pytensor
pandas-profiling
pytensor | pandas-profiling | |
---|---|---|
1 | 1 | |
252 | 8,962 | |
8.3% | - | |
9.5 | 8.5 | |
5 days ago | almost 2 years ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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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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data_algebra - Codd method-chained SQL generator and Pandas data processing in Python.
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kangas - 🦘 Explore multimedia datasets at scale
mito - The mitosheet package, trymito.io, and other public Mito code.
dtale - Visualizer for pandas data structures
vegafusion - Serverside scaling for Vega and Altair visualizations
monaco - Quantify uncertainty and sensitivities in your computer models with an industry-grade Monte Carlo library.