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Mastering Python 2E

Mastering Python 2E - Second Edition

By : Rick Hattem
4.6 (65)
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Mastering Python 2E

Mastering Python 2E

4.6 (65)
By: Rick 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)
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19
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20
Index

Plotting, graphing, and charting

Being able to read, process, and write data is important, of course, but to understand the meaning of data it is often far more convenient to create a plot, graph, or chart. As the old adage goes: “A picture is worth a thousand words.”

If you have experience with any of the libraries mentioned earlier in this chapter, you may know that many of them have options for graphical output. In (almost?) all cases, however, this is not really a built-in feature but a convenient shortcut to an external library such as matplotlib.

As is the case with several of the libraries mentioned in this chapter, there are multiple libraries with similar features and possibilities, so this is certainly not an exhaustive list. To make visual plotting easier, for these examples we will mostly rely on jupyter-notebook with the use of the ipywidgets to create interactive samples. As always, the code (in these cases, the jupyter-notebooks) can be found on...

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Mastering Python 2E
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