[closed]

I have a fair amount of experience with pandas, and find the notebooks very help to refer to! I would say it's worth noting that his book is organized by technology (e.g. numpy, then pandas, then plotting), which makes it feel more like a technical reference, than a walk-through of basic to advanced DS activities.

It's also worth checking out the notebooks for Wes McKinney's data science book. Daniel Chen doesn't have the code from his DS book on GitHub, but does have some useful notebooks he uses for workshops.

https://github.com/wesm/pydata-book

https://github.com/chendaniely/pandas_for_everyone/tree/mast...

[Tarq0n]

Hands-on Machine Learning with Scikit-Learn and TensorFlow [1] is more ML focused, but highly recommended. Out of the three books (Python for Data Analysis and Python Data Science Handbook) I learned the most from this one by far.

[1] https://github.com/ageron/handson-ml

[FranzFerdiNaN]

An incredibly critical review of McKinneys book can be found here: https://medium.com/dunder-data/python-for-data-analysis-a-cr...

[closed]

Ah thanks for pointing out--I mostly agree with his posts (and his minimally sufficient pandas is a great one!), and it's definitely worth reading. A common quirk with a lot of the python DS books is them being "reference manuals".

(I'm a little concerned with the aggressive way he's come at Wes McKinney in posts and on twitter, considering Wes has given a lot of his time working on open source contributions)

[wodenokoto]

I agree with that review. McKinneys book reads like a reference manual and an old one at that. I don't understand why it is recommended so often.