You're reading from Mastering Python 2E Write powerful and efficient code using the full range of Python's capabilities

Product type Paperback

Published in May 2022

Last Updated in May 2022

Publisher Packt

ISBN-13 9781800207721

Length 710 pages

Edition 2nd Edition

Languages

Python

Tools

Dask

Concepts

Programming Language

Author (1):

Rick Hattem

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Table of Contents (21) Chapters

Preface

1. Getting Started – One Environment per Project FREE CHAPTER

2. Interactive Python Interpreters

3. Pythonic Syntax and Common Pitfalls

4. Pythonic Design Patterns

5. Functional Programming – Readability Versus Brevity

6. Decorators – Enabling Code Reuse by Decorating

7. Generators and Coroutines – Infinity, One Step at a Time

8. Metaclasses – Making Classes (Not Instances) Smarter

9. Documentation – How to Use Sphinx and reStructuredText

10. Testing and Logging – Preparing for Bugs

11. Debugging – Solving the Bugs

12. Performance – Tracking and Reducing Your Memory and CPU Usage

13. asyncio – Multithreading without Threads

14. Multiprocessing – When a Single CPU Core Is Not Enough

15. Scientific Python and Plotting

16. Artificial Intelligence

17. Extensions in C/C++, System Calls, and C/C++ Libraries

18. Packaging – Creating Your Own Libraries or Applications

19. Other Books You May Enjoy

20. Index

Scientific Python and Plotting

The Python programming language is quite suited for scientific work. This is due to it being really easy to program for while being powerful enough to do almost anything you need. This combination has spawned a whole bunch of (very large) Python projects, such as numpy, scipy, matplotlib, pandas, and so on, over the years. While these libraries are all large enough to warrant entire books for themselves, we can offer a little insight into where and when they can be useful so you have an idea of where to start.

The major topics and libraries covered in this chapter are split into three sections:

Arrays and matrices: NumPy, Numba, SciPy, Pandas, statsmodels, and xarray
Mathematics and precise calculations: gmpy2, Sage, mpmath, SymPy, and Patsy
Plotting, graphing, and charting: Matplotlib, Seaborn, Yellowbrick, Plotly, Bokeh, and Datashader

It is very likely that not all libraries in this chapter are relevant to you,...