Expert Python Programming

Python is a dynamic programming language, used in a wide range of domains by programmers who find it simple, yet powerful. From the earliest version 15 years ago to the current one, it has constantly evolved with productivity and code readability in mind.

Even if you find writing Python code easy, writing code that is efficient and easy to maintain and reuse is not so straightforward. This book will show you how to do just that: it will show you how Python development should be done. Python expert Tarek Ziadé takes you on a practical tour of Python application development, beginning with setting up the best development environment, and along the way looking at agile methodologies in Python, and applying proven object-oriented principles to your design.

This book is an authorative exploration of Python's best practices and applications of agile methodologies to Python, illustrated with practical and real-world examples.

What you will learn from this book:

• Set up a productive development environment
• Customize the Python prompt and deploy setuptools
• Write efficient syntax: iterators, generators, and decorators
• Build arguments by design and follow the best practices for working on API
• Build, release, and distribute your applications
• Write an application based on several eggs
• Distribute and deploy your application with zc.buildout
• Build and release your packages and set up a development cycle
• Manage your code with distributed version control and continuous integration
• Use an iterative and incremental approach to write software
• Practice Test-Driven Development
• Profile and optimize your code to speed up your programs
• Apply design patterns to your applications

• Preface

• Chapter 1: Getting started
• Installing Python
• Python Implementations
• Jython
• IronPython
• PyPy
• Other Implementations
• Linux Installation
• Package Installation
• Compiling the Sources
• Windows Installation
• Installing Python
• Installing MinGW
• Installing MSYS
• Mac OS X Installation
• Package Installation
• Compiling the Source
• The Python Prompt
• Customizing the Interactive Prompt
• iPython: An Advanced Prompt
• Installing setuptools
• Understanding How It Works
• setuptools Installation Using EasyInstall
• Hooking MinGW into distutils
• Working Environment
• Using an Editor and Complementary Tools
• Code Editor
• Installing and Configuring Vim
• Using Another Editor
• Extra Binaries
• Using an Integrated Development Environment
• Installing Eclipse with PyDev
• Summary

• Chapter 2: Syntax Best Practices—Below the Class Level
• List Comprehensions
• Iterators and Generators
• Generators
• Coroutines
• Generator Expressions
• The itertools Module
• islice: The Window Iterator
• tee: The Back and Forth Iterator
• groupby: The uniq Iterator
• Other Functions
• Decorators
• How to Write a Decorator
• Argument checking
• Caching
• Proxy
• Context Provider
• with and contextlib
• The contextlib Module
• Context Example
• Summary

• Chapter 3: Syntax Best Practices—Above the Class Level
• Subclassing Built-in Types
• Accessing Methods from Superclasses
• Understanding Python's Method Resolution Order (MRO)
• super Pitfalls
• Mixing super and classic Calls
• Heterogeneous Arguments
• Best Practices
• Descriptors and Properties
• Descriptors
• Introspection Descriptor
• Meta-descriptor
• Properties
• Slots
• Meta-programming
• The__new__ Method
• __metaclass__ Method
• Summary

• Chapter 4: Choosing Good Names
• PEP 8 and Naming Best Practices
• Naming Styles
• Variables
• Constants
• Public and Private Variables
• Functions and Methods
• The Private Controversy
• Special Methods
• Arguments
• Properties
• Classes
• Modules and Packages
• Naming Guide
• Use "has" or "is" Prefix for Boolean Elements
• Use Plural for Elements That Are Sequences
• Use Explicit Names for Dictionaries
• Avoid Generic Names
• Avoid Existing Names
• Best Practices for Arguments
• Build Arguments by Iterative Design
• Trust the Arguments and Your Tests
• Use *args and **kw Magic Arguments Carefully
• Class Names
• Module and Package Names
• Working on APIs
• Tracking Verbosity
• Building the Namespace Tree
• Splitting the Code
• Using Eggs
• Using a Deprecation Process
• Useful Tools
• Pylint
• CloneDigger
• Summary

• Chapter 5: Writing a Package
• A Common Pattern for All Packages
• setup.py, the Script That Controls Everything
• sdist
• build and bdist
• bdist_egg
• install
• How to Uninstall a Package
• develop
• test
• register and upload
• Creating a New Command
• setup.py Usage Summary
• Other Important Metadata
• The Template-Based Approach
• Python Paste
• Creating Templates
• Creating the Package Template
• Development Cycle
• Summary

• Chapter 6: Writing an Application
• Atomisator: An Introduction
• Overall Picture
• Working Environment
• Adding a Test Runner
• Adding a packages Structure
• Writing the Packages
• atomisator.parser
• Creating the Initial Package
• Creating the Initial doctest
• Building the Test Environment
• Writing the Code
• atomisator.db
• SQLAlchemy
• Providing the APIs
• atomisator.feed
• atomisator.main
• Distributing Atomisator
• Dependencies between Packages
• Summary

• Chapter 7: Working with zc.buildout
• zc.buildout Philosophy
• Configuration File Structure
• Minimum Configuration File
• [buildout] Section Options
• The buildout Command
• Recipes
• Notable Recipes
• Creating Recipes
• Atomisator buildout Environment
• buildout Folder Structure
• Going Further
• Releasing and Distributing
• Releasing the Packages
• Adding a Release Configuration File
• Building and Releasing the Application
• Summary

• Chapter 8: Managing Code
• Version Control Systems
• Centralized Systems
• Distributed Systems
• Distributed Strategies
• Centralized or Distributed?
• Mercurial
• Project Management with Mercurial
• Setting Up a Dedicated Folder
• Configuring hgwebdir
• Configuring Apache
• Setting Up Authorizations
• Setting Up the Client Side
• Continuous Integration
• Buildbot
• Installing Buildbot
• Hooking Buildbot and Mercurial
• Hooking Apache and Buildbot
• Summary

• Chapter 9: Managing Life Cycle
• Different Approaches
• Waterfall Development Model
• Spiral Development Model
• Iterative Development Model
• Defining a Life Cycle
• Planning
• Development
• Global Debug
• Release
• Setting Up a Tracking System
• Trac
• Installation
• Apache Settings
• Permission Settings
• Project Life Cycle with Trac
• Planning
• Development
• Cleaning
• Release
• Summary

• Chapter 10: Documenting Your Project
• The Seven Rules of Technical Writing
• Write in Two Steps
• Target the Readership
• Use a Simple Style
• Limit the Scope of the Information
• Use Realistic Code Examples
• Use a Light but Sufficient Approach
• Use Templates
• A reStructuredText Primer
• Section Structure
• Lists
• Inline Markup
• Literal Block
• Links
• Building the Documentation
• Building the Portfolio
• Design
• Usage
• Operations
• Make Your Own Portfolio
• Building the Landscape
• Producer's Layout
• Consumer's Layout
• Summary

• Chapter 11: Test-Driven Development
• I Don't Test
• Test-Driven Development Principles
• Preventing Software Regression
• Improving Code Quality
• Providing the Best Developer Documentation
• Producing Robust Code Faster
• What Kind of Tests?
• Acceptance Tests
• Unit Tests
• Python Standard Test Tools
• I Do Test
• Unittest Pitfalls
• Unittest Alternatives
• nose
• py.test
• Fakes and Mocks
• Building a Fake
• Using Mocks
• Document-Driven Development
• Writing a Story
• Summary

• Chapter 12: Optimization: General Principles and Profiling Techniques
• The Three Rules of Optimization
• Make It Work First
• Work from the User's Point of View
• Keep the Code Readable(and thus maintainable)
• Optimization Strategy
• Find Another Culprit
• Scale the Hardware
• Write a Speed Test
• Finding Bottlenecks
• Profiling CPU Usage
• Macro-Profiling
• Micro-Profiling
• Measuring Pystones
• Profiling Memory Usage
• How Python Deals with Memory
• Profiling Memory
• Profiling Network Usage
• Summary

• Chapter 13: Optimization: Solutions
• Reducing the Complexity
• Measuring Cyclomatic Complexity
• Measuring the Big-O Notation
• Simplifying
• Searching in a List
• Using a Set Instead of a List
• Cut the External Calls, Reduce the Workload
• Using Collections
• Multithreading
• What is Multithreading?
• How Python Deals with Threads
• When Should Threading Be Used?
• Building Responsive Interfaces
• Delegating Work
• Multi-User Applications
• Simple Example
• Multiprocessing
• Pyprocessing
• Caching
• Deterministic Caching
• Non-Deterministic Caching
• Pro-Active Caching
• Memcached
• Summary

• Chapter 14: Useful Design Patterns
• Creational Patterns
• Singleton
• Structural Patterns
• Adapter
• Interfaces
• Proxy
• Facade
• Behavioral Patterns
• Observer
• Visitor
• Template
• Summary

A very good overview of a lot of the finer points about Python (I knew I learned a lot).

But it is not really a book about "Expert Python Programming", as it is "a book about best practices in program development, as illustrated thru Python" more than anything else. And it does a wonderful work on that, as it gives you (the pupil) the necessary concepts and why they are good or bad, and with some examples, and then leaves out a lot of details, which forces you to find the info somewhere else. I would love to use it on a course for new developers, with some side reading thrown in.

It is better than just very good, but it fails to achieve excellence, due to the lack of details and the extremelly bad editing and cheap printing (some pictures are un-eligble).