Mastering Python
Rick van Hattem
- 710 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Mastering Python
Rick van Hattem
About This Book
Use advanced features of Python to write high-quality, readable code and packagesKey Features⢠Extensively updated for Python 3.10 with new chapters on design patterns, scientific programming, machine learning, and interactive Python⢠Shape your scripts using key concepts like concurrency, performance optimization, asyncio, and multiprocessing⢠Learn how advanced Python features fit together to produce maintainable codeBook DescriptionEven if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python's capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10. Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code's performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community. If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challengesWhat you will learn⢠Write beautiful Pythonic code and avoid common Python coding mistakes⢠Apply the power of decorators, generators, coroutines, and metaclasses⢠Use different testing systems like pytest, unittest, and doctest⢠Track and optimize application performance for both memory and CPU usage⢠Debug your applications with PDB, Werkzeug, and faulthandler⢠Improve your performance through asyncio, multiprocessing, and distributed computing⢠Explore popular libraries like Dask, NumPy, SciPy, pandas, TensorFlow, and scikit-learn⢠Extend Python's capabilities with C/C++ libraries and system callsWho this book is forThis book will benefit more experienced Python programmers who wish to upskill, serving as a reference for best practices and some of the more intricate Python techniques. Even if you have been using Python for years, chances are that you haven't yet encountered every topic discussed in this book. A good understanding of Python programming is necessary
Frequently asked questions
Information
10
Testing and Logging â Preparing for Bugs
doctest, unittest, and test modules. The doctest module allows you to combine tests with example documentation. The unittest module allows you to easily write regression tests. The test module is meant for internal usage only, so unless you are planning to modify the Python core, you probably wonât need this one.doctestpy.test(and why itâs more convenient thanunittest)unittest.mock
py.test module has roughly the same purpose as the unittest module, but itâs much more convenient to use and has many more options and plugins available.logging module in Python is highly configurable and can be adjusted for just about any use case. If youâve ever written Java code, you should feel right at home with the logging module, as its design is largely based on the log4j module and is very similar in both implementation and naming. The latter makes it a bit of an odd module in Python as well, as it is one of the few modules that does not follow the pep8 naming standards.- Combining documentation with tests using
doctest - Regression and unit tests using
py.testandunittest - Testing with fake objects using
unittest.mock - Testing multiple environments using
tox - Using the
loggingmodule effectively - Combining
loggingandpy.test
Using documentation as tests with doctest
doctest module is one of the most useful modules within Python. It allows you to combine documenting your code with tests to make sure that it keeps working as it is supposed to.doctest format, which offers the advantage that both the input and the output are shown intertwined. Especially in demonstrations, this is much more convenient than having a block of code followed by the output. A simple doctest example
def square(n: int) -> int: ''' Returns the input number, squared >>> square(0) 0 >>> square(1) 1 >>> square(2) 4 >>> square(3) 9 >>> square() Traceback (most recent call last): ... TypeError: square() missing 1 required positional argument: 'n' >>> square('x') Traceback (most recent call last): ... TypeError: can't multiply sequence by non-int of type 'str' Args: n (int): The number to square Returns: int: The squared result ''' return n * n if __name__ == '__main__': import doctest doctest.testmod()