Python 聚合各个日期的时间间隔

我有一个带有两个时间戳列的数据帧start和end

其目的是汇总在给定日期记录的小时数。所以在我的例子中

我将创建日期范围，并在多个条目上增加小时数

2014-08-28 -> 7 hrs
2014-08-29 -> 10 hrs + 1 hr 15 min => 11 hrs 15 mins
2014-08-30 -> 24 hrs
2014-08-31 -> 24 hrs
2014-09-01 -> 17 hrs + 4 hrs => 21 hrs

我尝试过使用timedelta，但它只在绝对小时内拆分，而不是在每天的基础上拆分

我还尝试分解行（即按天分割行，但我只能使其在日期级别工作，而不能在时间戳级别工作）

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非常感谢您的任何建议。

希望对您有所帮助。我想你能适应以达到你的目的。思考的方法是在第二天——在口述中存储日期和相应的时间。如果是同一天——就写下差异。否则写时间到第一个午夜，需要时迭代，写时间从最后一个午夜到结束。仅供参考。。。我想2014-09-01的结果可能是21小时

from datetime import datetime, timedelta
from collections import defaultdict


s = [('2014-08-28 17:00:00', '2014-08-29 22:00:00'),
     ('2014-08-29 10:45:00', '2014-09-01 17:00:00'),
     ('2014-09-01 15:00:00', '2014-09-01 19:00:00') ]


def aggreate(time):
    store = defaultdict(timedelta)

    for slice in time:
        start = datetime.strptime(slice[0], "%Y-%m-%d %H:%M:%S")
        end = datetime.strptime(slice[1], "%Y-%m-%d %H:%M:%S")

        start_date = start.date()
        end_date = end.date()

        if start_date == end_date:
            store[start_date] += end - start

        else:
            midnight = datetime(start.year, start.month, start.day + 1, 0, 0, 0)
            part1 = midnight - start
            store[start_date] += part1

            for i in range(1, (end_date - start_date).days):
                next_date = start_date + timedelta(days=i)
                store[next_date] += timedelta(hours=24)

            last_midnight = datetime(end_date.year, end_date.month, end_date.day, 0, 0, 0)
            store[end_date] += end - last_midnight

    return store


r = aggreate(s)

for i in r:
    print(i, r[i])

2014-08-28 7:00:00
2014-08-29 1 day, 11:15:00
2014-08-30 1 day, 0:00:00
2014-08-31 1 day, 0:00:00
2014-09-01 21:00:00

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您可以使用

pd.date\u range

创建所花费的每一天的

分钟间隔

，然后您可以计算所花费的分钟数并将其转换为时间增量

start   end
0   2014-08-28 17:00:00 2014-08-29 22:00:00
1   2014-08-29 10:45:00 2014-09-01 17:00:00
2   2014-09-01 15:00:00 2014-09-01 19:00:00


#Creating the minute to minute time intervals from start to end date of each line and creating as one series of dates 
a = pd.Series(sum(df.apply(lambda x: pd.date_range(x['start'],x['end'],freq='min').tolist(),1).tolist(),[])).dt.date
# Counting the each mintue intervals and converting to time stamps
a.value_counts().apply(lambda x: pd.to_timedelta(x,'m'))

输出：

start   end
0   2014-08-28 17:00:00 2014-08-29 22:00:00
1   2014-08-29 10:45:00 2014-09-01 17:00:00
2   2014-09-01 15:00:00 2014-09-01 19:00:00


#Creating the minute to minute time intervals from start to end date of each line and creating as one series of dates 
a = pd.Series(sum(df.apply(lambda x: pd.date_range(x['start'],x['end'],freq='min').tolist(),1).tolist(),[])).dt.date
# Counting the each mintue intervals and converting to time stamps
a.value_counts().apply(lambda x: pd.to_timedelta(x,'m'))

2014-08-29   1 days 11:16:00
2014-08-30   1 days 00:00:00
2014-08-31   1 days 00:00:00
2014-09-01   0 days 21:02:00
2014-08-28   0 days 07:00:00
dtype: timedelta64[ns]