Excluding IDs with some value day after day - pandas

Question

I have this df:
ID Date X Y
A 16-07-19 123 56
A 17-07-19 456 84
A 18-07-19 0 58
A 19-07-19 123 81
B 19-07-19 456 70
B 21-07-19 789 46
B 22-07-19 0 19
B 23-07-19 0 91
C 14-07-19 0 86
C 16-07-19 456 91
C 17-07-19 456 86
C 18-07-19 0 41
C 19-07-19 456 26
C 20-07-19 456 17
D 06-07-19 789 98
D 08-07-19 789 90
D 09-07-19 0 94
I want to exclude IDs that have any value in X column (except for 0) day after day.
For example: A has the value 123 on 16-07-19, and 456 on 17-07-19. So all A's observations should be excluded.
Expected result:
ID Date X Y
B 19-07-19 456 70
B 21-07-19 789 46
B 22-07-19 0 19
B 23-07-19 0 91
D 06-07-19 789 98
D 08-07-19 789 90
D 09-07-19 0 94

Answer 1

Let's do this in a vectorized manner, to keep our code as efficient as possible
(meaning: we avoid using GroupBy.apply)
First we check if the difference in Date is equal to 1 day
We check if X column is not equal to 0
we create a temporary column m where we check if both conditions are True
We groupby on ID and remove all groups where any of the rows are True
# df['Date'] = pd.to_datetime(df['Date']) <- if Date is not datetime type
m1 = df['Date'].diff(1).eq(pd.Timedelta(1, unit='d'))
m2 = df['X'].ne(0)
df['m'] = m1&m2
df = df[~df.groupby('ID')['m'].transform('any')].drop(columns='m')
ID Date X Y
4 B 2019-07-19 456 70
5 B 2019-07-21 789 46
6 B 2019-07-22 0 19
7 B 2019-07-23 0 91
14 D 2019-06-07 789 98
15 D 2019-08-07 789 90
16 D 2019-09-07 0 94