Python Books

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The Definitive Guide to Pylons

The Definitive Guide to Pylons

By James Gardner (568 pages)

Pylons is a lightweight web framework built on standard Python tools that provides a robust environment for writing modern web applications. It is well known for its clean architecture and loosely coupled approach, both of which make web development fast, flexible, and easy.

The Definitive Guide to Pylons teaches you everything you need to know about web development with Pylons – from how to create your first “Hello World!” application to how to use each of Pylons’ core tools including FormEncode, Mako, SQLAlchemy, and Routes to how to perform more advanced tasks such as testing, using Unicode, internationalizing your application, authenticating users, and more.

It also helps developers make use of the software’s built-in support for session management, web services, and Ajax.

This book is made available under the terms of the GNU Free Documentation License, Version 1.2 or any later version.

From Python to Numpy

From Python to Numpy

By Nicolas P. Rougier (HTML)

From Python to Numpy offers a different approach focusing on the migration from Python to NumPy through vectorization. The book explains some of the techniques. You need an intermediate level in Python and a beginner level in NumPy.

NumPy is the fundamental package for scientific computing with Python.

This book is licensed under a Creative Commons Attribution-Non Commercial-Share Alike 4.0 International License.

Full Stack Python

Full Stack Python

By Matt Makai (HTML)

Full Stack Python is an open book that explains how to create, deploy, and operate Python web applications. Concepts are explained in plain language.

Chapters cover development environments, testing, documentation, security, web development (frameworks, template engines web design, Javascript, task queries, architectures, static site generators), Web App deployment, data, web APIs, and DevOps.

The book is published under the MIT License.

Building Skills in Object-Oriented Design

Building Skills in Object-Oriented Design

By Steven F. Lott (285 pages)

This book teaches how to move from object-orientated programming to object-oriented design through a series of design exercises. Build applications step-by-step with real-world sophistication. The code examples focus on the Python programming language. It uses casino table games (Roulette, Craps and Blackjack) as its context.

The purpose of this book is to build skills in object-oriented design prior to a project with fixed cost and deadline.

High Performance Python

By Ian Ozsvald (55 pages)

This book shows you different ways of optimizing Python code. The speed improvements can be between 10-500 times faster.

Techniques covered:

  • Python profiling (cProfile, RunSnake, line_profiler) – find bottlenecks
  • PyPy – Python’s new Just In Time compiler
  • Cython – annotate your code and compile to C
  • numpy integration with Cython – fast numerical Python library wrapped by Cython
  • ShedSkin – automatic code annotation and conversion to C
  • numpy vectors – fast vector operations using numpy arrays
  • NumExpr on numpy vectors – automatic numpy compilation to multiple CPUs and vector units
  • multiprocessing – built-in module to use multiple CPUs
  • ParallelPython – run tasks on multiple computers
  • pyCUDA – run tasks on your Graphics Processing Unit

High Performance Python is published under Creative Commons by Attribution.

Think Stats: Exploratory Data Analysis

Think Stats: Exploratory Data Analysis in Python

By Allen B. Downey (264 pages)

Think Stats is an introduction to Probability and Statistics for Python programmers. Most ideas are expressed using Python code.

Chapters examine:

  • Exploratory data analysis
  • Distributions
  • Probability mass functions
  • Cumulative distribution functions
  • Modeling distributions
  • Probability density functions
  • Relationships between variables
  • Estimation
  • Hypothesis testing
  • Linear least squares
  • Regression
  • Time series analysis
  • Survival analysis
  • Analytic methods

The book presents a case study using data from the National Institutes of Health and the National Survey of Family Growth and the Behavioral Risk Factor Surveillance System. Each chapter presents exercises to help develop a reader’s understanding.

Think Stats is published under the Creative Commons Attribution-NonCommercial 3.0 Unported License.

Fundamentals of Python Programming

Fundamentals of Python Programming

By Richard L. Halterman (669 pages)

This book offers a veritable feast of information. Chapters cover:

  • Values and Variables
  • Expressions and Arithmetic
  • Conditional Execution – Boolean expressions, the simple if statement, the if/else statement and more
  • Iteration – including nested and infinite loops, with iteration examples
  • Using Functions
  • Writing Functions
  • More on Functions
  • Objects
  • Lists
  • Tuples, Dictionaries, and Sets
  • Handling Exceptions
  • Custom Types
  • Class Design: Composition and Inheritance
  • Algorithm Quality
  • Representing Relationships with Graphs

The book is published under an open-source compatible license.

Python Module of the Week

Python 3 Module of the Week

By Doug Hellmann (HTML)

Python Module of the Week (PyMOTW) was started as a way to build the habit of writing something on a regular basis. The focus of the series is building a set of example code for the modules in the Python standard library.

PyMOTW is a good source of documentation for Python modules.

PyMOTW includes a command line program, motw, to make it easier to access the examples while you are developing.

This work is made available under the terms of the Creative Commons Attribution-NonCommercial Share-alike 3.0 license.

Python for you and me

Python for you and me

By Kushal Das (115 pages)

This book is designed for newcomers to the Python programming language.

Topics covered include:

  • Variables and Datatypes
  • Operators and expressions
  • Control flow
  • Looping
  • Data structures
  • Strings
  • Functions
  • File handling
  • Class
  • Modules
  • Collections module
  • Virtual Python Environment builder

It is released under the GNU Free Documentation License v1.2 or later.

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