fixed mkdocs issue

DeaMariaLeon · Nov 3, 2024 · 73cc573 · 73cc573
1 parent 0cdb0c3
commit 73cc573
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Showing 2 changed files with 30 additions and 30 deletions.
diff --git a/narwhals/expr.py b/narwhals/expr.py
@@ -485,36 +485,36 @@ def ewm_mean(
             │ 2.428571 │
             └──────────┘
 
-             pandas and Polars handle nulls differently. So, When calculating ewm over
-             a sequence with null values, leading to distinct results:
+            pandas and Polars handle nulls differently. So, calculating ewm over
+            a sequence with null values leads to distinct results:
 
-             >>> data = {"a": [2.0, 4.0, None, 3.0, float("nan"), 3.0]}
-             >>> df_pd2 = pd.DataFrame(data)
-             >>> df_pl2 = pl.DataFrame(data)
+            >>> data = {"a": [2.0, 4.0, None, 3.0, float("nan"), 3.0]}
+            >>> df_pd2 = pd.DataFrame(data)
+            >>> df_pl2 = pl.DataFrame(data)
 
-             >>> func(df_pd2)
-                       a
-             0  2.000000
-             1  3.333333
-             2  3.333333
-             3  3.090909
-             4  3.090909
-             5  3.023256
-
-             >>> func(df_pl2)  # doctest: +NORMALIZE_WHITESPACE
-             shape: (6, 1)
-             ┌──────────┐
-             │ a        │
-             │ ---      │
-             │ f64      │
-             ╞══════════╡
-             │ 2.0      │
-             │ 3.333333 │
-             │ null     │
-             │ 3.090909 │
-             │ NaN      │
-             │ NaN      │
-             └──────────┘
+            >>> func(df_pd2)
+                      a
+            0  2.000000
+            1  3.333333
+            2  3.333333
+            3  3.090909
+            4  3.090909
+            5  3.023256
+
+            >>> func(df_pl2)  # doctest: +NORMALIZE_WHITESPACE
+            shape: (6, 1)
+            ┌──────────┐
+            │ a        │
+            │ ---      │
+            │ f64      │
+            ╞══════════╡
+            │ 2.0      │
+            │ 3.333333 │
+            │ null     │
+            │ 3.090909 │
+            │ NaN      │
+            │ NaN      │
+            └──────────┘
         """
         return self.__class__(
             lambda plx: self._call(plx).ewm_mean(

diff --git a/narwhals/series.py b/narwhals/series.py
@@ -464,8 +464,8 @@ def ewm_mean(
                2.428571
             ]
 
-            pandas and Polars handle nulls differently. So, When calculating ewm over
-            a sequence with null values, leading to distinct results:
+            pandas and Polars handle nulls differently. So, calculating ewm over
+            a sequence with null values leads to distinct results:
 
             >>> data = [2.0, 4.0, None, 3.0, float("nan"), 3.0]
             >>> s_pd2 = pd.Series(name="a", data=data)