Merge pull request #17 from MarcoGorelli/old-pandas-compat

Old pandas compat

.github/workflows/pytest.yml

@@ -9,7 +9,7 @@ jobs:
   tox:
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
         os: [windows-latest, ubuntu-latest]
 
     runs-on: ${{ matrix.os }}
@@ -28,5 +28,7 @@ jobs:
           key: ${{ runner.os }}-build-${{ matrix.python-version }}
       - name: install-reqs
         run: python -m pip install --upgrade tox virtualenv setuptools pip -r requirements-dev.txt
+      - name: install-modin
+        run: python -m pip install --upgrade modin[dask]
       - name: Run pytest
         run: pytest tests --cov=narwhals --cov=tests --cov-fail-under=50

narwhals/dataframe.py

@@ -169,15 +169,17 @@ def __init__(
         elif (pd := get_pandas()) is not None and isinstance(df, pd.DataFrame):
             self._dataframe = PandasDataFrame(df, implementation="pandas")
             self._implementation = "pandas"
-        elif (mpd := get_modin()) is not None and isinstance(df, mpd.DataFrame):
+        elif (mpd := get_modin()) is not None and isinstance(
+            df, mpd.DataFrame
+        ):  # pragma: no cover
             self._dataframe = PandasDataFrame(df, implementation="modin")
             self._implementation = "modin"
         elif (cudf := get_cudf()) is not None and isinstance(
             df, cudf.DataFrame
         ):  # pragma: no cover
             self._dataframe = PandasDataFrame(df, implementation="cudf")
             self._implementation = "cudf"
-        elif hasattr(df, "__narwhals_dataframe__"):
+        elif hasattr(df, "__narwhals_dataframe__"):  # pragma: no cover
             self._dataframe = df.__narwhals_dataframe__()
             self._implementation = "custom"
         else:
@@ -222,7 +224,9 @@ def __init__(
         elif (pd := get_pandas()) is not None and isinstance(df, pd.DataFrame):
             self._dataframe = PandasDataFrame(df, implementation="pandas")
             self._implementation = "pandas"
-        elif (mpd := get_modin()) is not None and isinstance(df, mpd.DataFrame):
+        elif (mpd := get_modin()) is not None and isinstance(
+            df, mpd.DataFrame
+        ):  # pragma: no cover
             self._dataframe = PandasDataFrame(df, implementation="modin")
             self._implementation = "modin"
         elif (cudf := get_cudf()) is not None and isinstance(

narwhals/pandas_like/group_by.py

@@ -8,6 +8,8 @@
 from typing import Callable
 from typing import Iterable
 
+from packaging.version import parse
+
 from narwhals.pandas_like.utils import dataframe_from_dict
 from narwhals.pandas_like.utils import evaluate_simple_aggregation
 from narwhals.pandas_like.utils import horizontal_concat
@@ -122,7 +124,10 @@ def func(df: Any) -> Any:
                 out_names.append(result_keys.name)
         return pd.Series(out_group, index=out_names)
 
-    result_complex = grouped.apply(func, include_groups=False)
+    if parse(pd.__version__) < parse("2.2.0"):
+        result_complex = grouped.apply(func)
+    else:
+        result_complex = grouped.apply(func, include_groups=False)
 
     if result_simple is not None:
         result = pd.concat(

requirements-dev.txt

@@ -1,5 +1,4 @@
 covdefaults
-modin[dask]
 pandas
 polars
 pre-commit

tests/test_common.py

@@ -1,9 +1,9 @@
 from __future__ import annotations
 
+import os
 import warnings
 from typing import Any
 
-import modin.pandas as mpd
 import numpy as np
 import pandas as pd
 import polars as pl
@@ -15,11 +15,17 @@
 df_pandas = pd.DataFrame({"a": [1, 3, 2], "b": [4, 4, 6], "z": [7.0, 8, 9]})
 df_polars = pl.DataFrame({"a": [1, 3, 2], "b": [4, 4, 6], "z": [7.0, 8, 9]})
 df_lazy = pl.LazyFrame({"a": [1, 3, 2], "b": [4, 4, 6], "z": [7.0, 8, 9]})
-with warnings.catch_warnings():
-    warnings.filterwarnings("ignore", category=UserWarning)
-    df_mpd = mpd.DataFrame(
-        mpd.DataFrame({"a": [1, 3, 2], "b": [4, 4, 6], "z": [7.0, 8, 9]})
-    )
+
+if os.environ.get("CI", None):
+    import modin.pandas as mpd
+
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=UserWarning)
+        df_mpd = mpd.DataFrame(
+            pd.DataFrame({"a": [1, 3, 2], "b": [4, 4, 6], "z": [7.0, 8, 9]})
+        )
+else:
+    df_mpd = df_pandas.copy()
 
 
 @pytest.mark.parametrize(
@@ -234,6 +240,9 @@ def test_convert_pandas(df_raw: Any) -> None:
 
 
 @pytest.mark.parametrize("df_raw", [df_polars, df_pandas, df_mpd])
+@pytest.mark.filterwarnings(
+    r"ignore:np\.find_common_type is deprecated\.:DeprecationWarning"
+)
 def test_convert_numpy(df_raw: Any) -> None:
     result = nw.DataFrame(df_raw).to_numpy()
     expected = np.array([[1, 3, 2], [4, 4, 6], [7.0, 8, 9]]).T