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

Lettuce

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

Nose

https://nose.readthedocs.io/en/latest/
Programming language

Python

Python

Category

Unit Testing, Acceptance Testing

Unit Testing, unittest Extensions

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

Nose is a Python unit test framework

This is a Python unit test framework that intergrates well with doctests, unnittests, and 'no-boilerplate tests', that is tests written from scratch without a specific boilerplate.
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

nose is a unit testing tool which is very similar to unittest. It is basically unittest with extensions therefore just like unittest is can test front-end components and behaviour
Server-side
Allows testing the bahovior of a server-side code

Yes

Lettuce can test various server and database behaviours and interactions

Yes

Nose can test back-end components and functionality as small units. One can write tests for each function that provides back-end 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

nose supports fixtures at the package, module, class, and test case levels, so that initialization which can be expensive is done as infrequently as possible.
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

Group fixtures are allowed with nose, where a multitest state can be defined.
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

Through use of third party libraries like test-generator and from the 'unittest.TestCase' library
Licence
Licence type governing the use and redistribution of the software

Unknown

GNU Library or Lesser General Public License (LGPL) (GNU LGPL)

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

The nose library extends the built-in Python unittest module therefore has access to unittest.mock
Grouping
Allows organizing tests in groups

Yes

It allows grouping of tests

Yes

With nose it collects tests automatically and there’s no need to manually collect test cases into test suites.
Other
Other useful information about the testing framework