an\n * Observer.\n * @param complete The `complete` callback of an\n * Observer.\n * @return A Subscriber wrapping the (partially defined)\n * Observer represented by the given arguments.\n * @nocollapse\n * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n * method, and there is no reason to be creating instances of `Subscriber` directly.\n * If you have a specific use case, please file an issue.\n */\n static create(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber {\n return new SafeSubscriber(next, error, complete);\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected isStopped: boolean = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected destination: Subscriber | Observer; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n */\n constructor(destination?: Subscriber | Observer) {\n super();\n if (destination) {\n this.destination = destination;\n // Automatically chain subscriptions together here.\n // if destination is a Subscription, then it is a Subscriber.\n if (isSubscription(destination)) {\n destination.add(this);\n }\n } else {\n this.destination = EMPTY_OBSERVER;\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `next` from\n * the Observable, with a value. The Observable may call this method 0 or more\n * times.\n * @param {T} [value] The `next` value.\n * @return {void}\n */\n next(value?: T): void {\n if (this.isStopped) {\n handleStoppedNotification(nextNotification(value), this);\n } else {\n this._next(value!);\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `error` from\n * the Observable, with an attached `Error`. Notifies the Observer that\n * the Observable has experienced an error condition.\n * @param {any} [err] The `error` exception.\n * @return {void}\n */\n error(err?: any): void {\n if (this.isStopped) {\n handleStoppedNotification(errorNotification(err), this);\n } else {\n this.isStopped = true;\n this._error(err);\n }\n }\n\n /**\n * The {@link Observer} callback to receive a valueless notification of type\n * `complete` from the Observable. Notifies the Observer that the Observable\n * has finished sending push-based notifications.\n * @return {void}\n */\n complete(): void {\n if (this.isStopped) {\n handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n } else {\n this.isStopped = true;\n this._complete();\n }\n }\n\n unsubscribe(): void {\n if (!this.closed) {\n this.isStopped = true;\n super.unsubscribe();\n this.destination = null!;\n }\n }\n\n protected _next(value: T): void {\n this.destination.next(value);\n }\n\n protected _error(err: any): void {\n try {\n this.destination.error(err);\n } finally {\n this.unsubscribe();\n }\n }\n\n protected _complete(): void {\n try {\n this.destination.complete();\n } finally {\n this.unsubscribe();\n }\n }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind any>(fn: Fn, thisArg: any): Fn {\n return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver implements Observer {\n constructor(private partialObserver: Partial>) {}\n\n next(value: T): void {\n const { partialObserver } = this;\n if (partialObserver.next) {\n try {\n partialObserver.next(value);\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n\n error(err: any): void {\n const { partialObserver } = this;\n if (partialObserver.error) {\n try {\n partialObserver.error(err);\n } catch (error) {\n handleUnhandledError(error);\n }\n } else {\n handleUnhandledError(err);\n }\n }\n\n complete(): void {\n const { partialObserver } = this;\n if (partialObserver.complete) {\n try {\n partialObserver.complete();\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n}\n\nexport class SafeSubscriber extends Subscriber {\n constructor(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((e?: any) => void) | null,\n complete?: (() => void) | null\n ) {\n super();\n\n let partialObserver: Partial>;\n if (isFunction(observerOrNext) || !observerOrNext) {\n // The first argument is a function, not an observer. The next\n // two arguments *could* be observers, or they could be empty.\n partialObserver = {\n next: (observerOrNext ?? undefined) as (((value: T) => void) | undefined),\n error: error ?? undefined,\n complete: complete ?? undefined,\n };\n } else {\n // The first argument is a partial observer.\n let context: any;\n if (this && config.useDeprecatedNextContext) {\n // This is a deprecated path that made `this.unsubscribe()` available in\n // next handler functions passed to subscribe. This only exists behind a flag\n // now, as it is *very* slow.\n context = Object.create(observerOrNext);\n context.unsubscribe = () => this.unsubscribe();\n partialObserver = {\n next: observerOrNext.next && bind(observerOrNext.next, context),\n error: observerOrNext.error && bind(observerOrNext.error, context),\n complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n };\n } else {\n // The \"normal\" path. Just use the partial observer directly.\n partialObserver = observerOrNext;\n }\n }\n\n // Wrap the partial observer to ensure it's a full observer, and\n // make sure proper error handling is accounted for.\n this.destination = new ConsumerObserver(partialObserver);\n }\n}\n\nfunction handleUnhandledError(error: any) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n captureError(error);\n } else {\n // Ideal path, we report this as an unhandled error,\n // which is thrown on a new call stack.\n reportUnhandledError(error);\n }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent\n * @param subscriber The stopped subscriber\n */\nfunction handleStoppedNotification(notification: ObservableNotification, subscriber: Subscriber) {\n const { onStoppedNotification } = config;\n onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly> & { closed: true } = {\n closed: true,\n next: noop,\n error: defaultErrorHandler,\n complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n * map(i => range(i)),\n * mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity(x: T): T {\n return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe(fn1: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction, fn3: UnaryFunction): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction,\n ...fns: UnaryFunction[]\n): UnaryFunction;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on. \n */\nexport function pipe(...fns: Array>): UnaryFunction {\n return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray(fns: Array>): UnaryFunction {\n if (fns.length === 0) {\n return identity as UnaryFunction;\n }\n\n if (fns.length === 1) {\n return fns[0];\n }\n\n return function piped(input: T): R {\n return fns.reduce((prev: any, fn: UnaryFunction) => fn(prev), input as any);\n };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n *\n * @class Observable\n */\nexport class Observable implements Subscribable {\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n source: Observable | undefined;\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n operator: Operator | undefined;\n\n /**\n * @constructor\n * @param {Function} subscribe the function that is called when the Observable is\n * initially subscribed to. This function is given a Subscriber, to which new values\n * can be `next`ed, or an `error` method can be called to raise an error, or\n * `complete` can be called to notify of a successful completion.\n */\n constructor(subscribe?: (this: Observable, subscriber: Subscriber) => TeardownLogic) {\n if (subscribe) {\n this._subscribe = subscribe;\n }\n }\n\n // HACK: Since TypeScript inherits static properties too, we have to\n // fight against TypeScript here so Subject can have a different static create signature\n /**\n * Creates a new Observable by calling the Observable constructor\n * @owner Observable\n * @method create\n * @param {Function} subscribe? the subscriber function to be passed to the Observable constructor\n * @return {Observable} a new observable\n * @nocollapse\n * @deprecated Use `new Observable()` instead. Will be removed in v8.\n */\n static create: (...args: any[]) => any = (subscribe?: (subscriber: Subscriber) => TeardownLogic) => {\n return new Observable(subscribe);\n };\n\n /**\n * Creates a new Observable, with this Observable instance as the source, and the passed\n * operator defined as the new observable's operator.\n * @method lift\n * @param operator the operator defining the operation to take on the observable\n * @return a new observable with the Operator applied\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * If you have implemented an operator using `lift`, it is recommended that you create an\n * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n * scratch\" section here: https://rxjs.dev/guide/operators\n */\n lift(operator?: Operator): Observable {\n const observable = new Observable();\n observable.source = this;\n observable.operator = operator;\n return observable;\n }\n\n subscribe(observerOrNext?: Partial> | ((value: T) => void)): Subscription;\n /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n /**\n * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n *\n * Use it when you have all these Observables, but still nothing is happening.\n *\n * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n * might be for example a function that you passed to Observable's constructor, but most of the time it is\n * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n * the thought.\n *\n * Apart from starting the execution of an Observable, this method allows you to listen for values\n * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n * of the following ways.\n *\n * The first way is creating an object that implements {@link Observer} interface. It should have methods\n * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n * an `error` method to avoid missing thrown errors.\n *\n * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n *\n * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n * and you also handled emissions internally by using operators (e.g. using `tap`).\n *\n * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n *\n * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n * It is an Observable itself that decides when these functions will be called. For example {@link of}\n * by default emits all its values synchronously. Always check documentation for how given Observable\n * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n *\n * #### Examples\n *\n * Subscribe with an {@link guide/observer Observer}\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * const sumObserver = {\n * sum: 0,\n * next(value) {\n * console.log('Adding: ' + value);\n * this.sum = this.sum + value;\n * },\n * error() {\n * // We actually could just remove this method,\n * // since we do not really care about errors right now.\n * },\n * complete() {\n * console.log('Sum equals: ' + this.sum);\n * }\n * };\n *\n * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n * .subscribe(sumObserver);\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n *\n * ```ts\n * import { of } from 'rxjs'\n *\n * let sum = 0;\n *\n * of(1, 2, 3).subscribe(\n * value => {\n * console.log('Adding: ' + value);\n * sum = sum + value;\n * },\n * undefined,\n * () => console.log('Sum equals: ' + sum)\n * );\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Cancel a subscription\n *\n * ```ts\n * import { interval } from 'rxjs';\n *\n * const subscription = interval(1000).subscribe({\n * next(num) {\n * console.log(num)\n * },\n * complete() {\n * // Will not be called, even when cancelling subscription.\n * console.log('completed!');\n * }\n * });\n *\n * setTimeout(() => {\n * subscription.unsubscribe();\n * console.log('unsubscribed!');\n * }, 2500);\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 'unsubscribed!' after 2.5s\n * ```\n *\n * @param {Observer|Function} observerOrNext (optional) Either an observer with methods to be called,\n * or the first of three possible handlers, which is the handler for each value emitted from the subscribed\n * Observable.\n * @param {Function} error (optional) A handler for a terminal event resulting from an error. If no error handler is provided,\n * the error will be thrown asynchronously as unhandled.\n * @param {Function} complete (optional) A handler for a terminal event resulting from successful completion.\n * @return {Subscription} a subscription reference to the registered handlers\n * @method subscribe\n */\n subscribe(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((error: any) => void) | null,\n complete?: (() => void) | null\n ): Subscription {\n const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n errorContext(() => {\n const { operator, source } = this;\n subscriber.add(\n operator\n ? // We're dealing with a subscription in the\n // operator chain to one of our lifted operators.\n operator.call(subscriber, source)\n : source\n ? // If `source` has a value, but `operator` does not, something that\n // had intimate knowledge of our API, like our `Subject`, must have\n // set it. We're going to just call `_subscribe` directly.\n this._subscribe(subscriber)\n : // In all other cases, we're likely wrapping a user-provided initializer\n // function, so we need to catch errors and handle them appropriately.\n this._trySubscribe(subscriber)\n );\n });\n\n return subscriber;\n }\n\n /** @internal */\n protected _trySubscribe(sink: Subscriber): TeardownLogic {\n try {\n return this._subscribe(sink);\n } catch (err) {\n // We don't need to return anything in this case,\n // because it's just going to try to `add()` to a subscription\n // above.\n sink.error(err);\n }\n }\n\n /**\n * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * #### Example\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(4));\n *\n * async function getTotal() {\n * let total = 0;\n *\n * await source$.forEach(value => {\n * total += value;\n * console.log('observable -> ' + value);\n * });\n *\n * return total;\n * }\n *\n * getTotal().then(\n * total => console.log('Total: ' + total)\n * );\n *\n * // Expected:\n * // 'observable -> 0'\n * // 'observable -> 1'\n * // 'observable -> 2'\n * // 'observable -> 3'\n * // 'Total: 6'\n * ```\n *\n * @param next a handler for each value emitted by the observable\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n */\n forEach(next: (value: T) => void): Promise;\n\n /**\n * @param next a handler for each value emitted by the observable\n * @param promiseCtor a constructor function used to instantiate the Promise\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n * @deprecated Passing a Promise constructor will no longer be available\n * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n * little benefit. If you need this functionality, it is recommended that you either\n * polyfill Promise, or you create an adapter to convert the returned native promise\n * to whatever promise implementation you wanted. Will be removed in v8.\n */\n forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise;\n\n forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n const subscriber = new SafeSubscriber({\n next: (value) => {\n try {\n next(value);\n } catch (err) {\n reject(err);\n subscriber.unsubscribe();\n }\n },\n error: reject,\n complete: resolve,\n });\n this.subscribe(subscriber);\n }) as Promise;\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): TeardownLogic {\n return this.source?.subscribe(subscriber);\n }\n\n /**\n * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n * @method Symbol.observable\n * @return {Observable} this instance of the observable\n */\n [Symbol_observable]() {\n return this;\n }\n\n /* tslint:disable:max-line-length */\n pipe(): Observable;\n pipe(op1: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction, op3: OperatorFunction): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction,\n ...operations: OperatorFunction[]\n ): Observable;\n /* tslint:enable:max-line-length */\n\n /**\n * Used to stitch together functional operators into a chain.\n * @method pipe\n * @return {Observable} the Observable result of all of the operators having\n * been called in the order they were passed in.\n *\n * ## Example\n *\n * ```ts\n * import { interval, filter, map, scan } from 'rxjs';\n *\n * interval(1000)\n * .pipe(\n * filter(x => x % 2 === 0),\n * map(x => x + x),\n * scan((acc, x) => acc + x)\n * )\n * .subscribe(x => console.log(x));\n * ```\n */\n pipe(...operations: OperatorFunction[]): Observable {\n return pipeFromArray(operations)(this);\n }\n\n /* tslint:disable:max-line-length */\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: typeof Promise): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: PromiseConstructorLike): Promise;\n /* tslint:enable:max-line-length */\n\n /**\n * Subscribe to this Observable and get a Promise resolving on\n * `complete` with the last emission (if any).\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * @method toPromise\n * @param [promiseCtor] a constructor function used to instantiate\n * the Promise\n * @return A Promise that resolves with the last value emit, or\n * rejects on an error. If there were no emissions, Promise\n * resolves with undefined.\n * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n */\n toPromise(promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n let value: T | undefined;\n this.subscribe(\n (x: T) => (value = x),\n (err: any) => reject(err),\n () => resolve(value)\n );\n }) as Promise;\n }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver(value: any): value is Observer {\n return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber(value: any): value is Subscriber {\n return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType['lift'] } {\n return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate(\n init: (liftedSource: Observable, subscriber: Subscriber) => (() => void) | void\n): OperatorFunction {\n return (source: Observable) => {\n if (hasLift(source)) {\n return source.lift(function (this: Subscriber, liftedSource: Observable) {\n try {\n return init(liftedSource, this);\n } catch (err) {\n this.error(err);\n }\n });\n }\n throw new TypeError('Unable to lift unknown Observable type');\n };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n onFinalize?: () => void\n): Subscriber {\n return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber extends Subscriber {\n /**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n */\n constructor(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n private onFinalize?: () => void,\n private shouldUnsubscribe?: () => boolean\n ) {\n // It's important - for performance reasons - that all of this class's\n // members are initialized and that they are always initialized in the same\n // order. This will ensure that all OperatorSubscriber instances have the\n // same hidden class in V8. This, in turn, will help keep the number of\n // hidden classes involved in property accesses within the base class as\n // low as possible. If the number of hidden classes involved exceeds four,\n // the property accesses will become megamorphic and performance penalties\n // will be incurred - i.e. inline caches won't be used.\n //\n // The reasons for ensuring all instances have the same hidden class are\n // further discussed in this blog post from Benedikt Meurer:\n // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n super(destination);\n this._next = onNext\n ? function (this: OperatorSubscriber, value: T) {\n try {\n onNext(value);\n } catch (err) {\n destination.error(err);\n }\n }\n : super._next;\n this._error = onError\n ? function (this: OperatorSubscriber, err: any) {\n try {\n onError(err);\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._error;\n this._complete = onComplete\n ? function (this: OperatorSubscriber) {\n try {\n onComplete();\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._complete;\n }\n\n unsubscribe() {\n if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n const { closed } = this;\n super.unsubscribe();\n // Execute additional teardown if we have any and we didn't already do so.\n !closed && this.onFinalize?.();\n }\n }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n schedule(callback: FrameRequestCallback): Subscription;\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n delegate:\n | {\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n }\n | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n schedule(callback) {\n let request = requestAnimationFrame;\n let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n const { delegate } = animationFrameProvider;\n if (delegate) {\n request = delegate.requestAnimationFrame;\n cancel = delegate.cancelAnimationFrame;\n }\n const handle = request((timestamp) => {\n // Clear the cancel function. The request has been fulfilled, so\n // attempting to cancel the request upon unsubscription would be\n // pointless.\n cancel = undefined;\n callback(timestamp);\n });\n return new Subscription(() => cancel?.(handle));\n },\n requestAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n },\n cancelAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n },\n delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n (_super) =>\n function ObjectUnsubscribedErrorImpl(this: any) {\n _super(this);\n this.name = 'ObjectUnsubscribedError';\n this.message = 'object unsubscribed';\n }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject extends Observable implements SubscriptionLike {\n closed = false;\n\n private currentObservers: Observer[] | null = null;\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n observers: Observer[] = [];\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n isStopped = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n hasError = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n thrownError: any = null;\n\n /**\n * Creates a \"subject\" by basically gluing an observer to an observable.\n *\n * @nocollapse\n * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n */\n static create: (...args: any[]) => any = (destination: Observer, source: Observable): AnonymousSubject => {\n return new AnonymousSubject(destination, source);\n };\n\n constructor() {\n // NOTE: This must be here to obscure Observable's constructor.\n super();\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n lift(operator: Operator): Observable {\n const subject = new AnonymousSubject(this, this);\n subject.operator = operator as any;\n return subject as any;\n }\n\n /** @internal */\n protected _throwIfClosed() {\n if (this.closed) {\n throw new ObjectUnsubscribedError();\n }\n }\n\n next(value: T) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n if (!this.currentObservers) {\n this.currentObservers = Array.from(this.observers);\n }\n for (const observer of this.currentObservers) {\n observer.next(value);\n }\n }\n });\n }\n\n error(err: any) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.hasError = this.isStopped = true;\n this.thrownError = err;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.error(err);\n }\n }\n });\n }\n\n complete() {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.isStopped = true;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.complete();\n }\n }\n });\n }\n\n unsubscribe() {\n this.isStopped = this.closed = true;\n this.observers = this.currentObservers = null!;\n }\n\n get observed() {\n return this.observers?.length > 0;\n }\n\n /** @internal */\n protected _trySubscribe(subscriber: Subscriber): TeardownLogic {\n this._throwIfClosed();\n return super._trySubscribe(subscriber);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._checkFinalizedStatuses(subscriber);\n return this._innerSubscribe(subscriber);\n }\n\n /** @internal */\n protected _innerSubscribe(subscriber: Subscriber) {\n const { hasError, isStopped, observers } = this;\n if (hasError || isStopped) {\n return EMPTY_SUBSCRIPTION;\n }\n this.currentObservers = null;\n observers.push(subscriber);\n return new Subscription(() => {\n this.currentObservers = null;\n arrRemove(observers, subscriber);\n });\n }\n\n /** @internal */\n protected _checkFinalizedStatuses(subscriber: Subscriber) {\n const { hasError, thrownError, isStopped } = this;\n if (hasError) {\n subscriber.error(thrownError);\n } else if (isStopped) {\n subscriber.complete();\n }\n }\n\n /**\n * Creates a new Observable with this Subject as the source. You can do this\n * to create custom Observer-side logic of the Subject and conceal it from\n * code that uses the Observable.\n * @return {Observable} Observable that the Subject casts to\n */\n asObservable(): Observable {\n const observable: any = new Observable();\n observable.source = this;\n return observable;\n }\n}\n\n/**\n * @class AnonymousSubject\n */\nexport class AnonymousSubject extends Subject {\n constructor(\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n public destination?: Observer,\n source?: Observable\n ) {\n super();\n this.source = source;\n }\n\n next(value: T) {\n this.destination?.next?.(value);\n }\n\n error(err: any) {\n this.destination?.error?.(err);\n }\n\n complete() {\n this.destination?.complete?.();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n }\n}\n", "import { Subject } from './Subject';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\n\n/**\n * A variant of Subject that requires an initial value and emits its current\n * value whenever it is subscribed to.\n *\n * @class BehaviorSubject\n */\nexport class BehaviorSubject extends Subject {\n constructor(private _value: T) {\n super();\n }\n\n get value(): T {\n return this.getValue();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n const subscription = super._subscribe(subscriber);\n !subscription.closed && subscriber.next(this._value);\n return subscription;\n }\n\n getValue(): T {\n const { hasError, thrownError, _value } = this;\n if (hasError) {\n throw thrownError;\n }\n this._throwIfClosed();\n return _value;\n }\n\n next(value: T): void {\n super.next((this._value = value));\n }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n now() {\n // Use the variable rather than `this` so that the function can be called\n // without being bound to the provider.\n return (dateTimestampProvider.delegate || Date).now();\n },\n delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject extends Subject {\n private _buffer: (T | number)[] = [];\n private _infiniteTimeWindow = true;\n\n /**\n * @param bufferSize The size of the buffer to replay on subscription\n * @param windowTime The amount of time the buffered items will stay buffered\n * @param timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n * calculate the amount of time something has been buffered.\n */\n constructor(\n private _bufferSize = Infinity,\n private _windowTime = Infinity,\n private _timestampProvider: TimestampProvider = dateTimestampProvider\n ) {\n super();\n this._infiniteTimeWindow = _windowTime === Infinity;\n this._bufferSize = Math.max(1, _bufferSize);\n this._windowTime = Math.max(1, _windowTime);\n }\n\n next(value: T): void {\n const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n if (!isStopped) {\n _buffer.push(value);\n !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n }\n this._trimBuffer();\n super.next(value);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._trimBuffer();\n\n const subscription = this._innerSubscribe(subscriber);\n\n const { _infiniteTimeWindow, _buffer } = this;\n // We use a copy here, so reentrant code does not mutate our array while we're\n // emitting it to a new subscriber.\n const copy = _buffer.slice();\n for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n subscriber.next(copy[i] as T);\n }\n\n this._checkFinalizedStatuses(subscriber);\n\n return subscription;\n }\n\n private _trimBuffer() {\n const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n // If we don't have an infinite buffer size, and we're over the length,\n // use splice to truncate the old buffer values off. Note that we have to\n // double the size for instances where we're not using an infinite time window\n // because we're storing the values and the timestamps in the same array.\n const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n // Now, if we're not in an infinite time window, remove all values where the time is\n // older than what is allowed.\n if (!_infiniteTimeWindow) {\n const now = _timestampProvider.now();\n let last = 0;\n // Search the array for the first timestamp that isn't expired and\n // truncate the buffer up to that point.\n for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n last = i;\n }\n last && _buffer.splice(0, last + 1);\n }\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action extends Subscription {\n * new (scheduler: Scheduler, work: (state?: T) => void);\n * schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n *\n * @class Action\n */\nexport class Action extends Subscription {\n constructor(scheduler: Scheduler, work: (this: SchedulerAction, state?: T) => void) {\n super();\n }\n /**\n * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n * some context object, `state`. May happen at some point in the future,\n * according to the `delay` parameter, if specified.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler.\n * @return {void}\n */\n public schedule(state?: T, delay: number = 0): Subscription {\n return this;\n }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n delegate:\n | {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n }\n | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setInterval(handler: () => void, timeout?: number, ...args) {\n const { delegate } = intervalProvider;\n if (delegate?.setInterval) {\n return delegate.setInterval(handler, timeout, ...args);\n }\n return setInterval(handler, timeout, ...args);\n },\n clearInterval(handle) {\n const { delegate } = intervalProvider;\n return (delegate?.clearInterval || clearInterval)(handle as any);\n },\n delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction extends Action {\n public id: TimerHandle | undefined;\n public state?: T;\n // @ts-ignore: Property has no initializer and is not definitely assigned\n public delay: number;\n protected pending: boolean = false;\n\n constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (this.closed) {\n return this;\n }\n\n // Always replace the current state with the new state.\n this.state = state;\n\n const id = this.id;\n const scheduler = this.scheduler;\n\n //\n // Important implementation note:\n //\n // Actions only execute once by default, unless rescheduled from within the\n // scheduled callback. This allows us to implement single and repeat\n // actions via the same code path, without adding API surface area, as well\n // as mimic traditional recursion but across asynchronous boundaries.\n //\n // However, JS runtimes and timers distinguish between intervals achieved by\n // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n // serial `setTimeout` calls can be individually delayed, which delays\n // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n // guarantee the interval callback will be invoked more precisely to the\n // interval period, regardless of load.\n //\n // Therefore, we use `setInterval` to schedule single and repeat actions.\n // If the action reschedules itself with the same delay, the interval is not\n // canceled. If the action doesn't reschedule, or reschedules with a\n // different delay, the interval will be canceled after scheduled callback\n // execution.\n //\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, delay);\n }\n\n // Set the pending flag indicating that this action has been scheduled, or\n // has recursively rescheduled itself.\n this.pending = true;\n\n this.delay = delay;\n // If this action has already an async Id, don't request a new one.\n this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n return this;\n }\n\n protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n }\n\n protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n // If this action is rescheduled with the same delay time, don't clear the interval id.\n if (delay != null && this.delay === delay && this.pending === false) {\n return id;\n }\n // Otherwise, if the action's delay time is different from the current delay,\n // or the action has been rescheduled before it's executed, clear the interval id\n if (id != null) {\n intervalProvider.clearInterval(id);\n }\n\n return undefined;\n }\n\n /**\n * Immediately executes this action and the `work` it contains.\n * @return {any}\n */\n public execute(state: T, delay: number): any {\n if (this.closed) {\n return new Error('executing a cancelled action');\n }\n\n this.pending = false;\n const error = this._execute(state, delay);\n if (error) {\n return error;\n } else if (this.pending === false && this.id != null) {\n // Dequeue if the action didn't reschedule itself. Don't call\n // unsubscribe(), because the action could reschedule later.\n // For example:\n // ```\n // scheduler.schedule(function doWork(counter) {\n // /* ... I'm a busy worker bee ... */\n // var originalAction = this;\n // /* wait 100ms before rescheduling the action */\n // setTimeout(function () {\n // originalAction.schedule(counter + 1);\n // }, 100);\n // }, 1000);\n // ```\n this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n }\n }\n\n protected _execute(state: T, _delay: number): any {\n let errored: boolean = false;\n let errorValue: any;\n try {\n this.work(state);\n } catch (e) {\n errored = true;\n // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n // return here, we can't have it return \"\" or 0 or false.\n // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n errorValue = e ? e : new Error('Scheduled action threw falsy error');\n }\n if (errored) {\n this.unsubscribe();\n return errorValue;\n }\n }\n\n unsubscribe() {\n if (!this.closed) {\n const { id, scheduler } = this;\n const { actions } = scheduler;\n\n this.work = this.state = this.scheduler = null!;\n this.pending = false;\n\n arrRemove(actions, this);\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, null);\n }\n\n this.delay = null!;\n super.unsubscribe();\n }\n }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n * now(): number;\n * schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @class Scheduler\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n public static now: () => number = dateTimestampProvider.now;\n\n constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n this.now = now;\n }\n\n /**\n * A getter method that returns a number representing the current time\n * (at the time this function was called) according to the scheduler's own\n * internal clock.\n * @return {number} A number that represents the current time. May or may not\n * have a relation to wall-clock time. May or may not refer to a time unit\n * (e.g. milliseconds).\n */\n public now: () => number;\n\n /**\n * Schedules a function, `work`, for execution. May happen at some point in\n * the future, according to the `delay` parameter, if specified. May be passed\n * some context object, `state`, which will be passed to the `work` function.\n *\n * The given arguments will be processed an stored as an Action object in a\n * queue of actions.\n *\n * @param {function(state: ?T): ?Subscription} work A function representing a\n * task, or some unit of work to be executed by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler itself.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @return {Subscription} A subscription in order to be able to unsubscribe\n * the scheduled work.\n */\n public schedule(work: (this: SchedulerAction, state?: T) => void, delay: number = 0, state?: T): Subscription {\n return new this.schedulerActionCtor(this, work).schedule(state, delay);\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n public actions: Array> = [];\n /**\n * A flag to indicate whether the Scheduler is currently executing a batch of\n * queued actions.\n * @type {boolean}\n * @internal\n */\n public _active: boolean = false;\n /**\n * An internal ID used to track the latest asynchronous task such as those\n * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n * others.\n * @type {any}\n * @internal\n */\n public _scheduled: TimerHandle | undefined;\n\n constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n super(SchedulerAction, now);\n }\n\n public flush(action: AsyncAction): void {\n const { actions } = this;\n\n if (this._active) {\n actions.push(action);\n return;\n }\n\n let error: any;\n this._active = true;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n this._active = false;\n\n if (error) {\n while ((action = actions.shift()!)) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * Schedule task as if you used setTimeout(task, duration)\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n * console.log(state);\n * this.schedule(state + 1, 1000); // `this` references currently executing Action,\n * // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { Subscription } from '../Subscription';\nimport { QueueScheduler } from './QueueScheduler';\nimport { SchedulerAction } from '../types';\nimport { TimerHandle } from './timerHandle';\n\nexport class QueueAction extends AsyncAction {\n constructor(protected scheduler: QueueScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (delay > 0) {\n return super.schedule(state, delay);\n }\n this.delay = delay;\n this.state = state;\n this.scheduler.flush(this);\n return this;\n }\n\n public execute(state: T, delay: number): any {\n return delay > 0 || this.closed ? super.execute(state, delay) : this._execute(state, delay);\n }\n\n protected requestAsyncId(scheduler: QueueScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n\n if ((delay != null && delay > 0) || (delay == null && this.delay > 0)) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n\n // Otherwise flush the scheduler starting with this action.\n scheduler.flush(this);\n\n // HACK: In the past, this was returning `void`. However, `void` isn't a valid\n // `TimerHandle`, and generally the return value here isn't really used. So the\n // compromise is to return `0` which is both \"falsy\" and a valid `TimerHandle`,\n // as opposed to refactoring every other instanceo of `requestAsyncId`.\n return 0;\n }\n}\n", "import { AsyncScheduler } from './AsyncScheduler';\n\nexport class QueueScheduler extends AsyncScheduler {\n}\n", "import { QueueAction } from './QueueAction';\nimport { QueueScheduler } from './QueueScheduler';\n\n/**\n *\n * Queue Scheduler\n *\n * Put every next task on a queue, instead of executing it immediately\n *\n * `queue` scheduler, when used with delay, behaves the same as {@link asyncScheduler} scheduler.\n *\n * When used without delay, it schedules given task synchronously - executes it right when\n * it is scheduled. However when called recursively, that is when inside the scheduled task,\n * another task is scheduled with queue scheduler, instead of executing immediately as well,\n * that task will be put on a queue and wait for current one to finish.\n *\n * This means that when you execute task with `queue` scheduler, you are sure it will end\n * before any other task scheduled with that scheduler will start.\n *\n * ## Examples\n * Schedule recursively first, then do something\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(() => {\n * queueScheduler.schedule(() => console.log('second')); // will not happen now, but will be put on a queue\n *\n * console.log('first');\n * });\n *\n * // Logs:\n * // \"first\"\n * // \"second\"\n * ```\n *\n * Reschedule itself recursively\n * ```ts\n * import { queueScheduler } from 'rxjs';\n *\n * queueScheduler.schedule(function(state) {\n * if (state !== 0) {\n * console.log('before', state);\n * this.schedule(state - 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * console.log('after', state);\n * }\n * }, 0, 3);\n *\n * // In scheduler that runs recursively, you would expect:\n * // \"before\", 3\n * // \"before\", 2\n * // \"before\", 1\n * // \"after\", 1\n * // \"after\", 2\n * // \"after\", 3\n *\n * // But with queue it logs:\n * // \"before\", 3\n * // \"after\", 3\n * // \"before\", 2\n * // \"after\", 2\n * // \"before\", 1\n * // \"after\", 1\n * ```\n */\n\nexport const queueScheduler = new QueueScheduler(QueueAction);\n\n/**\n * @deprecated Renamed to {@link queueScheduler}. Will be removed in v8.\n */\nexport const queue = queueScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction extends AsyncAction {\n constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay is greater than 0, request as an async action.\n if (delay !== null && delay > 0) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n // Push the action to the end of the scheduler queue.\n scheduler.actions.push(this);\n // If an animation frame has already been requested, don't request another\n // one. If an animation frame hasn't been requested yet, request one. Return\n // the current animation frame request id.\n return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n }\n\n protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n if (delay != null ? delay > 0 : this.delay > 0) {\n return super.recycleAsyncId(scheduler, id, delay);\n }\n // If the scheduler queue has no remaining actions with the same async id,\n // cancel the requested animation frame and set the scheduled flag to\n // undefined so the next AnimationFrameAction will request its own.\n const { actions } = scheduler;\n if (id != null && actions[actions.length - 1]?.id !== id) {\n animationFrameProvider.cancelAnimationFrame(id as number);\n scheduler._scheduled = undefined;\n }\n // Return undefined so the action knows to request a new async id if it's rescheduled.\n return undefined;\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n public flush(action?: AsyncAction): void {\n this._active = true;\n // The async id that effects a call to flush is stored in _scheduled.\n // Before executing an action, it's necessary to check the action's async\n // id to determine whether it's supposed to be executed in the current\n // flush.\n // Previous implementations of this method used a count to determine this,\n // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n // are removed from the actions array and that can shift actions that are\n // scheduled to be executed in a subsequent flush into positions at which\n // they are executed within the current flush.\n const flushId = this._scheduled;\n this._scheduled = undefined;\n\n const { actions } = this;\n let error: any;\n action = action || actions.shift()!;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n this._active = false;\n\n if (error) {\n while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * Perform task when `window.requestAnimationFrame` would fire\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html:
\n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n * div.style.height = height + \"px\";\n *\n * this.schedule(height + 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * Just emits 'complete', and nothing else.\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n * next: () => console.log('Next'),\n * complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n * mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n return new Observable((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last(arr: T[]): T | undefined {\n return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = ((x: any): x is ArrayLike => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike {\n return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable {\n return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable(obj: any): obj is AsyncIterable {\n return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n // TODO: We should create error codes that can be looked up, so this can be less verbose.\n return new TypeError(\n `You provided ${\n input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n );\n}\n", "export function getSymbolIterator(): symbol {\n if (typeof Symbol !== 'function' || !Symbol.iterator) {\n return '@@iterator' as any;\n }\n\n return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable {\n return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator(readableStream: ReadableStreamLike): AsyncGenerator {\n const reader = readableStream.getReader();\n try {\n while (true) {\n const { value, done } = await reader.read();\n if (done) {\n return;\n }\n yield value!;\n }\n } finally {\n reader.releaseLock();\n }\n}\n\nexport function isReadableStreamLike(obj: any): obj is ReadableStreamLike {\n // We don't want to use instanceof checks because they would return\n // false for instances from another Realm, like an