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

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

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.

Dvc objects provides a filesystem and object-db level abstractions to use in dvc and dvc-data.

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.

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.

Quantities is designed to handle arithmetic and conversions of physical quantities, which have a magnitude, dimensionality specified by various units, and possibly an uncertainty. It builds on the NumPy library and is designed to work with numpy.ufuncs, many of which are already supported.

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

This package provides tools to easily search and download French data from INSEE and IGN APIs. This data includes more than 150 000 macroeconomic series, a dozen datasets of local french data, numerous sources available on insee.fr, geographical limits of administrative areas taken from IGN as well as key metadata and SIRENE database containing data on all French compagnies.

This package implements a functionality to tell whether two images look nearly identical. The image hash algorithms (average, perceptual, difference, wavelet) analyse the image structure on luminance (without color information). The color hash algorithm analyses the color distribution and black & gray fractions (without position information).

Features:

• average hashing

• perceptual hashing

• difference hashing

• wavelet hashing

• HSV color hashing (colorhash)

• crop-resistant hashing

An efficient Python implementation of the Apriori algorithm, which uncovers hidden structures in categorical data

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.

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

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

Pythran is an ahead of time compiler for a subset of the Python language, with a focus on scientific computing. It takes a Python module annotated with a few interface descriptions and turns it into a native Python module with the same interface, but (hopefully) faster.

openPMD is an open meta-data schema that provides meaning and self-description for data sets in science and engineering. See the openPMD standard for details of this schema.

This library provides a reference API for openPMD data handling. Since openPMD is a schema (or markup) on top of portable, hierarchical file formats, this library implements various backends such as HDF5, ADIOS2 and JSON. Writing & reading through those backends and their associated files are supported for serial and MPI-parallel workflows.

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

xarray_einstats provides wrappers around some NumPy and SciPy functions and around einops with an API and features adapted to xarray.

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

Polars is a DataFrame library for manipulating structured data.

This package provides a Pytest plugin that facilitates the comparison of images produced by PyVista, generating cached images from tests and comparing subsequent results against that cache.

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

pyts is a Python package for time series classification. It aims to make time series classification easily accessible by providing preprocessing and utility tools, and implementations of state-of-the-art algorithms. Most of these algorithms transform time series, thus pyts provides several tools to perform these transformations.

python-pandera provides a flexible and expressive API for performing data validation on dataframe-like objects to make data processing pipelines more readable and robust. Dataframes contain information that python-pandera explicitly validates at runtime. This is useful in production-critical data pipelines or reproducible research settings. With python-pandera, you can:

• Define a schema once and use it to validate different dataframe types.

• Check the types and properties of columns.

• Perform more complex statistical validation like hypothesis testing.

• Seamlessly integrate with existing data pipelines via function decorators.

• Define dataframe models with the class-based API with pydantic-style syntax.

• Synthesize data from schema objects for property-based testing.

• Lazily validate dataframes so that all validation rules are executed.

• Integrate with a rich ecosystem of tools like python-pydantic, python-fastapi and python-mypy.