pandas :将Bin日期间隔30分钟并计算平均值 [英] Pandas: Bin dates into 30 minute intervals and calculate averages

问题描述

我有一个熊猫数据框，其中有两列，分别是speed和time.

I have a Pandas dataframe with two columns which are speed and time.

speed   date
54.72   1:33:56
49.37   1:33:59
37.03   1:34:03
24.02   7:39:58
28.02   7:40:01
24.04   7:40:04
24.02   7:40:07
25.35   7:40:10
26.69   7:40:13
32.04   7:40:16
28.02   11:05:43
30.71   11:05:46
29.36   11:05:49
18.68   11:05:52
54.72   11:05:55
34.69   10:31:34
25.03   10:31:38
56.04   10:31:40
44.03   10:31:43

我想计算每30分钟的垃圾箱平均速度.例如，第4个bin(1:31-2:00)期间的平均速度为(54.72 + 49.37 + 37.03)/3.我曾考虑过将小时，分钟和秒从00:00转换为秒，然后将其设为1800秒.我曾尝试从scipy.stats中使用binned_statistic，但是我的主要问题是我无法找到一种基于日期来分离垃圾箱并获取平均速度的方法.

I want to calculate the average of speeds per bins of 30 minutes. For example, the average speed during the 4th bin (1:31 - 2:00) is (54.72 + 49.37 + 37.03)/3. I have thought of converting hours, minutes and seconds to seconds from 00:00 and then have bins of 1800 seconds. I have tried to do use binned_statistic from scipy.stats but my main issue is that I cannot find a way to separate bins based on date and get the average of speeds.

有什么想法吗?

推荐答案

转换为日期时间并使用pandas.Grouper +

Converting to datetime and using pandas.Grouper + Offset Aliases:

df['date'] = pd.to_datetime(df.date)
df.groupby(pd.Grouper(key='date', freq='30min')).mean().dropna()

    speed
date    
2018-09-20 01:30:00     47.040000
2018-09-20 07:30:00     26.311429
2018-09-20 10:30:00     39.947500
2018-09-20 11:00:00     32.298000

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