【问题标题】：Getting the closest date to a given date获取最接近给定日期的日期
【发布时间】：2013-06-22 09:29:30
【问题描述】：

鉴于此基准日期：

base_date = "10/29 06:58 AM"

我想在列表中找到一个包含最接近base_date 的日期的元组，但它不能是更早的日期。

list_date = [('10/30 02:18 PM', '-103', '-107'), ('10/30 02:17 PM', '+100', '-110'), \
             ('10/29 02:15 AM', '-101', '-109')

所以这里的输出应该是('10/30 02:17 PM', '+100', '-110')（它不能是第三个元组，因为那里的日期早于基准日期）

我的问题是，是否存在用于此类日期比较的任何模块？我尝试首先将数据全部更改为AM 格式，然后进行比较，但我的代码因大量切片而变得难看。

@edit：

要测试的大列表：

[('10/30 02:18 PM', '+13 -103', '-13 -107'), ('10/30 02:17 PM', '+13 +100', '-13 -110'), ('10/30 02:15 PM', '+13 -101', '-13 -109'), ('10/30 02:14 PM', '+13 -103', '-13 -107'), ('10/30 01:59 PM', '+13 -105', '-13 -105'), ('10/30 01:46 PM', '+13 -106', '-13 -104'), ('10/30 01:37 PM', '+13 -105', '-13 -105'), ('10/30 01:24 PM', '+13 -107', '-13 -103'), ('10/30 01:23 PM', '+13 -106', '-13 -104'), ('10/30 01:05 PM', '+13 -103', '-13 -107'), ('10/30 01:02 PM', '+13 -104', '-13 -106'), ('10/30 12:55 PM', '+13 -103', '-13 -107'), ('10/30 12:51 PM', '+13.5 -110', '-13.5 +100'), ('10/30 12:44 PM', '+13.5 -108', '-13.5 -102'), ('10/30 12:38 PM', '+13.5 -107', '-13.5 -103'), ('10/30 12:35 PM', '+13 -102', '-13 -108'), ('10/30 12:34 PM', '+13 -103', '-13 -107'), ('10/30 12:06 PM', '+13.5 -110', '-13.5 +100'), ('10/30 11:57 AM', '+13.5 -108', '-13.5 -102'), ('10/30 11:36 AM', '+13.5 -107', '-13.5 -103'), ('10/30 09:01 AM', '+13.5 -110', '-13.5 +100'), ('10/30 08:59 AM', '+13.5 -108', '-13.5 -102'), ('10/30 08:13 AM', '+13.5 -105', '-13.5 -105'), ('10/30 06:11 AM', '+13.5 +100', '-13.5 -110'), ('10/30 06:09 AM', '+13.5 -105', '-13.5 -105'), ('10/30 06:04 AM', '+13.5 -110', '-13.5 +100'), ('10/30 05:32 AM', '+13.5 -105', '-13.5 -105'), ('10/30 04:48 AM', '+13.5 -107', '-13.5 -103'), ('10/30 12:51 AM', '+13.5 -110', '-13.5 +100'), ('10/29 01:31 PM', '+13.5 -105', '-13.5 -105'), ('10/29 01:31 PM', '+13 +103', '-13 -113'), ('10/29 01:28 PM', '+13 -102', '-13 -108'), ('10/29 07:59 AM', '+13 -105', '-13 -105'), ('10/29 07:20 AM', '+13 -103', '-13 -107'), ('10/29 07:14 AM', '+13 -105', '-13 -105'), ('10/29 04:47 AM', '+13 +100', '-13 -110'), ('10/29 04:14 AM', '+13 -105', '-13 -105'), ('10/28 08:17 PM', '+12.5 +100', '-12.5 -110'), ('10/28 12:52 PM', '+12.5 -105', '-12.5 -105')]

要测试的大列表2：

[('10/30 04:30 PM', '+1.5 -111', '-1.5 +101'), ('10/30 04:24 PM', '+1.5 -110', '-1.5     +100'), ('10/30 04:21 PM', '+1.5 -111', '-1.5 +101'), ('10/30 04:15 PM', '+1.5 -112', '-1.5 +102'), ('10/30 04:14 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:57 PM', '+1.5 -111', '-1.5 +101'), ('10/30 03:40 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:31 PM', '+1.5 -111', '-1.5 +101'), ('10/30 03:30 PM', '+1.5 -109', '-1.5 -101'), ('10/30 03:25 PM', '+1.5 -107', '-1.5 -103'), ('10/30 03:24 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:23 PM', '+1.5 -108', '-1.5 -102'), ('10/30 03:22 PM', '+1.5 -106', '-1.5 -104'), ('10/30 02:14 PM', '+1.5 -104', '-1.5 -106'), ('10/30 01:41 PM', '+1.5 -105', '-1.5 -105'), ('10/30 01:37 PM', '+1.5 -107', '-1.5 -103'), ('10/30 01:36 PM', '+1.5 -105', '-1.5 -105'), ('10/30 01:06 PM', '+1.5 -103', '-1.5 -107'), ('10/30 12:56 PM', '+2 -111', '-2 +101'), ('10/30 12:53 PM', '+2 -110', '-2 +100'), ('10/30 12:50 PM', '+2 -113', '-2 +103'), ('10/30 12:49 PM', '+2 -112', '-2 +102'), ('10/30 12:46 PM', '+2 -113', '-2 +103'), ('10/30 12:45 PM', '+2 -110', '-2 +100'), ('10/30 12:43 PM', '+2 -108', '-2 -102'), ('10/30 12:38 PM', '+2.5 -116', '-2.5 +106'), ('10/30 12:38 PM', '+2.5 -113', '-2.5 +103'), ('10/30 12:37 PM', '+2.5 -110', '-2.5 +100'), ('10/30 10:30 AM', '+2.5 -105', '-2.5 -105'), ('10/30 10:07 AM', '+3 -113', '-3 +103'), ('10/30 09:55 AM', '+3 -112', '-3 +102'), ('10/30 09:51 AM', '+3 -110', '-3 +100'), ('10/30 09:32 AM', '+3 -109', '-3 -101'), ('10/30 06:04 AM', '+3 -110', '-3 +100'), ('10/30 03:16 AM', '+3 -107', '-3 -103'), ('10/30 03:14 AM', '+3.5 -116', '-3.5 +106'), ('10/30 01:03 AM', '+3.5 -115', '-3.5 +105'), ('10/30 12:17 AM', '+3.5 -110', '-3.5 +100'), ('10/29 08:52 PM', '+3.5 -108', '-3.5 -102'), ('10/29 01:31 PM', '+3.5 -105', '-3.5 -105'), ('10/29 06:48 AM', '+3.5 -110', '-3.5 +100'), ('10/29 06:47 AM', '+3.5 -109', '-3.5 -101'), ('10/29 05:39 AM', '+3.5 -113', '-3.5 +103'), ('10/29 03:34 AM', '+3.5 -108', '-3.5 -102'), ('10/29 12:44 AM', '+3.5 -110', '-3.5 +100'), ('10/29 12:41 AM', '+3.5 -107', '-3.5 -103'), ('10/29 12:40 AM', '+3.5 -105', '-3.5 -105'), ('10/28 12:52 PM', '+4 -105', '-4 -105')]

【问题讨论】：

没有年份，你将如何比较两个日期？是否假定它们属于同一年份？
是的，它们永远属于同一年份

标签： python date python-2.7

【解决方案1】：

这可以使用datetime模块来完成，它能够将日期字符串解析为日期时间对象，支持与日期的比较和算术：

from datetime import datetime

# function for parsing strings using specific format
get_datetime = lambda s: datetime.strptime(s, "%m/%d %I:%M %p")

base = get_datetime(base_date)
later = filter(lambda d: get_datetime(d[0]) > base, list_date)
closest_date = min(later, key = lambda d: get_datetime(d[0]))

【讨论】：

整洁，一个非常pythonic的解决方案:-)
有关日期解析的更多信息，请查看datetime documentation。它能够解析基本上以您能想到的任何方式格式化的日期。
@NilsWerner 确实，非常pythonic。我怎么知道？超过一半的行包含lambda。（当然，这是一个非常好的解决方案。）
我喜欢这个解决方案，但恐怕它会返回错误的输出。
如果你真的想让事情“看起来很实用”，请废弃所有这些 lambda 表达式并使用 partial 和 compose。 filter(compose(partial(operator.gt, base), compose(operator.itemgetter(0), get_datetime), list_date) 不是更好吗？ :)

【解决方案2】：

>>> from datetime import timedelta, datetime
>>> base_date = "10/29 06:58 AM"
>>> b_d = datetime.strptime(base_date, "%m/%d %I:%M %p")
def func(x):
    d =  datetime.strptime(x[0], "%m/%d %I:%M %p")
    delta =  d - b_d if d > b_d else timedelta.max
    return delta
... 
>>> min(list_date, key = func)
('10/30 02:17 PM', '+100', '-110')

datetime.strptime 将日期转换为日期时间对象，因此b_d 现在看起来像这样：

>>> b_d
datetime.datetime(1900, 10, 29, 6, 58)

现在我们可以写一个函数，可以传递给min的key参数：

delta =  d - b_d if d > b_d else timedelta.max

如果d > b_d 即如果传递给min 的日期大于base_date，则将它们的差异分配给delta，否则将timedelta.max 分配给它。

>>> timedelta.max
datetime.timedelta(999999999, 86399, 999999)

更新：

>>> from datetime import timedelta, datetime
>>> base_date = '10/29 06:59 AM'
>>> b_d = datetime.strptime(base_date, "%m/%d %I:%M %p")
>>> def func(x):
...         d =  datetime.strptime(x[0], "%m/%d %I:%M %p")
...         delta =  d - b_d if d > b_d else timedelta.max
...         return delta
... 
>>> lis2 = [('10/30 04:30 PM', '+1.5 -111', '-1.5 +101'), ('10/30 04:24 PM', '+1.5 -110', '-1.5     +100'), ('10/30 04:21 PM', '+1.5 -111', '-1.5 +101'), ('10/30 04:15 PM', '+1.5 -112', '-1.5 +102'), ('10/30 04:14 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:57 PM', '+1.5 -111', '-1.5 +101'), ('10/30 03:40 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:31 PM', '+1.5 -111', '-1.5 +101'), ('10/30 03:30 PM', '+1.5 -109', '-1.5 -101'), ('10/30 03:25 PM', '+1.5 -107', '-1.5 -103'), ('10/30 03:24 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:23 PM', '+1.5 -108', '-1.5 -102'), ('10/30 03:22 PM', '+1.5 -106', '-1.5 -104'), ('10/30 02:14 PM', '+1.5 -104', '-1.5 -106'), ('10/30 01:41 PM', '+1.5 -105', '-1.5 -105'), ('10/30 01:37 PM', '+1.5 -107', '-1.5 -103'), ('10/30 01:36 PM', '+1.5 -105', '-1.5 -105'), ('10/30 01:06 PM', '+1.5 -103', '-1.5 -107'), ('10/30 12:56 PM', '+2 -111', '-2 +101'), ('10/30 12:53 PM', '+2 -110', '-2 +100'), ('10/30 12:50 PM', '+2 -113', '-2 +103'), ('10/30 12:49 PM', '+2 -112', '-2 +102'), ('10/30 12:46 PM', '+2 -113', '-2 +103'), ('10/30 12:45 PM', '+2 -110', '-2 +100'), ('10/30 12:43 PM', '+2 -108', '-2 -102'), ('10/30 12:38 PM', '+2.5 -116', '-2.5 +106'), ('10/30 12:38 PM', '+2.5 -113', '-2.5 +103'), ('10/30 12:37 PM', '+2.5 -110', '-2.5 +100'), ('10/30 10:30 AM', '+2.5 -105', '-2.5 -105'), ('10/30 10:07 AM', '+3 -113', '-3 +103'), ('10/30 09:55 AM', '+3 -112', '-3 +102'), ('10/30 09:51 AM', '+3 -110', '-3 +100'), ('10/30 09:32 AM', '+3 -109', '-3 -101'), ('10/30 06:04 AM', '+3 -110', '-3 +100'), ('10/30 03:16 AM', '+3 -107', '-3 -103'), ('10/30 03:14 AM', '+3.5 -116', '-3.5 +106'), ('10/30 01:03 AM', '+3.5 -115', '-3.5 +105'), ('10/30 12:17 AM', '+3.5 -110', '-3.5 +100'), ('10/29 08:52 PM', '+3.5 -108', '-3.5 -102'), ('10/29 01:31 PM', '+3.5 -105', '-3.5 -105'), ('10/29 06:48 AM', '+3.5 -110', '-3.5 +100'), ('10/29 06:47 AM', '+3.5 -109', '-3.5 -101'), ('10/29 05:39 AM', '+3.5 -113', '-3.5 +103'), ('10/29 03:34 AM', '+3.5 -108', '-3.5 -102'), ('10/29 12:44 AM', '+3.5 -110', '-3.5 +100'), ('10/29 12:41 AM', '+3.5 -107', '-3.5 -103'), ('10/29 12:40 AM', '+3.5 -105', '-3.5 -105'), ('10/28 12:52 PM', '+4 -105', '-4 -105')]
>>> min(lis2, key = func)
('10/29 01:31 PM', '+3.5 -105', '-3.5 -105')

时间比较：

脚本：

from datetime import datetime, timedelta
import sys
import time
list_date = [('10/30 04:30 PM', '+1.5 -111', '-1.5 +101'), ('10/30 04:24 PM', '+1.5 -110', '-1.5     +100'), ('10/30 04:21 PM', '+1.5 -111', '-1.5 +101'), ('10/30 04:15 PM', '+1.5 -112', '-1.5 +102'), ('10/30 04:14 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:57 PM', '+1.5 -111', '-1.5 +101'), ('10/30 03:40 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:31 PM', '+1.5 -111', '-1.5 +101'), ('10/30 03:30 PM', '+1.5 -109', '-1.5 -101'), ('10/30 03:25 PM', '+1.5 -107', '-1.5 -103'), ('10/30 03:24 PM', '+1.5 -110', '-1.5 +100'), ('10/30 03:23 PM', '+1.5 -108', '-1.5 -102'), ('10/30 03:22 PM', '+1.5 -106', '-1.5 -104'), ('10/30 02:14 PM', '+1.5 -104', '-1.5 -106'), ('10/30 01:41 PM', '+1.5 -105', '-1.5 -105'), ('10/30 01:37 PM', '+1.5 -107', '-1.5 -103'), ('10/30 01:36 PM', '+1.5 -105', '-1.5 -105'), ('10/30 01:06 PM', '+1.5 -103', '-1.5 -107'), ('10/30 12:56 PM', '+2 -111', '-2 +101'), ('10/30 12:53 PM', '+2 -110', '-2 +100'), ('10/30 12:50 PM', '+2 -113', '-2 +103'), ('10/30 12:49 PM', '+2 -112', '-2 +102'), ('10/30 12:46 PM', '+2 -113', '-2 +103'), ('10/30 12:45 PM', '+2 -110', '-2 +100'), ('10/30 12:43 PM', '+2 -108', '-2 -102'), ('10/30 12:38 PM', '+2.5 -116', '-2.5 +106'), ('10/30 12:38 PM', '+2.5 -113', '-2.5 +103'), ('10/30 12:37 PM', '+2.5 -110', '-2.5 +100'), ('10/30 10:30 AM', '+2.5 -105', '-2.5 -105'), ('10/30 10:07 AM', '+3 -113', '-3 +103'), ('10/30 09:55 AM', '+3 -112', '-3 +102'), ('10/30 09:51 AM', '+3 -110', '-3 +100'), ('10/30 09:32 AM', '+3 -109', '-3 -101'), ('10/30 06:04 AM', '+3 -110', '-3 +100'), ('10/30 03:16 AM', '+3 -107', '-3 -103'), ('10/30 03:14 AM', '+3.5 -116', '-3.5 +106'), ('10/30 01:03 AM', '+3.5 -115', '-3.5 +105'), ('10/30 12:17 AM', '+3.5 -110', '-3.5 +100'), ('10/29 08:52 PM', '+3.5 -108', '-3.5 -102'), ('10/29 01:31 PM', '+3.5 -105', '-3.5 -105'), ('10/29 06:48 AM', '+3.5 -110', '-3.5 +100'), ('10/29 06:47 AM', '+3.5 -109', '-3.5 -101'), ('10/29 05:39 AM', '+3.5 -113', '-3.5 +103'), ('10/29 03:34 AM', '+3.5 -108', '-3.5 -102'), ('10/29 12:44 AM', '+3.5 -110', '-3.5 +100'), ('10/29 12:41 AM', '+3.5 -107', '-3.5 -103'), ('10/29 12:40 AM', '+3.5 -105', '-3.5 -105'), ('10/28 12:52 PM', '+4 -105', '-4 -105')]

base_date = "10/29 06:58 AM"

def func1(list_date):
    #http://stackoverflow.com/a/17249420/846892
    get_datetime = lambda s: datetime.strptime(s, "%m/%d %I:%M %p")
    base = get_datetime(base_date)
    later = filter(lambda d: get_datetime(d[0]) > base, list_date)
    return min(later, key = lambda d: get_datetime(d[0]))

def func2(list_date):
    #http://stackoverflow.com/a/17249470/846892
    b_d = datetime.strptime(base_date, "%m/%d %I:%M %p")
    def func(x):
       d =  datetime.strptime(x[0], "%m/%d %I:%M %p")
       delta =  d - b_d if d > b_d else timedelta.max
       return delta
    return min(list_date, key = func)

def func3(list_date):
    #http://stackoverflow.com/a/17249529/846892
    fmt = '%m/%d %I:%M %p'
    d = datetime.strptime(base_date, fmt)
    def foo(x):
        return (datetime.strptime(x[0],fmt)-d).total_seconds() > 0
    return sorted(list_date, key=foo)[-1]

def func4(list_date):
    #http://stackoverflow.com/a/17249441/846892
    fmt = '%m/%d %I:%M %p'
    base_d = datetime.strptime(base_date, fmt)
    candidates = ((datetime.strptime(d, fmt), d, x, y) for d, x, y in list_date)
    candidates = min((dt, d, x, y) for dt, d, x, y in candidates if dt > base_d)
    return  candidates[1:]

结果：

>>> from so import *

#check output irst
>>> func1(list_date)
('10/29 01:31 PM', '+3.5 -105', '-3.5 -105')
>>> func2(list_date)
('10/29 01:31 PM', '+3.5 -105', '-3.5 -105')
>>> func3(list_date)
('10/29 01:31 PM', '+3.5 -105', '-3.5 -105')
>>> func4(list_date)
('10/29 01:31 PM', '+3.5 -105', '-3.5 -105')

>>> %timeit func1(list_date)
100 loops, best of 3: 3.07 ms per loop
>>> %timeit func2(list_date)
100 loops, best of 3: 1.59 ms per loop      #winner
>>> %timeit func3(list_date)
100 loops, best of 3: 1.91 ms per loop
>>> %timeit func4(list_date)
1000 loops, best of 3: 2.02 ms per loop

#increase the input size
>>> list_date = list_date *10**3
>>> len(list_date)
48000
>>> %timeit func1(list_date)
1 loops, best of 3: 3.6 s per loop
>>> %timeit func2(list_date)            #winner
1 loops, best of 3: 1.99 s per loop      
>>> %timeit func3(list_date)
1 loops, best of 3: 2.09 s per loop
>>> %timeit func4(list_date)
1 loops, best of 3: 2.02 s per loop


#increase the input size again

>>> list_date = list_date *10
>>> len(list_date)
480000
>>> %timeit func1(list_date)
1 loops, best of 3: 36.4 s per loop
>>> %timeit func2(list_date)                  #winner
1 loops, best of 3: 20.2 s per loop           
>>> %timeit func3(list_date)
1 loops, best of 3: 22.8 s per loop
>>> %timeit func4(list_date)
1 loops, best of 3: 22.7 s per loop

【讨论】：

您好，感谢您的解决方案，但它区分 AM 和 PM 吗？
如果您有时间，请您检查一下 OP 的Big list to test2 吗？作为base_date，请插入10/29 06:59 AM。正确的输出应该是('10/29 01:31 PM', '+3.5 -105', '-3.5 -105')，但您的代码生成的('10/29 08:52 PM', '+3.5 -108', '-3.5 -102') 是不正确的。
@nutship 查看我更新的解决方案，我得到了正确的输出。
嗨，实际上你的代码对我来说一直运行良好，但是这一次，由于 Andrei 的解决方案获得了 5 票，我认为接受他的回答可能是公平的，嗯？
@nutship 我将比较在此线程上发布的解决方案的计时结果并发布它们（基于此选择最佳的），您不应该仅仅因为它获得更多选票而选择答案。（这并不意味着安德烈的解决方案是错误的）

【解决方案3】：

您可以考虑将日期列表放入 Pandas 索引中，然后使用 'truncate' 或 'get_loc' 函数。

import pandas as pd

##Initial inputs
list_date = [('10/30 02:18 PM', '-103', '-107'),('10/29 02:15 AM', '-101', '-109') , ('10/30 02:17 PM', '+100', '-110'), \
             ]  # reordered to show the method is input order insensitive
base_date = "10/29 06:58 AM"


##Make a data frame with data
df=pd.DataFrame(list_date)
df.columns=['date','val1','val2']
dateIndex=pd.to_datetime(df['date'], format='%m/%d %I:%M %p')
df=df.set_index(dateIndex) 
df=df.sort_index(ascending=False) #earliest comes on top 

##Find the result
base_dateObj=pd.to_datetime(base_date, format='%m/%d %I:%M %p')
result=df.truncate(after=base_dateObj).iloc[-1]  #take the bottom value, or the 1st after the base date
(result['date'],result['val1'], result['val2']) # result is ('10/30 02:17 PM', '+100', '-110')

参考：this link

【讨论】：

【解决方案4】：

装饰、过滤、查找最接近的日期、取消装饰

>>> base_date = "10/29 06:58 AM"
>>> list_date = [
...     ('10/30 02:18 PM', '-103', '-107'),
...     ('10/30 02:17 PM', '+100', '-110'),
...     ('10/29 02:15 AM', '-101', '-109')
... ]
>>> import datetime
>>> fmt = '%m/%d %H:%M %p'
>>> base_d = datetime.datetime.strptime(base_date, fmt)
>>> candidates = ((datetime.datetime.strptime(d, fmt), d, x, y) for d, x, y in list_date)
>>> candidates = min((dt, d, x, y) for dt, d, x, y in candidates if dt > base_d)
>>> print candidates[1:]
('10/30 02:17 PM', '+100', '-110')

【讨论】：

【解决方案5】：

线性搜索？

import sys
import time

base_date = "10/29 06:58 AM"

def str_to_my_time(my_str):
    return time.mktime(time.strptime(my_str, "%m/%d %I:%M %p")) 
                # assume year 1900...

base_dt = str_to_my_time(base_date)

list_date = [('10/30 02:18 PM', '-103', '-107'), 
             ('10/30 02:17 PM', '+100', '-110'),
             ('10/29 02:15 AM', '-101', '-109')]


best_delta = sys.maxint
best_match = None

for t in list_date:
    the_dt = str_to_my_time(t[0])
    delta_sec = the_dt - base_dt
    if (delta_sec >= 0) and (delta_sec < best_delta):
        best_delta = delta_sec
        best_match = t

print best_match, best_delta

制作：

('10/30 02:17 PM', '+100', '-110') 112740.0

【讨论】：

【解决方案6】：

import time
import sys

#The Function
def to_sec(date_string):
    return time.mktime(time.strptime(date_string, '%m/%d %I:%M %p'))


#The Test
base_date = "10/29 06:58 AM"
base_date_sec = to_sec(base_date)
result = None
difference = sys.maxint
list_date = [
        ('10/30 02:18 PM', '-103', '-107'),
        ('10/30 02:17 PM', '+100', '-110'), 
        ('10/29 02:15 AM', '-101', '-109') ]
for date_str in list_date:
    diff_sec = to_sec(date_str[0])-base_date_sec
    if diff_sec >= 0 and diff_sec < difference:
        result = date_str
        difference = diff_sec
print result

【讨论】：

【解决方案7】：

import datetime

fmt = '%m/%d %H:%M %p'
d = datetime.datetime.strptime(base_date, fmt)
def foo(x):
   return (datetime.datetime.strptime(x[0],fmt)-d).total_seconds() > 0
sorted(list_date, key=foo)[-1]

【讨论】：

【解决方案8】：

我正在查找这个问题并找到了一些答案，其中大部分都检查了所有元素。我已经对日期进行了排序（假设大多数人都这样做了），所以如果你也这样做了，请使用 numpy：

import numpy as np
// dates is a numpy array of np.datetime64 objects
dates = np.array([date1, date2, date3, ...], dtype=np.datetime64)
timestamp = np.datetime64('Your date')
np.searchsorted(dates, timestamp)

searchsorted 使用二进制搜索，它使用日期排序的事实，因此非常有效。如果你使用 pandas，这是可能的：

dates = df.index # df is a DatetimeIndex-ed dataframe
timestamp = pd.to_datetime('your date here', format='its format')
np.searchsorted(dates, timestamp)

函数返回最近日期的索引（如果搜索的日期包含在日期中，则返回其索引[如果不需要，使用 side='right' 作为函数的参数]），所以要获取日期，请执行以下操作：

dates[np.searchsorted(dates, timestamp)]

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