DOC: Enforce Numpy Docstring Validation for pandas.Series.mean (#58574)

* DOC: add more examples

* DOC: add more examples

Author: tuhinsharma121

ci/code_checks.sh

@@ -200,7 +200,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.list.flatten SA01" \
         -i "pandas.Series.list.len SA01" \
         -i "pandas.Series.lt PR07,SA01" \
-        -i "pandas.Series.mean RT03,SA01" \
         -i "pandas.Series.min RT03" \
         -i "pandas.Series.mod PR07" \
         -i "pandas.Series.mode SA01" \

pandas/core/series.py

@@ -6266,14 +6266,55 @@ def prod(
         )
 
     @deprecate_nonkeyword_arguments(version="3.0", allowed_args=["self"], name="mean")
-    @doc(make_doc("mean", ndim=1))
     def mean(
         self,
         axis: Axis | None = 0,
         skipna: bool = True,
         numeric_only: bool = False,
         **kwargs,
     ) -> Any:
+        """
+        Return the mean of the values over the requested axis.
+
+        Parameters
+        ----------
+        axis : {index (0)}
+            Axis for the function to be applied on.
+            For `Series` this parameter is unused and defaults to 0.
+
+            For DataFrames, specifying ``axis=None`` will apply the aggregation
+            across both axes.
+
+            .. versionadded:: 2.0.0
+
+        skipna : bool, default True
+            Exclude NA/null values when computing the result.
+        numeric_only : bool, default False
+            Include only float, int, boolean columns.
+        **kwargs
+            Additional keyword arguments to be passed to the function.
+
+        Returns
+        -------
+        scalar or Series (if level specified)
+            Median of the values for the requested axis.
+
+        See Also
+        --------
+        numpy.median : Equivalent numpy function for computing median.
+        Series.sum : Sum of the values.
+        Series.median : Median of the values.
+        Series.std : Standard deviation of the values.
+        Series.var : Variance of the values.
+        Series.min : Minimum value.
+        Series.max : Maximum value.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2, 3])
+        >>> s.mean()
+        2.0
+        """
         return NDFrame.mean(
             self, axis=axis, skipna=skipna, numeric_only=numeric_only, **kwargs
         )