timedelta64和日期时间转换 [英] timedelta64 and datetime conversion

问题描述

我的数据框中有两个日期时间(时间戳)格式的列，分别是 df ['start']，df ['end'] .我想得到两个日期之间的持续时间.因此，我创建了工期列

I have two datetime (Timestamp) formatted columns in my dataframe, df['start'], df['end']. I'd like to get the duration between the two dates. So I create the duration column

df['duration'] = df['start'] - df['end']

但是，现在 duration 列的格式设置为 numpy.timedelta64 ，而不是我期望的 datetime.timedelta .

However, now the duration column is formatted as numpy.timedelta64, instead of datetime.timedelta as I would expect.

>>> df['duration'][0]
>>> numpy.timedelta64(0,'ns')

而

>>> df['start'][0] - df['end'][0]
>>> datetime.timedelta(0)

有人可以向我解释为什么数组减法会更改 timedelta 类型吗?有没有一种方法可以保存 datetime.timedelta ，因为它更易于使用?

Can someone explain to me why the array subtraction change the timedelta type? Is there a way that I keep the datetime.timedelta as it is easier to work with?

推荐答案

这是在熊猫0.15.0中实现Timedelta标量的动机之一.在此处

This was one of the motivations for implementing a Timedelta scalar in pandas 0.15.0. See full docs here

在> = 0.15.0中， timedelta64 [ns] 系列的实现仍然是 np.timedelta64 [ns] 的内幕，但是所有内容都完全隐藏了起来.用户使用 datetime.timedelta 子类标量 Timedelta (基本上是timedelta和numpy版本的有用超集).

In >= 0.15.0 the implementation of a timedelta64[ns] Series is still np.timedelta64[ns] under the hood, but all is completely hidden from the user in a datetime.timedelta sub-classed scalar, Timedelta (which is basically a useful superset of timedelta and the numpy version).

In [1]: df = DataFrame([[pd.Timestamp('20130102'),pd.Timestamp('20130101')]],columns=list('AB'))

In [2]: df['diff'] = df['A']-df['B']

In [3]: df.dtypes
Out[3]: 
A        datetime64[ns]
B        datetime64[ns]
diff    timedelta64[ns]
dtype: object

# this will return a Timedelta in 0.15.2
In [4]: df['A'][0]-df['B'][0]
Out[4]: datetime.timedelta(1)

In [5]: (df['A']-df['B'])[0] 
Out[5]: Timedelta('1 days 00:00:00')

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