# How to Apply a function to multiple columns in Pandas

by Abdul Rawoof A R Updated: Jan 31, 2023

Solution Kit

By passing a list of column names to the apply() method in Pandas, you can apply a function to many columns at once. You can use the applymap() and map() functions to apply a function to each element of a DataFrame and a series, respectively.

Here are a few examples of the many possible uses for applying a function to several columns in a Pandas DataFrame:

- Data transformation: You can use the apply() function to transform data in multiple columns, for example, by normalizing numerical values or encoding categorical variables.
- Data aggregation: You can use the apply() function to compute summary statistics for multiple columns at once, for example, by calculating the mean or median of each column.
- Data cleaning: You can use the apply() function to clean up data in multiple columns.
- Data visualization: You can use the apply() function to prepare data for visualization.
- Data analysis: You can use the apply() function to perform complex data analysis on multiple columns.

Here is how you can apply a function to multiple columns in Pandas:

Fig : Preview of the output that you will get on running this code from your IDE.

### Code

In this solution we're using Pandas and NumPy libraries.

### Instructions

__Follow the steps carefully to get the output easily.__

- Install pandas on your IDE(Any of your favorite IDE).
- Copy the snippet using the '
**copy'**and paste it in your IDE. - Add required dependencies and import them in Python file.
- Run the file to generate the output.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

*I found this code snippet by searching for **'How to apply a function to multiple columns in Pandas' **in kandi. You can try any such use case!*

### Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

- The solution is created in PyCharm 2021.3.
- The solution is tested on Python 3.9.7.
- Pandas version-v1.5.2.
- NumPy version-1.24.0.

Using this solution, we are able to apply a function for multiple columns in pandas with simple steps. This process also facilities an easy way to use, **hassle-free** method to create a hands-on working version of code which would help us to apply a function for multiple columns in pandas.

### Dependent Libraries

pandasby pandas-dev

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

pandasby pandas-dev

Python 37415 Version:v2.0.0rc1 License: Permissive (BSD-3-Clause)

numpyby numpy

The fundamental package for scientific computing with Python.

numpyby numpy

Python 23030 Version:v1.24.2 License: Permissive (BSD-3-Clause)

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