w3resource

Pandas Series: apply() function

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)
Pandas Series apply image

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
Pandas Series apply image

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

Previous: Compute the dot product between the Series and the columns in Pandas
Next: Aggregation in Pandas



Share this Tutorial / Exercise on : Facebook and Twitter