10 Little-Known Python Libraries for Working with Data

• PyGWalker: PyGWalker simplifies the data analysis and visualization workflow in Jupyter Notebook by turning a pandas data frame (or polars data frame) into a Tableau-style UI for visual exploration.

• SciencePlots: Create professional matplotlib plots for presentations, research papers, and more.

• CleverCSV: Eliminate parsing errors when reading CSV files with Pandas.

• Bottleneck: Speeds up NumPy methods by 25x. Especially when the array has NaN values.

• Fastparquet: Speeds up pandas I/O by 5x.

• Multipledispatch: Provides methods for overloading functions in Python.

• Aquarel: Additional matplotlib plot styles.

• Nbcommands: Helps easily search code in Jupyter notebooks instead of manually searching.

• Modelstore: A machine learning model library for better tracking of model performance.

• Pigeon: Helps annotate data with mouse clicks in a Jupyter notebook.