Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter, Third Edition

Data Analysis with Python, Taught by pandas’ Creator

Wes McKinney built pandas, the library that reshaped how Python handles tabular data. In Python for Data Analysis, he turns that experience into a practical, example-driven guide to working with data in Python. The third edition updates the material for Python 3.10 and pandas 1.4, so the tools and techniques stay close to what modern data analysts actually use.

The book begins with Python language basics, IPython, and Jupyter notebooks, then moves into NumPy arrays, pandas DataFrames, data loading, cleaning, wrangling, aggregation, visualization, time series, and modeling libraries. Each topic is presented through concrete examples rather than abstract theory.

What You’ll Work With 💻

• The Jupyter notebook and IPython shell for exploratory computing
• NumPy fundamentals and advanced array operations
• pandas Series and DataFrame structures, indexing, and alignment
• Reading and writing data in text, JSON, XML, HTML, Excel, HDF5, and databases
• Missing-data handling, transformation, binning, and outlier detection
• Joining, merging, reshaping, and pivoting datasets
• Plotting with matplotlib and seaborn
• GroupBy, aggregation, pivot tables, and cross-tabulation
• Time series data, date ranges, resampling, moving windows
• Connecting pandas to statistical modeling libraries

A Hands-On Path Through Real Data Problems 🐍

McKinney’s approach is grounded in the work analysts do every day: loading messy files, fixing missing values, reshaping tables, summarizing groups, and turning raw numbers into clear visuals. Because the author created pandas, the explanations often include the reasoning behind the API, not just the syntax. That context can help you make better decisions when your own data does not fit a tidy example.

Who This Book Is For

The third edition is aimed at analysts new to Python and Python programmers new to data science and scientific computing. If you already know some Python but have not used it for data cleaning or analysis, the book meets you where you are and builds from the fundamentals. If you work with spreadsheets or SQL and want to move into reproducible Python workflows, the progression from basic data structures to advanced wrangling provides a clear path.

Updated for Modern Python and pandas

The third edition reflects Python 3.10 and pandas 1.4. That means the code and explanations are aligned with current versions of the core libraries, reducing the friction of translating older examples to a modern environment. Data files and related material are available on GitHub, so you can follow along and experiment with the examples.

Why It Belongs on Your Digital Shelf

Python for Data Analysis is not a quick list of recipes. It is a structured guide that helps you understand how NumPy, pandas, and Jupyter fit together in a real analysis workflow. Whether you are cleaning a CSV for a report, joining tables for a dashboard, or exploring a time series, the book gives you the vocabulary and techniques to work confidently in Python.