diff --git a/narwhals/expr.py b/narwhals/expr.py
index ea7271984..ddacd4fff 100644
--- a/narwhals/expr.py
+++ b/narwhals/expr.py
@@ -459,7 +459,6 @@ def ewm_mean(
                   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
@@ -503,37 +502,6 @@ def ewm_mean(
             │ 1.666667 │
             │ 2.428571 │
             └──────────┘
-
-            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)
-
-            >>> func(df_pd2)
-                      a
-            0  2.000000
-            1  3.333333
-            2       NaN
-            3  3.090909
-            4       NaN
-            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
index 7e60b1581..01829cb04 100644
--- a/narwhals/series.py
+++ b/narwhals/series.py
@@ -418,7 +418,6 @@ def ewm_mean(
                   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
@@ -459,35 +458,6 @@ def ewm_mean(
                1.666667
                2.428571
             ]
-
-            pandas and Polars handle NaN differently. So, calculating ewm over
-            a sequence with null values leads to distinct results. Narwhals
-            matches Polars' results when "None" is in the input.
-
-            >>> data = [2.0, 4.0, None, 3.0, float("nan"), 3.0]
-            >>> s_pd2 = pd.Series(name="a", data=data)
-            >>> s_pl2 = pl.Series(name="a", values=data)
-
-            >>> func(s_pd2)
-            0    2.000000
-            1    3.333333
-            2         NaN
-            3    3.090909
-            4         NaN
-            5    3.023256
-            Name: a, dtype: float64
-
-            >>> func(s_pl2)  # doctest: +NORMALIZE_WHITESPACE
-            shape: (6,)
-            Series: 'a' [f64]
-            [
-               2.0
-               3.333333
-               null
-               3.090909
-               NaN
-               NaN
-            ]
         """
         return self._from_compliant_series(
             self._compliant_series.ewm_mean(
diff --git a/narwhals/stable/v1/__init__.py b/narwhals/stable/v1/__init__.py
index 174115813..12016d80d 100644
--- a/narwhals/stable/v1/__init__.py
+++ b/narwhals/stable/v1/__init__.py
@@ -535,7 +535,6 @@ def ewm_mean(
                   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
@@ -576,35 +575,6 @@ def ewm_mean(
                1.666667
                2.428571
             ]
-
-            pandas and Polars handle NaN differently. So, calculating ewm over
-            a sequence with null values leads to distinct results. Narwhals
-            matches Polars' results when "None" is in the input.
-
-            >>> data = [2.0, 4.0, None, 3.0, float("nan"), 3.0]
-            >>> s_pd2 = pd.Series(name="a", data=data)
-            >>> s_pl2 = pl.Series(name="a", values=data)
-
-            >>> func(s_pd2)
-            0    2.000000
-            1    3.333333
-            2         NaN
-            3    3.090909
-            4         NaN
-            5    3.023256
-            Name: a, dtype: float64
-
-            >>> func(s_pl2)  # doctest: +NORMALIZE_WHITESPACE
-            shape: (6,)
-            Series: 'a' [f64]
-            [
-               2.0
-               3.333333
-               null
-               3.090909
-               NaN
-               NaN
-            ]
         """
         from narwhals.exceptions import NarwhalsUnstableWarning
         from narwhals.utils import find_stacklevel
@@ -762,7 +732,6 @@ def ewm_mean(
                   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
@@ -806,37 +775,6 @@ def ewm_mean(
             │ 1.666667 │
             │ 2.428571 │
             └──────────┘
-
-            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)
-
-            >>> func(df_pd2)
-                      a
-            0  2.000000
-            1  3.333333
-            2       NaN
-            3  3.090909
-            4       NaN
-            5  3.023256
-
-            >>> func(df_pl2)  # doctest: +NORMALIZE_WHITESPACE
-            shape: (6, 1)
-            ┌──────────┐
-            │ a        │
-            │ ---      │
-            │ f64      │
-            ╞══════════╡
-            │ 2.0      │
-            │ 3.333333 │
-            │ null     │
-            │ 3.090909 │
-            │ NaN      │
-            │ NaN      │
-            └──────────┘
         """
         from narwhals.exceptions import NarwhalsUnstableWarning
         from narwhals.utils import find_stacklevel