Pandas Series: rename() function

Last update on September 15 2022 12:55:33 (UTC/GMT +8 hours)

Alter Series index in Pandas

The rename() function is used to alter Series index labels or name.

Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error.

Alternatively, change Series.name with a scalar value.

Syntax:

Series.rename(self, index=None, **kwargs)

Parameters:

Name	Description	Type/Default Value	Required / Optional
index	dict-like or functions are transformations to apply to the index. Scalar or hashable sequence-like will alter the Series.name attribute.	scalar, hashable sequence, dict-like or function	optional
copy	Whether to copy underlying data.	bool Default Value: True	Required
inplace	Whether to return a new Series. If True then value of copy is ignored.	bool Default Value: False	Required
level	In case of a MultiIndex, only rename labels in the specified level.	int or level name Default Value: None	Required

Returns: Series
Series with index labels or name altered.

Example:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([2, 3, 4])
s

Output:

0    2
1    3
2    4
dtype: int64

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([2, 3, 4])
s.rename("my_name")  # scalar, changes Series.name

Output:

0    2
1    3
2    4
Name: my_name, dtype: int64

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([2, 3, 4])
s.rename(lambda x: x ** 2)  # function, changes labels

Output:

0    2
1    3
4    4
dtype: int64

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([2, 3, 4])
s.rename({1: 4, 2: 5})  # mapping, changes labels

Output:

0    2
4    3
5    4
dtype: int64

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