# [SOLVED] Aggregate efficiently between dates

## Issue

Hello I Have a Df Look like that:

HostName      Date
0   B   2021-01-01 12:42:00
1   B   2021-02-01 12:30:00
2   B   2021-02-01 12:40:00
3   B   2021-02-25 12:40:00
4   B   2021-03-01 12:41:00
5   B   2021-03-01 12:42:00
6   B   2021-03-02 12:43:00
7   B   2021-03-03 12:44:00
8   B   2021-04-04 12:44:00
9   B   2021-06-05 12:44:00
10  B   2021-08-06 12:44:00
11  B   2021-09-07 12:44:00
12  A   2021-03-12 12:45:00
13  A   2021-03-13 12:46:00

i what do to aggregation this is how I solved the problem but its not efficient at all and if there are 1M rows
it will take a long time
is there a better way to Aggregate efficiently between dates?

end results:

HostName      Date        ds
0   B   2021-01-01 12:42:00  1
1   B   2021-02-01 12:30:00  2
2   B   2021-02-01 12:40:00  3
3   B   2021-02-25 12:40:00  3
4   B   2021-03-01 12:41:00  2
5   B   2021-03-01 12:42:00  3
6   B   2021-03-02 12:43:00  4
7   B   2021-03-03 12:44:00  5
8   B   2021-04-04 12:44:00  1
9   B   2021-06-05 12:44:00  1
10  B   2021-08-06 12:44:00  1
11  B   2021-09-07 12:44:00  1
12  A   2021-03-12 12:45:00  1
13  A   2021-03-13 12:46:00  2
TheList = []
for index, row in df.iterrows():
TheList.append((df[(df['Date'] > (df['Date'].iloc[index] - pd.DateOffset(months=1))) & (df['Date'] <= df['Date'].iloc[index])].groupby(['HostName']).size()[row[0]]))
df['ds'] = TheList

is there is a better way to do it but with the same result?

## Solution

Here is used broadcasting between groups and for count Trues is used sum in custom function in GroupBy.transform:

Notice: Performance depends also by length of groups, if few very big groups here should be problem with memory.

df['Date'] = pd.to_datetime(df['Date'])

def f(x):
a = x.to_numpy()
b = x.sub(pd.DateOffset(months=1)).to_numpy()
return np.sum((a > b[:, None]) & (a <= a[:, None]), axis=1)

df['ds'] = df.groupby('HostName')['Date'].transform(f)

print (df)
HostName                Date  ds
0         B 2021-01-01 12:42:00   1
1         B 2021-02-01 12:30:00   2
2         B 2021-02-01 12:40:00   3
3         B 2021-02-25 12:40:00   3
4         B 2021-03-01 12:41:00   2
5         B 2021-03-01 12:42:00   3
6         B 2021-03-02 12:43:00   4
7         B 2021-03-03 12:44:00   5
8         B 2021-04-04 12:44:00   1
9         B 2021-06-05 12:44:00   1
10        B 2021-08-06 12:44:00   1
11        B 2021-09-07 12:44:00   1
12        A 2021-03-12 12:45:00   1
13        A 2021-03-13 12:46:00   2

Unfortunately need loops if memory problems:

df['Date'] = pd.to_datetime(df['Date'])
df['Date1'] = pd.to_datetime(df['Date']).sub(pd.DateOffset(months=1))

def f(x):
one = x['Date'].to_numpy()
both = x[['Date','Date1']].to_numpy()

x['ds'] = [np.sum((one > b) & (one <= a))  for a, b in both]
return x

df = df.groupby('HostName').apply(f)
print (df)
HostName                Date               Date1  ds
0         B 2021-01-01 12:42:00 2020-12-01 12:42:00   1
1         B 2021-02-01 12:30:00 2021-01-01 12:30:00   2
2         B 2021-02-01 12:40:00 2021-01-01 12:40:00   3
3         B 2021-02-25 12:40:00 2021-01-25 12:40:00   3
4         B 2021-03-01 12:41:00 2021-02-01 12:41:00   2
5         B 2021-03-01 12:42:00 2021-02-01 12:42:00   3
6         B 2021-03-02 12:43:00 2021-02-02 12:43:00   4
7         B 2021-03-03 12:44:00 2021-02-03 12:44:00   5
8         B 2021-04-04 12:44:00 2021-03-04 12:44:00   1
9         B 2021-06-05 12:44:00 2021-05-05 12:44:00   1
10        B 2021-08-06 12:44:00 2021-07-06 12:44:00   1
11        B 2021-09-07 12:44:00 2021-08-07 12:44:00   1
12        A 2021-03-12 12:45:00 2021-02-12 12:45:00   1
13        A 2021-03-13 12:46:00 2021-02-13 12:46:00   2