[英]timedelta64 and datetime conversion
I have two datetime (Timestamp) formatted columns in my dataframe, df['start'], df['end']
. 我的数据帧中有两个datetime(时间戳)格式的列
df['start'], df['end']
。 I'd like to get the duration between the two dates. 我想获取两个日期之间的持续时间。 So I create the duration column
所以我创建了工期列
df['duration'] = df['start'] - df['end']
However, now the duration
column is formatted as numpy.timedelta64
, instead of datetime.timedelta
as I would expect. 但是,现在
duration
列的格式设置为numpy.timedelta64
,而不是我期望的datetime.timedelta
。
>>> df['duration'][0]
>>> numpy.timedelta64(0,'ns')
While 而
>>> df['start'][0] - df['end'][0]
>>> datetime.timedelta(0)
Can someone explain to me why the array subtraction change the timedelta
type? 有人可以向我解释为什么数组减法会更改
timedelta
类型吗? Is there a way that I keep the datetime.timedelta
as it is easier to work with? 有没有一种方法可以保留
datetime.timedelta
因为它更易于使用?
This was one of the motivations for implementing a Timedelta scalar in pandas 0.15.0. 这是在熊猫0.15.0中实现Timedelta标量的动机之一。 See full docs here
在这里查看完整的文档
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). 在> =
timedelta64[ns]
系列的实现仍然是np.timedelta64[ns]
,但在datetime.timedelta
子类化标量Timedelta
(基本上是timedelta和numpy版本的有用超集)。
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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