scientific-visualization-book VS datatable

knowing matplotlib really well gets really pro viz tbh, this https://github.com/rougier/scientific-visualization-book is the best resource for it imo. Its a bit more work but you can get really great results

I had the same problem until I found this tutorial:

https://github.com/rougier/matplotlib-tutorial

If you wan something deeper the same person has written a book:

https://github.com/rougier/scientific-visualization-book

i saw this one : > https://github.com/rougier/scientific-visualization-book

I re-created the hatching using matplotlib as shown here.

From the open access book "Scientific Visualization: Python + Matplotlib. Code: dungeon.py

For python this open access book is excellent: https://github.com/rougier/scientific-visualization-book

Aside from that, there is a Python translation of data.table (see documentation here), which might be worth looking into. However, it hasn't had any major updates in a while: the last release 2 years ago ...

There's datatable. I haven't used it much, but the R version (data.table) is phenomenal.

just use HDF5 or Parquet, or CSV + https://github.com/h2oai/datatable to speed up the file reading.

> For me I had with pandas the most issues using it's multiindex.

Yessss. I loathe indices, and have never been in a situation where I was better off with them than without them.

> Regarding fast you have something like Vaex on python sid

I've never used Vaex, but I've used datatable (https://github.com/h2oai/datatable) and polars (https://github.com/pola-rs/polars). Polars is my favorite API, but datatable was faster at reading data (Polars was faster in execution). I'll have to give Vaex a try at some point.

Yep. I think this is the downside to a package being entirely maintained by volunteers. In any case, Pandas is still the leading data wrangling package for Python. (I'm excited to see how datatable evolves.)

h2o's data.table clone is fine

https://github.com/h2oai/datatable