Sales Analysis with Pandas

Turn raw sales data into business insights with Pandas. Retail companies generate massive amounts of sales data every day, and analyzing this information is key to understanding performance. In this project, you’ll work with a retail sales dataset to practice using Pandas for organizing, cleaning, and analyzing transactions. Your goal is to uncover trends in revenue, products, and customer behavior by applying data manipulation techniques. You’ll begin by loading the dataset into a Pandas DataFrame and exploring its structure. Next, you’ll clean the data by checking for missing values, correcting data types (such as dates and numerical fields), and removing duplicates. Once prepared, you’ll use filtering and indexing to focus on specific products, stores, or time periods. The project continues with groupby and aggregation, where you’ll calculate total and average sales by product, category, or region. You’ll also practice creating new calculated columns—for example, revenue (quantity × price) or sales growth over time. Sorting and ranking will help identify top-selling items, seasonal patterns, and underperforming categories. By the end of this project, you will be able to: Clean and prepare a sales dataset with Pandas. Filter and subset data for products, categories, and time ranges. Apply grouping and aggregation to analyze sales performance. Create calculated metrics such as revenue and growth. Highlight top products, peak months, and important trends. This project mirrors the type of work done by analysts in retail and e-commerce. By working hands-on with Pandas, you’ll learn how raw sales data can be transformed into insights that drive decisions—skills that are directly valuable in business and industry.