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...

Haven't read this yet, but just from Jake Vanderplas' reputation, I think it's probably worth

If you want some more recs, my two favorites are Chris Albon's Machine Learning with Python Cookbook and Joel Grus' Data Science from Scratch: First Principles with Python

This is the first book on the subject matter which I actually finished. When I started using it, I was completely noob in data analysis/ml.

Personally I'd say this is a good book. Sections dealing with Numpy, Pandas and Matplotlib are great.

However, I am hesitant to say the same about ML section. I felt like this book assumes some familiarity with general ML concepts. I also felt like ML chapters progressed a bit fast from beginning to the core of chapter.

In all, book is great. Sections on Numpy, Pandas etc are great. But as for ML section, don't use that section as an introduction/first course for ML.

"These notebooks are just Python code. They even have #-comments instead of markdown. For awesome Python notebooks, see

http://norvig.com/ipython/README.html "

https://news.ycombinator.com/item?id=12470109

I have the paperback version and I have read the Jupyter, Numpy and Matplotlib chapters as well as most of the Pandas chapter (I haven't read the scikit-learn chapter at all). So far I like it. It's well written, well edited, overall a good quality book. It's really focused on the tools and it shows you how they work with small, contrived examples. This is good because you can use it as a reference, pick up pretty much any section and understand it. However it doesn't teach you much about the process of data science, which would require larger examples. In other words it's focused on the how but not on the what and why. Maybe a more accurate title would be Python Data Science Tooling Guide. In my opinion it should be perfect for people who've already done some data science in another environment and are switching to Python. Other people might need to seek additional guidance elsewhere.

For me, it is one of the best books for beginners. It covers almost completely from numpy, pandas, matplotlib/seaborn to ML algorithms.