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Mastering Python: Write powerful and efficient code using the full range of Python's capabilities, 2nd Edition (English Edition) Kindle电子书
Use advanced features of Python to write high-quality, readable code and packages
- 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 code
Even 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 challenges
What 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 calls
Who this book is for
This 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
Table of Contents
- Getting Started – One Environment per Project
- Interactive Python Interpreters
- Pythonic Syntax and Common Pitfalls
- Pythonic Design Patterns
- Functional Programming – Readability Versus Brevity
- Decorators – Enabling Code Reuse by Decorating
- Generators and Coroutines – Infinity, One Step at a Time
- Metaclasses – Making Classes (Not Instances) Smarter
- Documentation – How to Use Sphinx and reStructuredText
- Testing and Logging – Preparing for Bugs
- Debugging – Solving the Bugs
- Performance – Tracking and Reducing Your Memory and CPU Usage
- asyncio – Multithreading without Threads
- Multiprocessing – When a Single CPU Core Is Not Enough
- Scientific Python and Plotting
- Artificial Intelligence
- Extensions in C/C++, System Calls, and C/C++ Libraries
- Packaging – Creating Your Own Libraries or Applications
- ASIN : B09MMM2W2N
- 出版社 : Packt Publishing; 第 2nd 版 (2022年5月20日)
- 出版日期 : 2022年5月20日
- 语言 : 英语
- 文件大小 : 8577 KB
- 标准语音朗读 : 已启用
- X-Ray : 未启用
- 生词提示功能 : 未启用
- 纸书页数 : 710页
- 亚马逊热销商品排名: 商品里排第41,353名Kindle商店 (查看商品销售排行榜Kindle商店)
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- This book covers a broad depth of subjects from custom data structures to machine learning. While not comprehensive (which would be practically impossible), it manages to cover the “big rocks” of important concepts.
- Wonderful introduction to environments, syntax, and “Pythonic” coding style. I personally gained a lot of tips and tricks from the first five chapters.
- In spite the advanced subject matter, it has good readability.
- Plenty of code examples throughout. Use cases are a bit generic but that’s not necessarily a negative thing.
- Chapter 15 (on Scientific Python) was jam-packed with helpful briefs on various data-wrangling packages. Anyone who handles even a moderate amount of data in their day-to-day would do well to thumb through this chapter.
- Readers with narrow use cases or scopes of work (e.g. business intelligence analysts or anyone not writing copious amounts of code) might struggle with applicability. This does not diminish the usefulness of the material presented, however, this may be a negative point for some people.
The book went over basics of python, making reference on simplicity of the code, design patterns and time complexity in general and data structures available in python. Mutable and immutable data,
functional programming, list comprehensions and lambda functions. It also explained in a detailed way the itertool library and the use of decorators which I personally found very useful.
The testing and logging section were pretty well structured and helped me understand better the testing options in python. Also, the debugging chapter was very useful for troubleshooting code.
CPU and memory handling chapter helps make code more efficient and see the impact on the computer. Multi-threading is something I was a lot nowadays and it was a great chapter to learn about it.
I was impressed with the chapters on scientific python and AI/Machine learning, I wasn't expecting this in the book, goes over some of the algorithms and some code examples which was very useful.
Great book overall, easy to digest and code examples were great to follow along and replicate the results. Exercises where a good additional feature for further practice.
I now rate this book as a solid "keeper" for my library and definitely recommend it to others who want to up their Python game. The writing is clear, and the mostly short code examples adequately and clearly illustrate the author's points. The examples also can be good starting points for trying out your own variations and seeing what works or what blows up.
"Mastering Python Second Edition" is hefty, spanning some 680 pages. But that makes room even for some focus on obscure but useful functions such as *compress*, which "applies a Boolean filter to your iterable, making it return only the elements you actually need." The author's explanations of list, dict, set, and generator comprehensions have proved enlightening for me, and--grabbing at another random example--so has using *mpmath* for "convenient, precise calculations" involving trigonometry, calculus, matrices, and other operators. (My math skills are far from the best, so good help is always needed!) And, while doing some tests, it's good to know that "when measuring the execution time of a code snippet, there will always be some variation present."
It will take me a while to work my way through all of the chapters and topics--and the numerous "try to" exercises (with answers posted on GitHub). Nonetheless, "Mastering Python" already has introduced me to a wide array of packages, tools, and topics that are helping me raise my capabilities, including functional programming style, testing, debugging, and working with scientific Python and plotting. My thanks to Packt Publishing for sending me a review copy to consider. This is a solid and comprehensive guide to producing better, more effective code using Python's wide-ranging capabilities, libraries, and tools.