pandas.DataFrame.set_index

DataFrame.set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)

Set the DataFrame index using existing columns.

Set the DataFrame index (row labels) using one or more existing columns. The index can replace the existing index or expand on it.

Parameters:

keys : label or list of label

Name or names of the columns that will be used as the index.

drop : bool, default True

Delete columns to be used as the new index.

append : bool, default False

Whether to append columns to existing index.

inplace : bool, default False

Modify the DataFrame in place (do not create a new object).

verify_integrity : bool, default False

Check the new index for duplicates. Otherwise defer the check until necessary. Setting to False will improve the performance of this method.

Returns:

DataFrame

Changed row labels.

See also

DataFrame.reset_index

Opposite of set_index.

DataFrame.reindex

Change to new indices or expand indices.

DataFrame.reindex_like

Change 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 multi-index 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 multi-index using a set of values and a column:

>>> df.set_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