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使用python中的日期列表循环24小时

datetime64 python-datetime datetime python-3.x python

2

tda · 技术社区 · 6 年前

我有一个清单 np.datetime64 python中的日期:

['2016-12-01T02:00:00.000000000', '2016-12-01T04:00:00.000000000',
 '2016-12-01T06:00:00.000000000', '2016-12-01T08:00:00.000000000',
 '2016-12-01T10:00:00.000000000', '2016-12-01T12:00:00.000000000', 
 '2016-12-01T14:00:00.000000000', '2016-12-01T16:00:00.000000000', 
 '2016-12-01T18:00:00.000000000', '2016-12-01T20:00:00.000000000', 
 '2016-12-01T22:00:00.000000000', '2016-12-02T00:00:00.000000000', 
 '2016-12-02T02:00:00.000000000', '2016-12-02T04:00:00.000000000', 
 '2016-12-02T06:00:00.000000000', '2016-12-02T08:00:00.000000000', 
 '2016-12-02T10:00:00.000000000', '2016-12-02T12:00:00.000000000', 
 '2016-12-02T14:00:00.000000000', '2016-12-02T16:00:00.000000000', 
 '2016-12-02T18:00:00.000000000', '2016-12-02T20:00:00.000000000', 
 '2016-12-02T22:00:00.000000000', '2016-12-03T00:00:00.000000000', 
 '2016-12-03T02:00:00.000000000', '2016-12-03T04:00:00.000000000',
 '2016-12-03T06:00:00.000000000', '2016-12-03T08:00:00.000000000', 
 '2016-12-03T10:00:00.000000000', '2016-12-03T12:00:00.000000000', 
 '2016-12-03T14:00:00.000000000', '2016-12-03T16:00:00.000000000', 
 '2016-12-03T18:00:00.000000000', '2016-12-03T20:00:00.000000000', 
 '2016-12-03T22:00:00.000000000']

我希望在列表中循环显示每个日历日。我试图从列表中提取每个唯一的日期(即查找最小和最大日期,并在这些日期之间创建一个日期列表),但这并不适合我要做的事情。

我希望得到的结果是有代码允许我循环遍历列表中的每个日期/日历日,并获得与此日期对应的日期时间 :

for each_date in date_list:
    ***get all datetimes corresponding to each_date***

(loop would occur 3 times in this example)

音符:

1)迭代每个[n:n+24]或任何不起作用的方法,因为不是每天都有相同的时间步数。

2 回复 | 直到 6 年前

1

3

willeM_ Van Onsem 6 年前

如果时间戳是命令 ,我们可以使用 itertools.groupby 函数按相应的日期对数组元素进行分组。

这一天可以通过 np.datetime64.astype(..., dtype='datetime64[D]') ,所以我们可以这样写:

from numpy import datetime64
from functools import partial
from itertools import groupby

for day, timestamps in groupby(data_array,
                               partial(datetime64.astype, dtype='datetime64[D]')):
    # process day and timestamps
    pass

在这里 day 是一个 datetime64[D] numpy对象(它只包含天),以及 timestamps 是一个 可迭代的 (不是列表,但我们可以将其转换为列表)对应的时间戳。 data_array 是包含初始数据的数组。

例如:

>>> for day, timestamps in groupby(data_array,
...                                partial(datetime64.astype, dtype='datetime64[D]')):
...     print((day, list(timestamps)))
... 
(numpy.datetime64('2016-12-01'), [numpy.datetime64('2016-12-01T02:00:00.000000000'), numpy.datetime64('2016-12-01T04:00:00.000000000'), numpy.datetime64('2016-12-01T06:00:00.000000000'), numpy.datetime64('2016-12-01T08:00:00.000000000'), numpy.datetime64('2016-12-01T10:00:00.000000000'), numpy.datetime64('2016-12-01T12:00:00.000000000'), numpy.datetime64('2016-12-01T14:00:00.000000000'), numpy.datetime64('2016-12-01T16:00:00.000000000'), numpy.datetime64('2016-12-01T18:00:00.000000000'), numpy.datetime64('2016-12-01T20:00:00.000000000'), numpy.datetime64('2016-12-01T22:00:00.000000000')])
(numpy.datetime64('2016-12-02'), [numpy.datetime64('2016-12-02T00:00:00.000000000'), numpy.datetime64('2016-12-02T02:00:00.000000000'), numpy.datetime64('2016-12-02T04:00:00.000000000'), numpy.datetime64('2016-12-02T06:00:00.000000000'), numpy.datetime64('2016-12-02T08:00:00.000000000'), numpy.datetime64('2016-12-02T10:00:00.000000000'), numpy.datetime64('2016-12-02T12:00:00.000000000'), numpy.datetime64('2016-12-02T14:00:00.000000000'), numpy.datetime64('2016-12-02T16:00:00.000000000'), numpy.datetime64('2016-12-02T18:00:00.000000000'), numpy.datetime64('2016-12-02T20:00:00.000000000'), numpy.datetime64('2016-12-02T22:00:00.000000000')])
(numpy.datetime64('2016-12-03'), [numpy.datetime64('2016-12-03T00:00:00.000000000'), numpy.datetime64('2016-12-03T02:00:00.000000000'), numpy.datetime64('2016-12-03T04:00:00.000000000'), numpy.datetime64('2016-12-03T06:00:00.000000000'), numpy.datetime64('2016-12-03T08:00:00.000000000'), numpy.datetime64('2016-12-03T10:00:00.000000000'), numpy.datetime64('2016-12-03T12:00:00.000000000'), numpy.datetime64('2016-12-03T14:00:00.000000000'), numpy.datetime64('2016-12-03T16:00:00.000000000'), numpy.datetime64('2016-12-03T18:00:00.000000000'), numpy.datetime64('2016-12-03T20:00:00.000000000'), numpy.datetime64('2016-12-03T22:00:00.000000000')])

所以在这里,我们每天都会打印一份 时间戳 ,但这是当然的一所有的选择。如示例所示,并非所有切片都具有相同的长度(最后两个切片有一个额外的元素)

请注意 时间戳 是迭代器,如果不将其转换为列表,则在一个循环之后,迭代器将 筋疲力尽的 .

这个 groupby 工作在线性时间内,因为每次它都检查“组键”是否与前一个元素相同,但如前所述,约束是必须对数据进行排序的。

2

1

jpp 6 年前

你可以使用 collections.defaultdict 对于O(N)溶液。你可以用熊猫来规范你的 datetime 对象,尽管这也可以通过numpy实现。

import pandas as pd
from collections import defaultdict

d = defaultdict(list)

for item in L:
    day = pd.to_datetime(item).normalize().to_datetime64()
    d[day].append(item)

print(d)

defaultdict(list,
            {numpy.datetime64('2016-12-01T00:00:00.000000000'):
                 [numpy.datetime64('2016-12-01T02:00:00.000000000'),
                  ...
                  numpy.datetime64('2016-12-01T22:00:00.000000000')],
             numpy.datetime64('2016-12-02T00:00:00.000000000'):
                 [numpy.datetime64('2016-12-02T00:00:00.000000000'),
                  ...
                  numpy.datetime64('2016-12-02T22:00:00.000000000')],
             numpy.datetime64('2016-12-03T00:00:00.000000000'):
                 [numpy.datetime64('2016-12-03T00:00:00.000000000'),
                  ...
                  numpy.datetime64('2016-12-03T22:00:00.000000000')]})