@@ -2681,8 +2681,15 @@ def sortlevel(
26812681 """
26822682 Sort MultiIndex at the requested level.
26832683
2684- The result will respect the original ordering of the associated
2685- factor at that level.
2684+ This method is useful when dealing with MultiIndex objects, allowing for
2685+ sorting at a specific level of the index. The function preserves the
2686+ relative ordering of data within the same level while sorting
2687+ the overall MultiIndex. The method provides flexibility with the `ascending`
2688+ parameter to define the sort order and with the `sort_remaining` parameter to
2689+ control whether the remaining levels should also be sorted. Sorting a
2690+ MultiIndex can be crucial when performing operations that require ordered
2691+ indices, such as grouping or merging datasets. The `na_position` argument is
2692+ important in handling missing values consistently across different levels.
26862693
26872694 Parameters
26882695 ----------
@@ -2692,7 +2699,9 @@ def sortlevel(
26922699 ascending : bool, default True
26932700 False to sort in descending order.
26942701 Can also be a list to specify a directed ordering.
2695- sort_remaining : sort by the remaining levels after level
2702+ sort_remaining : bool, default True
2703+ If True, sorts by the remaining levels after sorting by the specified
2704+ `level`.
26962705 na_position : {'first' or 'last'}, default 'first'
26972706 Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
26982707 the end.
@@ -2706,6 +2715,13 @@ def sortlevel(
27062715 indexer : np.ndarray[np.intp]
27072716 Indices of output values in original index.
27082717
2718+ See Also
2719+ --------
2720+ MultiIndex : A multi-level, or hierarchical, index object for pandas objects.
2721+ Index.sort_values : Sort Index values.
2722+ DataFrame.sort_index : Sort DataFrame by the index.
2723+ Series.sort_index : Sort Series by the index.
2724+
27092725 Examples
27102726 --------
27112727 >>> mi = pd.MultiIndex.from_arrays([[0, 0], [2, 1]])
0 commit comments