Pandas resample and aggregate with condition

Question

I have a DataFrame which have a column with statuses like this:

datetime               |    session    |    try       |    status
2020-09-17 10:00:01    |    '1a'       |    '1a_1'    |    'success'
2020-09-17 10:00:02    |    '2a'       |    '2a_1'    |    'fail'
2020-09-17 10:00:03    |    '2a'       |    '2a_2'    |    'success'
2020-09-17 10:00:03    |    '3a'       |    '3a_1'    |    'interrupted'
2020-09-18 13:00:04    |    '4a'       |    '4a_1'    |    'fail'

I want to resample data by day with counting status types by condition in sessions (not tries).

I can resample it by tries easyly like this:

df['date'] = df['datetime'].dt.date
df['ones'] = np.ones(df.shape[0])
piv = df.pivot_table(index='date', columns='status', values='ones', aggfunc=len).fillna(0)

And have:

day           |    success    |    fail    |    interrupted
2020-09-17    |    2          |    2       |    1
2020-09-18    |    0          |    1       |    0

But I want to aggregate it by session with condition no matter how much tries in session.:

• if 'success' status in session tries, then success +1, fail +0, interrupted +0;
• if 'interrupted' status in session AND NO 'success' status in session, then success +0, fail +0, interrupted +1;
• if NO 'interrupted' AND 'success' statuses in session, then then success +0, fail +1, interrupted +0.

So I shoul get something like this:

day           |    success    |    fail    |    interrupted
2020-09-17    |    2          |    0       |    1
2020-09-18    |    0          |    1       |    0

I stuck with function and all I come up with ends with "ValueError: The truth value of a Series is ambiguous". I will be very greatfull for any ideas.

Answer 1

My idea is convert values of statust to ordered categories, sorting and get row only with most important value which is defined in list passed to parameter categories:

print (df)
             datetime session   try       status
0 2020-09-17 10:00:01      1a  1a_1      success
1 2020-09-17 10:00:02      2a  2a_1         fail
2 2020-09-17 10:00:03      2a  2a_2      success
3 2020-09-17 10:00:03      3a  3a_1  interrupted
4 2020-09-18 13:00:04      4a  4a_1         fail
5 2020-09-19 10:00:01      1a  1a_1  interrupted
6 2020-09-19 10:00:02      1a  2a_1         fail
7 2020-09-19 10:00:03      2a  2a_2      success
8 2020-09-19 10:00:03      2a  3a_1  interrupted

df['status'] = pd.Categorical(df['status'], 
                              ordered=True, 
                              categories=['success','interrupted','fail'])
df['date'] = df['datetime'].dt.date

df1 = df.sort_values(['date','status']).drop_duplicates(['date','session'])
print (df1)
             datetime session   try       status        date
0 2020-09-17 10:00:01      1a  1a_1      success  2020-09-17
2 2020-09-17 10:00:03      2a  2a_2      success  2020-09-17
3 2020-09-17 10:00:03      3a  3a_1  interrupted  2020-09-17
4 2020-09-18 13:00:04      4a  4a_1         fail  2020-09-18
7 2020-09-19 10:00:03      2a  2a_2      success  2020-09-19
5 2020-09-19 10:00:01      1a  1a_1  interrupted  2020-09-19

piv = pd.crosstab(df1['date'], df1['status'])
print (piv)
status      success  interrupted  fail
date                                  
2020-09-17        2            1     0
2020-09-18        0            0     1
2020-09-19        1            1     0