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
Some what interested	24	32	12
Not interested	32	42	4
very intersted	21	31	53

Always Right Never Left · Accepted Answer · 2022-03-16 23:14:32Z

1

Combine value_counts with apply to do it per column:

df.apply(pd.value_counts)

answered Mar 16, 2022 at 23:14

Always Right Never Left

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2 Comments

user17242583 Over a year ago

pd.value_counts instead of pd.Series.value_counts? Nice! Didn't know about that one. (+1 soon!) :)

Always Right Never Left Over a year ago

pd.Series.value_counts is simply a reference to pd.value_counts . Series module does not contain a definition for value_counts, it is defined in core.algorithms and is referenced in core. DataFrame.value_counts is a different thing though.

user17242583 · Accepted Answer · 2022-03-16 23:14:31Z

0

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)

answered Mar 16, 2022 at 23:14

user17242583

1 Comment

user17242583 Over a year ago

@JeStra does this not solve your problem?

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