@@ -6464,6 +6464,57 @@ def swaplevel(self, i: Axis = -2, j: Axis = -1, axis: Axis = 0) -> DataFrame:
64646464 Returns
64656465 -------
64666466 DataFrame
6467+
6468+ Examples
6469+ --------
6470+ >>> df = pd.DataFrame(
6471+ ... {"Grade": ["A", "B", "A", "C"]},
6472+ ... index=[
6473+ ... ["Final exam", "Final exam", "Coursework", "Coursework"],
6474+ ... ["History", "Geography", "History", "Geography"],
6475+ ... ["January", "February", "March", "April"],
6476+ ... ],
6477+ ... )
6478+ >>> df
6479+ Grade
6480+ Final exam History January A
6481+ Geography February B
6482+ Coursework History March A
6483+ Geography April C
6484+
6485+ In the following example, we will swap the levels of the indices.
6486+ Here, we will swap the levels column-wise, but levels can be swapped row-wise
6487+ in a similar manner. Note that column-wise is the default behaviour.
6488+ By not supplying any arguments for i and j, we swap the last and second to
6489+ last indices.
6490+
6491+ >>> df.swaplevel()
6492+ Grade
6493+ Final exam January History A
6494+ February Geography B
6495+ Coursework March History A
6496+ April Geography C
6497+
6498+ By supplying one argument, we can choose which index to swap the last
6499+ index with. We can for example swap the first index with the last one as
6500+ follows.
6501+
6502+ >>> df.swaplevel(0)
6503+ Grade
6504+ January History Final exam A
6505+ February Geography Final exam B
6506+ March History Coursework A
6507+ April Geography Coursework C
6508+
6509+ We can also define explicitly which indices we want to swap by supplying values
6510+ for both i and j. Here, we for example swap the first and second indices.
6511+
6512+ >>> df.swaplevel(0, 1)
6513+ Grade
6514+ History Final exam January A
6515+ Geography Final exam February B
6516+ History Coursework March A
6517+ Geography Coursework April C
64676518 """
64686519 result = self .copy ()
64696520
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