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

Green

https://github.com/CleanCut/green

Lettuce

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

Python

Python

Category

Unit Testing

Unit Testing, Acceptance Testing

General info

Green is a clean, colorful, fast Python test runner

This is a test runner that has pretty printing on output that makes results easy to read and understand. Some of its features include: Tests running in independent processes (fast), low redundancy in output (clean), supports pretty printing that is the terminal output, makes good use of color when the terminal supports it (colorful)

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
xUnit
Set of frameworks originating from SUnit (Smalltalk's testing framework). They share similar structure and functionality.

No

No

However It can generate xml results for behaviour tests xUnit style
Client-side
Allows testing code execution on the client, such as a web browser

Yes

It can test front-end components of the django framework

Yes

By integrating Lettuce with Selenium’s Python bindings, you have a robust framework for testing Django applications. It can test front-end behaviour
Server-side
Allows testing the bahovior of a server-side code

Yes

It can test server-side behaviours of web applications written with Python

Yes

Lettuce can test various server and database behaviours and interactions
Fixtures
Allows defining a fixed, specific states of data (fixtures) that are test-local. This ensures specific environment for a single test

N/A

N/A

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

N/A

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

By using a third party library
Licence
Licence type governing the use and redistribution of the software

MIT License

Unknown

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

Through the use of Python's mock library

By adding the lettuce-tools library one has access to the Mock module to implement a configurable http REST mock.
Grouping
Allows organizing tests in groups

N/A

Yes

It allows grouping of tests
Other
Other useful information about the testing framework