Question

目标：查找GREEN和YELLOW状态之间的平均经过时间。首先，我需要删除所有不必要的行。要查找已用时间，我需要第一个GREEN实例，然后是第一个黄色实例，一遍又一遍地重复。以下是100,000多行的摘录。

在下面的示例中，我希望保留行 1,2,5,6,9,13,14,15,16,21

Row #   Serial Number   Time Stamp  Status <br>
1   1400004 3/10/14 11:52   GREEN <br>
2   1400004 3/15/14 11:45   YELLOW <br>
3   1400004 3/29/14 7:59    YELLOW <br>
4   1400004 4/16/14 15:59   YELLOW <br>
5   1400004 5/10/14 8:18    GREEN <br>
6   1400004 5/11/14 15:28   YELLOW <br>
7   1400004 5/23/14 14:10   YELLOW <br>
8   1400004 5/24/14 7:56    YELLOW <br>
9   1400004 5/26/14 7:59    GREEN <br>
10  1400004 5/28/14 8:26    GREEN <br>
11  1400004 5/30/14 7:28    GREEN <br>
12  1400004 6/1/14 16:56    GREEN <br>
13  1400004 6/13/14 17:29   YELLOW <br>
14  1400004 6/15/14 15:12   GREEN <br>
15  1400004 6/17/14 8:57    YELLOW <br>
16  1400007 1/3/14 11:55    GREEN <br>
17  1400007 1/4/14 15:31    GREEN <br>
18  1400007 1/15/14 14:44   GREEN <br>
19  1400007 1/17/14 5:37    GREEN <br>
20  1400007 1/18/14 5:35    GREEN <br>
21  1400007 1/18/14 18:32   YELLOW <br>
22  1400007 1/19/14 21:50   YELLOW <br>

Answer 1

以下内容可用于获取您要查找的行：

from itertools import groupby
from datetime import datetime, timedelta

with open('input.csv', 'rb') as f_input:
    csv_input = csv.reader(f_input)
    header = next(csv_input)

    for k, g in groupby(csv_input, lambda x: x[4]):
        first_in_group = next(g)
        print first_in_group[0]     # show first column entry

这会显示：

为了扩展这一点，我建议采用以下方法：

from itertools import groupby
from datetime import datetime, timedelta

with open('input.csv', 'rb') as f_input:
    csv_input = csv.reader(f_input)
    header = next(csv_input)

    for k1, g1 in groupby(csv_input, lambda x: x[1]):   # group by serial number
        last = None
        entries = []
        for k, g in groupby(g1, lambda x: x[4]):    # group by status
            first = next(g)
            start = datetime.strptime('{} {}'.format(first[2], first[3]), '%m/%d/%y %H:%M')

            if last:
                entries.append((first[0], k, start - last))
                print '{:4} {:7} {:>20}'.format(first[0], k, start - last)

            last = start

        average_seconds = sum((t[2] for t in entries), timedelta()).total_seconds() / float(len(entries))
        print "Entries: {} Average mins: {}".format(len(entries), average_seconds / 60)
        print

这将显示给定数据的以下输出：

2    YELLOW      4 days, 23:53:00
5    GREEN      55 days, 20:33:00
6    YELLOW        1 day, 7:10:00
9    GREEN      14 days, 16:31:00
13   YELLOW      18 days, 9:30:00
14   GREEN        1 day, 21:43:00
15   YELLOW       1 day, 17:45:00
Entries: 7 Average mins: 20340.7142857

21   YELLOW      15 days, 6:37:00
Entries: 1 Average mins: 21997.0

一个问题是您的时间戳会为每个新的序列号重置，因此如果您计算差异，您将获得非常负的时间。此外，目前尚不清楚您的日期和时间是一列还是两列？此脚本假定有两列，例如

Row,#,Serial,Number,Time,Stamp,Status
1,1400004,3/10/14,11:52,GREEN

使用Python删除CSV文件中的某些行

1 个答案: