Tf-idf for SO posts (where tag can only occur once )
If you want to filter out tags that are so common, you can use conditional probability. eg: python is so common on posts taged pytorch, so P(python|pytorch) will be hight, likes:0.9. You can find a threshold to filter those tags. 
Association rule learning is more suitable and more complex than the above.
Using the stackoverflow data dump, I am analyzing SO posts that are tagged with pytorch or keras. Specifically, I count how many times each co tag occurs (ie the tags that aren't pytorch in a pytorch tagged post). 
I'd like to filter out the tags that are so common they've lost real meaning for my analysis (like the python tag).
I am looking into Tf-idf
TF reprensents the frequency of word for each document. However, each co-tag can only occur once for a given post (ie you can't tag your post 'html' five times). So the tf for most words would be 1/5, and others less (because post only has 4 tags for instance). Is it still possible to do Tf-Idf given this context?