16. Making Games with Python & Pygame by Al Sweigart
Making Games with Python & Pygame teaches you how to make graphical computer games in the Python programming language using the Pygame library. It’s targeted at the intermediate programmer.
The book features the source code to 11 games. Each chapter focuses on a single game program, explaining how different parts of the code work. The games are clones of classics such as Nibbles, Tetris, Simon, Bejeweled, Othello, Ink Spill, Connect Four, Flood It, and others. The programs are less than 600 lines long.
The book and the programs are available under a Creative Commons Attribution-Noncommercial-Share Alike license.
17. Natural Language Processing with Python by Steven Bird, Ewan Klein, Edward Loper
Natural Language Processing with Python offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.
Learn how to write Python programs that work with large collections of unstructured text. Access richly annotated datasets using a comprehensive range of linguistic data structures, and understand the main algorithms for analyzing the content and structure of written communication.
The book is updated for Python 3 and NLTK 3.
This book helps to:
- Extract information from unstructured text, either to guess the topic or identify “named entities”.
- Analyze linguistic structure in text, including parsing and semantic analysis.
- Access popular linguistic databases, including WordNet and treebanks.
- Integrate techniques drawn from fields as diverse as linguistics and artificial intelligence.
This book is made available under the terms of the Creative Commons Attribution Noncommercial No-Derivative-Works 3.0 US License.
18. Problem Solving with Algorithms and Data Structures using Python by Bradley M. Kuhn and David L. Ranum
This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum.
Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving.
The authors cover abstract data types and data structures, writing algorithms, and solving problems. They look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.
The objectives of this book are:
- To review the ideas of computer science, programming, and problem-solving.
- To understand abstraction and the role it plays in the problem-solving process.
- To understand and implement the notion of an abstract data type.
- To review the Python programming language:
- Getting started with data:
- Built-in atomic data types.
- Built-in collection data types.
- Input and output:
- String formatting.
- Control structures.
- Exception handling.
- Defining functions.
- Object-oriented programming in Python.
- Getting started with data:
- Discussion questions.
- Programming exercises.
Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version.
19. Think DSP by Allen B. Downey
Think DSP is an introduction to Digital Signal Processing in Python and includes processes for synthesizing, transforming, and analyzing signals. All of the code is written to work in both Python 2 and Python 3 with no translation.
The author explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material.
Permission is granted to copy, distribute, and/or modify this book under the terms of the Creative Commons Attribution-NonCommercial 3.0 Unported License.
20. Python for Informatics: Exploring Information by Charles Severance
The goal of Python for Informatics – Exploring Information is to provide an Informatics-oriented introduction to programming. This book is designed to introduce students to programming and computational thinking through the lens of exploring data.
The primary difference between a computer science approach and the Informatics approach taken in this book is a greater focus on using Python to solve data analysis problems common in the world of Informatics
All the supporting materials for the book are available under open and remixable licenses.
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 – How to Make Mistakes in Python and more books
Page 7 – Text Processing in Python and more books
Page 8 – The Coder’s Apprentice and more books
Page 9 – Building Skills in Object-Oriented Design and more books
All books in this series:
|Free Programming Books|
|Java||General-purpose, concurrent, class-based, object-oriented, high-level language|
|C||General-purpose, procedural, portable, high-level language|
|Python||General-purpose, structured, powerful language|
|C++||General-purpose, portable, free-form, multi-paradigm language|
|C#||Combines the power and flexibility of C++ with the simplicity of Visual Basic|
|PHP||PHP has been at the helm of the web for many years|
|HTML||HyperText Markup Language|
|SQL||Access and manipulate data held in a relational database management system|
|Ruby||General purpose, scripting, structured, flexible, fully object-oriented language|
|Assembly||As close to writing machine code without writing in pure hexadecimal|
|Swift||Powerful and intuitive general-purpose programming language|
|Groovy||Powerful, optionally typed and dynamic language|
|Go||Compiled, statically typed programming language|
|Pascal||Imperative and procedural language designed in the late 1960s|
|Perl||High-level, general-purpose, interpreted, scripting, dynamic language|
|R||De facto standard among statisticians and data analysts|
|COBOL||Common Business-Oriented Language|
|Scala||Modern, object-functional, multi-paradigm, Java-based language|
|Fortran||The first high-level language, using the first compiler|
|Scratch||Visual programming language designed for 8-16 year-old children|
|Lua||Designed as an embeddable scripting language|
|Logo||Dialect of Lisp that features interactivity, modularity, extensibility|
|Rust||Ideal for systems, embedded, and other performance critical code|
|Lisp||Unique features - excellent to study programming constructs|
|Ada||ALGOL-like programming language, extended from Pascal and other languages|
|Haskell||Standardized, general-purpose, polymorphically, statically typed language|
|Scheme||A general-purpose, functional language descended from Lisp and Algol|
|Prolog||A general purpose, declarative, logic programming language|
|Forth||Imperative stack-based programming language|
|Clojure||Dialect of the Lisp programming language|
|Julia||High-level, high-performance language for technical computing|
|Awk||Versatile language designed for pattern scanning and processing language|
|BASIC||Beginner’s All-purpose Symbolic Instruction Code|
|Erlang||General-purpose, concurrent, declarative, functional language|
|VimL||Powerful scripting language of the Vim editor|
|OCaml||The main implementation of the Caml language|
|ECMAScript||Best known as the language embedded in web browsers|
|Bash||Shell and command language; popular both as a shell and a scripting language|
|LaTeX||Professional document preparation system and document markup language|
|TeX||Markup and programming language - create professional quality typeset text|
|Arduino||Inexpensive, flexible, open source microcontroller platform|
|Elixir||Relatively new functional language running on the Erlang virtual machine|
|F#||Uses functional, imperative, and object-oriented programming methods|
|Tcl||Dynamic language based on concepts of Lisp, C, and Unix shells|
|Factor||Dynamic stack-based programming language|
|Eiffel||Object-oriented language designed by Bertrand Meyer|
|Agda||Dependently typed functional language based on intuitionistic Type Theory|
|Icon||Wide variety of features for processing and presenting symbolic data|
|XML||Rules for defining semantic tags describing structure ad meaning|
|Vala||Object-oriented language, syntactically similar to C#|
|Standard ML||General-purpose functional language characterized as "Lisp with types"|
|D||General-purpose systems programming language with a C-like syntax|
|Dart||Client-optimized language for fast apps on multiple platforms|
|Markdown||Plain text formatting syntax designed to be easy-to-read and easy-to-write|
|Kotlin||More modern version of Java|
|Objective-C||Object-oriented language that adds Smalltalk-style messaging to C|
|VHDL||Hardware description language used in electronic design automation|
|J||Array programming language based primarily on APL|
|LabVIEW||Designed to enable domain experts to build power systems quickly|