How to calculate cumulative groupby counts in Pandas with point in time?

Question

I have a df that contains multiple weekly snapshots of JIRA tickets. I want to calculate the YTD counts of tickets.

the df looks like this:

pointInTime   ticketId
2008-01-01         111
2008-01-01         222
2008-01-01         333
2008-01-07         444
2008-01-07         555
2008-01-07         666
2008-01-14         777
2008-01-14         888
2008-01-14         999

So if I df.groupby(['pointInTime'])['ticketId'].count() I can get the count of Ids in every snaphsots. But what I want to achieve is calculate the cumulative sum.

and have a df looks like this:

pointInTime   ticketId   cumCount
2008-01-01         111   3
2008-01-01         222   3
2008-01-01         333   3
2008-01-07         444   6
2008-01-07         555   6
2008-01-07         666   6
2008-01-14         777   9
2008-01-14         888   9
2008-01-14         999   9

so for 2008-01-07 number of ticket would be count of 2008-01-07 + count of 2008-01-01.

Answer 1

Use GroupBy.count and cumsum, then map the result back to "pointInTime":

df['cumCount'] = (
    df['pointInTime'].map(df.groupby('pointInTime')['ticketId'].count().cumsum()))
df

  pointInTime  ticketId  cumCount
0  2008-01-01       111         3
1  2008-01-01       222         3
2  2008-01-01       333         3
3  2008-01-07       444         6
4  2008-01-07       555         6
5  2008-01-07       666         6
6  2008-01-14       777         9
7  2008-01-14       888         9
8  2008-01-14       999         9

Answer 2

I am using value_counts

df.pointInTime.map(df.pointInTime.value_counts().sort_index().cumsum())
Out[207]: 
0    3
1    3
2    3
3    6
4    6
5    6
6    9
7    9
8    9
Name: pointInTime, dtype: int64

Or

pd.Series(np.arange(len(df))+1,index=df.index).groupby(df['pointInTime']).transform('last')
Out[216]: 
0    3
1    3
2    3
3    6
4    6
5    6
6    9
7    9
8    9
dtype: int32

Answer 3

Here's an approach transforming with the size and multiplying by the result of taking pd.factorize on pointInTime:

df['cumCount'] = (df.groupby('pointInTime').ticketId
                    .transform('size')
                    .mul(pd.factorize(df.pointInTime)[0]+1))

 pointInTime  ticketId  cumCount
0  2008-01-01       111         3
1  2008-01-01       222         3
2  2008-01-01       333         3
3  2008-01-07       444         6
4  2008-01-07       555         6
5  2008-01-07       666         6
6  2008-01-14       777         9
7  2008-01-14       888         9
8  2008-01-14       999         9