距重采样截止点至少 n 秒的最后一个值

Question

考虑以下 DF：

import pandas

mydata = pandas.DataFrame({'TRADE_PRICE': {pandas.Timestamp('2016-07-12 10:52:58.850899935'): 9.23,

  pandas.Timestamp('2016-07-12 10:55:13.832099915'): 9.23,

  pandas.Timestamp('2016-07-12 11:09:17.775099993'): 9.22,

  pandas.Timestamp('2016-07-12 11:09:25.811100006'): 9.22,

  pandas.Timestamp('2016-07-12 11:09:26.020699978'): 9.22,

  pandas.Timestamp('2016-07-12 11:09:27.408600092'): 9.22,

  pandas.Timestamp('2016-07-12 11:11:48.448199987'): 9.22,

  pandas.Timestamp('2016-07-12 11:11:58.801599979'): 9.21,

  pandas.Timestamp('2016-07-12 11:11:58.810499907'): 9.21,

  pandas.Timestamp('2016-07-12 11:11:59.049000025'): 9.21}})

现在，

mydata.resample('1Min',label = 'right', closed = 'right').last()

给我重采样期结束前的最后一笔交易。

我需要的是距离重采样期结束至少 5 秒的最后一笔交易。

基本上，我想要一种 last() ，它会忽略在重采样期结束前 5 秒内发生的所有交易。

所以我希望得到类似的东西：

                     TRADE_PRICE

2016-07-12 10:53:00          NaN

2016-07-12 10:54:00          NaN

2016-07-12 10:55:00          NaN

2016-07-12 10:56:00         9.23

2016-07-12 10:57:00          NaN

2016-07-12 10:58:00          NaN

2016-07-12 10:59:00          NaN

2016-07-12 11:00:00          NaN

2016-07-12 11:01:00          NaN

2016-07-12 11:02:00          NaN

2016-07-12 11:03:00          NaN

2016-07-12 11:04:00          NaN

2016-07-12 11:05:00          NaN

2016-07-12 11:06:00          NaN

2016-07-12 11:07:00          NaN

2016-07-12 11:08:00          NaN

2016-07-12 11:09:00          NaN

2016-07-12 11:10:00         9.22

2016-07-12 11:11:00          NaN

2016-07-12 11:12:00         9.22

这可能吗？

Answer 1

您可以先过滤掉任何超过 55 秒的记录，然后按原样继续。mydata_ = mydata[mydata.index.second <= 55]mydata_.resample('1Min',label = 'right', closed = 'right').last()返回：&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; TRADE_PRICE2016-07-12 10:56:00 9.232016-07-12 10:57:00 NaN2016-07-12 10:58:00 NaN2016-07-12 10:59:00 NaN2016-07-12 11:00:00 NaN2016-07-12 11:01:00 NaN2016-07-12 11:02:00 NaN2016-07-12 11:03:00 NaN2016-07-12 11:04:00 NaN2016-07-12 11:05:00 NaN2016-07-12 11:06:00 NaN2016-07-12 11:07:00 NaN2016-07-12 11:08:00 NaN2016-07-12 11:09:00 NaN2016-07-12 11:10:00 9.222016-07-12 11:11:00 NaN2016-07-12 11:12:00 9.22