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Mastering Python 2E
Mastering Python 2E

Mastering Python 2E: Write powerful and efficient code using the full range of Python's capabilities , Second Edition

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Mastering Python 2E

Interactive Python Interpreters

Now that we have a working Python installation, we need to run some code. The most obvious way is to create a Python file and execute it. Often, it can be faster to interactively develop code from an interactive Python interpreter, however. While the standard Python interpreter is already quite powerful, many enhancements and alternatives are available.

The alternative interpreters/shells offer features such as:

  • Smart autocompletion
  • Syntax highlighting
  • Saving and loading sessions
  • Automatic indenting
  • Graphing/charting output

In this chapter, you will learn about:

  • Alternative interpreters:
    • bpython
    • ptpython
    • ipython
    • jupyter
  • How to enhance interpreters

The Python interpreter

The standard Python interpreter is already fairly powerful, but more options are available through customization. First, let’s start with a 'Hello world!'. Because the interpreter uses REPL, all output will be automatically printed and we can simply create a string.

Sometimes interactive interpreters are referred to as REPL. This stands for Read-Eval-Print-Loop. This effectively means that all of your statements will be executed and printed to your screen immediately.

First, we need to start the interpreter; after that, we can type our commands:

$ python3
Python 3.9.0
[GCC 7.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> 'Hello world!'
'Hello world!'

That was easy enough. And note that we didn’t have to use print('Hello world!') to show the output.

Many interpreters have only...

Alternative interpreters

Now that you have seen some of the features of the regular Python interpreter, let’s look at some enhanced alternatives. There are many options available, but we will limit ourselves to the most popular ones here:

  • bpython
  • ptpython
  • ipython
  • jupyter (web-based ipython)

Let’s get started.

bpython

The bpython interpreter is a curses interface for the Python interpreter that offers many useful features, while still being very similar to the regular Python interpreter.

The curses library allows you to create a fully functioning text-based user interface (TUI). A TUI gives you full control over where you want to write to the screen. The regular Python interpreter is a command-line interface (CLI), which normally only allows you to append to the screen. With a TUI, you can write to any position on the screen, making its features somewhat comparable to a graphical user interface (GUI).

Some...

Exercises

  1. The rlcompleter enhancement we created currently only handles dictionaries. Try and extend the code so it supports lists, strings, and tuples as well.
  2. Add colors to the completer (hint: use colorama for the coloring).
  3. Instead of manually completing using our own object introspection, try and use the jedi library for autocompletion, which does static code analysis.

    Static code analysis inspects code without executing it. This means it’s entirely safe to run, even on foreign code, as opposed to the autocompletion we wrote earlier, which runs the code in object.keys().

  4. Try to create a Hello <ipywidget> so the name of the person can be edited through a notebook without code changes.
  5. Try and create a script that will look for a given pattern through all of your previous ipython sessions.

Example answers for these exercises can be found on GitHub: https://github.com/mastering-python/exercises...

Summary

This chapter has shown you several of the available Python interpreters and some of the pros and cons. Additionally, you have had a small glimpse of what IPython and Jupyter can offer us. Chapter 15, Scientific Python and Plotting, almost exclusively uses Jupyter Notebooks and demonstrates a few more powerful features, such as plotting integration.

For most generic Python programmers, I would suggest using either bpython or ptpython, since they are really fast and lightweight interpreters to (re-)start that still offer a lot of useful features.

If your focus is more on scientific programming and/or handling large datasets in your shell, then IPython or JupyterLab are probably more useful. These are far more powerful tools, but they come at the cost of having slightly higher start up times and system requirements. I personally use both depending on the use case. When testing a few simple lines of Python and/or verifying the behavior of a small code block, I mostly use...

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Key benefits

  • 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

Description

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

Who is this book 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

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

Product Details

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Last updated date : May 13, 2022
Publication date : May 20, 2022
Length: 710 pages
Edition : 2nd
Language : English
ISBN-13 : 9781800207721
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Product Details

Last updated date : May 13, 2022
Publication date : May 20, 2022
Length: 710 pages
Edition : 2nd
Language : English
ISBN-13 : 9781800207721
Category :
Languages :
Tools :

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Table of Contents

20 Chapters
Getting Started – One Environment per Project Chevron down icon Chevron up icon
Interactive Python Interpreters Chevron down icon Chevron up icon
Pythonic Syntax and Common Pitfalls Chevron down icon Chevron up icon
Pythonic Design Patterns Chevron down icon Chevron up icon
Functional Programming – Readability Versus Brevity Chevron down icon Chevron up icon
Decorators – Enabling Code Reuse by Decorating Chevron down icon Chevron up icon
Generators and Coroutines – Infinity, One Step at a Time Chevron down icon Chevron up icon
Metaclasses – Making Classes (Not Instances) Smarter Chevron down icon Chevron up icon
Documentation – How to Use Sphinx and reStructuredText Chevron down icon Chevron up icon
Testing and Logging – Preparing for Bugs Chevron down icon Chevron up icon
Debugging – Solving the Bugs Chevron down icon Chevron up icon
Performance – Tracking and Reducing Your Memory and CPU Usage Chevron down icon Chevron up icon
asyncio – Multithreading without Threads Chevron down icon Chevron up icon
Multiprocessing – When a Single CPU Core Is Not Enough Chevron down icon Chevron up icon
Scientific Python and Plotting Chevron down icon Chevron up icon
Artificial Intelligence Chevron down icon Chevron up icon
Extensions in C/C++, System Calls, and C/C++ Libraries Chevron down icon Chevron up icon
Packaging – Creating Your Own Libraries or Applications Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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4 star 21.5%
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1 star 3.1%
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Si Dunn Jul 30, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I had never used virtual environments for Python projects, so this book's first chapter gave me a headache trying to figure out which of several approaches to take and what might be the best way to manage and track dependencies. It was a case of being shown too much too soon for someone who was expecting an easier, calmer entrance into intermediate and higher-level Python. However, once I got past that intimidating first chapter, I quickly started finding many topics and code examples that are helping me improve my capabilities. (Actually, Chapter 1 also helped me, even if it did give me a headache. I settled on Anaconda and was able to set up virtual environments that are working as described.)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.
Amazon Verified review Amazon
Fed Mar 01, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I know python at a decent level but I’m always looking for more books to pick up new ideas and ways of coding. Good book for my needs. Probably not the friendliest book for a total beginner.
Amazon Verified review Amazon
Madhu Bhargav Oct 27, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book takes a very comprehensive approach in discussing about various topics in a structured way. It is more targeted towards someone who already nailed down the basics and want to learn and use python in an actual project. The introduction to Environment and Interpertetters was very well put together.The overall structure of the book caught me off guard though. I wish there was more emphasis on unit testing, packaging and C++ extensions earlier in the book before dealing with AI and ML. This my personal take as a Django Developer.In the debugging chapter, I wish the author talks about ipdb in depth instead of just a couple of pages. Nevertheless the number of topics covered in the book from Unit Testing to Tensorflow was really good for a intermediate level book.
Amazon Verified review Amazon
Ninad Jul 24, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Content is designed well to aid the learning of new as well as experienced developer.Each concept is explained in detailed manner.Good amount coding examples.
Amazon Verified review Amazon
Anvesh Chinta Jun 30, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Pretty much covers everything you need to know about python, right from the basics. You can learn basic python from watching some YouTube videos or some blogs, but after that you struggle with What's next?. This book lays out all the paths that we can take after learning python and the best part of the book is it also mentions the pitfalls that we may encounter. I feel that's important. If you are that average learner who learnt python and curious about what's next this is the go to book.The only drawback is abundance of information. For the one who is really interested and have a path clear in mind it will be easy, otherwise it can be little overwhelming.
Amazon Verified review Amazon
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