pytensor
pandas-profiling
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| pytensor | pandas-profiling | |
|---|---|---|
| 1 | 1 | |
| 244 | 8,962 | |
| 11.5% | - | |
| 9.5 | 8.5 | |
| 5 days ago | almost 2 years ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
pytensor
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[D] Programming language for developing computational statistics algorithms
I wouldn't overlook also how many goodies these existing PPLs are coming with. Starting from scratch means that one would have code up all the utilities to assess convergence, chain properties, etc. etc. on their own. And more advanced tricks like auto-differentiations that by themselves are huge perks are also unavailable (goodbye easy ADVI). For example PyMC, now uses Numba and JAX (via Aesara/PyTensor depending on your version), so theoretically (and practically) you can include arbitary PyTensor/JAX code in your model. I use PyMC as an example here, but this extends to most other PPLs too depending on their backends.
pandas-profiling
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Mito – Excel-like interface for Pandas dataframes in Jupyter notebook
For those who are going through the thread finding new tools: pandas-profiling[0] is a library for automatic EDA (part of what bamboolib[1] does).
[0]: https://github.com/pandas-profiling/pandas-profiling
What are some alternatives?
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mito - The mitosheet package, trymito.io, and other public Mito code.
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monaco - Quantify uncertainty and sensitivities in your computer models with an industry-grade Monte Carlo library.