pytest vs mocha-parallel-tests comparison of testing frameworks
What are the differences between pytest and mocha-parallel-tests?

	pytest https://docs.pytest.org/en/latest/	mocha-parallel-tests https://www.npmjs.com/package/mocha-parallel-tests
Programming language	Python	JavaScript
Category	Unit Testing	Unit Testing, Intergration Testing, End-to-End Testing
General info	Pytest is the TDD 'all in one' testing framework for Python Pytest is a powerful Python testing framework that can test all and levels of software. It is considered by many to be the best testing framework in Python with many projects on the internet having switched to it from other frameworks, including Mozilla and Dropbox. This is due to its many powerful features such as ‘assert‘ rewriting, a third-party plugin model and a powerful yet simple fixture model.	mocha-parallel-tests is a test runner for tests written with mocha testing framework. mocha-parallel-tests allows you to run your tests in parallel and executes each of your test files in a separate process while still maintaining the output of mocha
xUnit Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.	No	N/A
Client-side Allows testing code execution on the client, such as a web browser	Yes pytest can test any part of the stack including front-end components	Yes Mocha-parallel-tests Runs in the browser and is used widely to test front-end components and functionality. It can test various DOM elements, front-end functions and so on.
Server-side Allows testing the bahovior of a server-side code	Yes pytest is powerful enough to test database and server components and functionality	Yes Mocha-parallel-tests provides convenient ways of testing the Node server.
Fixtures Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test	Yes Pytest has a powerful yet simple fixture model that is unmatched in any other testing framework.	Mocha, which is the the framework which mocha-parallel-tests runs provides the hooks before(), after(), beforeEach(), and afterEach() to set up preconditions and clean up after your tests
Group fixtures Allows defining a fixed, specific states of data for a group of tests (group-fixtures). This ensures specific environment for a given group of tests.	Yes Pytest's powerful fixture model allows grouping of fixtures	Yes Group fixtures are available
Generators Supports data generators for tests. Data generators generate input data for test. The test is then run for each input data produced in this way.	Yes pytest has a hook function called pytest_generate_tests hook which is called when collecting a test function and one can use it to generate data	N/A
Licence Licence type governing the use and redistribution of the software	MIT License	MIT License
Mocks Mocks are objects that simulate the behavior of real objects. Using mocks allows testing some part of the code in isolation (with other parts mocked when needed)	Yes By either using unittest.mock or using pytest-mock a thin wrapper that provides mock functionality for pytest	Provides Mocking capabilities through third party Libraries like sinon.js, simple-mock and nock
Grouping Allows organizing tests in groups	Yes Tests can be grouped with pytest by use of markers which are applied to various tests and one can run tests with the marker applied	Yes Grouping is supported and is accomplished by the using a nested 'describe()'
Other Other useful information about the testing framework