python - 将 1 分钟间隔内的最后一个值分配给 pandas DataFrame 的行

Question

我有一个这样的DataFrame

    time                    val

    2019-10-01 04:00:00.000 568830.000
    2019-10-01 17:47:29.794 37612.000
    2019-10-01 19:41:03.697 37.000
    2019-10-01 19:41:03.698 37612.000
    2019-10-02 00:16:07.224 376.000
    2019-10-02 00:16:07.225 37612.000
    2019-10-02 01:19:05.135 3761.000
    2019-10-02 01:19:05.136 37612.000
    2019-10-02 08:26:27.623 36612.000
    2019-10-02 08:26:27.624 36493.000
    2019-10-03 07:08:54.455 364.000
    2019-10-03 07:08:54.456 36493.000

我想创建一个包含 X 分钟间隔内的last 值的新列，例如val 列 1 分钟。

预期输出:

    time                    val        result

    2019-10-01 04:00:00.000 568830.000 568830.000
    2019-10-01 17:47:29.794 37612.000  37612.000
    2019-10-01 19:41:03.697 37.000     37612.000
    2019-10-01 19:41:03.698 37612.000  37612.000
    2019-10-02 00:16:07.224 376.000    37612.000
    2019-10-02 00:16:07.225 37612.000  37612.000
    2019-10-02 01:19:05.135 3761.000   37612.000
    2019-10-02 01:19:05.136 37612.000  37612.000
    2019-10-02 08:26:27.623 36612.000  36493.000 
    2019-10-02 08:26:27.624 36493.000  36493.000
    2019-10-03 07:08:54.455 364.000    36493.000
    2019-10-03 07:08:54.456 36493.000  36493.000

Answer 1

将 DatetimeIndex DataFrame.resample 与 Resampler.transform 和 last 一起使用:

df['result'] = df.resample('T')['val'].transform('last')
print (df)
                              val    result
time                                       
2019-10-01 04:00:00.000  568830.0  568830.0
2019-10-01 17:47:29.794   37612.0   37612.0
2019-10-01 19:41:03.697      37.0   37612.0
2019-10-01 19:41:03.698   37612.0   37612.0
2019-10-02 00:16:07.224     376.0   37612.0
2019-10-02 00:16:07.225   37612.0   37612.0
2019-10-02 01:19:05.135    3761.0   37612.0
2019-10-02 01:19:05.136   37612.0   37612.0
2019-10-02 08:26:27.623   36612.0   36493.0
2019-10-02 08:26:27.624   36493.0   36493.0
2019-10-03 07:08:54.455     364.0   36493.0
2019-10-03 07:08:54.456   36493.0   36493.0

并为 time 列添加参数 on:

df['result'] = df.resample('T', on='time')['val'].transform('last')
print (df)
                      time       val    result
0  2019-10-01 04:00:00.000  568830.0  568830.0
1  2019-10-01 17:47:29.794   37612.0   37612.0
2  2019-10-01 19:41:03.697      37.0   37612.0
3  2019-10-01 19:41:03.698   37612.0   37612.0
4  2019-10-02 00:16:07.224     376.0   37612.0
5  2019-10-02 00:16:07.225   37612.0   37612.0
6  2019-10-02 01:19:05.135    3761.0   37612.0
7  2019-10-02 01:19:05.136   37612.0   37612.0
8  2019-10-02 08:26:27.623   36612.0   36493.0
9  2019-10-02 08:26:27.624   36493.0   36493.0
10 2019-10-03 07:08:54.455     364.0   36493.0
11 2019-10-03 07:08:54.456   36493.0   36493.0