Book Image

Daniel Arbuckle's Mastering Python

By : Daniel Arbuckle
Book Image

Daniel Arbuckle's Mastering Python

By: Daniel Arbuckle

Overview of this book

Daniel Arbuckle's Mastering Python covers the basics of operating in a Python development environment, before moving on to more advanced topics. Daniel presents you with real-world solutions to Python 3.6 and advanced-level concepts, such as reactive programming, microservices, ctypes, and Cython tools. You don't need to be familiar with the Python language to use this book, as Daniel starts with a Python primer. Throughout, Daniel highlights the major aspects of managing your Python development environment, shows you how to handle parallel computation, and helps you to master asynchronous I/O with Python 3.6 to improve performance. Finally, Daniel will teach you the secrets of metaprogramming and unit testing in Python, helping you acquire the perfect skillset to be a Python expert. Daniel will get you up to speed on everything from basic programming practices to high-end tools and techniques, things that will help set you apart as a successful Python programmer.

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Table of Contents (13 chapters)
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
Python Primer
Python Primer
Python basic syntax and block structure
Python's built-in data structures and comprehensions
First-class functions and classes
The standard library
What's new in modern Python
Summary
2
Setting Up
Setting Up
Downloading and installing Python
Using the command line and the interactive shell
Installing packages with pip
Finding packages in the Python Package Index
Summary
3
Making a Package
Making a Package
Creating an empty package
Adding modules to the package
Accessing code from other modules
Adding static data files to the package
Summary
4
Basic Best Practices
Basic Best Practices
PEP 8 and writing readable code
Using version control
Using venv to create a stable and isolated work area
Getting the most out of docstrings
Summary
5
Making a Command-Line Utility
Making a Command-Line Utility
Making a package executable via Python -m
Handling command-line arguments with argparse
Python tools to interact with the user
Executing other programs with subprocess
Setting up a shell script or batch file to launch the program
Summary
6
Parallel Processing
Parallel Processing
Using the concurrent.futures package
Using the multiprocessing packages
Summary
7
Coroutines and Asynchronous I/O
Coroutines and Asynchronous I/O
The difference between asynchronous processing and parallel processing
Using the asyncio event loop and coroutine scheduler
Awaiting data availability
Synchronizing multiple tasks
Communicating across the network
Summary
8
Metaprogramming
Metaprogramming
Using function decorators
Function annotations
Class decorators
Metaclasses
Context managers
Descriptors
Summary
9
Unit Testing
Unit Testing
Understanding the principle of unit testing
Using the unittest package
Using unittest.mock
Using unittest's test discovery
Using nose for unified test discovery and reporting
Summary
10
Reactive Programming
Reactive Programming
The concept of reactive programming
Building a simple reactive programming framework
Using the reactive extensions for Python (RxPY)
Summary
11
Microservices
Microservices
Microservices and the advantages of process isolation
Building high-level microservices with Flask
Building high-level microservices with nameko
Summary
12
Extension Modules and Compiled Code
Extension Modules and Compiled Code
Advantages and disadvantages of compiled code
Accessing a dynamic library using ctypes
Interfacing with C code using Cython
Summary

Parallel Processing

In the previous chapter, we created a text mode utility program and you learned about several of Python's built-in packages. In this chapter, we're going to see how to use both the high-level concurrent.futures package and the lower-level multiprocessing package to help us write parallel programs. Both are part of the Python standard library.

We will cover the following two topics in detail:

  • Using the concurrent.futures package
  • Using multiprocessing packages
Previous Section
End of Section 1
Next Section