Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Seaborn | 11,624 | 6,393 | 4,946 | 3 months ago | 35 | September 29, 2023 | 129 | bsd-3-clause | Python | |
Statistical data visualization in Python | ||||||||||
Ml Workspace | 3,197 | 5 months ago | 2 | apache-2.0 | Jupyter Notebook | |||||
🛠 All-in-one web-based IDE specialized for machine learning and data science. | ||||||||||
Dtreeviz | 2,720 | 2 | 22 | 4 months ago | 41 | July 07, 2022 | 61 | mit | Jupyter Notebook | |
A python library for decision tree visualization and model interpretation. | ||||||||||
Moviegeek | 730 | 2 years ago | 11 | mit | Python | |||||
A django website used in the book Practical Recommender Systems to illustrate how recommender algorithms can be implemented. | ||||||||||
Statistical Rethinking With Python And Pymc3 | 674 | 6 years ago | 2 | Jupyter Notebook | ||||||
Python/PyMC3 port of the examples in " Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath | ||||||||||
Anaconda Project | 214 | 2 | 6 months ago | 13 | August 12, 2022 | 114 | other | Python | ||
Tool for encapsulating, running, and reproducing data science projects | ||||||||||
Bestpractices | 142 | 5 months ago | 6 | mit | Jupyter Notebook | |||||
Things that you should (and should not) do in your Materials Informatics research. | ||||||||||
Learnpythonforresearch | 137 | 4 years ago | Jupyter Notebook | |||||||
This repository provides everything you need to get started with Python for (social science) research. | ||||||||||
Pycon Ds 2018 | 135 | 6 years ago | 1 | Jupyter Notebook | ||||||
Introduction to Python for Data Science for PyCon 2018 | ||||||||||
Data Science At Scale | 95 | 2 years ago | 4 | mit | Jupyter Notebook | |||||
A Pythonic introduction to methods for scaling your data science and machine learning work to larger datasets and larger models, using the tools and APIs you know and love from the PyData stack (such as numpy, pandas, and scikit-learn). |