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 ofIterator
.- 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