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From DatetimeIndex to list of times

Tags:

python

list

datetime

pandas

datetimeindex

My objective is to have a lists of times (in seconds), already packaged in lists of times in 5 minutes for a whole day. This is my code to package the whole day of "2016-07-08" by 5 minutes :

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

The result :

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')

And this is the code to have all times (by second) included in every 5 minutes:

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)

The result :

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

This is perfect for me! I want now to have lists, not a pandas.tseries.index.DatetimeIndex.. The .tolist() method give this :

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()

The result :

[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]

I want to have something like this :

           [['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]

Any ideas ?

like image 496
Arij SEDIRI Avatar asked Sep 20 '16 15:09

Arij SEDIRI

People also ask

How do I convert DateTimeIndex to series?

To convert the DateTimeIndex to Series, use the DateTimeIndex. to_series() method.

How to convert DateTimeIndex to String?

You can convert or cast pandas DatetimeIndex to String by using pd. to_datetime() and DatetimeIndex. strftime() functions. DatetimeIndex class is an Immutable ndarray which is used to store datetime64 data (internally stores as int64).

What is a DateTimeIndex?

DatetimeIndex [source] Immutable ndarray of datetime64 data, represented internally as int64, and which can be boxed to Timestamp objects that are subclasses of datetime and carry metadata such as frequency information.

2 Answers

I think you can use DatetimeIndex.strftime:

I try remove some code (in sample is not necessary, maybe in real code is important)

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']
...
...

If need output as nested lists append data in loop to 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...
like image 188
jezrael Avatar answered Oct 28 '22 17:10

jezrael

five_minute_data = five_minute_date_range.strftime("%Y-%m-%d %H:%M:%S").tolist() does return data in the way OP required it to be.

like image 21
Nick Avatar answered Oct 28 '22 17:10

Nick
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