Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. Six supports every Python version since 2.5. It is contained in only one Python file, so it can be easily copied into your project.

importlib_metadata is a library which provides an API for accessing an installed Python package's metadata, such as its entry points or its top-level name. This functionality intends to replace most uses of pkg_resources entry point API and metadata API. Along with importlib.resources in Python 3.7 and newer, this can eliminate the need to use the older and less efficient pkg_resources package.

This module is primarily a backport of the Python 3.2 contextlib to earlier Python versions. Like contextlib, it provides utilities for common tasks involving decorators and context managers. It also contains additional features that are not part of the standard library.

This module is primarily a backport of the Python 3.2 contextlib to earlier Python versions. Like contextlib, it provides utilities for common tasks involving decorators and context managers. It also contains additional features that are not part of the standard library.

Python bindings for GLib, GObject, and GIO.

This package provides a pathlib-compatible Zipfile object wrapper. It provides a backport of the Path object.

This package provides an implementation of importlib.resources for older versions of Python.

The lxml XML toolkit is a Pythonic binding for the C libraries libxml2 and libxslt.

Thin-wrapper around the mock package for easier use with pytest

This package provides a Python library intended for use in automated tests. One difficulty when testing software is that the code under test might need to read or write to files in the local file system. If the file system is not set up in just the right way, it might cause a spurious error during the test. The pyfakefs library provides a solution to problems like this by mocking file system interactions. In other words, it arranges for the code under test to interact with a fake file system instead of the real file system. The code under test requires no modification to work with pyfakefs.

NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran code, useful linear algebra, Fourier transform, and random number capabilities.

The pyparsing module is an alternative approach to creating and executing simple grammars, vs. the traditional lex/yacc approach, or the use of regular expressions. The pyparsing module provides a library of classes that client code uses to construct the grammar directly in Python code.

This package provides a pathlib-compatible Zipfile object wrapper. It provides a backport of the Path object.

Unittest2 is a replacement for the unittest module in the Python standard library.

This package provides chardet, a Python module that can automatically detect a wide range of file encodings.

ScientificPython is a collection of Python modules that are useful for scientific computing. Most modules are rather general (Geometry, physical units, automatic derivatives, ...) whereas others are more domain-specific (e.g. netCDF and PDB support). The library is currently not actively maintained and works only with Python 2 and NumPy < 1.9.

Pluggy is an extraction of the plugin manager as used by Pytest but stripped of Pytest specific details.

Coverage measures code coverage, typically during test execution. It uses the code analysis tools and tracing hooks provided in the Python standard library to determine which lines are executable, and which have been executed.

Pytest-cov produces coverage reports. It supports centralised testing and distributed testing in both load and each modes. It also supports coverage of subprocesses.

Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It’s based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow.

Packaging is a Python module for dealing with Python packages. It offers an interface for working with package versions, names, and dependency information.

Soup Sieve is a CSS selector library designed to be used with Beautiful Soup 4. It aims to provide selecting, matching, and filtering using modern CSS selectors. Soup Sieve currently provides selectors from the CSS level 1 specifications up through the latest CSS level 4 drafts and beyond (though some are not yet implemented).

Mock is a library for testing in Python. It allows you to replace parts of your system under test with mock objects and make assertions about how they have been used. This library is now part of Python (since Python 3.3), available via the unittest.mock module.

This is a backport of the subprocess standard library module from Python 3.2 and 3.3 for use on Python 2. It includes bugfixes and some new features. On POSIX systems it is guaranteed to be reliable when used in threaded applications. It includes timeout support from Python 3.3 but otherwise matches 3.2’s API.