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Advanced Python Programming, Second Edition, published by Packt

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Advanced Python Programming

This is the code repository for Advanced Python Programming , published by Packt. Accelerate your Python programs using proven techniques and design patterns

What is this book about?

Write efficient numerical code with NumPy, pandas, and Xarray
Use Cython and Numba to achieve native performance
Find bottlenecks in your Python code using profilers
Optimize your machine learning models with JAX
Implement multithreaded, multiprocessing, and asynchronous programs
Solve common problems in concurrent programming, such as deadlocks
Tackle architecture challenges with design patterns

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

fork = threading.Lock() partner1 = Spouse('Wife', None) partner2 = Spouse('Husband', partner1) partner1.partner = partner2 partner1.start()

Following is what you need for this book: This book is for intermediate to experienced Python programmers who are looking to scale up their applications in a systematic and robust manner. Programmers from a range of backgrounds will find this book useful, including software engineers, scientific programmers, and software architects.

With the following software and hardware list you can run all code files present in the book (Chapter 1-26).

Software and Hardware List

Chapter	Software required	OS required
1	Python 3	Windows, Mac OS X, and Linux (Any)

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Get to Know the Author

Quan Nguyen is a Python programmer and machine learning enthusiast. He is interested in solving decision-making problems under uncertainty. Quan has authored several books on Python programming and scientific computing. He is currently pursuing a Ph.D. degree in computer science at Washington University in St. Louis, researching Bayesian methods in machine learning.

Download a free PDF

If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.