Book Image

Mastering Python 2E - Second Edition

By : Rick van Hattem
5 (1)
Book Image

Mastering Python 2E - Second Edition

5 (1)
By: Rick van Hattem

Overview of this book

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
Table of Contents (21 chapters)
19
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20
Index

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...