Lettuce vs pytest comparison of testing frameworks
What are the differences between Lettuce and pytest?

Lettuce

https://pypi.org/project/lettuce/

pytest

https://docs.pytest.org/en/latest/
Programming language

Python

Python

Category

Unit Testing, Acceptance Testing

Unit Testing

General info

Lettuce is a BDD testing tool for Python

Lettuce is a testing tool for Python which is inspired by Ruby's Cucumber that supports Gherkin. It can execute plain-text functional descriptions as automated tests for Python projects just like Cucumber does for Ruby

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.

No

However It can generate xml results for behaviour tests xUnit style

No

Client-side
Allows testing code execution on the client, such as a web browser

Yes

By integrating Lettuce with Selenium’s Python bindings, you have a robust framework for testing Django applications. It can test front-end behaviour

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

Lettuce can test various server and database behaviours and interactions

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

N/A

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.

N/A

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.

Yes

By using a third party library

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

Unknown

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)

By adding the lettuce-tools library one has access to the Mock module to implement a configurable http REST mock.

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

It allows grouping of tests

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