What are the advantages/disadvantages of using tfidf on n-grams generated through countvectorizer when your end goal is to see the frequent occurring terms in the corpus with the occurrence percentage?
If the goal is to study term frequency, there is no point using TF-IDF since TF-IDF weights are different from frequency. TF-IDF is used to reduce the weight of tokens which appear frequently compared to tokens which appear rarely. Moreover, TF-IDF weights are at the level of a document, so they cannot be used as a measure of global comparison across all documents. Across documents you could use the IDF (Inverse Document Frequency) part only, but then why not simply use Document Frequency.
Note that the same applies to a token count matrix: the values are at the level of a document. In order to find the global frequency one has to sum across the documents for every token.
Finally if you are trying to find the most frequent terms/n-grams of any length, it's difficult to compare frequencies between n-grams of different length. Additionally you're going to find real "terms" mixed with frequent grammatical constructs, for example "it is" is not a term but it's a frequent n-gram.