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Quick questions about the High Performance Python book

 
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Hi. I looked at Manning's page for this book and it looks VERY interesting. I have 2 quick questions.
Do you go discuss Jupyter notebooks at all? All of the data analytic work I've done so far used Jupyter as a platform for the Python work.
Do you talk about tips and tricks to make "vanilla Python" highly performant, or does the book concentrate on Cython for the big-time performance enhancements?
Thanks!
 
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In my previous book - in the field of Bioinformatics - I used Jupyter quite extensively. Most of the content of this book happens more at the library level - not at the analysis level - so I assume mostly the standard Python interpreter. I see environments like Jupyter being great for exploratory analysis of data (data science). This book is more about the guts of processing. That being said I do talk a bit about Jupyter (especially IPython magics that can be useful in many situations e.g. for quick profiling or cython development)

I discuss quite a bit of vanilla Python. Data structures and memory allocation. Multi-processing, ...
Then there is Cython as you refer. Also a lot of stuff about NumPy (being a book targeted at data analytics). And Pandas. And Numba.
And then some more advanced topics on Python/Numpy optimizations for CPU caching, GPU usage with Python. And also file system and advanced storage formats for data analysis (Apache parquet for example).

A side comment about Jupyter: I tend to prefer Notebooks formatted as jupytext: as they allow you to use normal text editors: https://github.com/mwouts/jupytext
 
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Hi Tiago!

Could the techniques described in your book also be useful for other applications than data analysis? Like building APIs or games in Python?
 
Tiago Antao
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Hi,

There are plenty of other applications for book. Games for sure (given that they are performance sensitive). API construction would be less obvious to me that the book would be useful.

 
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