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.

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

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.

pyjanitor provides a set of data cleaning routines for pandas DataFrames. These routines extend the method chaining API defined by pandas for a subset of its methods. Originally, this package was a port of the R package by the same name and it is inspired by the ease-of-use and expressiveness of the dplyr package.

Xarray (formerly xray) makes working with labelled multi-dimensional arrays simple, efficient, and fun!

Xarray introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like arrays, which allows for a more intuitive, more concise, and less error-prone developer experience. The package includes a large and growing library of domain-agnostic functions for advanced analytics and visualization with these data structures.

This package provides utilities for exploratory analysis of large scale genetic variation data.

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.

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

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.

xarray-dataclass is a Python package that makes it easy to create xarray's DataArray and Dataset objects that are "typed" (i.e. fixed dimensions, data type, coordinates, attributes, and name) using the Python's dataclass. It's a successor of not maintained https://github.com/astropenguin/xarray-dataclasses.

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 simplified scipy.signal.spectral module to do spectral analysis in Python.

This Python module uses matplotlib to visualize multidimensional samples using a scatterplot matrix. In these visualizations, each one- and two-dimensional projection of the sample is plotted to reveal covariances. corner was originally conceived to display the results of Markov Chain Monte Carlo simulations and the defaults are chosen with this application in mind but it can be used for displaying many qualitatively different samples.

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

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

This is the Python package for ECOS: Embedded Cone Solver. ECOS is numerical software for solving convex second-order cone programs (SOCPs).

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

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.

Polars is an analytical query engine written for DataFrames. It is designed to be fast, easy to use and expressive.

vedo is a fast and lightweight python module for scientific analysis and visualization. The package provides a wide range of functionalities for working with three-dimensional meshes and point clouds. It can also be used to generate high quality two-dimensional renderings such as scatter plots and histograms. vedo is based on vtk and numpy.

unyt is a Python library working with data that has physical units. It defines the unyt.array.unyt_array and unyt.array.unyt_quantity classes (subclasses of NumPy’s ndarray class) for handling arrays and scalars with units,respectively

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

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

Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization. skopt aims to be accessible and easy to use in many contexts.