Python 如何获得数据帧之间的总时间重叠量？

假设我有两个数据帧：

import pandas as pd

df1 = pd.DataFrame(
    {
        "Start": {
            0: "2019-07-19 07:00:00",
            1: "2019-07-19 08:00:00",
            2: "2019-07-19 10:00:00",
        },
        "Finish": {
            0: "2019-07-19 07:30:00",
            1: "2019-07-19 08:30:00",
            2: "2019-07-19 10:30:00",
        },
    }
)

df2 = pd.DataFrame(
    {
        "Start": {0: "2019-07-19 07:30:00", 1: "2019-07-19 08:15:00",},
        "Finish": {0: "2019-07-19 08:00:00", 1: "2019-07-19 09:00:00",},
    }
)
df1.Start = pd.to_datetime(df1.Start)
df2.Finish = pd.to_datetime(df2.Finish)

from tabulate import tabulate

print(df1.pipe(tabulate, headers="keys", tablefmt="pipe"))
print(df2.pipe(tabulate, headers="keys", tablefmt="pipe"))

它们看起来像这样：

|    | Start               | Finish              |
|---:|:--------------------|:--------------------|
|  0 | 2019-07-19 07:00:00 | 2019-07-19 07:30:00 |
|  1 | 2019-07-19 08:00:00 | 2019-07-19 08:30:00 |
|  2 | 2019-07-19 10:00:00 | 2019-07-19 10:30:00 |

|    | Start               | Finish              |
|---:|:--------------------|:--------------------|
|  0 | 2019-07-19 07:30:00 | 2019-07-19 08:00:00 |
|  1 | 2019-07-19 08:15:00 | 2019-07-19 09:00:00 |

下面是如果我绘制它们的样子（在每行的

开始

和

结束

之间的部分着色）：

把它想象成

df1

在

TV1

打开时记录时间，而

df2

在

TV2

打开时记录时间。我想找出所有电视的总播放时间。在上面的图中，这用线

df1或df2

显示

补遗 下面是我如何制作情节的：

import plotly.figure_factory as ff

df3 = pd.DataFrame(
    {
        "Start": {0: "2019-07-19 07:00:00", 1: "2019-07-19 10:00:00",},
        "Finish": {0: "2019-07-19 09:00:00", 1: "2019-07-19 10:30:00",},
    }
)
df1['Resource'] = ['df1']*3
df2['Resource'] = ['df2']*2
df3['Resource'] = ['df1 or df2']*2
df1['Task'] = ['df1']*3
df2['Task'] = ['df2']*2
df3['Task'] = ['df1 or df2']*2

fig = ff.create_gantt(
    pd.concat([df1, df2, df3]).reset_index(drop=True),
    group_tasks=True,
    index_col="Resource",
)
fig.show()

这是我打印出来的数据框：

import pandas as pd

df1 = pd.DataFrame(
    {
        "Start": {
            0: "2019-07-19 07:00:00",
            1: "2019-07-19 08:00:00",
            2: "2019-07-19 10:00:00",
        },
        "Finish": {
            0: "2019-07-19 07:30:00",
            1: "2019-07-19 08:30:00",
            2: "2019-07-19 10:30:00",
        },
    }
)

df2 = pd.DataFrame(
    {
        "Start": {0: "2019-07-19 07:30:00", 1: "2019-07-19 08:15:00",},
        "Finish": {0: "2019-07-19 08:00:00", 1: "2019-07-19 09:00:00",},
    }
)
df1.Start = pd.to_datetime(df1.Start)
df2.Finish = pd.to_datetime(df2.Finish)

from tabulate import tabulate

print(df1.pipe(tabulate, headers="keys", tablefmt="pipe"))
print(df2.pipe(tabulate, headers="keys", tablefmt="pipe"))

---

注意，此处的输入基于原始问题

---

我不确定这是否可以很好地完成，因为您总是比较行，但有一种方法：

df1['start_time'] = pd.to_datetime(df1['start_time'])
df2['start_time'] = pd.to_datetime(df2['start_time'])
df1['end_time'] = pd.to_datetime(df1['end_time'])
df2['end_time'] = pd.to_datetime(df2['end_time'])

all_events = pd.concat((df1, df2)).sort_values('start_time')
result = all_events.iloc[0:1].copy()
for _, row in all_events.iterrows():
    if row['start_time'] <= result['end_time'].iloc[-1]:
        if row['end_time'] > result['end_time'].iloc[-1]:
            result['end_time'].iloc[-1] = row['end_time']
    else:
        result = result.append(row, ignore_index=True)

print(all_events)
print(result)

实际的三角洲是：

>>> print(result['end_time'] - result['start_time'])
0   00:02:13
1   00:01:08
2   00:02:35
3   00:01:01
4   00:01:52
5   00:06:59
6   00:02:02

---

事件

列在哪里？或者每一行代表一个事件？此外，分钟数的不同是否重要，或者您只需要小时数的差异？每一行代表一个事件。我将用秒来表示差异，所以整个时间戳都很重要谢谢！很抱歉更改了输入，这只是为了让我可以用

plotly

制作一个很好的绘图。感谢您的回答和解释-在这样的时刻，我感到尴尬的是，对问题的赞成票和对问题的反对票一样重要answers@ign不客气。不要尴尬，你会惊讶地发现好问题是多么困难。