If you want to learn how to manipulate, process, clean, and crunch data sets with Python, you can use this book, Python for Data Analysis (Third Edition), as a reference. Python for Data Analysis is written by Wes McKinney, the creator of the Pandas library.

Python for Data Analysis is jam-packed with real-world examples of how to use Python, Pandas, NumPy, and Jupyter to analyze data effectively. Beginner Python programmers and analysts who are new to data science and scientific computing should read this book.

Python for Data Analysis has 13 chapters:

  1. Preliminaries
  2. Python Language Basics, IPython, and Jupyter Notebooks
  3. Built-In Data Structures, Functions, and Files
  4. NumPy Basics: Arrays and Vectorized Computation
  5. Getting Started with pandas
  6. Data Loading, Storage, and File Formats
  7. Data Cleaning and Preparation
  8. Data Wrangling: Join, Combine, and Reshape
  9. Plotting and Visualization
  10. Data Aggregation and Group Operations
  11. Time Series
  12. Introduction to Modeling Libraries in Python
  13. Data Analysis Examples

The third edition of Python for Data Analysis is now available as a “Open Access” HTML version on this website: wesmckinney.com/book.

If you find the online version of the book useful, please consider buying a paper copy or a DRM-free eBook (in PDF and EPUB formats) to show your appreciation to the author.