The pandas apply() function can be used to apply a function across rows or columns of a pandas DataFrame.

This function is different from other functions like **drop()** and **replace()** that provide an inplace argument:

df.drop(['column1'], inplace=True) df.rename({'old_column' : 'new_column'}, inplace=True)

The **apply()** function has no inplace argument, so we must use the following syntax to transform a DataFrame inplace:

df = df.apply(lambda x: x*2)

The following examples show how to use this syntax in practice with the following pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'points': [25, 12, 15, 14, 19, 23, 25, 29], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #view DataFrame df points assists rebounds 0 25 5 11 1 12 7 8 2 15 7 10 3 14 9 6 4 19 12 6 5 23 9 5 6 25 9 9 7 29 4 12

**Example 1: Use apply() inplace for One Column**

The following code shows how to use **apply()** to transform one data frame column inplace:

#multiply all values in 'points' column by 2 inplace df.loc[:, 'points'] = df.points.apply(lambda x: x*2) #view updated DataFrame df points assists rebounds 0 50 5 11 1 24 7 8 2 30 7 10 3 28 9 6 4 38 12 6 5 46 9 5 6 50 9 9 7 58 4 12

**Example 2: Use apply() inplace for Multiple Columns**

The following code shows how to use **apply()** to transform multiple data frame columns inplace:

multiply all values in 'points' and 'rebounds' column by 2 inplace df[['points', 'rebounds']] = df[['points', 'rebounds']].apply(lambda x: x*2) #view updated DataFrame df points assists rebounds 0 50 5 22 1 24 7 16 2 30 7 20 3 28 9 12 4 38 12 12 5 46 9 10 6 50 9 18 7 58 4 24

**Example 3: Use apply() inplace for All Columns**

The following code shows how to use **apply()** to transform all data frame columns inplace:

#multiply values in all columns by 2 df = df.apply(lambda x: x*2) #view updated DataFrame df points assists rebounds 0 50 10 22 1 24 14 16 2 30 14 20 3 28 18 12 4 38 24 12 5 46 18 10 6 50 18 18 7 58 8 24

**Additional Resources**

The following tutorials explain how to perform other common functions in pandas:

How to Calculate the Sum of Columns in Pandas

How to Calculate the Mean of Columns in Pandas

How to Find the Max Value of Columns in Pandas