guipsamora/pandas_exercises

This repository is a collection of practice exercises for pandas, a Python library widely used for working with tabular data. The creator made it because they found plenty of tutorials explaining pandas concepts but almost no places to actually practice using them. The exercises fill that gap. Each topic in the collection comes in three files: one with the exercise instructions and no answers, one with the expected output but no code, and one with full code and explanations. The suggested approach is to attempt each exercise after reading about a topic, check only the expected output if stuck, and look at the code solution only as a last resort. The exercises are organized by skill area. Topics include loading data and exploring its structure, filtering rows and sorting, grouping data to compute summaries across categories, applying custom functions to data, merging multiple datasets, computing statistics, creating charts, working with time-series data, and deleting rows or columns. Each topic uses a different real-world or familiar dataset, such as Chipotle restaurant orders, Titanic passenger records, Apple stock prices, US baby name statistics, and Pokemon data. The exercises are Jupyter notebooks, which run interactively in a browser. You write code in cells, run each cell, and immediately see whether your output matches the expected result. No special setup is described beyond having pandas and Jupyter available. Video walkthroughs of the exercises are also linked at the bottom of the README for learners who prefer to watch someone work through the problems before or after attempting them independently. Contributions of new exercises are welcome through the standard GitHub pull request process.