Pandas Series: rename_axis() function
Set the name of the axis in Pandas
The rename_axis() function is used to set the name of the axis for the index or columns.
Syntax:
Series.rename_axis(self, mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False)
Parameters:
| Name | Description | Type/Default Value | Required / Optional |
|---|---|---|---|
| mapper | Value to set the axis name attribute. | scalar, list-like | optional |
| index, columns | A scalar, list-like, dict-like or functions transformations to apply to that axis’ values. Use either mapper and axis to specify the axis to target with mapper, or index and/or columns. |
scalar, list-like, dict-like or function, | optional |
| axis | The axis to rename. | {0 or ‘index’, 1 or ‘columns’} Default Value: 0 |
Required |
| copy | Also copy underlying data. | bool Default Value: True |
Required |
| inplace | Modifies the object directly, instead of creating a new Series or DataFrame. | bool Default Value: False |
Required |
Returns: Series,DataFrame, or None - The same type as the caller or None if inplace is True.
Notes: DataFrame.rename_axis supports two calling conventions
- (index=index_mapper, columns=columns_mapper, ...)
- mapper, axis={'index', 'columns'}, ...)
The second calling convention will modify the names of the the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis labels.
We highly recommend using keyword arguments to clarify your intent.
Example - Series:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(["lion", "fox", "monkey"])
s
Output:
0 lion 1 fox 2 monkey dtype: object
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(["lion", "fox", "monkey"])
s.rename_axis("animal")
Output:
animal 0 lion 1 fox 2 monkey dtype: object
Example - DataFrame:
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame({"num_legs": [4, 4, 2],
"num_arms": [0, 0, 2]},
["lion", "fox", "monkey"])
df
Output:
num_legs num_arms
lion 4 0
fox 4 0
monkey 2 2
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame({"num_legs": [4, 4, 2],
"num_arms": [0, 0, 2]},
["lion", "fox", "monkey"])
df = df.rename_axis("animal")
df
Output:
num_legs num_arms
animal
lion 4 0
fox 4 0
monkey 2 2
Python-Pandas Code:
import numpy as np
import pandas as pd
df = pd.DataFrame({"num_legs": [4, 4, 2],
"num_arms": [0, 0, 2]},
["lion", "fox", "monkey"])
df = df.rename_axis("limbs", axis="columns")
df
Output:
limbs num_legs num_arms animal lion 4 0 fox 4 0 monkey 2 2
Example - MultiIndex:
Python-Pandas Code:
import numpy as np
import pandas as pd
df.index = pd.MultiIndex.from_product([['mammal'],
['lion', 'fox', 'monkey']],
names=['type', 'name'])
df
Output:
imbs num_legs num_arms
type name
mammal lion 4 0
fox 4 0
monkey 2 2
Python-Pandas Code:
import numpy as np
import pandas as pd
df.index = pd.MultiIndex.from_product([['mammal'],
['lion', 'fox', 'monkey']],
names=['type', 'name'])
df.rename_axis(index={'type': 'class'})
Output:
limbs num_legs num_arms
class name
mammal lion 4 0
fox 4 0
monkey 2 2
Python-Pandas Code:
import numpy as np
import pandas as pd
df.index = pd.MultiIndex.from_product([['mammal'],
['lion', 'fox', 'monkey']],
names=['type', 'name'])
df.rename_axis(columns=str.upper)
Output:
LIMBS num_legs num_arms
type name
mammal lion 4 0
fox 4 0
monkey 2 2
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