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TA贡献1966条经验 获得超4个赞
假设begin和end已经是Timestamp类型:
# Generate a series of Timedeltas for each row
n = (
(df['end'].dt.normalize() - df['begin'].dt.normalize())
.apply(lambda d: [pd.Timedelta(days=i) for i in range(d.days+1)])
.explode()
).rename('n')
df = df.join(n)
# Adjust the begin and end of each row
adjusted_begin = np.max([
df['begin'],
df['begin'].dt.normalize() + df['n']
], axis=0)
adjusted_end = np.min([
df['end'],
pd.Series(adjusted_begin).dt.normalize() + pd.Timedelta(days=1, milliseconds=-100)
], axis=0)
# Final assembly
df = df.assign(begin_=adjusted_begin, end_=adjusted_end)
结果:
begin end info n begin_ end_
0 2019-10-25 10:39:58.352073 2019-10-25 10:40:06.266782 toto 0 days 2019-10-25 10:39:58.352073 2019-10-25 10:40:06.266782
1 2019-10-25 16:35:22.485574 2019-10-27 09:50:31.713179 tata 0 days 2019-10-25 16:35:22.485574 2019-10-25 23:59:59.900000
1 2019-10-25 16:35:22.485574 2019-10-27 09:50:31.713179 tata 1 days 2019-10-26 00:00:00.000000 2019-10-26 23:59:59.900000
1 2019-10-25 16:35:22.485574 2019-10-27 09:50:31.713179 tata 2 days 2019-10-27 00:00:00.000000 2019-10-27 09:50:31.713179
2 2019-10-27 09:50:31.713179 2019-10-27 09:50:31.713192 titi 0 days 2019-10-27 09:50:31.713179 2019-10-27 09:50:31.713192
3 2019-10-28 14:04:33.095633 2019-10-28 14:05:07.639344 tete 0 days 2019-10-28 14:04:33.095633 2019-10-28 14:05:07.639344
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