How to apply a lambda function to pandas DataFrame?

In this article, we’ll look at how to use the apply method to apply a lambda function to a DataFrame.

Applying a Lambda Function to a Python DataFrame

A lambda function is a small, anonymous function in Python that can take any number of arguments but can only have one expression.

• It’s often used to simplify code and make it more readable, and can be especially useful when working with data in a Pandas DataFrame.

To apply a lambda function to a DataFrame, you can use the apply method. This method applies a function to each element in a specified axis (row or column) of a DataFrame.

Here’s an example of using a lambda function to calculate the square of each element in a DataFrame:

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

df['A'] = df['A'].apply(lambda x: x**2)

print(df)

In this example, the lambda function takes an argument x and returns x**2. The apply method is used to apply this function to each element in the 'A' column of the DataFrame df. The result is a new DataFrame with the square of each element in the 'A' column.

You can also use a lambda function to apply a more complex operation to a DataFrame. For example…