8

我的目标是有一个时间列表（以秒为单位），已经在 5 分钟内打包了一整天的时间列表。这是我将“2016-07-08”全天打包 5 分钟的代码：

pd.date_range('2016-07-08 00:00:00', '2016-07-08 23:59:00', freq='5Min')

结果 ：

DatetimeIndex(['2016-07-08 00:00:00', '2016-07-08 00:05:00',
           '2016-07-08 00:10:00', '2016-07-08 00:15:00',
           '2016-07-08 00:20:00', '2016-07-08 00:25:00',
           '2016-07-08 00:30:00', '2016-07-08 00:35:00',
           '2016-07-08 00:40:00', '2016-07-08 00:45:00',
           ...
           '2016-07-08 23:10:00', '2016-07-08 23:15:00',
           '2016-07-08 23:20:00', '2016-07-08 23:25:00',
           '2016-07-08 23:30:00', '2016-07-08 23:35:00',
           '2016-07-08 23:40:00', '2016-07-08 23:45:00',
           '2016-07-08 23:50:00', '2016-07-08 23:55:00'],
          dtype='datetime64[ns]', length=288, freq='5T')

这是每 5 分钟包含所有时间（按秒计）的代码：

for time in pd.date_range('2016-07-08 00:00:00', '2016-07-08 23:59:00', freq='5Min').tolist():    
    time_by_5_min = datetime.datetime.strftime(time.to_datetime(), "%Y-%m-%d %H:%M:%S")
    print pd.date_range(time_by_5_min, freq='S', periods=60)

结果 ：

DatetimeIndex(['2016-07-08 00:00:00', '2016-07-08 00:00:01',
           '2016-07-08 00:00:02', '2016-07-08 00:00:03',
           '2016-07-08 00:00:04', '2016-07-08 00:00:05',
           '2016-07-08 00:00:06', '2016-07-08 00:00:07',
           '2016-07-08 00:00:08', '2016-07-08 00:00:09',
           '2016-07-08 00:00:10', '2016-07-08 00:00:11',
           '2016-07-08 00:00:12', '2016-07-08 00:00:13',
           '2016-07-08 00:00:14', '2016-07-08 00:00:15',
           '2016-07-08 00:00:16', '2016-07-08 00:00:17',
           '2016-07-08 00:00:18', '2016-07-08 00:00:19',
           '2016-07-08 00:00:20', '2016-07-08 00:00:21',
           '2016-07-08 00:00:22', '2016-07-08 00:00:23',
           '2016-07-08 00:00:24', '2016-07-08 00:00:25',
           '2016-07-08 00:00:26', '2016-07-08 00:00:27',
           '2016-07-08 00:00:28', '2016-07-08 00:00:29',
           '2016-07-08 00:00:30', '2016-07-08 00:00:31',
           '2016-07-08 00:00:32', '2016-07-08 00:00:33',
           '2016-07-08 00:00:34', '2016-07-08 00:00:35',
           '2016-07-08 00:00:36', '2016-07-08 00:00:37',
           '2016-07-08 00:00:38', '2016-07-08 00:00:39',
           '2016-07-08 00:00:40', '2016-07-08 00:00:41',
           '2016-07-08 00:00:42', '2016-07-08 00:00:43',
           '2016-07-08 00:00:44', '2016-07-08 00:00:45',
           '2016-07-08 00:00:46', '2016-07-08 00:00:47',
           '2016-07-08 00:00:48', '2016-07-08 00:00:49',
           '2016-07-08 00:00:50', '2016-07-08 00:00:51',
           '2016-07-08 00:00:52', '2016-07-08 00:00:53',
           '2016-07-08 00:00:54', '2016-07-08 00:00:55',
           '2016-07-08 00:00:56', '2016-07-08 00:00:57',
           '2016-07-08 00:00:58', '2016-07-08 00:00:59'],
          dtype='datetime64[ns]', freq='S')
DatetimeIndex(['2016-07-08 00:05:00', '2016-07-08 00:05:01',
           '2016-07-08 00:05:02', '2016-07-08 00:05:03',
           '2016-07-08 00:05:04', '2016-07-08 00:05:05',
           '2016-07-08 00:05:06', '2016-07-08 00:05:07',
           '2016-07-08 00:05:08', '2016-07-08 00:05:09',
           '2016-07-08 00:05:10', '2016-07-08 00:05:11',
           '2016-07-08 00:05:12', '2016-07-08 00:05:13',
           '2016-07-08 00:05:14', '2016-07-08 00:05:15',
           '2016-07-08 00:05:16', '2016-07-08 00:05:17',
           '2016-07-08 00:05:18', '2016-07-08 00:05:19',
           '2016-07-08 00:05:20', '2016-07-08 00:05:21',
           '2016-07-08 00:05:22', '2016-07-08 00:05:23',
           '2016-07-08 00:05:24', '2016-07-08 00:05:25',
           '2016-07-08 00:05:26', '2016-07-08 00:05:27',
           '2016-07-08 00:05:28', '2016-07-08 00:05:29',
           '2016-07-08 00:05:30', '2016-07-08 00:05:31',
           '2016-07-08 00:05:32', '2016-07-08 00:05:33',
           '2016-07-08 00:05:34', '2016-07-08 00:05:35',
           '2016-07-08 00:05:36', '2016-07-08 00:05:37',
           '2016-07-08 00:05:38', '2016-07-08 00:05:39',
           '2016-07-08 00:05:40', '2016-07-08 00:05:41',
           '2016-07-08 00:05:42', '2016-07-08 00:05:43',
           '2016-07-08 00:05:44', '2016-07-08 00:05:45',
           '2016-07-08 00:05:46', '2016-07-08 00:05:47',
           '2016-07-08 00:05:48', '2016-07-08 00:05:49',
           '2016-07-08 00:05:50', '2016-07-08 00:05:51',
           '2016-07-08 00:05:52', '2016-07-08 00:05:53',
           '2016-07-08 00:05:54', '2016-07-08 00:05:55',
           '2016-07-08 00:05:56', '2016-07-08 00:05:57',
           '2016-07-08 00:05:58', '2016-07-08 00:05:59'],
          dtype='datetime64[ns]', freq='S')
etc

这对我来说太完美了！我现在想要列表，而不是 pandas.tseries.index.DatetimeIndex .. .tolist() 方法给出了这个：

for time in pd.date_range('2016-07-08 00:00:00', '2016-07-08 23:59:00', freq='5Min').tolist():    
    time_by_5_min = datetime.datetime.strftime(time.to_datetime(), "%Y-%m-%d %H:%M:%S")
    print (pd.date_range(time_by_5_min, freq='S', periods=60)).tolist()

结果 ：

[Timestamp('2016-07-08 00:00:00', offset='S'), Timestamp('2016-07-08 00:00:01', offset='S'), Timestamp('2016-07-08 00:00:02', offset='S'), Timestamp('2016-07-08 00:00:03', offset='S'), Timestamp('2016-07-08 00:00:04', offset='S'), Timestamp('2016-07-08 00:00:05', offset='S'), Timestamp('2016-07-08 00:00:06', offset='S'), etc]

我想要这样的东西：

           [['2016-07-08 00:00:00', '2016-07-08 00:00:01',
           '2016-07-08 00:00:02', '2016-07-08 00:00:03',
           '2016-07-08 00:00:04', '2016-07-08 00:00:05',
           '2016-07-08 00:00:06', '2016-07-08 00:00:07',
           '2016-07-08 00:00:08', '2016-07-08 00:00:09',
           '2016-07-08 00:00:10', '2016-07-08 00:00:11',
           '2016-07-08 00:00:12', '2016-07-08 00:00:13',
           '2016-07-08 00:00:14', '2016-07-08 00:00:15',
           '2016-07-08 00:00:16', '2016-07-08 00:00:17',
           '2016-07-08 00:00:18', '2016-07-08 00:00:19',
           '2016-07-08 00:00:20', '2016-07-08 00:00:21',
           '2016-07-08 00:00:22', '2016-07-08 00:00:23',
           '2016-07-08 00:00:24', '2016-07-08 00:00:25',
           '2016-07-08 00:00:26', '2016-07-08 00:00:27',
           '2016-07-08 00:00:28', '2016-07-08 00:00:29',
           '2016-07-08 00:00:30', '2016-07-08 00:00:31',
           '2016-07-08 00:00:32', '2016-07-08 00:00:33',
           '2016-07-08 00:00:34', '2016-07-08 00:00:35',
           '2016-07-08 00:00:36', '2016-07-08 00:00:37',
           '2016-07-08 00:00:38', '2016-07-08 00:00:39',
           '2016-07-08 00:00:40', '2016-07-08 00:00:41',
           '2016-07-08 00:00:42', '2016-07-08 00:00:43',
           '2016-07-08 00:00:44', '2016-07-08 00:00:45',
           '2016-07-08 00:00:46', '2016-07-08 00:00:47',
           '2016-07-08 00:00:48', '2016-07-08 00:00:49',
           '2016-07-08 00:00:50', '2016-07-08 00:00:51',
           '2016-07-08 00:00:52', '2016-07-08 00:00:53',
           '2016-07-08 00:00:54', '2016-07-08 00:00:55',
           '2016-07-08 00:00:56', '2016-07-08 00:00:57',
           '2016-07-08 00:00:58', '2016-07-08 00:00:59'],

           ['2016-07-08 00:05:00', '2016-07-08 00:05:01',
           '2016-07-08 00:05:02', '2016-07-08 00:05:03',
           '2016-07-08 00:05:04', '2016-07-08 00:05:05',
           '2016-07-08 00:05:06', '2016-07-08 00:05:07',
           '2016-07-08 00:05:08', '2016-07-08 00:05:09',
           '2016-07-08 00:05:10', '2016-07-08 00:05:11',
           '2016-07-08 00:05:12', '2016-07-08 00:05:13',
           '2016-07-08 00:05:14', '2016-07-08 00:05:15',
           '2016-07-08 00:05:16', '2016-07-08 00:05:17',
           '2016-07-08 00:05:18', '2016-07-08 00:05:19',
           '2016-07-08 00:05:20', '2016-07-08 00:05:21',
           '2016-07-08 00:05:22', '2016-07-08 00:05:23',
           '2016-07-08 00:05:24', '2016-07-08 00:05:25',
           '2016-07-08 00:05:26', '2016-07-08 00:05:27',
           '2016-07-08 00:05:28', '2016-07-08 00:05:29',
           '2016-07-08 00:05:30', '2016-07-08 00:05:31',
           '2016-07-08 00:05:32', '2016-07-08 00:05:33',
           '2016-07-08 00:05:34', '2016-07-08 00:05:35',
           '2016-07-08 00:05:36', '2016-07-08 00:05:37',
           '2016-07-08 00:05:38', '2016-07-08 00:05:39',
           '2016-07-08 00:05:40', '2016-07-08 00:05:41',
           '2016-07-08 00:05:42', '2016-07-08 00:05:43',
           '2016-07-08 00:05:44', '2016-07-08 00:05:45',
           '2016-07-08 00:05:46', '2016-07-08 00:05:47',
           '2016-07-08 00:05:48', '2016-07-08 00:05:49',
           '2016-07-08 00:05:50', '2016-07-08 00:05:51',
           '2016-07-08 00:05:52', '2016-07-08 00:05:53',
           '2016-07-08 00:05:54', '2016-07-08 00:05:55',
           '2016-07-08 00:05:56', '2016-07-08 00:05:57',
           '2016-07-08 00:05:58', '2016-07-08 00:05:59'], etc]

有任何想法吗 ？

4

3 回答 3

9

我认为您可以使用DatetimeIndex.strftime：

我尝试删除一些代码（在示例中没有必要，也许在实际代码中很重要）

for time in pd.date_range('2016-07-08 00:00:00', '2016-07-08 23:59:00', freq='5Min'):    
    print (pd.date_range(time, freq='S', periods=60).strftime("%Y-%m-%d %H:%M:%S").tolist())

['2016-07-08 00:00:00', '2016-07-08 00:00:01', '2016-07-08 00:00:02', '2016-07-08 00:00:03', '2016-07-08 00:00:04', '2016-07-08 00:00:05', '2016-07-08 00:00:06', '2016-07-08 00:00:07', '2016-07-08 00:00:08', '2016-07-08 00:00:09', '2016-07-08 00:00:10', '2016-07-08 00:00:11', '2016-07-08 00:00:12', '2016-07-08 00:00:13', '2016-07-08 00:00:14', '2016-07-08 00:00:15', '2016-07-08 00:00:16', '2016-07-08 00:00:17', '2016-07-08 00:00:18', '2016-07-08 00:00:19', '2016-07-08 00:00:20', '2016-07-08 00:00:21', '2016-07-08 00:00:22', '2016-07-08 00:00:23', '2016-07-08 00:00:24', '2016-07-08 00:00:25', '2016-07-08 00:00:26', '2016-07-08 00:00:27', '2016-07-08 00:00:28', '2016-07-08 00:00:29', '2016-07-08 00:00:30', '2016-07-08 00:00:31', '2016-07-08 00:00:32', '2016-07-08 00:00:33', '2016-07-08 00:00:34', '2016-07-08 00:00:35', '2016-07-08 00:00:36', '2016-07-08 00:00:37', '2016-07-08 00:00:38', '2016-07-08 00:00:39', '2016-07-08 00:00:40', '2016-07-08 00:00:41', '2016-07-08 00:00:42', '2016-07-08 00:00:43', '2016-07-08 00:00:44', '2016-07-08 00:00:45', '2016-07-08 00:00:46', '2016-07-08 00:00:47', '2016-07-08 00:00:48', '2016-07-08 00:00:49', '2016-07-08 00:00:50', '2016-07-08 00:00:51', '2016-07-08 00:00:52', '2016-07-08 00:00:53', '2016-07-08 00:00:54', '2016-07-08 00:00:55', '2016-07-08 00:00:56', '2016-07-08 00:00:57', '2016-07-08 00:00:58', '2016-07-08 00:00:59']
['2016-07-08 00:05:00', '2016-07-08 00:05:01', '2016-07-08 00:05:02', '2016-07-08 00:05:03', '2016-07-08 00:05:04', '2016-07-08 00:05:05', '2016-07-08 00:05:06', '2016-07-08 00:05:07', '2016-07-08 00:05:08', '2016-07-08 00:05:09', '2016-07-08 00:05:10', '2016-07-08 00:05:11', '2016-07-08 00:05:12', '2016-07-08 00:05:13', '2016-07-08 00:05:14', '2016-07-08 00:05:15', '2016-07-08 00:05:16', '2016-07-08 00:05:17', '2016-07-08 00:05:18', '2016-07-08 00:05:19', '2016-07-08 00:05:20', '2016-07-08 00:05:21', '2016-07-08 00:05:22', '2016-07-08 00:05:23', '2016-07-08 00:05:24', '2016-07-08 00:05:25', '2016-07-08 00:05:26', '2016-07-08 00:05:27', '2016-07-08 00:05:28', '2016-07-08 00:05:29', '2016-07-08 00:05:30', '2016-07-08 00:05:31', '2016-07-08 00:05:32', '2016-07-08 00:05:33', '2016-07-08 00:05:34', '2016-07-08 00:05:35', '2016-07-08 00:05:36', '2016-07-08 00:05:37', '2016-07-08 00:05:38', '2016-07-08 00:05:39', '2016-07-08 00:05:40', '2016-07-08 00:05:41', '2016-07-08 00:05:42', '2016-07-08 00:05:43', '2016-07-08 00:05:44', '2016-07-08 00:05:45', '2016-07-08 00:05:46', '2016-07-08 00:05:47', '2016-07-08 00:05:48', '2016-07-08 00:05:49', '2016-07-08 00:05:50', '2016-07-08 00:05:51', '2016-07-08 00:05:52', '2016-07-08 00:05:53', '2016-07-08 00:05:54', '2016-07-08 00:05:55', '2016-07-08 00:05:56', '2016-07-08 00:05:57', '2016-07-08 00:05:58', '2016-07-08 00:05:59']
...
...

lists append如果需要在循环中作为嵌套数据输出到L：

import pandas as pd

L = []
for time in pd.date_range('2016-07-08 00:00:00', '2016-07-08 23:59:00', freq='5Min'):    
    print (pd.date_range(time, freq='S', periods=60).strftime("%Y-%m-%d %H:%M:%S").tolist())
    L.append(pd.date_range(time, freq='S', periods=60).strftime("%Y-%m-%d %H:%M:%S").tolist())

print (L)

[['2016-07-08 00:00:00', '2016-07-08 00:00:01', '2016-07-08 00:00:02', '2016-07-08 00:00:03', '2016-07-08 00:00:04', '2016-07-08 00:00:05', '2016-07-08 00:00:06', '2016-07-08 00:00:07', '2016-07-08 00:00:08', '2016-07-08 00:00:09', '2016-07-08 00:00:10', '2016-07-08 00:00:11', '2016-07-08 00:00:12', '2016-07-08 00:00:13', '2016-07-08 00:00:14', '2016-07-08 00:00:15', '2016-07-08 00:00:16', '2016-07-08 00:00:17', '2016-07-08 00:00:18', '2016-07-08 00:00:19', '2016-07-08 00:00:20', '2016-07-08 00:00:21', '2016-07-08 00:00:22', '2016-07-08 00:00:23', '2016-07-08 00:00:24', '2016-07-08 00:00:25', '2016-07-08 00:00:26', '2016-07-08 00:00:27', '2016-07-08 00:00:28', '2016-07-08 00:00:29', '2016-07-08 00:00:30', '2016-07-08 00:00:31', '2016-07-08 00:00:32', '2016-07-08 00:00:33', '2016-07-08 00:00:34', '2016-07-08 00:00:35', '2016-07-08 00:00:36', '2016-07-08 00:00:37', '2016-07-08 00:00:38', '2016-07-08 00:00:39', '2016-07-08 00:00:40', '2016-07-08 00:00:41', '2016-07-08 00:00:42', '2016-07-08 00:00:43', '2016-07-08 00:00:44', '2016-07-08 00:00:45', '2016-07-08 00:00:46', '2016-07-08 00:00:47', '2016-07-08 00:00:48', '2016-07-08 00:00:49', '2016-07-08 00:00:50', '2016-07-08 00:00:51', '2016-07-08 00:00:52', '2016-07-08 00:00:53', '2016-07-08 00:00:54', '2016-07-08 00:00:55', '2016-07-08 00:00:56', '2016-07-08 00:00:57', '2016-07-08 00:00:58', '2016-07-08 00:00:59'], ['2016-07-08 00:05:00', '2016-07-08 00:05:01', '2016-07-08 00:05:02', '2016-07-08 00:05:03', '2016-07-08 00:05:04', '2016-07-08 00:05:05', '2016-07-08 00:05:06', '2016-07-08 00:05:07', '2016-07-08 00:05:08', '2016-07-08 00:05:09', '2016-07-08 00:05:10', '2016-07-08 00:05:11', '2016-07-08 00:05:12', '2016-07-08 00:05:13', '2016-07-08 00:05:14', '2016-07-08 00:05:15', '2016-07-08 00:05:16', '2016-07-08 00:05:17', '2016-07-08 00:05:18', '2016-07-08 00:05:19', '2016-07-08 00:05:20', '2016-07-08 00:05:21', '2016-07-08 00:05:22', '2016-07-08 00:05:23', '2016-07-08 00:05:24', '2016-07-08 00:05:25', '2016-07-08 00:05:26', '2016-07-08 00:05:27', '2016-07-08 00:05:28', '2016-07-08 00:05:29', '2016-07-08 00:05:30', '2016-07-08 00:05:31', '2016-07-08 00:05:32', '2016-07-08 00:05:33', '2016-07-08 00:05:34', '2016-07-08 00:05:35', '2016-07-08 00:05:36', '2016-07-08 00:05:37', '2016-07-08 00:05:38', '2016-07-08 00:05:39', '2016-07-08 00:05:40', '2016-07-08 00:05:41', '2016-07-08 00:05:42', '2016-07-08 00:05:43', '2016-07-08 00:05:44', '2016-07-08 00:05:45', '2016-07-08 00:05:46', '2016-07-08 00:05:47', '2016-07-08...
于 2016-09-20T15:09:44.090 回答
5

five_minute_data = five_minute_date_range.strftime("%Y-%m-%d %H:%M:%S").tolist()确实以 OP 要求的方式返回数据。

于 2019-11-12T10:29:43.177 回答
0

如果您使用的是 Pandas，我认为将返回值作为数据框有助于进一步处理。

  1. 第一个创建一个包含时间间隔为 5 分钟的数据框。然后，为其中的每一个创建一个嵌套列表，间隔为 1 秒。

    df = pd.date_range('2016-07-08 00:00:00', '2016-07-08 00:15:00', freq='5Min').to_frame()
df = [pd.date_range(time, freq='S', periods=10).to_pydatetime for time in df[0]]

  2. 类似的返回 ndarray 而不是数据框。也是单行与列表理解。

    arr = [pd.date_range(five_minute, freq='S', periods=60).to_pydatetime
       for five_minute in pd.date_range('2016-07-08 00:00:00', '2016-07-08 00:15:00', freq='5Min')]
于 2018-09-23T02:19:31.137 回答