47 Excellent Free Books to Learn Python

6. Supporting Python 3 by Lennart Regebro

Supporting Python 3 is an expertly written in-depth book which guides the reader through the process of adding Python 3 support, from choosing a strategy to solving distribution issues.

The process of switching to Python 3 is most often called “porting”. The first two editions of this book were also called “Porting to Python 3”. However, this gave the false impression that this was a lot of work, and that Python 3 was “a different language” than Python 2. Although moving to Python 3 can be hard work, for the most time it is not, and as Python 2.5 and Python 3.2 are rapidly falling out of use, it gets even easier.

If you want to ‘port’ Python 2 code to Python 3, read this book.

The author has been using Python since 2008.

This book is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.

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7. Building Skills in Python by Steven F. Lott

Building Skills in Python is a 42 chapter book which helps build Python programming skills through a series of exercises. It includes useful projects from straightforward to sophisticated that will help solidify your Python skills.

This book is a close-to-complete presentation of the Python language, updated to cover Python 2.6 and some elements of Python 3.1. It’s oriented toward learning, which involves accumulating many closely intertwined concepts. This book is primarily targeted at professional programmers.

The first three parts are organized in a way that builds up the language in layers from central concepts to more advanced features. Programming exercises are provided to encourage further exploration of each layer. The last two parts cover the extension modules and provide specifications for some complex exercises that will help solidify programming skills.

The book explores a wide range of topics including:

• Numeric Expressions and Output.
• Advanced Expressions.
• Variables, Assignment and Input.
• Truth, Comparison and Conditional Processing.
• Loops and Iterative Processing.
• Functions.

This book is made available under a Creative Commons Attribution-Noncommercial-No Derivative Works License.

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8. Think Complexity by Allen B. Downey

Think Complexity is about data structures and algorithms, intermediate programming in Python, computational modeling and the philosophy of science.

After reading the material, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.

Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations.

Topics covered include:

• Graphs including random and connected graphs.
• Analysis of algorithms – the branch of computer science that considers the performance of algorithms.
• Small world graphs.
• Scale-free networks: Zipf’s law, cumulative, continuous and Pareto distributions.
• Cellular automata.
• Game of Life.
• Fractals.
• Self-organized criticality.
• Case studies.

Permission is granted to copy, distribute, transmit and adapt this work under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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9. Programming Computer Vision with Python by Jan Erik Solem

Programming Computer Vision with Python gives a hands-on introduction to the underlying theory and algorithms of computer vision (images, videos, etc).

It seeks to explain computer vision in simple terms, without becoming too embroiled in theory. You learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python.

There are complete code samples with accompanying explanations.

The Python language comes with many powerful modules for handling images, mathematical computing and data mining.

Topics covered include:

• Learn techniques used in robot navigation, medical image analysis, and other computer vision applications.
• Work with image mappings and transforms, such as texture warping and panorama creation.
• Compute 3D reconstructions from several images of the same scene.
• Organize images based on similarity or content, using clustering methods.
• Build efficient image retrieval techniques to search for images based on visual content.
• Use algorithms to classify image content and recognize objects.
• Access the popular OpenCV library through a Python interface.

The final draft of the book is released under a Creative Commons license.

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10. Annotated Algorithms in Python by Massimo Di Pierro

Annotated Algorithms in Python with Applications in Physics, Biology, and Finance is compiled from lectures given by the author over a period of 10 years at the School of Computing of DePaul University.

The lectures cover multiple classes, including Analysis and Design of Algorithms, Scientific Computing, Monte Carlo Simulations, and Parallel Algorithms. These lectures teach the core knowledge required by any scientist interested in numerical algorithms and by students interested in computational finance.

The book is published under the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

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Next page: Page 3 – Invent Your Own Computer Games with Python and more books

Pages in this article:
Page 1 – Think Python and more books
Page 2 – Supporting Python 3 and more books
Page 3 – Invent Your Own Computer Games with Python and more books
Page 4 – Making Games with Python & Pygame and more books
Page 5 – Hacking Secret Ciphers with Python and more books
Page 6 – Learn Python, Break Python and more books
Page 7 – Modeling Creativity: Case Studies in Python and more books
Page 8 – Python in Hydrology and more books
Page 9 – Tiny Python 3.6 Notebook and more books

All books in this series: