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

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

Histoprint uses a mix of terminal color codes and Unicode trickery (i.e. combining characters) to plot overlaying histograms.

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.

pyvistaqt is a helper module for pyvista to enable you to plot using Qt by placing a vtk-widget into a background renderer. This can be quite useful when you desire to update your plot in real-time.

The fast-histogram mini-package aims to provide simple and fast histogram functions for regular bins that don't compromise on performance. It doesn't do anything complicated - it just implements a simple histogram algorithm in C and keeps it simple. The aim is to have functions that are fast but also robust and reliable. The result is a 1D histogram function here that is 7-15x faster than numpy.histogram, and a 2D histogram function that is 20-25x faster than numpy.histogram2d.

Spyder is a scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts. It offers a combination of the advanced editing, analysis, debugging, and profiling functionality of a comprehensive development tool with the data exploration, interactive execution, deep inspection, and visualization capabilities of a scientific package.

This package contains public type stubs for python-pandas, following the convention of providing stubs in a separate package, as specified in PEP 561. The stubs cover the most typical use cases of python-pandas. In general, these stubs are narrower than what is possibly allowed by python-pandas, but follow a convention of suggesting best recommended practices for using python-pandas.

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.

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.

This package provides a logging plugin for Snakemake that utilizes python-rich for enhanced terminal styling and progress bars.

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

A Snakemake executor plugin for running SLURM jobs.

The TDDA Python module provides command-line and Python API support for the overall process of data analysis, through tools that perform reference testing, constraint discovery for data, automatic inference of regular expressions from text data and automatic test generation.

This package provides a fast units and dimensions library with support for static dimensionality checking and protobuffer serialization.

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.

Pyzo is a Python IDE focused on interactivity and introspection,which makes it very suitable for scientific computing. Its practical design is aimed at simplicity and efficiency.

It consists of two main components, the editor and the shell, and uses a set of pluggable tools to help the programmer in various ways. Some example tools are source structure, project manager, interactive help, workspace...

This package is an implementation of the Promises/A+ specification and test suite in Python.

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.

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

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

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

This package provides a simple and easy-to-use PID controller.