Pandas Comprehensive Tutorial

This is a concise demonstration of essential pandas operations for data manipulation and analysis.

This tutorial covers:

• reading data (CSV and Parquet formats) and some performance considerations,
• accessing data and some best practices,
• filtering data and a performance comparison,
• column operations such as adding new columns, naming, and datatype conversions,
• data export options,
• merging (join types) and concat data,
• handling null values,
• aggregating data (groupBy operations), and
• some advance functionality (shift operations, ranking systems, cumulative operations, ...).

About the datasets

The tutorial uses three main datasets:

• Coffee Sales Data: Daily coffee shop sales with different coffee types
• Biographical Data: Olympic athletes' biographical information
• Country Codes: NOC (National Olympic Committee) to country name mappings

All datasets are loaded directly from GitHub repositories, so no local files are required.

Installation

Using Google Colab

Download the pandas_comprehensive_tutorial.ipynb file. Go to Google Colab and upload it.

Using your Local Environment

Read the instructions from setup.md

Key Learning Points

Performance Best Practices

• Use vectorized operations over .apply() when possible
• Prefer .loc for explicit, readable code
• Choose appropriate data types for memory efficiency
• Use pd.cut() for binning operations instead of nested conditions

Code Quality

• Use .copy() when creating DataFrame variants to avoid name binding
• Handle null values explicitly
• Use descriptive column names
• Prefer explicit over implicit operations

License

This project is open source and available under the MIT License.

Acknowledgments

• Data sources from Keith Galli's pandas tutorial repository
• Pandas development team for creating this amazing library
• The open-source community for continuous improvements

---

Happy Data Engineering! 🐼📊