Pandas count consecutive date observations within groupby object

Question

This is an example of the data frame i'm working with:

d = {
'item_number':['bdsm1000', 'bdsm1000', 'bdsm1000', 'ZZRWB18','ZZRWB18', 'ZZRWB18', 'ZZRWB18', 'ZZHP1427BLK', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1427', 'ZZHP1414', 'ZZHP1414', 'ZZHP1414', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115WNTR', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE', 'WRM115SCFRE'],
'Comp_ID':[2454, 2454, 2454, 1395, 1395, 1395, 1395, 3378, 1266941, 660867, 43978, 1266941, 660867, 43978, 1266941, 660867, 43978, 1266941, 660867, 43978, 43978, 43978, 43978, 1197347907, 70745, 4737, 1197347907, 4737, 1197347907, 70745, 4737, 1197347907, 70745, 4737, 1197347907, 4737, 1197487704, 1197347907, 70745, 23872, 4737, 1197347907, 4737, 1197487704, 1197347907, 23872, 4737, 1197487704, 1197347907, 70745],
'date':['2016-11-22', '2016-11-20', '2016-11-19', '2016-11-22', '2016-11-20', '2016-11-19', '2016-11-18', '2016-11-22', '2016-11-22', '2016-11-22', '2016-11-22', '2016-11-20', '2016-11-20', '2016-11-20', '2016-11-19', '2016-11-19', '2016-11-19', '2016-11-18', '2016-11-18', '2016-11-18', '2016-11-22', '2016-11-20', '2016-11-19', '2016-11-22', '2016-11-22', '2016-11-22', '2016-11-21', '2016-11-21', '2016-11-20', '2016-11-20', '2016-11-20', '2016-11-19', '2016-11-19', '2016-11-19', '2016-11-18', '2016-11-18', '2016-11-22', '2016-11-22', '2016-11-22', '2016-11-22', '2016-11-22', '2016-11-21', '2016-11-21', '2016-11-20', '2016-11-20', '2016-11-20', '2016-11-20', '2016-11-19', '2016-11-19', '2016-11-19']}

df = pd.DataFrame(data=d)
df.date = pd.to_datetime(df.date)

I'd like to count consecutive observations starting from 2016-11-22 that there are grouped by Comp_ID and item_number.

Essentially, what I am looking to do, is count how many days in a row there is an observation counting back from todays date for each Comp_ID and item_number. (this example was put together on the 22nd of Nov) Consecutive observations observed weeks/ days prior to today are not relevant. Only sequences like today... yesterday... the day before yesterday... and so on are relevant.

I got this to work on a smaller sample, but it seems to be getting tripped up on a larger data-set.

Here is the code for the smaller sample. I need to find the consecutive dates with observations across thousands of sellers/ items. For some reason, the below code did not work on the larger data set.

d = {'item_number':['KIN005','KIN005','KIN005','KIN005','KIN005','A789B','A789B','A789B','G123H','G123H','G123H'],
'Comp_ID':['1395','1395','1395','1395','1395','7787','7787','7787','1395','1395','1395'],
'date':['2016-11-22','2016-11-21','2016-11-20','2016-11-14','2016-11-13','2016-11-22','2016-11-21','2016-11-12','2016-11-22','2016-11-21','2016-11-08']}

df = pd.DataFrame(data=d)
df.date = pd.to_datetime(df.date)
d = pd.Timedelta(1, 'D')

df = df.sort_values(['item_number','date','Comp_ID'],ascending=False)

g = df.groupby(['Comp_ID','item_number'])
sequence = g['date'].apply(lambda x: x.diff().fillna(0).abs().le(d)).reset_index()
sequence.set_index('index',inplace=True)
test = df.join(sequence)
test.columns = ['Comp_ID','date','item_number','consecutive']
g = test.groupby(['Comp_ID','item_number'])
g['consecutive'].apply(lambda x: x.idxmin() - x.idxmax() )

This gets the desired result for the smaller data-set:

Comp_ID  item_number
1395     G123H          2
         KIN005         3
7787     KIN005         2
Name: consecutive, dtype: int64

Answer 1

You can do it this way:

today = pd.to_datetime('2016-11-22')

# sort DF by `date` (descending)    
x = df.sort_values('date', ascending=0)
g = x.groupby(['Comp_ID','item_number'])
# compare the # of days to `today` with a consecutive day# in each group
x[(today - x['date']).dt.days == g.cumcount()].groupby(['Comp_ID','item_number']).size()

Result:

Comp_ID  item_number
1395     G123H          2
         KIN005         3
7787     A789B          2
dtype: int64

PS thanks to @DataSwede's for faster diff calculation!

Explanation:

In [124]: x[(today - x['date']).dt.days == g.cumcount()] \
           .sort_values(['Comp_ID','item_number','date'], ascending=[1,1,0])
Out[124]:
  Comp_ID       date item_number
8    1395 2016-11-22       G123H
9    1395 2016-11-21       G123H
0    1395 2016-11-22      KIN005
1    1395 2016-11-21      KIN005
2    1395 2016-11-20      KIN005
5    7787 2016-11-22       A789B
6    7787 2016-11-21       A789B