This package implements a functionality to solve automatic numerical differentiation problems in one or more variables. Finite differences are used in an adaptive manner, coupled with a Richardson extrapolation methodology to provide a maximally accurate result. The user can configure many options like; changing the order of the method or the extrapolation, even allowing the user to specify whether complex-step, central, forward or backward differences are used.

This is a Python implementation of UpSet plots by Lex et al. UpSet plots are used to visualize set overlaps; like Venn diagrams but more readable.

QuTiP is a library for simulating the dynamics of closed and open quantum systems. It aims to provide numerical simulations of a wide variety of quantum mechanical problems, including those with Hamiltonians and/or collapse operators with arbitrary time-dependence, commonly found in a wide range of physics applications.

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.

xarray-dataclasses is a Python package that makes it easy to create xarray's DataArray and Datase objects that are "typed" (i.e. fixed dimensions, data type, coordinates, attributes, and name) using the Python's dataclass.

Scikit-image is a collection of algorithms for image processing.

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.

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.

meshzoo is a mesh generator for finite element or finite volume computations for simple domains like regular polygons, disks, spheres, cubes, etc.

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

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

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

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

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.

Plotly's Python graphing library makes interactive,publication-quality graphs online. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.

The goal of this package is to provide a reference implementation of trait types for common data structures used in the scipy stack such as numpy arrays or pandas and xarray data structures. These are out of the scope of the main traitlets project but are a common requirement to build applications with traitlets in combination with the scipy stack.

SALib provides tools for global sensitivity analysis. It contains Sobol', Morris, FAST, DGSM, PAWN, HDMR, Moment Independent and fractional factorial methods.

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.

Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. This package provides the core modules of Vaex.

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

Hist is an analyst-friendly front-end for boost-histogram.

Trimesh is a pure Python library for loading and using triangular meshes with an emphasis on watertight surfaces. The goal of the library is to provide a full featured and well tested Trimesh object which allows for easy manipulation and analysis, in the style of the Polygon object in the Shapely library.