Как применить функцию к нескольким столбцам в Pandas?

Опубликовано: 27 Марта, 2022

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

Эта функция применяет функцию вдоль оси 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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Как применить функцию к нескольким столбцам в Pandas?

pandas.DataFrame.apply

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