How to Apply a function to multiple columns in Pandas?
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) |
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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) |
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Output :
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