docs: Add Pyarrow example to Expr.mean and nw.mean (#1207)

* Add Pyarrow example to Expr.mean

* Make mean docstring consistent with stable v1 API

narwhals/expr.py

@@ -367,17 +367,19 @@ def mean(self) -> Self:
         Examples:
             >>> import polars as pl
             >>> import pandas as pd
+            >>> import pyarrow as pa
             >>> import narwhals as nw
             >>> df_pd = pd.DataFrame({"a": [-1, 0, 1], "b": [2, 4, 6]})
             >>> df_pl = pl.DataFrame({"a": [-1, 0, 1], "b": [2, 4, 6]})
+            >>> df_pa = pa.table({"a": [-1, 0, 1], "b": [2, 4, 6]})
 
             Let's define a dataframe-agnostic function:
 
             >>> @nw.narwhalify
             ... def func(df):
             ...     return df.select(nw.col("a", "b").mean())
 
-            We can then pass either pandas or Polars to `func`:
+            We can pass any supported library such as Pandas, Polars, or PyArrow to `func`:
 
             >>> func(df_pd)
                  a    b
@@ -391,6 +393,13 @@ def mean(self) -> Self:
             ╞═════╪═════╡
             │ 0.0 ┆ 4.0 │
             └─────┴─────┘
+            >>> func(df_pa)
+            pyarrow.Table
+            a: double
+            b: double
+            ----
+            a: [[0]]
+            b: [[4]]
         """
         return self.__class__(lambda plx: self._call(plx).mean())
 
@@ -4054,17 +4063,19 @@ def mean(*columns: str) -> Expr:
     Examples:
         >>> import pandas as pd
         >>> import polars as pl
+        >>> import pyarrow as pa
         >>> import narwhals as nw
         >>> df_pl = pl.DataFrame({"a": [1, 8, 3]})
         >>> df_pd = pd.DataFrame({"a": [1, 8, 3]})
+        >>> df_pa = pa.table({"a": [1, 8, 3]})
 
         We define a dataframe agnostic function:
 
         >>> @nw.narwhalify
         ... def func(df):
         ...     return df.select(nw.mean("a"))
 
-        We can then pass either pandas or Polars to `func`:
+        We can pass any supported library such as Pandas, Polars, or PyArrow to `func`:
 
         >>> func(df_pd)
              a
@@ -4078,6 +4089,11 @@ def mean(*columns: str) -> Expr:
         ╞═════╡
         │ 4.0 │
         └─────┘
+        >>> func(df_pa)
+        pyarrow.Table
+        a: double
+        ----
+        a: [[4]]
     """
 
     return Expr(lambda plx: plx.mean(*columns))

narwhals/stable/v1/__init__.py

@@ -1254,17 +1254,19 @@ def mean(*columns: str) -> Expr:
     Examples:
         >>> import pandas as pd
         >>> import polars as pl
+        >>> import pyarrow as pa
         >>> import narwhals.stable.v1 as nw
         >>> df_pl = pl.DataFrame({"a": [1, 8, 3]})
         >>> df_pd = pd.DataFrame({"a": [1, 8, 3]})
+        >>> df_pa = pa.table({"a": [1, 8, 3]})
 
         We define a dataframe agnostic function:
 
         >>> @nw.narwhalify
         ... def func(df):
         ...     return df.select(nw.mean("a"))
 
-        We can then pass either pandas or Polars to `func`:
+        We can pass any supported library such as Pandas, Polars, or PyArrow to `func`:
 
         >>> func(df_pd)
              a
@@ -1278,6 +1280,11 @@ def mean(*columns: str) -> Expr:
         ╞═════╡
         │ 4.0 │
         └─────┘
+        >>> func(df_pa)
+        pyarrow.Table
+        a: double
+        ----
+        a: [[4]]
     """
     return _stableify(nw.mean(*columns))