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

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

PyVista is...

• Pythonic VTK: a high-level API to the Visualization Toolkit (VTK);

• mesh data structures and filtering methods for spatial datasets;

• 3D plotting made simple and built for large/complex data geometries.

This package provides a Pythonic, well-documented interface exposing VTK's powerful visualization backend to facilitate rapid prototyping, analysis, and visual integration of spatially referenced datasets.

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.

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.

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.

This package implements a functionality to create and manipulate plot legends for matplotlib.

iminuit is a Jupyter-friendly Python interface for the Minuit2 C++ library maintained by CERN's ROOT team.

Minuit was designed to optimize statistical cost functions, for maximum-likelihood and least-squares fits. It provides the best-fit parameters and error estimates from likelihood profile analysis.

Optionally, Iminuit supports SciPy minimizers as alternatives to Minuit's MIGRAD algorithm and Numba accelerated functions.

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

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.

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.

scikit-fem is a library for performing finite element assembly. Its main purpose is the transformation of bilinear forms into sparse matrices and linear forms into vectors.

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

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.

Marsilea is a Python library for creating composable visualizations in a declarative way. It is built on top of Matplotlib and provides a high-level API for you to puzzle different visualizations together like logo.

This is a Python implementation of the APTED algorithm,which supersedes the RTED algorithm for computing the tree edit distance.

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.

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

This package provides fast computations for principal component analysis (PCA), SVD, and eigendecompositions via randomized methods

Numpoly is a generic library for creating, manipulating and evaluating arrays of polynomials based on numpy.ndarray objects.

Dask.distributed is a lightweight library for distributed computing in Python. It extends both the concurrent.futures and dask APIs to moderate sized clusters.

Baycomp is a library for Bayesian comparison of classifiers. Functions in the library compare two classifiers on one or on multiple data sets. They compute three probabilities: the probability that the first classifier has higher scores than the second, the probability that differences are within the region of practical equivalence (rope), or that the second classifier has higher scores.

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.

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