35 Hidden Python Libraries That Are Absolute Gems

I reviewed 1,000+ Python libraries and discovered these hidden gems I never knew even existed.

Here are some of them that will make you fall in love with Python and its versatility (even more).

1. PyGWalker: Analyze Pandas dataframe in a tableau-like interface in Jupyter.

   • Link: https://bit.ly/pyg-walker

2. Science plots: Make professional matplotlib plots for presentations, research papers, etc.

   • Link: https://bit.ly/sciplt

3. CleverCSV: Resolve parsing errors while reading CSV files with Pandas.

   • Link: https://bit.ly/clv-csv

4. fastparquet: Speed-up parquet I/O of pandas by 5x.

   • Link: https://bit.ly/fparquet

5. Dovpanda: Generate helpful hints as you write your Pandas code.

   • Link: https://bit.ly/dv-pnda

6. Drawdata: Draw a 2D dataset of any shape in a notebook by dragging the mouse.

   • Link: https://bit.ly/data-dr

7. nbcommands: Search code in Jupyter notebooks easily rather than manually doing it.

   • Link: https://bit.ly/nb-cmnds

8. Bottleneck: Speedup NumPy methods 25x. Especially better if array has NaN values.

   • Link: https://bit.ly/btlneck

9. multipledispatch: Enable function overloading in python.

   • Link: https://bit.ly/func-ove

10. Aquarel: Style matplotlib plots.

    • Link: https://bit.ly/py-aql

11. Uniplot: Lightweight plotting in the terminal with Unicode.

    • Link: https://bit.ly/py-uni

12. pydbgen: Random pandas dataframe generator.

    • Link: https://bit.ly/pydbgen

13. modelstore: Version machine learning models for better tracking.

    • Link: https://bit.ly/mdl-str

14. Pigeon: Annotate data with button clicks in Jupyter notebook.

    • Link: https://bit.ly/py-pgn

15. Optuna: A framework for faster/better hyperparameter optimization.

    • Link: https://bit.ly/py-optuna

16. Pampy: Simple, intuitive and faster pattern matching. Works on numerous data structures.

    • Link: https://bit.ly/py-pmpy

17. Typeguard: Enforce type annotations in python.

    • Link: https://bit.ly/typeguard

18. KnockKnock: Decorator that notifies upon model training completion.

    • Link: https://bit.ly/knc-knc

19. Gradio: Create an elegant UI for ML model.

    • Link: https://bit.ly/py-grd

20. Parse: Reverse f-strings by specifying patterns.

    • Link: https://bit.ly/py-prs

21. handcalcs - Write and display mathematical equations in Jupyter

    • Link: https://bit.ly/py-hcals

22. Osquery: Write SQL-based queries to explore operating system data.

    • Link: https://bit.ly/py-osqry

23. D3Blocks: Create and export interactive plots as HTML. (Matplolib/Plotly lose interactivity when exported).

    • Link: https://bit.ly/py-d3

24. itables: Show Pandas dataframes as interactive tables.

    • Link: https://bit.ly/py-itbls

25. jellyfish: Perform approximate and phonetic string matching.

    • Link: https://bit.ly/jly-fsh

26. Hamilton: Create an automatic dataflow graph of python functions.

    • Link: https://bit.ly/py-hmltn

27. Folium: Powerful js-powered library for visualizing geospatial data.

    • Link: https://bit.ly/py-flm

28. Termcolor: Color formatting for output in terminal/notebook.

    • Link: https://bit.ly/trmclr

29. PyDataset: Access many datasets (in DataFrame format) using a single API.

    • Link: https://bit.ly/py-dataset

30. Spellchecker: Check if words are spelled correctly.

    • Link: https://bit.ly/spl-chk

31. plotapi: Create engaging and elegant visualization (also available as no-code).

    • Link: https://bit.ly/plt-api

32. animatplot: Animate matplotlib plots.

    • Link: https://bit.ly/ani-matplot

33. HyperTools: A single wrapper for many dimensionality reduction techniques and visualization.

    • Link: https://bit.ly/hyp-tls

34. Mercury: Build web apps in Jupyter with python.

    • Link: https://bit.ly/pymrcry

35. Lance: A columnar data format optimized for ML workflows and datasets.

    • Link: https://bit.ly/py-lance

That’s a wrap!!

What cool Python libraries would you add to this list?

👇 Drop your suggestions in the replies below 👇

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