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?
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).