40 Open-Source Tools to Supercharge Your Pandas Workflow

Pandas receives over 3M downloads per day. But 99% of its users are not using it to its full potential.

I discovered these open-source gems that will immensely supercharge your Pandas workflow the moment you start using them.

1. Jupyter-Datatables: Enrich the default preview of a DataFrame in jupyter notebook.

   1. Link: https://bit.ly/jupy-dtables

2. SummaryTools: Supercharge the describe() method in Pandas.

   1. Link: https://bit.ly/summarytools

3. Sidetable: Supercharge the value_counts() method in Pandas.

   1. Link: https://bit.ly/py-sidetable

4. Sketch: Generate code/insights about data by asking questions in natural language.

   1. Link: https://bit.ly/py-sketch

5. Deepchecks: Generate a comprehensive validation report of your data.

   1. Link: https://bit.ly/deepchks

6. Pandas Flavor: Extend Pandas to attach methods to the dataframe object.

   1. Link: https://bit.ly/pd-flavor

7. Pandarallel: Parallelize Pandas across multiple CPU cores.

   1. Link: https://bit.ly/pandarallel

8. PandasML: Pandas, sklearn and matplotlib integrated.

   1. Link: https://bit.ly/pandasml

9. Geopandas: Work with Geospatial data in Pandas.

   1. Link: https://bit.ly/geo-pd

10. DuckDB: Run SQL queries on dataframes.

    1. Link: https://bit.ly/duckdb

11. Modin: Boost Pandas' performance up to 70x by modifying the import.

    1. Link: https://bit.ly/modin-guide

12. PivotTableJS: Create pivot tables by using drag and drop functionality.

    1. Link: https://bit.ly/pivottablejs

13. Missingno: Visualize missing values in your dataset.

    1. Link: https://bit.ly/py-missingno

14. Pandas Alive: Create animated charts for pandas dataframes.

    1. Link: https://bit.ly/pd-alive

15. Skimpy: Supercharge the describe() method in Pandas.

    1. Link: https://bit.ly/py-skimpy

16. Pandas-log: Debug pandas pipeline using step-by-step logging.

    1. Link: https://bit.ly/py-log

17. tsflex: Process time series and perform feature extraction.

    1. Link: https://bit.ly/tsflex

18. pandas-profiling: Generate EDA report of data in one-line of code.

    1. Link: https://bit.ly/pd-profiling

19. Mars: A tensor-based framework for scaling numpy, pandas, scikit-learn, and Python functions.

    1. Link: https://bit.ly/py-mars

20. nptyping: Apply type hints for Pandas data frames.

    1. Link: https://bit.ly/nptyping

21. popmon: Profile your data to determine its stability.

    1. Link: https://bit.ly/py-popmon

22. Gspread-pandas: Interact with Google sheets through pandas dataframes.

    1. Link: https://bit.ly/pd-gsheets

23. pdpipe: Create pandas pipeline easily and intuitively.

    1. Link: https://bit.ly/py-pdpipe

24. PrettyPandas: Prettify the dataframe when printed.

    1. Link: https://bit.ly/PrettyPandas

25. Dora: An intuitive API for data cleaning, processing, feature selection, visualization, etc.

    1. Link: https://bit.ly/py-dora

26. Pandapy: The speed of NumPy combined with Pandas' elegance.

    1. Link: https://bit.ly/pandapy

27. PyJanitor: A clean API for cleaning data.

    1. Link: https://bit.ly/pyjanitor

28. swifter: Speed-up the apply() method in Pandas.

    1. Link: https://bit.ly/py-swifter

29. Mito: Analyze data in Jupyter by editing a spreadsheet.

    1. Link: https://bit.ly/mito-ds

30. Visual Python: GUI-based Python code generator for data science

    1. Link: https://bit.ly/visual-py

31. tqdm: Add progress bars to Pandas methods.

    1. Link: https://bit.ly/tqdm-pd

32. Lux: Automatic data visualization.

    1. Link: https://bit.ly/pd-lux

33. D-Tale: Visualizer for pandas dataframe.

    1. Link: https://bit.ly/py-dtale

34. AutoClean: Automated data preprocessing & cleaning.

    1. Link: https://bit.ly/py-autoclean

35. pytablewriter: Write a dataframe in various formats: AsciiDoc / CSV / HTML / JSON / LaTeX / Markdown / Excel / TOML / TSV / YAML, etc.

    1. Link: https://bit.ly/pytablewriter

36. itables: Pandas dataframes as interactive datatables.

    1. Link: https://bit.ly/itables

37. PandasGUI: A GUI for Pandas dataframes.

    1. Link: https://bit.ly/PandasGUI

38. tabula-py: Extract table from PDF into Pandas dataframe.

    1. Link: https://bit.ly/tabulapy

39. Pingouin: Perform statistical testing on Pandas dataframe.

    1. Link: https://bit.ly/pypingouin

40. Dexplot: Create many types of beautiful data visualizations with a simple, consistent, and intuitive API.

    1. Link: https://bit.ly/dexplot

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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Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn.