😎💡🔥A selection of unpopular but very useful Python libraries for working with data

Bottleneck is a library that speeds up NumPy methods up to 25 times, especially when processing arrays containing NaN values. It optimizes calculations such as finding minima, maxima, medians, and other aggregate functions. By using specialized algorithms and handling missing data, Bottleneck significantly speeds up work with large data sets, making it more efficient than standard NumPy methods.

Nbcommands is a tool that simplifies code search in Jupyter notebooks, eliminating the need for users to search manually. It allows you to find and manage code by keywords, functions, or other elements, which significantly speeds up working with large projects in Jupyter and helps users navigate their notes and code blocks more efficiently.

SciencePlots is a style library for matplotlib that allows you to create professional graphs for presentations, research papers, and other scientific publications. It offers a set of predefined styles that meet the requirements for data visualization in scientific papers, making graphs more readable and aesthetically pleasing. SciencePlots makes it easy to create high-quality graphs that meet the standards of academic publications and presentations.

Aquarel is a library that adds additional styles to visualizations in matplotlib. It allows you to improve the appearance of graphs, making them more attractive and professional. Aquarel simplifies the creation of custom styles, helping users create graphs with more interesting designs without having to manually configure all the visualization parameters.

Modelstore is a library for managing and tracking machine learning models. It helps organize, save, and version models, as well as track their lifecycle. With Modelstore, users can easily save models to various storages (S3, GCP, Azure, and others), manage their updates and restore. This makes it easier to deploy and monitor models in production environments, making working with models more convenient and controllable.

CleverCSV is a library that improves the process of parsing CSV files and helps avoid errors when reading them with Pandas. It automatically detects the correct delimiters and format of CSV files, which is especially useful when working with files that have non-standard or heterogeneous structures. CleverCSV simplifies working with data by eliminating errors associated with incorrect recognition of delimiters and other file format parameters.