Is there a pythonic way to group by a field and check if all elements of each resulting group have the same value?
Sample data:
datetime rating signal
0 2018-12-27 11:33:00 IG 0
1 2018-12-27 11:33:00 HY -1
2 2018-12-27 11:49:00 IG 0
3 2018-12-27 11:49:00 HY -1
4 2018-12-27 12:00:00 IG 0
5 2018-12-27 12:00:00 HY -1
6 2018-12-27 12:49:00 IG 0
7 2018-12-27 12:49:00 HY -1
8 2018-12-27 14:56:00 IG 0
9 2018-12-27 14:56:00 HY -1
10 2018-12-27 15:12:00 IG 0
11 2018-12-27 15:12:00 HY -1
12 2018-12-20 15:14:00 IG 0
13 2018-12-20 15:14:00 HY -1
14 2018-12-20 15:50:00 IG -1
15 2018-12-20 15:50:00 HY -1
16 2018-12-27 13:26:00 IG 0
17 2018-12-27 13:26:00 HY -1
18 2018-12-27 13:44:00 IG 0
19 2018-12-27 13:44:00 HY -1
20 2018-12-27 15:06:00 IG 0
21 2018-12-27 15:06:00 HY -1
22 2018-12-20 15:48:00 IG 0
23 2018-12-20 15:48:00 HY -1
The grouping part can be done by
df.groupby([datetime.dt.date,'rating'])
However, I'm sure there must be a simple way to leverage the grouper and use a transform statement to return 1 if all the values from signal are the same.
Desired output
2018-12-20 HY True
IG False
2018-12-27 HY True
IG True
Use groupby and nunique, and check whether the result is 1:
df.groupby([df.datetime.dt.date, 'rating']).signal.nunique().eq(1)
datetime rating
2018-12-20 HY True
IG False
2018-12-27 HY True
IG True
Name: signal, dtype: bool
Or, similarly, using apply with set conversion:
(df.groupby([df.datetime.dt.date, 'rating']).signal
.apply(lambda x: len(set(x)) == 1))
datetime rating
2018-12-20 HY True
IG False
2018-12-27 HY True
IG True
Name: signal, dtype: bool
PS., you don't need to assign a temp column, groupby takes arbitrary grouper arguments.