Serious Python

Serious Python Black-belt advice on deployment, scalability, testing, and more

Julien Danjou

Book - 2019

"Offers experienced coders advice and tips for improving knowledge of Python coding language. Includes interviews with Python experts and covers a wide range of common topics, from scaling and testing code to designing APIs"--

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Subjects
Python (Computer program language)
Published
San Francisco, CA : No Starch Press, Inc [2019]
Language
English
Main Author
Julien Danjou (author)
Physical Description
xvi, 218 pages ; 24 cm
ISBN
9781593278786
  • Acknowledgments
  • Introduction
  • Who Should Read This Book And Why
  • About This Book
  • 1. Starting Your Project
  • Versions of Python
  • Laying Out Your Project
  • What to Do
  • What Not to Do
  • Version Numbering
  • Coding Style and Automated Checks
  • Tools to Catch Style Errors
  • Tools to Catch Coding Errors
  • Joshua Harlow on Python
  • 2. Modules, Libraries, And Frameworks
  • The Import System
  • The sys Module
  • Import Paths
  • Custom Importers
  • Meta Path Finders
  • Useful Standard Libraries
  • External Libraries
  • The External Libraries Safety Checklist
  • Protecting Your Code With an API Wrapper
  • Package Installation: Getting More from pip
  • Using and Choosing Frameworks
  • Doug Hellmann, Python Core Developer, on Python Libraries
  • 3. Documentation And Good API Practice
  • Documenting with Sphinx
  • Getting Started with Sphinx and Rest
  • Sphinx Modules
  • Writing a Sphinx Extension
  • Managing Changes to YOUR APIs
  • Numbering API Versions
  • Documenting Your API Changes
  • Marking Deprecated Functions with the warnings Module
  • Summary
  • Christophe de Vienne on Developing APIs
  • 4. Handling Timestamps And Time Zones
  • The Problem of Missing Time Zones
  • Building Default datetime Objects
  • Time Zone-Aware Timestamps with dateutil
  • Serializing Time Zone-Aware datetime Objects
  • Solving Ambiguous Times
  • Summary
  • 5. Distributing Your Software
  • A Bit of setup.py History
  • Packaging with setup.cfg
  • The Wheel Format Distribution Standard
  • Sharing Your Work with the World
  • Entry Points
  • Visualizing Entry Points
  • Using Console Scripts
  • Using Plugins and Drivers
  • Summary
  • Nick Coghlan on Packaging
  • 6. Unit Testing
  • The Basics of Testing
  • Some Simple Tests
  • Skipping Tests
  • Running Particular Tests
  • Running Tests in Parallel
  • Creating Objects Used in Tests with Fixtures
  • Running Test Scenarios
  • Controlled Tests Using Mocking
  • Revealing Untested Code with coverage
  • Virtual Environments
  • Setting Up a Virtual Environment
  • Using virtualenv with tox
  • Re-creating an Environment
  • Using Different Python Versions
  • Integrating Other Tests
  • Testing Policy
  • Robert Collins on Testing
  • 7. Methods And Decorators
  • Decorators and When to Use Them
  • Creating Decorators
  • Writing Decorators
  • Stacking Decorators
  • Writing Class Decorators
  • How Methods Work in Python
  • Static Methods
  • Class Methods
  • Abstract Methods
  • Mixing Static, Class, and Abstract Methods
  • Putting Implementations in Abstract Methods
  • The Truth About super
  • Summary
  • 8. Functional Programming
  • Creatinq Pure Functions
  • Generators
  • Creating a Generator
  • Returning and Passing Values with yield
  • Inspecting Generators
  • List Comprehensions
  • Functional Functions Functioning
  • Applying Functions to Items with map()
  • Filtering Lists with filter()
  • Getting Indexes with enumerate()
  • Sorting a List with sorted()
  • Finding Items That Satisfy Conditions with any() and all()
  • Combining Lists with zip()
  • A Common Problem Solved
  • Useful itertools Functions
  • Summary
  • 9. The Abstract Syntax Tree, HY, And Lisp-Like Attributes
  • Looking at the AST
  • Writing a Program Using the AST
  • The AST Objects
  • Walking Through an AST
  • Extending flake8 with AST Checks
  • Writing the Class
  • Ignoring Irrelevant Code
  • Checking for the Correct Decorator
  • Looking for self
  • A Quick Introduction to Hy
  • Summary
  • Paul Tagliamonte on the AST and Hy
  • 10. Performances And Optimizations
  • Data Structures
  • Understanding Behavior Through Profiling
  • cProfile
  • Disassembling with the dis Module
  • Defining Functions Efficiently
  • Ordered Lists and bisect
  • Namedtuple and Slots
  • Memoization
  • Faster Python With PyPy
  • Achieving Zero Copy with the Buffer Protocol
  • Summary
  • Victor Stinner On Optimization
  • 11. Scaling And Architecture
  • Multithreading in Python and Its Limitations
  • Multiprocessing vs. Multithreading
  • Event-Driven Architecture
  • Other Options and asyncio
  • Service-Oriented Architecture
  • Interprocess Communication with ZeroMQ
  • Summary
  • 12. Managing Relational Databases
  • RDBMSs, ORMs, and When to Use Them
  • Database Backends
  • Streaming Data with Flask and PostgreSQL
  • Writing the Data-Streaming Application
  • Building the Application
  • Dimitri Fontaine on Databases
  • 13. Write Less, Code More
  • Using six for Python 2 and 3 Support
  • Strings And Unicode
  • Handling Python Modules Moves
  • The Modernize Module
  • Using Python Like Lisp to Make a Single Dispatcher
  • Creating Generic Methods In Lisp
  • Generic Methods With Python
  • Context Managers
  • Less Boilerplate With attr
  • Summary
  • Index

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