Python Data Science Handbook: Full Text in Jupyter Notebooks (opens in new tab)

hmm interesting idea. I've been trying all kinds of different tools for documenting my work/code snippets/notes.... But nothing sticked. Mind explaining more on that? Would it be stupid to have a wiki like system inside jupyter?

Yep, Jupyter notebooks have an execution API, you can find more of it here - https://nbconvert.readthedocs.io/en/latest/execute_api.html. Hosted notebooks as a service is a growing area of investment and those services presumably use this API.

I built ReviewNB [1] to see visual diff for Jupyter Notebook changes & do a code review on it (by writing comments on cell changes etc.).

One of the next feature for ReviewNB is a CI pipeline for Jupyter Notebooks on GitHub. The idea is to make it easy for users to specify notebook "tests"/"checks" that can then be run on every change.

Given the nature of Notebooks, it's a bit hard to design CI for it in a clean way, but I appreciate any inputs or use cases that you might want to see fulfilled.

[1] https://www.reviewnb.com/

There's the nbval plugin for pytest. https://nbval.readthedocs.io/en/latest/ I've used that in one of my packages (https://github.com/qucontrol/krotov) to verify example notebooks on every push, on Travis CI

At the very least you can use the command line tool to run/export them.

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.

Or maybe you should spend time on it, by creating a fork with all the good PRs applied?

Last commit was 3 months ago, and there are 48 open pull requests ...

Which one of us is accessing the wrong repo?

any one else reading this comment should know -- I did look through the PRs and many of them are typo fixes.

Its easy to make demands on open source code maintainers time. Not all PRs and "tickets" need attention. The maintainer does not owe us anything.

Is this an advertisement?

Some ideas / questions: - The documentation on GH is unreadable like this - On GH it says "Patent Pending" so is this not open source after that or is that phrase just a joke? - How is it related to the mentioned Data Science Handbook?