articles has columns article_id and tags, where tags holds a Python list of strings. An article can have no tags at all.

Return the top_n most frequent tags as a DataFrame with columns tag and count, ordered by count descending then tag alphabetically, with the index renumbered from 0.

Each list has to become one row per tag before anything can be counted.

Input

articles =
   article_id                   tags
0           1       [python, pandas]
1           2               [python]
2           3                     []
3           4  [pandas, sql, python]
top_n = 2

Output

      tag  count
0  python      3
1  pandas      2