Python用时间条件删除行 [英] Python removing rows with time condition
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问题描述
我有两组数据框，都具有唯一的标识符和日期时间数据，其格式如下
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和"12345"-唯一标识符和类型
"2020-01-01 00:00:01"-datetime and "12345" - unique identifier and Type
第一个问题，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
我想基于具有相同类型"的ID的第一个时间戳，并在此之后10分钟删除行:
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
我尝试探索时间范围/日期范围，但是找不到任何类似的编码概念.希望如果有人能指出我可以探索的方式而不是尝试寻求完整的解决方案.几年没有接触过python并且以前不熟悉它.谢谢
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
已更新了其他数据行，以获取更准确的示例
推荐答案
添加示例输入数据并简化了过程:
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]
详细信息...
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)
输出:
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
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