如何根据日期时间过滤您的列表？

Question

这是我的清单：

matched_rows_2 =[
    ['1', '07-09-2020', '8:43:02', '100', 'TTF'],
    ['2', '07-09-2020', '8:43:02', '100', 'GGY'],
    ['3', '07-09-2020', '7:53:08', '120', 'HHJ'],
    ['4', '07-09-2020', '7:54:01', '160', 'JJH'],
    ['5', '07-09-2020', '8:30:00', '160', 'RRT'],
    ['6', '07-09-2020', '10:10:10', '160', 'PPO'],
    ['7', '07-09-2020', '11:12:11', '100', 'KKG'],
    ['8', '07-09-2020', '11:31:55', '160', 'PPO']]

我正在尝试执行以下操作：

1. 对于每个车辆编号 (index[3])，我正在尝试获取日期时间最接近 chosen_datetime 的列表。

我尝试了多种方法，但似乎还没有奏效。 下面是我的鳕鱼e：

chosen_datetime = datetime.fromisoformat("2020-07-09 08:43:55+00:00")
dts = [datetime.strptime(sub[1] + ' ' + sub[2], "%d-%m-%Y  %H:%M:%S").replace(tzinfo=timezone.utc) for sub in matched_rows_2]

for x in matched_rows_2:
    closest_to_chosen_datetime = min(dts, key=lambda d: max( d, chosen_datetime) - min(d, chosen_datetime))
    if closest_to_chosen_datetime:
        print(x)

这是我想要的输出：

['1', '07-09-2020', '8:43:02', '100', 'TTF'],
['2', '07-09-2020', '8:43:02', '100', 'GGY'],
['3', '07-09-2020', '7:53:08', '120', 'HHJ'],
['5', '07-09-2020', '8:30:00', '160', 'RRT'],

这是我当前的输出：

['1', '07-09-2020', '8:43:02', '100', 'TTF'],
['2', '07-09-2020', '8:43:02', '100', 'GGY'],
['3', '07-09-2020', '7:53:08', '120', 'HHJ'],
['4', '07-09-2020', '7:54:01', '160', 'JJH'],
['5', '07-09-2020', '8:30:00', '160', 'RRT'],
['6', '07-09-2020', '10:10:10', '160', 'PPO'],
['7', '07-09-2020', '11:12:11', '100', 'KKG'],
['8', '07-09-2020', '11:31:55', '160', 'PPO']]

我真的不知道发生了什么，出了什么问题。

Answer 1

第一个问题是你有一个print 命令在你的循环。由于在循环遍历所有项目之前之后您不会知道哪些行的日期时间最接近chosen_datetime，因此这是为时过早的，并且是导致错误输出的重要原因。

其次，因为您正在寻找每辆车最近的日期时间 number 你需要一些逻辑来按车辆分组 号码。

一个选项是使用itertools.groupby 的解决方案；其他 解决方案——我在这里实现的——将结果存储在 以车号为关键字的字典。

以下代码中有几个 cmets，如果有请告诉我 你想要一些额外的细节。

from collections import defaultdict
from datetime import datetime, timezone, timedelta


matched_rows_2 = [
    ['1', '07-09-2020', '8:43:02', '100', 'TTF'],
    ['2', '07-09-2020', '8:43:02', '100', 'GGY'],
    ['3', '07-09-2020', '7:53:08', '120', 'HHJ'],
    ['4', '07-09-2020', '7:54:01', '160', 'JJH'],
    ['5', '07-09-2020', '8:30:00', '160', 'RRT'],
    ['6', '07-09-2020', '10:10:10', '160', 'PPO'],
    ['7', '07-09-2020', '11:12:11', '100', 'KKG'],
    ['8', '07-09-2020', '11:31:55', '160', 'PPO']]

chosen_datetime = datetime.fromisoformat("2020-07-09 08:43:55+00:00")
dts = [
    datetime.strptime(f'{row[1]} {row[2]}', '%m-%d-%Y %H:%M:%S').replace(tzinfo=timezone.utc)
    for row in matched_rows_2
]

mindelta = defaultdict(lambda: None)
minrows = defaultdict(lambda: None)

# use zip() to combine the timestamps in dts with the
# original data
for ts, row in zip(dts, matched_rows_2):
    # get the absolute difference from chosen_datetime
    delta = abs((ts - chosen_datetime).total_seconds())
    vid = row[3]

    # if it's the closest value for this vid (or if we haven't
    # processed the vid yet), update mindelta[vid] with the current
    # delta and set minrows[vid] to the current row.
    if mindelta[vid] is None or delta < mindelta[vid]:
        mindelta[vid] = delta
        minrows[vid] = [row]

    # if the current delta is equal to the existing closest delta,
    # just append the current row.
    elif delta == mindelta[vid]:
        minrows[vid].append(row)

for vid, rows in minrows.items():
    for row in rows:
        print(row)

运行上述程序会产生以下输出：

['1', '07-09-2020', '8:43:02', '100', 'TTF']
['2', '07-09-2020', '8:43:02', '100', 'GGY']
['3', '07-09-2020', '7:53:08', '120', 'HHJ']
['5', '07-09-2020', '8:30:00', '160', 'RRT']