Cyclomatic complexity metric practices for Python
I have a relatively large Python project that I work on, and we don't have any cyclomatic complexity tools as a part of our automated test and deployment process.
How important are cyclomatic complexity tools in Python? Do you or your project use them and find them effective? I'd like a nice before/after story if anyone has one so we can take a bit of the subjectiveness out of the answers (i.e. before we didn't have a cyclo-comp tool either, and after we introduced it, good thing A happened, bad thing B happened, etc). There are a lot of other general answers to this type of question, but I didn't find one for Python projects in particular.
I'm ultimately trying to decide whether or not it's worth it for me to add it to our processes, and what particular metric and tool/library is best for large Python projects. One of our major goals is long term maintenance.
Answer
We used the RADON tool in one of our projects which is related to Test Automation.
Depending on new features and requirements, we need to add/modify/update/delete codes in that project. Also, almost 4-5 people were working on this. So, as a part of review process, we identified and used RADON tools since we want our code maintainable and readable.
Depend on the RADON tool output, there were several times we re-factored our code, added more methods and modified the looping.
Please let me know if this is useful to you.