Pandas Series: apply() function

Last update on September 15 2022 12:56:42 (UTC/GMT +8 hours)

Invoke a python function on values of Pandas series

The apply() function is used to invoke a python function on values of Series.

Syntax:

Series.apply(self, func, convert_dtype=True, args=(), **kwds)

Parameters:

Name	Description	Type/Default Value	Required / Optional
func	Python function or NumPy ufunc to apply.	function	Required
convert_dtype	Try to find better dtype for elementwise function results. If False, leave as dtype=object.	bool Default Value: True	Required
args	Positional arguments passed to func after the series value.	tuple	Required
**kwds	Additional keyword arguments passed to func.		Required

Returns: Series or DataFrame
If func returns a Series object the result will be a DataFrame.

Example - Create a series with typical summer temperatures for each city:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([31, 27, 11],
              index=['Beijing', 'Los Angeles', 'Berlin'])
s

Output:

Beijing        31
Los Angeles    27
Berlin         11
dtype: int64

Example - Square the values by defining a function and passing it as an argument to apply():

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([31, 27, 11],
              index=['Beijing', 'Los Angeles', 'Berlin'])
def square(x):
    return x ** 2
s.apply(square)

Output:

Beijing        961
Los Angeles    729
Berlin         121
dtype: int64

Example - Square the values by passing an anonymous function as an argument to apply():

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([31, 27, 11],
              index=['Beijing', 'Los Angeles', 'Berlin'])
def square(x):
    return x ** 2
s.apply(lambda x: x ** 2)

Output:

Beijing        961
Los Angeles    729
Berlin         121
dtype: int64

Example - Define a custom function that needs additional positional arguments and pass these additional arguments using the args keyword:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([31, 27, 11],
              index=['Beijing', 'Los Angeles', 'Berlin'])
def subtract_custom_value(x, custom_value):
    return x - custom_value
s.apply(subtract_custom_value, args=(4,))

Output:

Beijing        27
Los Angeles    23
Berlin          7
dtype: int64

Example - Define a custom function that takes keyword arguments and pass these arguments to apply:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([31, 27, 11],
              index=['Beijing', 'Los Angeles', 'Berlin'])
def add_custom_values(x, **kwargs):
    for month in kwargs:
        x += kwargs[month]
    return x
s.apply(add_custom_values, november=18, december=16, january=15)

Output:

Beijing        80
Los Angeles    76
Berlin         60
dtype: int64

Example - Use a function from the Numpy library:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series([31, 27, 11],
              index=['Beijing', 'Los Angeles', 'Berlin'])
def add_custom_values(x, **kwargs):
    for month in kwargs:
        x += kwargs[month]
    return x
s.apply(np.log)

Output:

Beijing        3.433987
Los Angeles    3.295837
Berlin         2.397895
dtype: float64

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