Pytest is a testing tool that provides auto-discovery of test modules and functions, detailed info on failing assert statements, modular fixtures, and many external plugins.

This package provides a simple pytest plugin that provides some helpful fixtures for testing Tornado (version 5.0 or newer) apps and easy handling of plain (undecoratored) native coroutine tests.

This package provides a plugin for the pytest framework that allows developers to detect whether any file handles or other file-like objects were inadvertently left open at the end of a unit test.

This is a py.test plugin to facilitate the generation and comparison of data arrays produced during tests, in particular in cases where the arrays are too large to conveniently hard-code them in the tests.

This package provides a Python port of the ActiveResource project.

Active Resource attempts to provide a coherent wrapper object-relational mapping for REST web services. It follows the same philosophy as Active Record, in that one of its prime aims is to reduce the amount of code needed to map to these resources. This is made possible by relying on a number of code- and protocol-based conventions that make it easy for Active Resource to infer complex relations and structures.

This package provides a plugin to fake subprocess for Pytest.

This package provides a Pytest plugin to run pydocstyle.

Python-pytest-catchlog is a pytest plugin to catch log messages.

This package provides a virtualenv fixture for the py.test framework.

Pytest plugin library to test http clients without contacting the real http server.

This package provides a tokenizer/parser/executor for the PartiQL language, in Python.

PyTorch Lightning is just organized PyTorch; Lightning disentangles PyTorch code to decouple the science from the engineering.

Pydantic Settings provides optional Pydantic features for loading a settings or config class from environment variables or secrets files.

This package provides a plugin for the Pytest framework that allows developers to control unit tests that require access to data from the internet.

PyG is a library built upon PyTorch to easily write and train Graph Neural Networks for a wide range of applications related to structured data.

This is a pytest plugin, that enables you to test your code that relies on a running PostgreSQL Database. It allows you to specify fixtures for PostgreSQL process and client.

This pytest plugin manages dependencies of tests. It allows to mark some tests as dependent from other tests. These tests will then be skipped if any of the dependencies did fail or has been skipped.

This package provides a plugin to fake subprocess for pytest. The plugin adds the fake_process fixture (and fp as an alias). It can be used it to register subprocess results so you won't need to rely on the real processes. The plugin hooks on the subprocess.Popen(), which is the base for other subprocess functions. That makes the subprocess.run(),subprocess.call(), subprocess.check_call() and subprocess.check_output() methods also functional.

Easy to use fixtures to write regression tests.

This Pytest plugin enables creating Pytest parametrize decorators from external files.

This package provides a Pytest plugin to run tests multiple times and detect flakyness.

This package provides a plugin to run pycodestyle for the pytest framework.

This package provides a pytest plugin that checks URLs for HTML-containing files.

This Pytest plugin provides an IPython extension that allows for interactively selecting and running Pytest tests.