How to apply a function to columns of pandas DataFrame?

Apply a function to columns of pandas DataFrame

The apply() function can apply a given function to any row or column of a pandas DataFrame. We can apply any built-in function, user-defined functions, or even a one-liner lambda function.

To apply a function to columns of pandas DataFrame:

1. Use the apply() function.

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# Apply a function to columns of DataFrame in Python
import pandas as pd 
# Create a DataFrame
dt = pd.DataFrame([[15,26,33,82],[22,92,65,14]], columns = ['c1','c2','c3','c4'])
# Create a function
def x(a):
    return a + 1 
# Use the apply() function to apply a function to columns of DataFrame in Python
dt_new  = dt.apply(x, axis = 0)
print(dt_new)
# Output:   c1  c2  c3  c4
#        0  16  27  34  83
#        1  23  93  66  15    

​

In the above sample code, we create a function x() and a DataFrame. We use the apply() function to apply this function to the columns of pandas DataFrame.

The axis parameter specifies if the function is to be applied to a row or a column. We specify its value as one for rows and zero for columns.

Similarly, we can also apply a built-in function to a column of Pandas DataFrame.

See the code below.

​

# Apply a function to columns of DataFrame in Python
import pandas as pd 
# Create a DataFrame
dt = pd.DataFrame([[15,26,33,82],[22,92,65,14]], columns = ['c1','c2','c3','c4'])
# Use the apply() function to apply a function to columns of DataFrame in Python
dt_new  = dt[["c1","c2"]].apply(sum, axis = 0)
print(dt_new)
# Output:  c1     37
#           c2    118

​

In the above sample code, we apply the sum() function to two columns of a DataFrame.

To conclude.

This particular article demonstrates how to apply a function to columns of pandas DataFrame in Python using the apply() function.

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