简短回答
用.total_seconds() 转换为秒数,然后求和
长答案
创建您的 dataframe 和 duration 列
import pandas as pd
dt1 = ['2019-12-01 10:00:00', '2019-12-01 10:01:00', '2019-12-01 10:00:30', '2019-12-01 10:02:30', '2019-12-01 10:05:30']
dt2 = ['2019-12-01 10:10:00', '2019-12-01 11:06:00', '2019-12-01 10:01:00', '2019-12-01 10:02:30', '2019-12-01 10:07:30']
df = pd.DataFrame({'dt1': dt1, 'dt2': dt2})
df['dt1'] = pd.to_datetime(df['dt1'])
df['dt2'] = pd.to_datetime(df['dt2'])
df['duration'] = df['dt2'] - df['dt1']
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 3 columns):
dt1 5 non-null datetime64[ns]
dt2 5 non-null datetime64[ns]
duration 5 non-null timedelta64[ns]
dtypes: datetime64[ns](2), timedelta64[ns](1)
memory usage: 248.0 bytes
请注意,持续时间是类型timedelta。
现在用.total_seconds() 转换为秒数,然后求和。
df['duration_rolling_sum'] = pd.to_timedelta(df['duration'].dt.total_seconds().rolling(min_periods=3, window=5).sum(), unit='s')
df
dt1 dt2 duration duration_rolling_sum
0 2019-12-01 10:00:00 2019-12-01 10:10:00 00:10:00 NaT
1 2019-12-01 10:01:00 2019-12-01 11:06:00 01:05:00 NaT
2 2019-12-01 10:00:30 2019-12-01 10:01:00 00:00:30 01:15:30
3 2019-12-01 10:02:30 2019-12-01 10:02:30 00:00:00 01:15:30
4 2019-12-01 10:05:30 2019-12-01 10:07:30 00:02:00 01:17:30