Set the DataFrame index using existing columns.
Set the DataFrame index (row labels) using one or more existing columns or arrays (of the correct length). The index can replace the existing index or expand on it.
This parameter can be either a single column key, a single array of the same length as the calling DataFrame, or a list containing an arbitrary combination of column keys and arrays. Here, “array” encompasses Series, Index, np.ndarray, and instances of Iterator.
Delete columns to be used as the new index.
Whether to append columns to existing index.
Whether to modify the DataFrame rather than creating a new one.
Check the new index for duplicates. Otherwise defer the check until necessary. Setting to False will improve the performance of this method.
Changed row labels or None if inplace=True.
See also
DataFrame.reset_indexOpposite of set_index.
DataFrame.reindexChange to new indices or expand indices.
DataFrame.reindex_likeChange to same indices as other DataFrame.
Examples
>>> df = pd.DataFrame({'month': [1, 4, 7, 10],
... 'year': [2012, 2014, 2013, 2014],
... 'sale': [55, 40, 84, 31]})
>>> df
month year sale
0 1 2012 55
1 4 2014 40
2 7 2013 84
3 10 2014 31
Set the index to become the ‘month’ column:
>>> df.set_index('month')
year sale
month
1 2012 55
4 2014 40
7 2013 84
10 2014 31
Create a MultiIndex using columns ‘year’ and ‘month’:
>>> df.set_index(['year', 'month'])
sale
year month
2012 1 55
2014 4 40
2013 7 84
2014 10 31
Create a MultiIndex using an Index and a column:
>>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
month sale
year
1 2012 1 55
2 2014 4 40
3 2013 7 84
4 2014 10 31
Create a MultiIndex using two Series:
>>> s = pd.Series([1, 2, 3, 4])
>>> df.set_index([s, s**2])
month year sale
1 1 1 2012 55
2 4 4 2014 40
3 9 7 2013 84
4 16 10 2014 31
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Licensed under the 3-clause BSD License.
https://pandas.pydata.org/pandas-docs/version/2.3.0/reference/api/pandas.DataFrame.set_index.html