使用 timedelta 的 TypeError，无法求和时间 [英] TypeError using timedelta, cannot sum times

问题描述

我有这样的数据:

    user                in               out location  flag     Time
0    ron  12/21/2021 10:11  12/21/2016 17:50     home     0  4:19:03
1    ron  12/21/2016 13:26  12/21/2016 13:52   office     2  0:25:28
2  april   12/21/2016 8:12  12/21/2016 17:27   office     0  8:15:03
3  april  12/21/2016 18:54  12/21/2016 22:56   office     0  4:02:36
4   andy   12/21/2016 8:57  12/21/2016 12:15     home     0  2:59:40

我想根据标志对每个用户的时间求和或取最大值.所以我将列转换为 timedelta.

I want to sum or take the max value of time per user based on the flag. So I converted the column to timedelta.

sample.loc[:,'Time'] = pd.to_timedelta(sample['Time'])

但是，当我尝试通过对整列求和来测试这一点时

However, when I try to test this by summing the entire column

sum(sample['Time'])

我收到以下错误:

TypeError: unsupported operand type(s) for +: 'int' and 'Timedelta'

我在这里错过了什么?我以为你可以用 Timedelta 求和.

What am I missing here? I thought you could sum with Timedelta.

推荐答案

Python 的 sum，默认情况下假定您正在对整数求和.因此它尝试从 0 开始求和，这就是这个错误的来源.不可能将 0 添加到 timedelta.

Python's sum, by default, assumes you are summing integers. Hence it tries to start summing from 0, which is where this error comes from. It's impossible to add 0 to a timedelta.

这可以通过两种方式修复:

This can be fixed in 2 ways:

• 为 sum 提供一个不同的起始值，可能是一个空的"timedelta，作为 sum:

• Provide a different starting value to sum, perhaps an "empty" timedelta, as the second argument for sum:

from datetime import timedelta
...
sum(sample['Time'], timedelta())

使用Series.sum(无论如何它可能会有更好的性能):

Use Series.sum (which will probably have better performance anyway):

sample['Time'].sum()

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