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

	mocha-parallel-tests https://www.npmjs.com/package/mocha-parallel-tests	pytest https://docs.pytest.org/en/latest/
Programming language	JavaScript	Python
Category	Unit Testing, Intergration Testing, End-to-End Testing	Unit Testing
General info	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	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.
xUnit Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.	N/A	No
Client-side Allows testing code execution on the client, such as a web browser	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.	Yes pytest can test any part of the stack including front-end components
Server-side Allows testing the bahovior of a server-side code	Yes Mocha-parallel-tests provides convenient ways of testing the Node server.	Yes pytest is powerful enough to test database and server components and functionality
Fixtures Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test	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	Yes Pytest has a powerful yet simple fixture model that is unmatched in any other testing framework.
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 Group fixtures are available	Yes Pytest's powerful fixture model allows grouping of fixtures
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.	N/A	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
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)	Provides Mocking capabilities through third party Libraries like sinon.js, simple-mock and nock	Yes By either using unittest.mock or using pytest-mock a thin wrapper that provides mock functionality for pytest
Grouping Allows organizing tests in groups	Yes Grouping is supported and is accomplished by the using a nested 'describe()'	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
Other Other useful information about the testing framework