pandas.Series.reset_index#

Series.reset_index(level=None, *, drop=False, name=_NoDefault.no_default, inplace=False, allow_duplicates=False)[source]#

Generate a new DataFrame or Series with the index reset.

This is useful when the index needs to be treated as a column, or when the index is meaningless and needs to be reset to the default before another operation.

Parameters

levelint, str, tuple, or list, default optional: For a Series with a MultiIndex, only remove the specified levels from the index. Removes all levels by default.
dropbool, default False: Just reset the index, without inserting it as a column in the new DataFrame.
nameobject, optional: The name to use for the column containing the original Series values. Uses self.name by default. This argument is ignored when drop is True.
inplacebool, default False: Modify the Series in place (do not create a new object).
allow_duplicatesbool, default False: Allow duplicate column labels to be created.

New in version 1.5.0.

Returns

Series or DataFrame or None: When drop is False (the default), a DataFrame is returned. The newly created columns will come first in the DataFrame, followed by the original Series values. When drop is True, a Series is returned. In either case, if inplace=True, no value is returned.

See also

DataFrame.reset_index: Analogous function for DataFrame.

Examples

>>> s = pd.Series([1, 2, 3, 4], name='foo',
...               index=pd.Index(['a', 'b', 'c', 'd'], name='idx'))

Generate a DataFrame with default index.

>>> s.reset_index()
  idx  foo
0   a    1
1   b    2
2   c    3
3   d    4

To specify the name of the new column use name.

>>> s.reset_index(name='values')
  idx  values
0   a       1
1   b       2
2   c       3
3   d       4

To generate a new Series with the default set drop to True.

>>> s.reset_index(drop=True)
0    1
1    2
2    3
3    4
Name: foo, dtype: int64

To update the Series in place, without generating a new one set inplace to True. Note that it also requires drop=True.

>>> s.reset_index(inplace=True, drop=True)
>>> s
0    1
1    2
2    3
3    4
Name: foo, dtype: int64

The level parameter is interesting for Series with a multi-level index.

>>> arrays = [np.array(['bar', 'bar', 'baz', 'baz']),
...           np.array(['one', 'two', 'one', 'two'])]
>>> s2 = pd.Series(
...     range(4), name='foo',
...     index=pd.MultiIndex.from_arrays(arrays,
...                                     names=['a', 'b']))

To remove a specific level from the Index, use level.

>>> s2.reset_index(level='a')
       a  foo
b
one  bar    0
two  bar    1
one  baz    2
two  baz    3

If level is not set, all levels are removed from the Index.

>>> s2.reset_index()
     a    b  foo
0  bar  one    0
1  bar  two    1
2  baz  one    2
3  baz  two    3