创建每个 n 个元素的相同时间戳。

huangapple
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英文:

Create identical timestamp for each n elements

问题

I have a function which creates an artificial list of 100,000 timestamps going back in time. The frequency is 2 minutes.

datelist = pd.date_range(end = pd.datetime.today(), periods=100000, freq='2min00S').tolist()

The result looks like:

  1. [Timestamp('2018-12-03 19:48:35.874707', freq='2T'),
  2.  Timestamp('2018-12-03 19:50:35.874707', freq='2T'),
  3.  Timestamp('2018-12-03 19:52:35.874707', freq='2T'),
  4.  Timestamp('2018-12-03 19:54:35.874707', freq='2T'),
  5.  Timestamp('2018-12-03 19:56:35.874707', freq='2T'),
  6.  Timestamp('2018-12-03 19:58:35.874707', freq='2T'),
  7.  Timestamp('2018-12-03 20:00:35.874707', freq='2T'),
  8.  Timestamp('2018-12-03 20:02:35.874707', freq='2T'),
  9.  Timestamp('2018-12-03 20:04:35.874707', freq='2T'),
  10.  Timestamp('2018-12-03 20:06:35.874707', freq='2T'),
  11. ...]
  13. I would like to create an identical timestamp for each 50 elements.
  14. At the moment I have a different timestamp for each 100,000 elements. Any idea to do that?
  16. In other words: The frequency of 2 minutes remains the same but the timestamp will be identical for each 50 elements.
  18. This final list will be integrated as a new column into a pandas dataframe.
  20. ```data_pd['Timestamp'] = datelist```
  23. <details>
  24. <summary>英文:</summary>
  26. I have a function which creates an artificial list of 100.000 timestamps going back in time. The frequency is 2 minutes. 
  28. ```datelist = pd.date_range(end = pd.datetime.today(), periods=100000, freq=&#39;2min00S&#39;).tolist()```
  30. The result looks like:

[Timestamp('2018-12-03 19:48:35.874707', freq='2T'),
Timestamp('2018-12-03 19:50:35.874707', freq='2T'),
Timestamp('2018-12-03 19:52:35.874707', freq='2T'),
Timestamp('2018-12-03 19:54:35.874707', freq='2T'),
Timestamp('2018-12-03 19:56:35.874707', freq='2T'),
Timestamp('2018-12-03 19:58:35.874707', freq='2T'),
Timestamp('2018-12-03 20:00:35.874707', freq='2T'),
Timestamp('2018-12-03 20:02:35.874707', freq='2T'),
Timestamp('2018-12-03 20:04:35.874707', freq='2T'),
Timestamp('2018-12-03 20:06:35.874707', freq='2T'),
...]

  2. I would like to create an identical timestamp for each 50 elements. 
  3. At the moment I have a different timestamp for each 100.000 elements. Any idea to do that?
  5. In other words: The frequency of 2 minutes remains the same but the timestamp will be identical for each 50 elements. 
  8. This final list will be integrated as new column into a pandas dataframe. 
  10. ```data_pd[&#39;Timestamp&#39;] = datelist```
  14. </details>
  17. # 答案1
  18. **得分**: 0
  20. 我相信你需要在删除 `tolist()` 后使用整数除法 `50` 通过 `numpy.arange` 的数组对 `DatetimeIndex` 进行索引：
  22. ```python
  23. dates = pd.date_range(end=pd.datetime.today(), periods=100000, freq='2min00S')
  24. data_pd['Timestamp'] = dates[np.arange(len(data_pd)) // 50]

示例：（每5个值）

  1. dates = pd.date_range(end=pd.datetime.today(), periods=100000, freq='2min00S')
  2. data_pd = pd.DataFrame({'a': range(10)})
  3. data_pd['Timestamp'] = dates[np.arange(len(data_pd)) // 5]
  5. print(data_pd)
  6.    a                  Timestamp
  7. 0  0 2019-08-17 13:20:41.002125
  8. 1  1 2019-08-17 13:20:41.002125
  9. 2  2 2019-08-17 13:20:41.002125
  10. 3  3 2019-08-17 13:20:41.002125
  11. 4  4 2019-08-17 13:20:41.002125
  12. 5  5 2019-08-17 13:22:41.002125
  13. 6  6 2019-08-17 13:22:41.002125
  14. 7  7 2019-08-17 13:22:41.002125
  15. 8  8 2019-08-17 13:22:41.002125
  16. 9  9 2019-08-17 13:22:41.002125
英文:

I believe you need indexing DatetimeIndex after removed tolist() by array with integer division of 50 by numpy.arange by lenght of DataFrame:

  1. dates = pd.date_range(end = pd.datetime.today(), periods=100000, freq=&#39;2min00S&#39;)
  2. data_pd[&#39;Timestamp&#39;] = dates[np.arange(len(data_pd)) // 50]

Sample: (each 5 values)

  1. dates = pd.date_range(end = pd.datetime.today(), periods=100000, freq=&#39;2min00S&#39;)
  2. data_pd = pd.DataFrame({&#39;a&#39;:range(10)})
  3. data_pd[&#39;Timestamp&#39;] = dates[np.arange(len(data_pd)) // 5]
  5. print (data_pd)
  6.    a                  Timestamp
  7. 0  0 2019-08-17 13:20:41.002125
  8. 1  1 2019-08-17 13:20:41.002125
  9. 2  2 2019-08-17 13:20:41.002125
  10. 3  3 2019-08-17 13:20:41.002125
  11. 4  4 2019-08-17 13:20:41.002125
  12. 5  5 2019-08-17 13:22:41.002125
  13. 6  6 2019-08-17 13:22:41.002125
  14. 7  7 2019-08-17 13:22:41.002125
  15. 8  8 2019-08-17 13:22:41.002125
  16. 9  9 2019-08-17 13:22:41.002125

答案2

得分: 0

end_time = pd.datetime.today()
end_date = end_time.date()
datelist = pd.date_range(end=end_date, periods=100000, freq='2min00S').tolist()

将end_time转换为日期，而不是使用带有小数秒的时间。这将始终给您相同的时间

[Timestamp('2019-08-17 02:42:00', freq='2T'),
Timestamp('2019-08-17 02:44:00', freq='2T'),
Timestamp('2019-08-17 02:46:00', freq='2T'),
Timestamp('2019-08-17 02:48:00', freq='2T'),
Timestamp('2019-08-17 02:50:00', freq='2T'),
Timestamp('2019-08-17 02:52:00', freq='2T'),
Timestamp('2019-08-17 02:54:00', freq='2T'),
Timestamp('2019-08-17 02:56:00', freq='2T'),
Timestamp('2019-08-17 02:58:00', freq='2T'),
Timestamp('2019-08-17 03:00:00', freq='2T'),
Timestamp('2019-08-17 03:02:00', freq='2T'),
Timestamp('2019-08-17 03:04:00', freq='2T'),
Timestamp('2019-08-17 03:06:00', freq='2T'),

英文: 
  1. end_time = pd.datetime.today()
  2. end_date = end_time.date()
  3. datelist = pd.date_range(end = end_date, periods=100000, freq=&#39;2min00S&#39;).tolist()

convert the end_time to date instead of using a time with decimal seconds. This will always gives you the same time

  1. [Timestamp(&#39;2019-08-17 02:42:00&#39;, freq=&#39;2T&#39;),
  2.  Timestamp(&#39;2019-08-17 02:44:00&#39;, freq=&#39;2T&#39;),
  3.  Timestamp(&#39;2019-08-17 02:46:00&#39;, freq=&#39;2T&#39;),
  4.  Timestamp(&#39;2019-08-17 02:48:00&#39;, freq=&#39;2T&#39;),
  5.  Timestamp(&#39;2019-08-17 02:50:00&#39;, freq=&#39;2T&#39;),
  6.  Timestamp(&#39;2019-08-17 02:52:00&#39;, freq=&#39;2T&#39;),
  7.  Timestamp(&#39;2019-08-17 02:54:00&#39;, freq=&#39;2T&#39;),
  8.  Timestamp(&#39;2019-08-17 02:56:00&#39;, freq=&#39;2T&#39;),
  9.  Timestamp(&#39;2019-08-17 02:58:00&#39;, freq=&#39;2T&#39;),
  10.  Timestamp(&#39;2019-08-17 03:00:00&#39;, freq=&#39;2T&#39;),
  11.  Timestamp(&#39;2019-08-17 03:02:00&#39;, freq=&#39;2T&#39;),
  12.  Timestamp(&#39;2019-08-17 03:04:00&#39;, freq=&#39;2T&#39;),
  13.  Timestamp(&#39;2019-08-17 03:06:00&#39;, freq=&#39;2T&#39;),

huangapple
  • 本文由 发表于 2020年1月3日 17:27:41
  • 转载请务必保留本文链接：https://go.coder-hub.com/59575998.html
  • list
  • numpy
  • pandas
  • python
  • timestamp
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