modin.pandas.DataFrame.set_index

DataFrame.set_index(keys: IndexLabel | list[IndexLabel | pd.Index | pd.Series | list | np.ndarray | Iterable], drop: bool = True, append: bool = False, inplace: bool = False, verify_integrity: bool = False) → None | DataFrame[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:

  • keys (label 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.

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

  • 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

Note

When performing DataFrame.set_index where the length of the DataFrame object does not match with the new index’s length, a ValueError is not raised. When the DataFrame object is longer than the new index, the DataFrame’s new index is filled with NaN values for the “extra” elements. When the DataFrame object is shorter than the new index, the extra values in the new index are ignored—the DataFrame stays the same length n, and uses only the first n values of the new index.

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
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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
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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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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
Copy

Create a MultiIndex using two Series:

>>> s = pd.Series([1, 2, 3, 4])
>>> df.set_index([s, s**2]) 
        month  year  sale
1 1.0       1  2012    55
2 4.0       4  2014    40
3 9.0       7  2013    84
4 16.0     10  2014    31
Copy
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