Pandas for Data Engineering

Pandas is a fast, powerful, and flexible open-source data analysis and manipulation tool built on top of the Python programming language.

🔗 Learning Resources

• Official Documentation: Pandas Documentation
• Pandas Getting Started: 10 Minutes to Pandas - The best “first stop” for beginners.
• Kaggle Course: Pandas Tutorial - Interactive and specifically focused on data manipulation.
• Visualization: Pandas Plotting Guide

📝 Practice Tasks (The Basics)

1. The Schema Validator

Goal: Ensure incoming data matches your requirements.

• Scenario: You have a CSV of “Sales” data. You need to ensure no prices are negative and all dates are valid.
• Requirements:
  • Load sales.csv.
  • Use df.info() to check data types.
  • Use boolean indexing to find rows where price < 0 and drop them.
  • Convert the transaction_date column to datetime64[ns].
  • Output: A report of how many rows were deleted during cleaning.

2. The Aggregator (Group-By)

Goal: Create a summary report from raw events.

• Scenario: A CSV contains store_id, product_category, and revenue.
• Requirements:
  • Group the data by store_id.
  • Calculate the Total Revenue and Average Revenue per store.
  • Sort the results so the highest-earning store is at the top.
  • Output: Save the summary to store_performance.csv.