pandas dataframe group by multiple columns and count distinct values

Question

I currently have a Dataframe that looks something like this:

The unique values are ['Somewhat Interested', 'Not at all Interested', nan,'Very Interested']

How would I go about creating a new dataframe that would have the same columns as above but for the index values 'Somewhat Interested', 'Not at all Interested', nan,'Very Interested' and the values inside the cell are the counts of each type of response. Im thinking a pivot table might do the trick but Im not sure.

What I want

	In person meet ups	alumni webinars	alumni webinars	etc...
Some what interested	24	32	12
Not interested	32	42	4
very intersted	21	31	53

Answer 1

Combine value_counts with apply to do it per column:

df.apply(pd.value_counts)

Answer 2

Actually, you can just apply pd.Series.value_counts for each column:

counts = df.fillna('NaN').apply(pd.Series.value_counts).fillna(0).astype(int)