pandas.DataFrame.set_index#

DataFrame.set_index(keys, *, drop=True, append=False, inplace=False, verify_integrity=False)[source] #

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.

Parameters:

keyslabel or array-like or list of labels/arrays

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.

dropbool, default True

Delete columns to be used as the new index.

appendbool, default False

Whether to append columns to existing index.

inplacebool, default False

Whether to modify the DataFrame rather than creating a new one.

verify_integritybool, 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 or None

Changed row labels or None if inplace=True.

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 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