scientific-visualization-book
datatable
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| scientific-visualization-book | datatable | |
|---|---|---|
| 17 | 9 | |
| 10,017 | 1,785 | |
| - | 0.5% | |
| 3.6 | 6.1 | |
| 3 months ago | 5 months ago | |
| Python | C++ | |
| GNU General Public License v3.0 or later | Mozilla Public License 2.0 |
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scientific-visualization-book
- Book or web book recommendation request: a data visualization cookbook using Python for scientists.
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What's New in Matplotlib 3.6.0
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:
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Ask HN: What is the best book on data visualization in 2021?
For python this open access book is excellent: https://github.com/rougier/scientific-visualization-book
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Open access book on scientific visualization using Python and matplotlib
This book is distributed under a non commercial license (CC-BY-NC-SA 4.0) [0].
While I understand that language is evolving and that only under a "strict definition" of open access does it mean removing barriers to copying and reuse [1], it's seems pretty duplicitous to say it's "open" while putting it under a non-commercial license.
[0] https://github.com/rougier/scientific-visualization-book/blo...
[1] https://en.wikipedia.org/w/index.php?title=Open_access&oldid...
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Scikit-Learn Version 1.0
Speaking of what's possible in matplotlib, I am very much looking forward to reading this book: https://github.com/rougier/scientific-visualization-book
datatable
- Any advice on using Pandas as a data analyst?
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Alternative to Pandas
There's datatable. I haven't used it much, but the R version (data.table) is phenomenal.
- Massive R analysis of Data Science Language and Job Trends 2022
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Scikit-Learn Version 1.0
> 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.
- Show HN: Sheet2dict – simple Python XLSX/CSV reader/to dictionary converter
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Ditching Excel for Python in a Legacy Industry (Reinsurance)
h2o's data.table clone is fine
What are some alternatives?
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
DataFrame - C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
db-benchmark - reproducible benchmark of database-like ops
sktime - A unified framework for machine learning with time series
faiss - A library for efficient similarity search and clustering of dense vectors.
vinum - Vinum is a SQL processor for Python, designed for data analysis workflows and in-memory analytics.
Altair - Declarative statistical visualization library for Python
oz - Data visualizations in Clojure and ClojureScript using Vega and Vega-lite
mplcyberpunk - "Cyberpunk style" for matplotlib plots