如何按定义的时间间隔分组一个 pandas 数据框？ [英] How to group a pandas dataframe by a defined time interval?

问题描述

 我有一个这样的数据框，我想每60分钟分组一次，并在06:30开始分组。数据
索引
 2017-02-14 06:29:57 11198648 
 2017-02-14 06:30:01 11198650 
 2017-02-14 06:37:22 11198706 
 2017-02-14 23:11:13 11207728 
 2017-02-14 23:21:43 11207774 
 2017-02-14 23:22:36 11207776 
  

我正在使用：



  df.groupby（pd.TimeGrouper（freq ='60Min'））
  

分组：



 数据
索引
 2017-02-14 06:00:00 x1 
 2017-02-14 07:00:00 x2 
 2017-02-14 08:00:00 x3 
 2017-02-14 09:00:00 x4 
 2017-02- 14 10:00:00 x5 
  

但我正在寻找这个结果：

 数据
索引
 2017-02-14 06:30:00 x1 
 2017-02-14 07： 30:00 x 2 
 2017-02-14 08:30:00 x3 
 2017-02-14 09:30:00 x4 
 2017-02-14 10:30:00 x5 
  

如何告诉函数在6:30以1小时为间隔开始分组？

如果 .groupby（pd.TimeGrouper（freq ='60Min'））无法完成，那么最好的方法是怎么做的？ p>

敬礼并且非常感谢您提前致谢

解决方案

$ c> base = 30 连同 label ='right'参数 pd.Grouper 。

指定 label ='right'使时间段从6:30开始分组（高端）而不是5:30。
另外， base 设置为0 默认情况下为，因此需要将这些值抵消30以考虑日期的向前传播。

假设你想聚合每个子组的第一个元素，那么：

$ $ $ $ $ $ c $ df.groupby（pd.TimeGrouper（freq ='60Min'，base = 30，label ='right'））。first（）
＃使用resample的同一事物 - df.resample（'60Min'，base = 30，label ='right'）。 （）


产生：

 数据
索引
 2017-02-14 06:30:00 11198648.0 
 2017-02-14 07:30:00 11198650.0 
 2017-02 -14 08:30:00 NaN 
 2017-02-14 09:30:00 NaN 
 2017-02-14 10:30:00 NaN 
 2017-02-14 11:30 ：00 NaN 
 2017-02-14 12:30:00 NaN 
 2017-02-14 13:30:00 NaN 
 2017-02-1 4 14:30:00 NaN 
 2017-02-14 15:30:00 NaN 
 2017-02-14 16:30:00 NaN 
 2017-02-14 17:30： 00 NaN 
 2017-02-14 18:30:00 NaN 
 2017-02-14 19:30:00 NaN 
 2017-02-14 20:30:00 NaN 
 2017-02-14 21:30:00 NaN 
 2017-02-14 22:30:00 NaN 
 2017-02-14 23:30:00 11207728.0 
  

I have a dataFrame like this, I would like to group every 60 minutes and start grouping at 06:30.

                           data
index
2017-02-14 06:29:57    11198648
2017-02-14 06:30:01    11198650
2017-02-14 06:37:22    11198706
2017-02-14 23:11:13    11207728
2017-02-14 23:21:43    11207774
2017-02-14 23:22:36    11207776

I am using:

df.groupby(pd.TimeGrouper(freq='60Min'))

I get this grouping:

                      data
index       
2017-02-14 06:00:00     x1
2017-02-14 07:00:00     x2
2017-02-14 08:00:00     x3
2017-02-14 09:00:00     x4
2017-02-14 10:00:00     x5

but I am looking for this result:

                      data
index       
2017-02-14 06:30:00     x1
2017-02-14 07:30:00     x2
2017-02-14 08:30:00     x3
2017-02-14 09:30:00     x4
2017-02-14 10:30:00     x5

How can I tell the function to start grouping at 6:30 at one-hour intervals?

If it can not be done by the .groupby(pd.TimeGrouper(freq='60Min')), how is the best way to do it?

A salute and thanks very much in advance

解决方案

Use base=30 in conjunction with label='right' parameters in pd.Grouper.

Specifying label='right' makes the time-period to start grouping from 6:30 (higher side) and not 5:30. Also, base is set to 0 by default, hence the need to offset those by 30 to account for the forward propagation of dates.

Suppose, you want to aggregate the first element of every sub-group, then:

df.groupby(pd.TimeGrouper(freq='60Min', base=30, label='right')).first()
# same thing using resample - df.resample('60Min', base=30, label='right').first()

yields:

                           data
index                          
2017-02-14 06:30:00  11198648.0
2017-02-14 07:30:00  11198650.0
2017-02-14 08:30:00         NaN
2017-02-14 09:30:00         NaN
2017-02-14 10:30:00         NaN
2017-02-14 11:30:00         NaN
2017-02-14 12:30:00         NaN
2017-02-14 13:30:00         NaN
2017-02-14 14:30:00         NaN
2017-02-14 15:30:00         NaN
2017-02-14 16:30:00         NaN
2017-02-14 17:30:00         NaN
2017-02-14 18:30:00         NaN
2017-02-14 19:30:00         NaN
2017-02-14 20:30:00         NaN
2017-02-14 21:30:00         NaN
2017-02-14 22:30:00         NaN
2017-02-14 23:30:00  11207728.0

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