Thi package implements a functionality for mean-preserving interpolation of 1D data (for example, time series) with splines.

The fastcluster package implements seven common hierarchical clustering schemes efficiently. The package is made with two interfaces to standard software: R and Python.

This package provides an extremely lightweight compatibility layer between dataframe libraries.

• full API support: cuDF, Modin, pandas, Polars, PyArrow

• lazy-only support: Dask, DuckDB, Ibis, PySpark, SQLFrame

pynetdicom is a Python package that implements the DICOM networking protocol. It allows the easy creation of DICOM SCUs and SCPs.

unyt is a Python library working with data that has physical units. It defines the unyt.array.unyt_array and unyt.array.unyt_quantity classes (subclasses of NumPy’s ndarray class) for handling arrays and scalars with units,respectively

Datatree is a prototype implementation of a tree-like hierarchical data structure for xarray. Datatree is in the process of being merged upstream into xarray.

The fast-histogram mini-package aims to provide simple and fast histogram functions for regular bins that don't compromise on performance. It doesn't do anything complicated - it just implements a simple histogram algorithm in C and keeps it simple. The aim is to have functions that are fast but also robust and reliable. The result is a 1D histogram function here that is 7-15x faster than numpy.histogram, and a 2D histogram function that is 20-25x faster than numpy.histogram2d.

This package provides utilities and tools for open data science including tools for accessing data sets in Python.

Modin uses Ray or Dask to provide an effortless way to speed up your pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing pandas code.

This package provides a domain-specific language for modeling convex optimization problems in Python.

Scikit-build-core is a build backend for Python that uses CMake to build extension modules. It has a simple yet powerful static configuration system in pyproject.toml, and supports almost unlimited flexibility via CMake. It was initially developed to support the demanding needs of scientific users, but can build any sort of package that uses CMake.

Particle provides a pythonic interface to the Particle Data Group (PDG) particle data tables and particle identification codes, with extended particle information and extra goodies.

This package provides a simple and easy-to-use PID controller.

This package provides a Python library for building and analyzing recommender systems that deal with explicit rating data. It was designed with the following purposes in mind:

• Provide tools to handle downloaded or user-provided datasets.

• Provide ready-to-use prediction algorithms and similarity measures.

• Provide a base for creating custom algorithms.

• Provide tools to evaluate, analyse and compare algorithm performance.

• Provide documentation with precise details regarding library algorithms.

This package provides functionality to make it easy to make scatter density maps, both for interactive and non-interactive use.

This package implements schema validation for Xarray objects.

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

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 a Python package for time series classification.

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

This package provides a stable interface for interactions between Snakemake and its executor plugins.

This package provides a Python library for working with NeuroML descriptions of neuronal models

This package provides fast numerical derivatives for analytic functions with arbitrary round-off error and error propagation.

Pingouin is a statistical package written in Python 3 and based mostly on Pandas and NumPy. Its features include

• ANOVAs: N-ways, repeated measures, mixed, ancova

• Pairwise post-hocs tests (parametric and non-parametric) and pairwise correlations

• Robust, partial, distance and repeated measures correlations

• Linear/logistic regression and mediation analysis

• Bayes Factors

• Multivariate tests

• Reliability and consistency

• Effect sizes and power analysis

• Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient

• Circular statistics

• Chi-squared tests

• Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation, and more