- Series.swaplevel(i: Level = - 2, j: Level = - 1, copy: bool | None = None) Series #
Swap levels i and j in a
Default is to swap the two innermost levels of the index.
i (int or str) – Levels of the indices to be swapped. Can pass level name as string.
j (int or str) – Levels of the indices to be swapped. Can pass level name as string.
copy (bool, default True) – Whether to copy underlying data.
Series with levels swapped in MultiIndex.
- Return type
>>> s = pd.Series( ... ["A", "B", "A", "C"], ... index=[ ... ["Final exam", "Final exam", "Coursework", "Coursework"], ... ["History", "Geography", "History", "Geography"], ... ["January", "February", "March", "April"], ... ], ... ) >>> s Final exam History January A Geography February B Coursework History March A Geography April C dtype: object
In the following example, we will swap the levels of the indices. Here, we will swap the levels column-wise, but levels can be swapped row-wise in a similar manner. Note that column-wise is the default behaviour. By not supplying any arguments for i and j, we swap the last and second to last indices.
>>> s.swaplevel() Final exam January History A February Geography B Coursework March History A April Geography C dtype: object
By supplying one argument, we can choose which index to swap the last index with. We can for example swap the first index with the last one as follows.
>>> s.swaplevel(0) January History Final exam A February Geography Final exam B March History Coursework A April Geography Coursework C dtype: object
We can also define explicitly which indices we want to swap by supplying values for both i and j. Here, we for example swap the first and second indices.
>>> s.swaplevel(0, 1) History Final exam January A Geography Final exam February B History Coursework March A Geography Coursework April C dtype: object
This method has not been implemented yet. Xorbits will try to execute it with pandas.
This docstring was copied from pandas.core.series.Series.