在python中生成15分钟的时间间隔数组

Question

我是python的新手.我正在尝试生成时间间隔数组.例如: __CODE__ 1.它应该在祖鲁时间创建如上所述的元素,直到每天晚上9点.2.从上午7:00下以及3.启动时间之后应该产生用于下一个和一天中的元素-埃德时间下午九时,如果是的current_time> START_TIME然后生成15分钟的时间间隔阵列直到下午9点.然后生成第二天和第二天+ 2.间隔时间应该是7:00,7:15那样..不是在7:12,8:32

Answer 1

这是datetime_range您可以使用的通用名称.

码

from datetime import datetime, timedelta

def datetime_range(start, end, delta):
    current = start
    while current < end:
        yield current
        current += delta

dts = [dt.strftime('%Y-%m-%d T%H:%M Z') for dt in 
       datetime_range(datetime(2016, 9, 1, 7), datetime(2016, 9, 1, 9+12), 
       timedelta(minutes=15))]

print(dts)

产量

['2016-09-01 T07:00 Z','2016-09-01 T07:15 Z','2016-09-01 T07:30 Z','2016-09-01 T07:45 Z',' 2016-09-01 T08:00 Z','2016-09-01 T08:15 Z','2016-09-01 T08:30 Z','2016-09-01 T08:45 Z','2016- 09-01 T09:00 Z','2016-09-01 T09:15 Z','2016-09-01 T09:30 Z','2016-09-01 T09:45 Z'...]

Answer 2

查看数据文件，您应该使用内置的python日期时间对象。然后strftime格式化日期。

大致来说，您可以将下面的代码修改为想要首先创建开始日期的任意多个日期时间。

Today= datetime.datetime.today()

用任意数量的时间间隔替换100。

date_list = [Today + datetime.timedelta(minutes=15*x) for x in range(0, 100)]

最后，使用下面的代码以所需的方式格式化列表。

datetext=[x.strftime('%Y-%m-%d T%H:%M Z') for x in date_list]

Answer 3

这是使用任意日期时间的示例

from datetime import datetime

start = datetime(1900,1,1,0,0,0)
end = datetime(1900,1,2,0,0,0)

start现在您需要获取和之间的时间增量（两个日期或时间之间的差异。）end

seconds = (end - start).total_seconds()

定义 15 分钟间隔

from datetime import timedelta

step = timedelta(minutes=15)

以 15 分钟（900 秒）的时间增量步长迭代秒范围，并将其求和为start。

array = []
for i in range(0, int(seconds), int(step.total_seconds())):
    array.append(start + timedelta(seconds=i))

print array
[datetime.datetime(1900, 1, 1, 0, 0),
datetime.datetime(1900, 1, 1, 0, 15),
datetime.datetime(1900, 1, 1, 0, 30),
datetime.datetime(1900, 1, 1, 0, 45),
datetime.datetime(1900, 1, 1, 1, 0),
...

最后，您可以将日期时间对象格式化为 str 表示形式。

array = [i.strftime('%Y-%m-%d %H:%M%:%S') for i in array]

print array
['1900-01-01 00:00:00',
 '1900-01-01 00:15:00',
 '1900-01-01 00:30:00',
 '1900-01-01 00:45:00',
 '1900-01-01 01:00:00',
...

您可以在第一次迭代时格式化日期时间对象。但它可能会伤害你的眼睛

array.append((start + timedelta(seconds=i)).strftime('%Y-%m-%d %H:%M%:%S'))

Answer 4

这是熊猫的解决方案：

import pandas as pd

l = (pd.DataFrame(columns=['NULL'],
                  index=pd.date_range('2016-09-02T17:30:00Z', '2016-09-04T21:00:00Z',
                                      freq='15T'))
       .between_time('07:00','21:00')
       .index.strftime('%Y-%m-%dT%H:%M:%SZ')
       .tolist()
)

输出：

In [165]: l
Out[165]:
['2016-09-02T17:30:00Z',
 '2016-09-02T17:45:00Z',
 '2016-09-02T18:00:00Z',
 '2016-09-02T18:15:00Z',
 '2016-09-02T18:30:00Z',
 '2016-09-02T18:45:00Z',
 '2016-09-02T19:00:00Z',
 '2016-09-02T19:15:00Z',
 '2016-09-02T19:30:00Z',
 '2016-09-02T19:45:00Z',
 '2016-09-02T20:00:00Z',
 '2016-09-02T20:15:00Z',
 '2016-09-02T20:30:00Z',
 '2016-09-02T20:45:00Z',
 '2016-09-02T21:00:00Z',
 '2016-09-03T07:00:00Z',
 '2016-09-03T07:15:00Z',
 '2016-09-03T07:30:00Z',
 '2016-09-03T07:45:00Z',
 '2016-09-03T08:00:00Z',
 '2016-09-03T08:15:00Z',
 '2016-09-03T08:30:00Z',
 '2016-09-03T08:45:00Z',
 '2016-09-03T09:00:00Z',
 '2016-09-03T09:15:00Z',
 '2016-09-03T09:30:00Z',
 '2016-09-03T09:45:00Z',
 '2016-09-03T10:00:00Z',
 '2016-09-03T10:15:00Z',
 '2016-09-03T10:30:00Z',
 '2016-09-03T10:45:00Z',
 '2016-09-03T11:00:00Z',
 '2016-09-03T11:15:00Z',
 '2016-09-03T11:30:00Z',
 '2016-09-03T11:45:00Z',
 '2016-09-03T12:00:00Z',
 '2016-09-03T12:15:00Z',
 '2016-09-03T12:30:00Z',
 '2016-09-03T12:45:00Z',
 '2016-09-03T13:00:00Z',
 '2016-09-03T13:15:00Z',
 '2016-09-03T13:30:00Z',
 '2016-09-03T13:45:00Z',
 '2016-09-03T14:00:00Z',
 '2016-09-03T14:15:00Z',
 '2016-09-03T14:30:00Z',
 '2016-09-03T14:45:00Z',
 '2016-09-03T15:00:00Z',
 '2016-09-03T15:15:00Z',
 '2016-09-03T15:30:00Z',
 '2016-09-03T15:45:00Z',
 '2016-09-03T16:00:00Z',
 '2016-09-03T16:15:00Z',
 '2016-09-03T16:30:00Z',
 '2016-09-03T16:45:00Z',
 '2016-09-03T17:00:00Z',
 '2016-09-03T17:15:00Z',
 '2016-09-03T17:30:00Z',
 '2016-09-03T17:45:00Z',
 '2016-09-03T18:00:00Z',
 '2016-09-03T18:15:00Z',
 '2016-09-03T18:30:00Z',
 '2016-09-03T18:45:00Z',
 '2016-09-03T19:00:00Z',
 '2016-09-03T19:15:00Z',
 '2016-09-03T19:30:00Z',
 '2016-09-03T19:45:00Z',
 '2016-09-03T20:00:00Z',
 '2016-09-03T20:15:00Z',
 '2016-09-03T20:30:00Z',
 '2016-09-03T20:45:00Z',
 '2016-09-03T21:00:00Z',
 '2016-09-04T07:00:00Z',
 '2016-09-04T07:15:00Z',
 '2016-09-04T07:30:00Z',
 '2016-09-04T07:45:00Z',
 '2016-09-04T08:00:00Z',
 '2016-09-04T08:15:00Z',
 '2016-09-04T08:30:00Z',
 '2016-09-04T08:45:00Z',
 '2016-09-04T09:00:00Z',
 '2016-09-04T09:15:00Z',
 '2016-09-04T09:30:00Z',
 '2016-09-04T09:45:00Z',
 '2016-09-04T10:00:00Z',
 '2016-09-04T10:15:00Z',
 '2016-09-04T10:30:00Z',
 '2016-09-04T10:45:00Z',
 '2016-09-04T11:00:00Z',
 '2016-09-04T11:15:00Z',
 '2016-09-04T11:30:00Z',
 '2016-09-04T11:45:00Z',
 '2016-09-04T12:00:00Z',
 '2016-09-04T12:15:00Z',
 '2016-09-04T12:30:00Z',
 '2016-09-04T12:45:00Z',
 '2016-09-04T13:00:00Z',
 '2016-09-04T13:15:00Z',
 '2016-09-04T13:30:00Z',
 '2016-09-04T13:45:00Z',
 '2016-09-04T14:00:00Z',
 '2016-09-04T14:15:00Z',
 '2016-09-04T14:30:00Z',
 '2016-09-04T14:45:00Z',
 '2016-09-04T15:00:00Z',
 '2016-09-04T15:15:00Z',
 '2016-09-04T15:30:00Z',
 '2016-09-04T15:45:00Z',
 '2016-09-04T16:00:00Z',
 '2016-09-04T16:15:00Z',
 '2016-09-04T16:30:00Z',
 '2016-09-04T16:45:00Z',
 '2016-09-04T17:00:00Z',
 '2016-09-04T17:15:00Z',
 '2016-09-04T17:30:00Z',
 '2016-09-04T17:45:00Z',
 '2016-09-04T18:00:00Z',
 '2016-09-04T18:15:00Z',
 '2016-09-04T18:30:00Z',
 '2016-09-04T18:45:00Z',
 '2016-09-04T19:00:00Z',
 '2016-09-04T19:15:00Z',
 '2016-09-04T19:30:00Z',
 '2016-09-04T19:45:00Z',
 '2016-09-04T20:00:00Z',
 '2016-09-04T20:15:00Z',
 '2016-09-04T20:30:00Z',
 '2016-09-04T20:45:00Z',
 '2016-09-04T21:00:00Z']