在Python-Pandas中，如何按特定的日期时间索引值对数据框进行子集化? [英] In Python-Pandas, How can I subset a dataframe by specific datetime index values?

问题描述

我有很多这样的数据框....连续30分钟的行:

I have a dataframe of many days that look like this....consecutive rows of 30 min intervals:

                      a   b
2006-05-08 09:30:00  10  13
2006-05-08 10:00:00  11  12
                          .
                          .
                          .
2006-05-08 15:30:00  15  14
2006-05-08 16:00:00  16  15

但是，我只关心某些特定时间，所以我希望df的每一天都像这样:

However, I only care about certain specific times, so I want EVERY DAY of the df to look like:

2006-05-08 09:30:00  10  13
2006-05-08 11:30:00  14  15
2006-05-08 13:00:00  18  15
2006-05-08 16:00:00  16  15

意思是，我只想保留数据帧中所有不同日期的行(16、13、11:30、9:30).

Meaning, I just want to keep the rows at times (16, 13, 11:30, 9:30), for all the different days in the dataframe.

谢谢

更新:

我在使用方面取得了一些进步

I made a bit of progress, using

hour = df.index.hour
selector = ((hour == 16) | (hour == 13) | (hour == 11) | (hour == 9))
df = df[selector]

但是，我也需要考虑分钟数，所以我尝试了:

However, I need to account for the minutes too, so I tried:

minute = df.index.minute
selector = ((hour == 16) & (minute == 0) | (hour == 3) & (minute == 0) | (hour == 9) & (minute == 30) | (hour == 12) & (minute == 0))

但是我得到了错误:

ValueError: operands could not be broadcast together with shapes (96310,) (16500,) 

推荐答案

import numpy as np
import pandas as pd
N = 100
df = pd.DataFrame(range(N), index=pd.date_range('2000-1-1', freq='30T', 
                                                periods=N))
mask = np.in1d((df.index.hour)*100+(df.index.minute), [930, 1130, 1300, 1600])
print(df.loc[mask])

收益

                      0
2000-01-01 09:30:00  19
2000-01-01 11:30:00  23
2000-01-01 13:00:00  26
2000-01-01 16:00:00  32
2000-01-02 09:30:00  67
2000-01-02 11:30:00  71
2000-01-02 13:00:00  74
2000-01-02 16:00:00  80

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