DOC: change nsmallest docstring to inline from @doc for SeriesGroupBy

Author: acrossler

pandas/core/groupby/generic.py

@@ -1961,10 +1961,110 @@ def nlargest(
         result = self._python_apply_general(f, data, not_indexed_same=True)
         return result
 
-    @doc(Series.nsmallest.__doc__)
     def nsmallest(
         self, n: int = 5, keep: Literal["first", "last", "all"] = "first"
     ) -> Series:
+        """
+        Return the smallest `n` elements.
+
+        Parameters
+        ----------
+        n : int, default 5
+            Return this many ascending sorted values.
+        keep : {'first', 'last', 'all'}, default 'first'
+            When there are duplicate values that cannot all fit in a
+            Series of `n` elements:
+
+            - ``first`` : return the first `n` occurrences in order
+              of appearance.
+            - ``last`` : return the last `n` occurrences in reverse
+              order of appearance.
+            - ``all`` : keep all occurrences. This can result in a Series of
+              size larger than `n`.
+
+        Returns
+        -------
+        Series
+            The `n` smallest values in the Series, sorted in increasing order.
+
+        See Also
+        --------
+        Series.nlargest: Get the `n` largest elements.
+        Series.sort_values: Sort Series by values.
+        Series.head: Return the first `n` rows.
+
+        Notes
+        -----
+        Faster than ``.sort_values().head(n)`` for small `n` relative to
+        the size of the ``Series`` object.
+
+        Examples
+        --------
+        >>> countries_population = {
+        ...     "Italy": 59000000,
+        ...     "France": 65000000,
+        ...     "Brunei": 434000,
+        ...     "Malta": 434000,
+        ...     "Maldives": 434000,
+        ...     "Iceland": 337000,
+        ...     "Nauru": 11300,
+        ...     "Tuvalu": 11300,
+        ...     "Anguilla": 11300,
+        ...     "Montserrat": 5200,
+        ... }
+        >>> s = pd.Series(countries_population)
+        >>> s
+        Italy       59000000
+        France      65000000
+        Brunei        434000
+        Malta         434000
+        Maldives      434000
+        Iceland       337000
+        Nauru          11300
+        Tuvalu         11300
+        Anguilla       11300
+        Montserrat      5200
+        dtype: int64
+
+        The `n` smallest elements where ``n=5`` by default.
+
+        >>> s.nsmallest()
+        Montserrat    5200
+        Nauru        11300
+        Tuvalu       11300
+        Anguilla     11300
+        Iceland     337000
+        dtype: int64
+
+        The `n` smallest elements where ``n=3``. Default `keep` value is
+        'first' so Nauru and Tuvalu will be kept.
+
+        >>> s.nsmallest(3)
+        Montserrat   5200
+        Nauru       11300
+        Tuvalu      11300
+        dtype: int64
+
+        The `n` smallest elements where ``n=3`` and keeping the last
+        duplicates. Anguilla and Tuvalu will be kept since they are the last
+        with value 11300 based on the index order.
+
+        >>> s.nsmallest(3, keep="last")
+        Montserrat   5200
+        Anguilla    11300
+        Tuvalu      11300
+        dtype: int64
+
+        The `n` smallest elements where ``n=3`` with all duplicates kept. Note
+        that the returned Series has four elements due to the three duplicates.
+
+        >>> s.nsmallest(3, keep="all")
+        Montserrat   5200
+        Nauru       11300
+        Tuvalu      11300
+        Anguilla    11300
+        dtype: int64
+        """
         f = partial(Series.nsmallest, n=n, keep=keep)
         data = self._obj_with_exclusions
         # Don't change behavior if result index happens to be the same, i.e.