Anndata is a package for simple (functional) high-level APIs for data analysis pipelines. In this context, it provides an efficient, scalable way of keeping track of data together with learned annotations and reduces the code overhead typically encountered when using a mostly object-oriented library such as scikit-learn.

This package provides miscellaneous tools for data analysis and scientific computing.

Uproot is a Python library for reading and writing ROOT files. It uses NumPy and does not depend on C++ ROOT.

pyvistaqt is a helper module for pyvista to enable you to plot using Qt by placing a vtk-widget into a background renderer. This can be quite useful when you desire to update your plot in real-time.

This package provides a Python library for manipulating indices of ndarrays.

The osfclient is a python library and a command-line client for up- and downloading files to and from Open Science Framework projects. The OSF is an open source project which facilitates the open collaboration of researchers on the web, by sharing data and other research outputs.

This package provides optimized tools for group-indexing operations: aggregated sum and more.

This package provides an efficient implementation of Friedman's SuperSmoother based in Python. It makes use of numpy for fast numerical computation.

This package provides Python bindings for the Boost::Histogram library, one of the fastest libraries for histogramming.

The CMasher package provides a collection of scientific colormaps and utility functions to be used by different Python packages and projects, mainly in combination with Matplotlib. It offers several alternatives to commonly used colormaps, like chroma and rainforest for jet; sunburst for hot; neutral for binary; and fusion and redshift for coolwarm.

This package provides a Python package for calculating tissue-specificity metrics for gene expression.

The SciPy library is one of the core packages that make up the SciPy stack. It provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.

This package provides a Python interface to the QDLDL LDL factorization routine for quasi-definite linear system.

This package provides a set of tools and Python modules for setting up, manipulating, running, visualizing and analyzing atomistic simulations.

Clarabel.rs is a Rust implementation of an interior point numerical solver for convex optimization problems using a novel homogeneous embedding.

Bottleneck is a collection of fast, NaN-aware NumPy array functions written in C.

Scikit-survival is a Python module for survival analysis built on top of scikit-learn. It allows doing survival analysis while utilizing the power of scikit-learn, e.g., for pre-processing or doing cross-validation.

PyAMG is a Python library of Algebraic Multigrid (AMG) solvers. It features implementations of:

• Ruge-Stuben (RS) or Classical AMG

• AMG based on Smoothed Aggregation (SA)

• Adaptive Smoothed Aggregation (αSA)

• Compatible Relaxation (CR)

• Krylov methods such as CG, GMRES, FGMRES, BiCGStab, MINRES, etc.

The TDDA Python module provides command-line and Python API support for the overall process of data analysis, through tools that perform reference testing, constraint discovery for data, automatic inference of regular expressions from text data and automatic test generation.

flox mainly provides strategies for fast GroupBy reductions with dask.array. It uses the MapReduce paradigm (or a "tree reduction") to run the GroupBy operation in a parallel-native way totally avoiding a sort or shuffle operation. flox can use either dask or cubed as its backend.

PyQtGraph is a Pure-python graphics library for PyQt5, PyQt6, PySide2 and PySide6. It is intended for use in mathematics, scientific or engineering applications.

hepunits collects the most commonly used units and constants in the HEP System of Units, as derived from the basic units originally defined by the CLHEP project.

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with structured (tabular, multidimensional, potentially heterogeneous) and time series data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.

Einops provides a set of tensor operations for NumPy and multiple deep learning frameworks.