Examples

import numpy as np
import pandas as pd

s = pd.Series([2, 3, 4, 5], name='f1',
              index=pd.Index(['p', 'q', 'r', 's'], name='idx'))

Generate a DataFrame with default index.

s.reset_index()

	idx	f1
0	p	2
1	q	3
2	r	4
3	s	5

To specify the name of the new column use name.

s.reset_index(name='values')

	idx	values
0	p	2
1	q	3
2	r	4
3	s	5

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

s.reset_index(drop=True)

0    2
1    3
2    4
3    5
Name: f1, 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    2
1    3
2    4
3    5
Name: f1, dtype: int64

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

arrays = [np.array(['b1', 'b2', 's1', 's2']),
              np.array(['one', 'two', 'one', 'two'])]

s2 = pd.Series(
    range(4), name='f1',
    index=pd.MultiIndex.from_arrays(arrays,
                                    names=['p', 'q']))

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

s2.reset_index(level='p')

	p	f1
q
one	b1	0
two	b2	1
one	s1	2
two	s2	3

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

s2.reset_index()

	p	q	f1
0	b1	one	0
1	b2	two	1
2	s1	one	2
3	s2	two	3