计数是否:作业处于特定时间间隔内 [英] Count if: job is in a certain time interval

问题描述

我有一个包含三列的数据框df1:

I have a dataframe df1 that contains three columns:

No.     Start Time          End Time
1       07/28/15 08:03 AM   07/28/15 08:09 AM
2       07/28/15 08:06 AM   07/28/15 08:12 AM

开始和结束时间代表特定作业的开始和结束时间. 我想构建一个新的数据框，该数据框统计特定日期特定时间的活动作业数.像这样:

The start and end time represents the start and endtime of a certain job. I want to construct a new dataframe that counts the number of active jobs at a certain time at a specific day. Like this:

Hours   Number of tasks
0:00    
0:01    
..  
..  
11:59   

此数据框应在一天中的每一分钟显示活动的作业数.从8:03开始到8:09结束的工作应计入以下时间:(因为它在8:09结束并且在8:09不再活动)

This dataframe should display for every minute of the day how many jobs are active. A job that starts at 8:03 and ends at 8:09 should be counted for the following times: (Because it ends at 8:09 and is not active anymore at 8:09)

8:03
8:04
8:05
8:06
8:07
8:08

我应该如何以一种简单的方式做到这一点?

How should I do this in a simple way?

推荐答案

不是熊猫解决方案，但您可以循环和过滤.
基于小时的快速示例:

Not a pandas solution, but you could loop and filter.
Quick example base on the hour:

import datetime

jobs = [
    (datetime.datetime(15, 7, 28, 8, 3), datetime.datetime(15, 7, 28, 8, 9)),
    (datetime.datetime(15, 7, 28, 8, 3), datetime.datetime(15, 7, 28, 8, 58)),
    (datetime.datetime(15, 7, 28, 8, 3), datetime.datetime(15, 7, 28, 10, 3)),
    (datetime.datetime(15, 7, 28, 8, 3), datetime.datetime(15, 7, 28, 9, 3)),
    (datetime.datetime(15, 7, 28, 10, 3), datetime.datetime(15, 7, 28, 8, 3)),
]
data = {'hours': [], 'active_jobs': []}
for hour in range(24):
    current__active_jobs = 0
    for job in jobs:
        if job[0].hour == hour:
            current__active_jobs += 1
        elif job[0].hour < hour and job[1].hour >= hour:
            current__active_jobs += 1
    data['hour'].append(hour)
    data['active_jobs'].append(current__active_jobs)

print DataFrame(data)

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