How to Apply a function to multiple columns in Pandas?

Last Updated : 20 Aug, 2020

Let us see how to apply a function to multiple columns in a Pandas DataFrame. To execute this task will be using the apply() function.

pandas.DataFrame.apply

This function applies a function along an axis of the DataFrame.

Syntax : DataFrame.apply(parameters)
Parameters :
func : Function to apply to each column or row.
axis : Axis along which the function is applied
raw : Determines if row or column is passed as a Series or ndarray object.
result_type : ‘expand’, ‘reduce’, ‘broadcast’, None; default None
args : Positional arguments to pass to func in addition to the array/series.
**kwds : Additional keyword arguments to pass as keywords arguments to func.
Returns : Series or DataFrame

Example 1 : Prepending “Geek” before every element in two columns.

# imnport the module
import pandas as pd
  
# creating a DataFrame
df = pd.DataFrame({'String 1' :['Tom', 'Nick', 'Krish', 'Jack'], 
                   'String 2' :['Jane', 'John', 'Doe', 'Mohan']})
  
# displaying the DataFrame
display(df)
  
# function for prepending 'Geek'
def prepend_geek(name):
    return 'Geek ' + name
  
# executing the function
df[["String 1", "String 2"]] = df[["String 1", "String 2"]].apply(prepend_geek)
  
# displaying the DataFrame
display(df)

Output :

Example 2 : Multiplying the value of each element by 2

# imnport the module
import pandas as pd
  
# creating a DataFrame
df = pd.DataFrame({'Integers' :[1, 2, 3, 4, 5], 
                   'Float' :[1.1, 2.2, 3.3, 4.4 ,5.5]})
  
# displaying the DataFrame
display(df)
  
# function for prepending 'Geek'
def multiply_by_2(number):
    return 2 * number
  
# executing the function
df[["Integers", "Float"]] = df[["Integers", "Float"]].apply(multiply_by_2)
  
# displaying the DataFrame
display(df)

Output :

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How to Apply a function to multiple columns in Pandas?

pandas.DataFrame.apply