Determine the total count of distinct combinations within a Pandas data frame

I seem to be facing some difficulties (mental block) when it comes to creating basic summary statistics for my dataset.

What I am trying to accomplish is counting the instances of co-occurring "code" values across all "id"s. The data is structured as follows:

id    code  
1      A
2      A
2      B
3      A
3      B
4      A
5      A
5      C
6      A
6      B
6      C

The desired output would resemble the following table, or potentially by introducing a factorized column called "combo-id" for each unique combination in the raw data.

Combo    Count    combo-id
(A)      2        1
(A,B)    2        2
(A,C)    1        3
(A,B,C)  1        4

You can find a similar question and answer here, focusing on unique pairs only

pythonpandasgroup-bycombinations

Answer №1

To begin, form grouped tuples and then calculate the frequency using GroupBy.size:

s = df.groupby('id')['code'].apply(tuple).rename('Combo')
#if duplicates aren't a concern, thanks to @cripcate
#s = df.groupby('id')['code'].apply(set).rename('Combo')
df1 = s.groupby(s).size().reset_index(name='Count')
print (df1)
       Combo  Count
0       (A,)      2
1     (A, B)      2
2  (A, B, C)      1
3     (A, C)      1

Answer №2

Consider using .unique() method

Retrieve unique values with Series.unique()[source]

Get a list of unique values in a Series object.

The unique values are listed in the order they appear, as this function does not sort them.

Learn more here.

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