• 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.