Pandas Series: reset_index() function

Last update on September 15 2022 12:55:32 (UTC/GMT +8 hours)

Generate a new Pandas series with the index reset

The reset_index() function is used to 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

Syntax:

Series.reset_index(self, level=None, drop=False, name=None, inplace=False)

Parameters:

Name	Description	Type/Default Value	Required / Optional
level	For a Series with a MultiIndex, only remove the specified levels from the index. Removes all levels by default.	int, str, tuple, or list,	optional
drop	Just reset the index, without inserting it as a column in the new DataFrame.	bool Default Value: False	Required
name	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.	object	optional
inplace	Modify the Series in place (do not create a new object).	bool Default Value: False	Required

Returns:Series or DataFrame
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.

Example - Generate a DataFrame with default index:

Python-Pandas Code:

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'))
s.reset_index()

Output:

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

Example - To specify the name of the new column use name:

Python-Pandas Code:

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'))
s.reset_index(name='values')

Output:

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

Example - To generate a new Series with the default set drop to True:

Python-Pandas Code:

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'))
s.reset_index(drop=True)

Output:

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

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

Python-Pandas Code:

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'))
s.reset_index(inplace=True, drop=True)
s

Output:

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

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

Example - To remove a specific level from the Index, use level:

Python-Pandas Code:

import numpy as np
import pandas as pd
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']))
s2.reset_index(level='p')

Output:

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

Example - If level is not set, all levels are removed from the Index:

Python-Pandas Code:

import numpy as np
import pandas as pd
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']))
s2.reset_index()

Output:

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

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