Cumulative count of unique values in pandas

Question

I would like to cumulatively count unique values from a column in a pandas frame by week. For example, imagine that I have data like this:

df = pd.DataFrame({'user_id':[1,1,1,2,2,2],'week':[1,1,2,1,2,2],'module_id':['A','B','A','A','B','C']})

+---+---------+------+-----------+
|   | user_id | week | module_id |
+---+---------+------+-----------+
| 0 |       1 |    1 |         A |
| 1 |       1 |    1 |         B |
| 2 |       1 |    2 |         A |
| 3 |       2 |    1 |         A |
| 4 |       2 |    2 |         B |
| 5 |       2 |    2 |         C |
+---+---------+------+-----------+

What I want is a running count of the number of unique module_ids up to each week, i.e. something like this:

+---+---------+------+-------------------------+
|   | user_id | week | cumulative_module_count |
+---+---------+------+-------------------------+
| 0 |       1 |    1 |                       2 |
| 1 |       1 |    2 |                       2 |
| 2 |       2 |    1 |                       1 |
| 3 |       2 |    2 |                       3 |
+---+---------+------+-------------------------+

It is straightforward to do this as a loop, for example this works:

running_tally = {}
result = {}
for index, row in df.iterrows():
    if row['user_id'] not in running_tally:
        running_tally[row['user_id']] = set()
        result[row['user_id']] = {}
    running_tally[row['user_id']].add(row['module_id'])
    result[row['user_id']][row['week']] = len(running_tally[row['user_id']])
print(result)

{1: {1: 2, 2: 2}, 2: {1: 1, 2: 3}}

But my real data frame is enormous and so I would like a vectorised algorithm instead of a loop.

There's a similar sounding question here, but looking at the accepted answer (here) the original poster does not want uniqueness across dates cumulatively, as I do.

How would I do this vectorised in pandas?

Answer 1

Idea is create lists per groups by both columns and then use np.cumsum for cumulative lists, last convert values to sets and get length:

df1 = (df.groupby(['user_id','week'])['module_id']
         .apply(list)
         .groupby(level=0)
         .apply(np.cumsum)
         .apply(lambda x: len(set(x)))
         .reset_index(name='cumulative_module_count'))

print (df1)
   user_id  week  cumulative_module_count
0        1     1                        2
1        1     2                        2
2        2     1                        1
3        2     2                        3