maxframe.dataframe.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 array-like or list of labels) – This parameter can be either a single column key, or a list containing column keys.

  • 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) – If True, modifies 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:

Changed row labels or None if inplace=True.

Return type:

DataFrame or None

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

>>> import maxframe.dataframe as md

>>> df = md.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
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