Issue
I have 2 sets of Dataframe, both with an unique identifier and a datetime data in the format as such
"2020-01-01 00:00:01"-datetime and "12345" - unique identifier and Type
1st Question, DF1:
DatetimeX ID Type
2020-01-01 02:00:01 12345 C
2020-01-01 02:00:03 12345 C
2020-01-01 05:00:03 12345 C
2020-01-01 05:03:05 12345 C
2020-01-01 03:00:09 13333 D
2020-01-01 02:00:09 12345 C
2020-01-01 02:01:35 12345 C
2020-01-01 02:10:35 12345 C
2020-01-01 02:00:01 13333 D
2020-01-01 02:05:35 13333 D
2020-01-01 02:00:50 13333 E
2020-01-01 02:00:01 12211 C
2020-01-01 02:09:50 13333 E
2020-01-01 02:11:50 13333 E
I would like to based on the ID's 1st time stamp with the same "Type", and remove the rows 10mins after as such:
DatetimeX ID Type
2020-01-01 02:00:01 12345 C
2020-01-01 05:00:03 12345 C
2020-01-01 02:10:35 12345 C
2020-01-01 03:00:09 13333 D
2020-01-01 02:00:01 13333 D
2020-01-01 02:00:50 13333 E
2020-01-01 02:00:01 12211 C
2020-01-01 02:11:50 13333 E
I've tried to explore timerange/daterange but could not find any similar concept of coding. Would hope that if anyone can point out what kind of ways i can look into to explore and not trying to get a full solution. Have not touch python for a few years and not familiar with it previously. Thank you
Updated with additional data row for more accurate example
Solution
Add sample input data and simplfied the process:
Timestamp = pd.to_datetime
data = [{'DatetimeX': Timestamp('2020-01-01 02:00:01'), 'ID': 12345, 'Type': 'C'},
{'DatetimeX': Timestamp('2020-01-01 02:00:03'), 'ID': 12345, 'Type': 'C'},
{'DatetimeX': Timestamp('2020-01-01 05:00:03'), 'ID': 12345, 'Type': 'C'},
{'DatetimeX': Timestamp('2020-01-01 05:03:05'), 'ID': 12345, 'Type': 'C'},
{'DatetimeX': Timestamp('2020-01-01 03:00:09'), 'ID': 13333, 'Type': 'D'},
{'DatetimeX': Timestamp('2020-01-01 02:00:09'), 'ID': 12345, 'Type': 'C'},
{'DatetimeX': Timestamp('2020-01-01 02:01:35'), 'ID': 12345, 'Type': 'C'},
{'DatetimeX': Timestamp('2020-01-01 02:10:35'), 'ID': 12345, 'Type': 'C'},
{'DatetimeX': Timestamp('2020-01-01 02:00:01'), 'ID': 13333, 'Type': 'D'},
{'DatetimeX': Timestamp('2020-01-01 02:05:35'), 'ID': 13333, 'Type': 'D'},
{'DatetimeX': Timestamp('2020-01-01 02:00:50'), 'ID': 13333, 'Type': 'E'},
{'DatetimeX': Timestamp('2020-01-01 02:00:01'), 'ID': 12211, 'Type': 'C'},
{'DatetimeX': Timestamp('2020-01-01 02:09:50'), 'ID': 13333, 'Type': 'E'},
{'DatetimeX': Timestamp('2020-01-01 02:11:50'), 'ID': 13333, 'Type': 'E'}]
df1 = pd.DataFrame(data)
col_raw = df1.columns
while True:
df1.sort_values(['ID', 'Type', 'DatetimeX'], inplace=True)
df1['diff1_lt10min'] = df1.groupby(['ID', 'Type'])['DatetimeX'].diff().dt.seconds < 10 * 60
df1['tag_group'] = (~df1['diff1_lt10min']).cumsum()
if df1.duplicated('tag_group').sum()==0:
break
df1 = df1.merge((df1.groupby('tag_group')['DatetimeX'].first()
.reset_index()
.rename(columns={'DatetimeX':'DatetimeX_1st'})),
on='tag_group')
df1['diff2_lt10min'] = (df1.DatetimeX - df1.DatetimeX_1st).dt.seconds < 10 * 60
cond = df1['diff1_lt10min'] & df1['diff2_lt10min']
df1 = df1.loc[~cond, col_raw]
df1 = df1[col_raw]
Detail...
# repeat
col_raw = df1.columns
df4 = df1.copy()
n_round = 1
while True:
print('#'*20, f'round {n_round}', '#'*20)
# step 1 sort the values & group by ['Type', 'ID'] calculate the DatetimeX's time diff
# notice: the time-diff is not the actual wanted
df = df4[col_raw].copy()
df.sort_values(['ID', 'Type', 'DatetimeX'], inplace=True)
df['diff'] = df.groupby(['Type', 'ID'])['DatetimeX'].diff()
print('#'*10, 'step1', '#'*10)
print(df)
# step 2, create a tag column to store the first 10min gap from 'diff' column
cond = False
cond |= df['diff'].dt.seconds > 10 * 60
cond |= df['diff'].isnull()
df['tag'] = np.where(cond, 1, 0)
df['tag'] = df['tag'].cumsum().fillna(method = 'ffill')
print('#'*10, 'step2', '#'*10)
print(df)
# step 3, use 'tag' to judge to stop the while loop or not
# tag should be unique
break_sign = df.tag.duplicated().sum()
if break_sign == 0:
break
print('#'*10, 'step3', '#'*10)
print(break_sign)
# step 4:
# create a 'DatetimeX_1st' with the 'tag' group's first DatetimeX
# create a 'diff2' = 'DatetimeX' - 'DatetimeX_1st'
df2 = df.reset_index().set_index('tag')
df2['DatetimeX_1st'] = df.groupby('tag').first()['DatetimeX']
df2['diff2'] = df2['DatetimeX'] - df2['DatetimeX_1st']
print('#'*10, 'step4', '#'*10)
print(df2)
# step 5:
# drop the True < 10min gaps records
# 'diff' and 'diff2' should all < 10min
cond = (df2['diff2'].dt.seconds < 10 * 60) & (df2['diff'].dt.seconds < 10 * 60)
df3 = df2[~cond].copy()
print('#'*10, 'step5', '#'*10)
print(df3)
# step 6:
# reset index
cols = 'tag DatetimeX ID Type'.split()
df4 = df3.reset_index().set_index('index').sort_index()[cols]
print('#'*10, 'step6', '#'*10)
print(df4)
n_round += 1
print()
# get result
result = df[['DatetimeX', 'ID', 'Type']].copy()
result.index.name = None
print()
print('#'*10, 'result', '#'*10)
print(result)
output:
#################### round 1 ####################
########## step1 ##########
DatetimeX ID Type diff
11 2020-01-01 02:00:01 12211 C NaT
0 2020-01-01 02:00:01 12345 C NaT
1 2020-01-01 02:00:03 12345 C 0 days 00:00:02
5 2020-01-01 02:00:09 12345 C 0 days 00:00:06
6 2020-01-01 02:01:35 12345 C 0 days 00:01:26
7 2020-01-01 02:10:35 12345 C 0 days 00:09:00
2 2020-01-01 05:00:03 12345 C 0 days 02:49:28
3 2020-01-01 05:03:05 12345 C 0 days 00:03:02
8 2020-01-01 02:00:01 13333 D NaT
9 2020-01-01 02:05:35 13333 D 0 days 00:05:34
4 2020-01-01 03:00:09 13333 D 0 days 00:54:34
10 2020-01-01 02:00:50 13333 E NaT
12 2020-01-01 02:09:50 13333 E 0 days 00:09:00
13 2020-01-01 02:11:50 13333 E 0 days 00:02:00
########## step2 ##########
DatetimeX ID Type diff tag
11 2020-01-01 02:00:01 12211 C NaT 1
0 2020-01-01 02:00:01 12345 C NaT 2
1 2020-01-01 02:00:03 12345 C 0 days 00:00:02 2
5 2020-01-01 02:00:09 12345 C 0 days 00:00:06 2
6 2020-01-01 02:01:35 12345 C 0 days 00:01:26 2
7 2020-01-01 02:10:35 12345 C 0 days 00:09:00 2
2 2020-01-01 05:00:03 12345 C 0 days 02:49:28 3
3 2020-01-01 05:03:05 12345 C 0 days 00:03:02 3
8 2020-01-01 02:00:01 13333 D NaT 4
9 2020-01-01 02:05:35 13333 D 0 days 00:05:34 4
4 2020-01-01 03:00:09 13333 D 0 days 00:54:34 5
10 2020-01-01 02:00:50 13333 E NaT 6
12 2020-01-01 02:09:50 13333 E 0 days 00:09:00 6
13 2020-01-01 02:11:50 13333 E 0 days 00:02:00 6
########## step3 ##########
8
########## step4 ##########
index DatetimeX ID Type diff \
tag
1 11 2020-01-01 02:00:01 12211 C NaT
2 0 2020-01-01 02:00:01 12345 C NaT
2 1 2020-01-01 02:00:03 12345 C 0 days 00:00:02
2 5 2020-01-01 02:00:09 12345 C 0 days 00:00:06
2 6 2020-01-01 02:01:35 12345 C 0 days 00:01:26
2 7 2020-01-01 02:10:35 12345 C 0 days 00:09:00
3 2 2020-01-01 05:00:03 12345 C 0 days 02:49:28
3 3 2020-01-01 05:03:05 12345 C 0 days 00:03:02
4 8 2020-01-01 02:00:01 13333 D NaT
4 9 2020-01-01 02:05:35 13333 D 0 days 00:05:34
5 4 2020-01-01 03:00:09 13333 D 0 days 00:54:34
6 10 2020-01-01 02:00:50 13333 E NaT
6 12 2020-01-01 02:09:50 13333 E 0 days 00:09:00
6 13 2020-01-01 02:11:50 13333 E 0 days 00:02:00
DatetimeX_1st diff2
tag
1 2020-01-01 02:00:01 0 days 00:00:00
2 2020-01-01 02:00:01 0 days 00:00:00
2 2020-01-01 02:00:01 0 days 00:00:02
2 2020-01-01 02:00:01 0 days 00:00:08
2 2020-01-01 02:00:01 0 days 00:01:34
2 2020-01-01 02:00:01 0 days 00:10:34
3 2020-01-01 05:00:03 0 days 00:00:00
3 2020-01-01 05:00:03 0 days 00:03:02
4 2020-01-01 02:00:01 0 days 00:00:00
4 2020-01-01 02:00:01 0 days 00:05:34
5 2020-01-01 03:00:09 0 days 00:00:00
6 2020-01-01 02:00:50 0 days 00:00:00
6 2020-01-01 02:00:50 0 days 00:09:00
6 2020-01-01 02:00:50 0 days 00:11:00
########## step5 ##########
index DatetimeX ID Type diff \
tag
1 11 2020-01-01 02:00:01 12211 C NaT
2 0 2020-01-01 02:00:01 12345 C NaT
2 7 2020-01-01 02:10:35 12345 C 0 days 00:09:00
3 2 2020-01-01 05:00:03 12345 C 0 days 02:49:28
4 8 2020-01-01 02:00:01 13333 D NaT
5 4 2020-01-01 03:00:09 13333 D 0 days 00:54:34
6 10 2020-01-01 02:00:50 13333 E NaT
6 13 2020-01-01 02:11:50 13333 E 0 days 00:02:00
DatetimeX_1st diff2
tag
1 2020-01-01 02:00:01 0 days 00:00:00
2 2020-01-01 02:00:01 0 days 00:00:00
2 2020-01-01 02:00:01 0 days 00:10:34
3 2020-01-01 05:00:03 0 days 00:00:00
4 2020-01-01 02:00:01 0 days 00:00:00
5 2020-01-01 03:00:09 0 days 00:00:00
6 2020-01-01 02:00:50 0 days 00:00:00
6 2020-01-01 02:00:50 0 days 00:11:00
########## step6 ##########
tag DatetimeX ID Type
index
0 2 2020-01-01 02:00:01 12345 C
2 3 2020-01-01 05:00:03 12345 C
4 5 2020-01-01 03:00:09 13333 D
7 2 2020-01-01 02:10:35 12345 C
8 4 2020-01-01 02:00:01 13333 D
10 6 2020-01-01 02:00:50 13333 E
11 1 2020-01-01 02:00:01 12211 C
13 6 2020-01-01 02:11:50 13333 E
#################### round 2 ####################
########## step1 ##########
DatetimeX ID Type diff
index
11 2020-01-01 02:00:01 12211 C NaT
0 2020-01-01 02:00:01 12345 C NaT
7 2020-01-01 02:10:35 12345 C 0 days 00:10:34
2 2020-01-01 05:00:03 12345 C 0 days 02:49:28
8 2020-01-01 02:00:01 13333 D NaT
4 2020-01-01 03:00:09 13333 D 0 days 01:00:08
10 2020-01-01 02:00:50 13333 E NaT
13 2020-01-01 02:11:50 13333 E 0 days 00:11:00
########## step2 ##########
DatetimeX ID Type diff tag
index
11 2020-01-01 02:00:01 12211 C NaT 1
0 2020-01-01 02:00:01 12345 C NaT 2
7 2020-01-01 02:10:35 12345 C 0 days 00:10:34 3
2 2020-01-01 05:00:03 12345 C 0 days 02:49:28 4
8 2020-01-01 02:00:01 13333 D NaT 5
4 2020-01-01 03:00:09 13333 D 0 days 01:00:08 6
10 2020-01-01 02:00:50 13333 E NaT 7
13 2020-01-01 02:11:50 13333 E 0 days 00:11:00 8
########## result ##########
DatetimeX ID Type
11 2020-01-01 02:00:01 12211 C
0 2020-01-01 02:00:01 12345 C
7 2020-01-01 02:10:35 12345 C
2 2020-01-01 05:00:03 12345 C
8 2020-01-01 02:00:01 13333 D
4 2020-01-01 03:00:09 13333 D
10 2020-01-01 02:00:50 13333 E
13 2020-01-01 02:11:50 13333 E
Answered By - Ferris
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