data-science-ipython-notebooks
pandas_flavor
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| data-science-ipython-notebooks | pandas_flavor | |
|---|---|---|
| 1 | 2 | |
| 26,459 | 293 | |
| - | 0.3% | |
| 0.0 | 1.5 | |
| about 1 month ago | 9 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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data-science-ipython-notebooks
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Beginner in Python for Data Science
data science ipython notebooks
pandas_flavor
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This OOP habit disturbs me (super().__init__(args accumulation):)
There's established ways to extend pandas btw: - https://pandas.pydata.org/docs/development/extending.html - Also, https://github.com/pyjanitor-devs/pandas_flavor
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Using Python Classes to Streamline Data Modelling/Cleaning
Check out pandas-flavor. It's a library that lets you register methods to dataframes. There's definitely a time and a place for OO in pandas data processing but your examples can probably be more simply expressed as methods and pandas flavor can make them easy to "find" as extensions of the frame.
What are some alternatives?
manjaro-linux - Shell scripts for setting up Manjaro Linux for Python programming and deep learning
modin - Modin: Scale your Pandas workflows by changing a single line of code
BirdNET - Soundscape analysis with BirdNET.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
kmodes - Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data
sports-betting - Collection of sports betting AI tools.
PMapper - A tool for quickly evaluating IAM permissions in AWS.
data-science - :bar_chart: Path to a free self-taught education in Data Science!
listenbrainz-server - Server for the ListenBrainz project, including the front-end (javascript/react) code that it serves and all of the data processing components that LB uses.
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)