Groupby function in pandas Python

In Pandas, the groupby function groups data in a DataFrame depending on one or more columns. It is a powerful function that allows you to perform various operations on data after grouping it. 

The groupby function splits a DataFrame into groups depending on the values in one or more columns. Once the DataFrame is split, you can apply various aggregate functions, such as sum, mean, min, max, count, etc., to the groups to calculate summary statistics. The groupby function is particularly helpful when you have a large dataset and want to analyze subsets of data based on specific criteria. By grouping the data, you can perform calculations and analyses on each subset separately, which can be much more efficient than performing the same operations on the entire dataset. 

Overall, the groupby function is a powerful tool in Pandas for exploring and analyzing data, and it is widely used in data science and analytics applications

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

Code

In this solution we used pandas library and groupby function of python.

Instructions

Follow the steps carefully to get the output successfully:

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE(Pycharm Preferable).
2. 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 "Groupby function in pandas Python"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.

1. The solution is created in Python 3.11.1 Version
2. The solution is tested on pandas 1.5.2 Version

Using this solution, we can use Groupby function in pandas Python.This process also facilities an easy to use, hassle free method to create a hands-on working version of code in python.

Dependent Library

If you do not have pandas library that is required to run this code, you can install it by clicking on the above link.

You can search for any dependent library on kandi like pandas.

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