CSV files show up in almost every business workflow. You may export orders from an ecommerce platform, download leads from a CRM, receive finance data from another team, or pull product usage data from an internal dashboard.

The challenge is that a raw CSV file rarely answers a question directly. It may contain duplicate rows, missing values, inconsistent column names, and dates stored in a format that makes time analysis difficult.

In this tutorial, you will work through a realistic sales analysis project. Imagine you received an ecommerce order export and need to answer one practical question:

What can this CSV tell us about revenue by product, category, country, and month?

You will start with a raw CSV file and finish with cleaned data, useful summaries, visualizations, and exported result files.

By the end, you will have:

Loaded a CSV file into Pandas

Inspected rows, columns, data types, and missing values

Cleaned column names, duplicates, missing values, and data types

Created a revenue…