如何在python中创建一个滚动的数据透视表到一个df?

时间:2018-03-20 20:14:55

标签: python pandas

我有一个df(样本最后粘贴在这里)。 我希望找到每分钟tradePrice tradeVolume最多的 minute tradePrice Data 0 1 10 Total Result 12548 Sum - tradeVolume 3 3 Count - tradePrice 2 2 12548.5 Sum - tradeVolume 1 1 Count - tradePrice 1 1 12549 Sum - tradeVolume 1 1 Count - tradePrice 1 1 12549.5 Sum - tradeVolume 95 95 Count - tradePrice 5 5 12550 Sum - tradeVolume 6 6 Count - tradePrice 4 4 12559 Sum - tradeVolume 93 93 Count - tradePrice 1 1 12559.5 Sum - tradeVolume 1 1 Count - tradePrice 1 1 12560 Sum - tradeVolume 5 5 Count - tradePrice 4 4 12560.5 Sum - tradeVolume 3 5 8 Count - tradePrice 3 2 5 12561 Sum - tradeVolume 4 5 9 Count - tradePrice 2 3 5 12561.5 Sum - tradeVolume 3 2 5 Count - tradePrice 3 1 4 12562 Sum - tradeVolume 9 7 16 Count - tradePrice 8 1 9 12562.5 Sum - tradeVolume 6 2 8 Count - tradePrice 2 2 4 12563 Sum - tradeVolume 2 2 Count - tradePrice 1 1 Total Sum - tradeVolume 120 27 106 253 Total Count - tradePrice 20 14 13 47 。这是一个发送到新df的数据透视表。 xls中的数据透视表如下所示:

    Price   Volume
02:00:00 AM 12559   93
02:01:00 AM 12562   7
02:10:00 AM 12549.5 95

结果需要像这样搜索交易量最大的价格:

    Price   Volume
02:10:00 AM 12549.5 95

我的问题是: 1.我如何将这个转轴变成1分钟df? 2.如何获得相同的结果,但每个滚动时间段(不能少于1分钟),所以最后10分钟的预期结果(以最大交易量交易的价格)是:

    dateTime    tradePrice  tradeVolume 1min    time_of_day_10  time_of_day_30  date    hour    minute
0   2017-09-19 02:00:04 12559   93  2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
49  2017-09-19 02:00:11 12562   1   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
50  2017-09-19 02:00:12 12563   2   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
51  2017-09-19 02:00:12 12562   1   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
122 2017-09-19 02:00:34 12561.5 1   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
123 2017-09-19 02:00:34 12562   1   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
127 2017-09-19 02:00:34 12562   1   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
129 2017-09-19 02:00:35 12561   2   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
130 2017-09-19 02:00:35 12560.5 1   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
131 2017-09-19 02:00:35 12561.5 1   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
135 2017-09-19 02:00:39 12562   1   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
136 2017-09-19 02:00:39 12562   1   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
137 2017-09-19 02:00:43 12561.5 1   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
138 2017-09-19 02:00:43 12561   2   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
139 2017-09-19 02:00:43 12560.5 1   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
140 2017-09-19 02:00:43 12560.5 1   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
152 2017-09-19 02:00:45 12562   2   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
153 2017-09-19 02:00:46 12562.5 4   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
166 2017-09-19 02:00:58 12562   1   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
167 2017-09-19 02:00:58 12562.5 2   2017-09-19 02:00:00 02:00:00    02:00:00    2017-09-19  2   0
168 2017-09-19 02:01:00 12562   7   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
169 2017-09-19 02:01:00 12562.5 1   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
170 2017-09-19 02:01:00 12562.5 1   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
171 2017-09-19 02:01:00 12561.5 2   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
175 2017-09-19 02:01:03 12561   1   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
176 2017-09-19 02:01:03 12561   3   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
187 2017-09-19 02:01:07 12560.5 2   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
188 2017-09-19 02:01:08 12561   1   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
189 2017-09-19 02:01:10 12560   1   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
190 2017-09-19 02:01:10 12560   1   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
191 2017-09-19 02:01:10 12559.5 1   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
192 2017-09-19 02:01:11 12560   1   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
193 2017-09-19 02:01:12 12560   2   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
194 2017-09-19 02:01:12 12560.5 3   2017-09-19 02:01:00 02:00:00    02:00:00    2017-09-19  2   1
593 2017-09-19 02:10:00 12550   1   2017-09-19 02:10:00 02:10:00    02:00:00    2017-09-19  2   10
594 2017-09-19 02:10:00 12549.5 12  2017-09-19 02:10:00 02:10:00    02:00:00    2017-09-19  2   10
604 2017-09-19 02:10:12 12548.5 1   2017-09-19 02:10:00 02:10:00    02:00:00    2017-09-19  2   10
605 2017-09-19 02:10:15 12549.5 22  2017-09-19 02:10:00 02:10:00    02:00:00    2017-09-19  2   10
606 2017-09-19 02:10:16 12549.5 21  2017-09-19 02:10:00 02:10:00    02:00:00    2017-09-19  2   10
636 2017-09-19 02:10:45 12548   1   2017-09-19 02:10:00 02:10:00    02:00:00    2017-09-19  2   10
637 2017-09-19 02:10:47 12548   2   2017-09-19 02:10:00 02:10:00    02:00:00    2017-09-19  2   10
638 2017-09-19 02:10:47 12549.5 23  2017-09-19 02:10:00 02:10:00    02:00:00    2017-09-19  2   10
639 2017-09-19 02:10:48 12549.5 17  2017-09-19 02:10:00 02:10:00    02:00:00    2017-09-19  2   10
640 2017-09-19 02:10:49 12549   1   2017-09-19 02:10:00 02:10:00    02:00:00    2017-09-19  2   10
665 2017-09-19 02:10:58 12550   1   2017-09-19 02:10:00 02:10:00    02:00:00    2017-09-19  2   10
666 2017-09-19 02:10:58 12550   1   2017-09-19 02:10:00 02:10:00    02:00:00    2017-09-19  2   10
667 2017-09-19 02:10:58 12550   3   2017-09-19 02:10:00 02:10:00    02:00:00    2017-09-19  2   10

样本数据:

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