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

This is a rewrite of Dask DataFrame that includes query optimization and generally improved organization.

Xarray (formerly xray) makes working with labelled multi-dimensional arrays simple, efficient, and fun!

Xarray introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like arrays, which allows for a more intuitive, more concise, and less error-prone developer experience. The package includes a large and growing library of domain-agnostic functions for advanced analytics and visualization with these data structures.

This package provides a framework for building scientific applications. It aims to bring state of the art software design practices to scientific computing, with the goal of providing a strong skeleton on which to build scientific codes by steering the implementation towards usability and maintainability.

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

Often when we want to label multiple points on a graph the text will start heavily overlapping with both other labels and data points. This can be a major problem requiring manual solution. However this can be largely automated by smart placing of the labels (difficult) or iterative adjustment of their positions to minimize overlaps (relatively easy). This library implements the latter option to help with matplotlib graphs.

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

Pint is a Python package to define, operate and manipulate physical quantities: the product of a numerical value and a unit of measurement. It allows arithmetic operations between them and conversions from and to different units.

Ruffus is designed to allow scientific and other analyses to be automated with the minimum of fuss and the least effort.

This package implements many useful tools for projects involving fuzzy logic, also known as grey logic.

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.

Snakemake aims to reduce the complexity of creating workflows by providing a clean and modern domain specific specification language (DSL) in Python style, together with a fast and comfortable execution environment.

PyMCubes is an implementation of the marching cubes algorithm to extract iso-surfaces from volumetric data. The volumetric data can be given as a three-dimensional NumPy array or as a Python function f(x, y, z).

Snakemake aims to reduce the complexity of creating workflows by providing a clean and modern domain specific specification language (DSL) in Python style, together with a fast and comfortable execution environment.

climin is a Python package for optimization, heavily biased to machine learning scenarios. It works on top of numpy and (partially) gnumpy.

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

pykdtree is a kd-tree implementation for fast nearest neighbour search in Python.

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.

Scikit-opt (or sko) is a Python module implementing swarm intelligence algorithms: genetic algorithm, particle swarm optimization, simulated annealing, ant colony algorithm, immune algorithm, artificial fish swarm algorithm.

This package provides a Python library for calculating Evapotranspiration using various standard methods.

pynrrd is a Python module for reading and writing NRRD files (format designed to support scientific visualization and image processing involving N-dimensional raster data) into and from numpy arrays.

A LEMS simulator written in Python which can be used to run NeuroML2 models.

Formulaic is a high-performance implementation of Wilkinson formulas for Python.

Dask is a flexible parallel computing library for analytics. It consists of two components: dynamic task scheduling optimized for computation, and large data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.