pandas 如何按定义的时间间隔对熊猫数据框进行分组？
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How to group a pandas dataframe by a defined time interval?
提问by EduardoRL
I have a dataFrame like this, I would like to group every 60 minutes and start grouping at 06:30.
我有一个这样的数据帧，我想每 60 分钟分组一次，并在 06:30 开始分组。
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:
我在用：
df.groupby(pd.TimeGrouper(freq='60Min'))
I get this grouping:
我得到这个分组：
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:
但我正在寻找这个结果：
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?
我怎样才能告诉函数在 6:30 开始以一小时为间隔进行分组？
If it can not be done by the .groupby(pd.TimeGrouper(freq='60Min')), how is the best way to do it?
如果.groupby(pd.TimeGrouper(freq='60Min'))无法完成，最好的方法是什么？
A salute and thanks very much in advance
提前致以敬意和感谢
回答by Nickil Maveli
Use base=30in conjunction with label='right'parameters in pd.Grouper.
使用base=30会同label='right'中的参数pd.Grouper。
Specifying label='right'makes the time-period to start grouping from 6:30 (higher side) and not 5:30.
Also, baseis set to 0 by default, hence the need to offset those by 30 to account for the forward propagation of dates.
指定label='right'使时间段从 6:30（较高侧）而不是 5:30 开始分组。此外，默认base设置为 0 ，因此需要将这些偏移 30 以考虑日期的前向传播。
Suppose, you want to aggregate the first element of every sub-group, then:
假设，您要聚合每个子组的第一个元素，然后：
df.groupby(pd.Grouper(freq='60Min', base=30, label='right')).first()
# same thing using resample - df.resample('60Min', base=30, label='right').first()
yields:
产量：
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
回答by Erfan
Using DataFrame.resamplewhich is a dedicated method for resampling time series, this way we dont need DataFrame.GroupByand pd.Grouper:
使用DataFrame.resamplewhich 是重新采样时间序列的专用方法，这样我们就不需要DataFrame.GroupBy和pd.Grouper：
df.resample('60min', base=30, label='right').first()
Output
输出
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
Notice: when you have multiple columns in your dataframe, you have to specify the column you want to aggregate on:
注意：当您的数据框中有多列时，您必须指定要聚合的列：
df.resample('60min', base=30, label='right')['data'].first()
