Pandas 0.23 added a simple API for registering accessors with Pandas objects. Pandas-flavor extends Pandas' extension API by

• adding support for registering methods as well

• making each of these functions backwards compatible with older versions of Pandas

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.

spin is a simple interface for common development tasks. It comes with a few common build commands out the box, but can easily be customized per project.

The impetus behind developing the tool was the mass migration of scientific Python libraries (SciPy, scikit-image, and NumPy, etc.) to Meson, after distutils was deprecated. When many of the build and installation commands changed, it made sense to abstract away the nuisance of having to re-learn them.

This package provides a Python wrapper around Fabio Crameri's perceptually uniform colormaps for scientific data visualization.

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

This is a package meant primarily for documenting histogram indexing and the PlottableHistogram Protocol and any future cross-library standards. It also contains the code for the PlottableHistogram Protocol, to be used in type checking libraries wanting to conform to the protocol. It is not usually a runtime dependency, but only a type checking, testing, and/or docs dependency in support of other libraries.

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

Optimized einsum can significantly reduce the overall execution time of einsum-like expressions by optimizing the expression's contraction order and dispatching many operations to canonical BLAS, cuBLAS, or other specialized routines. Optimized einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch, Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially any library which conforms to a standard API. See the documentation for more information.

pyfma provides an implementation of fused multiply-add which computes (x*y) + z with a single rounding. This is useful for dot products, matrix multiplications, polynomial evaluations (e.g., with Horner's rule), Newton's method for evaluating functions, convolutions, artificial neural networks etc.

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.

This package implements schema validation for Xarray objects.

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.

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.

Deepdish is a Python library to load and save HDF5 files. The primary feature of deepdish is its ability to save and load all kinds of data as HDF5. It can save any Python data structure, offering the same ease of use as pickling or numpy.save, but with the language interoperability offered by HDF5.

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.

Plotnine is a Python implementation of the Grammar of Graphics. It is a powerful graphics concept for creating plots and visualizations in a structured and declarative manner. It is inspired by the R package ggplot2 and aims to provide a similar API and functionality in Python.

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

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

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

This package provides Python tools for the Open Reflectometry Standards Organization (ORSO). It includes utilities for working with reflectometry data files and the ORSO file format.

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

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

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).