Pandas DataFrame apply() Examples

import pandas as pd

df = pd.DataFrame({'A': [1, 2], 'B': [10, 20]})


def square(x):
    return x * x


df1 = df.apply(square)

print(df)
print(df1)

Output:

   A   B
0  1  10
1  2  20

   A    B
0  1  100
1  4  400

The DataFrame on which apply() function is called remains unchanged. The apply() function returns a new DataFrame object after applying the function to its elements.

If you look at the above example, our square() function is very simple. We can easily convert it into a lambda function. We can create a lambda function while calling the apply() function.

df1 = df.apply(lambda x: x * x)

The output will remain the same as the last example.

We can apply a function along the axis. But, in the last example, there is no use of the axis. The function is being applied to all the elements of the DataFrame. The use of axis becomes clear when we call an aggregate function on the DataFrame rows or columns. Let’s say we want to get the sum of elements along the columns or indexes. The output will be different based on the value of the axis argument.

import pandas as pd
import numpy as np

df = pd.DataFrame({'A': [1, 2], 'B': [10, 20]})

df1 = df.apply(np.sum, axis=0)
print(df1)

df1 = df.apply(np.sum, axis=1)
print(df1)

Output:

A     3
B    30
dtype: int64

0    11
1    22
dtype: int64

In the first example, the sum of elements along the column is calculated. Whereas in the second example, the sum of the elements along the row is calculated.

Let’s say we want to apply a function that accepts more than one parameter. In that case, we can pass the additional parameters using the ‘args’ argument.

import pandas as pd


def sum(x, y, z):
    return x + y + z


df = pd.DataFrame({'A': [1, 2], 'B': [10, 20]})

df1 = df.apply(sum, args=(1, 2))
print(df1)

Output:

   A   B
0  4  13
1  5  23

Let’s look at an example where we will use both ‘args’ and ‘kwargs’ parameters to pass positional and keyword arguments to the function.

import pandas as pd


def sum(x, y, z, m):
    return (x + y + z) * m


df = pd.DataFrame({'A': [1, 2], 'B': [10, 20]})

df1 = df.apply(sum, args=(1, 2), m=10)
print(df1)

Output:

    A    B
0  40  130
1  50  230