#  Introduction   Pandas is one of the most popular Python libraries for data analysis. It gives you simple tools for cleaning, reshaping, summarizing, and exploring structured data. One of the most useful features in pandas is GroupBy . It helps you answer questions that require grouping rows by one or more categories. For example, if you are working with sales data, you may want to calculate total revenue by region, average order value by product category, or the number of orders handled by each sales representative. Instead of manually filtering each category one by one, GroupBy lets you perform these calculations in a clean and efficient way. In this tutorial, we will walk through practical examples of using Pandas GroupBy with a small sales dataset. I am using Deepnote as the coding environment, so some outputs are shown as notebook screenshots directly under the code blocks.…