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python - 如何避免将 np.datetime64 添加到 numpy 数组时自动转换为日期时间？

问题描述

对于以下示例，具有 dtype 的元素在添加到另一个 numpy 数组时np.datetime64会自动转换为。datetime.datetime

如何避免这种自动转换？

import numpy as np
a = np.array([['2018-04-01T15:30:00'],
       ['2018-04-01T15:31:00'],
       ['2018-04-01T15:32:00'],
       ['2018-04-01T15:33:00'],
       ['2018-04-01T15:34:00']], dtype='datetime64[s]')
c = np.array([0,1,2,3,4]).reshape(-1,1)
c = c.astype("object")
d = np.append(c,a,axis=1)
d

.

array([[0, datetime.datetime(2018, 4, 1, 15, 30)],
       [1, datetime.datetime(2018, 4, 1, 15, 31)],
       [2, datetime.datetime(2018, 4, 1, 15, 32)],
       [3, datetime.datetime(2018, 4, 1, 15, 33)],
       [4, datetime.datetime(2018, 4, 1, 15, 34)]], dtype=object)

标签： pythonnumpy

解决方案

有时我们必须制作一个“空白”对象数组，并逐个填充它。

In [57]: d = np.empty((5,2), object)
In [58]: d
Out[58]: 
array([[None, None],
       [None, None],
       [None, None],
       [None, None],
       [None, None]], dtype=object)

我们可以按列填充它，但结果与concatenate（不要使用np.append）一样：

In [59]: d[:,0] = c.ravel()
In [60]: d[:,1] = a.ravel()
In [61]: d
Out[61]: 
array([[0, datetime.datetime(2018, 4, 1, 15, 30)],
       [1, datetime.datetime(2018, 4, 1, 15, 31)],
       [2, datetime.datetime(2018, 4, 1, 15, 32)],
       [3, datetime.datetime(2018, 4, 1, 15, 33)],
       [4, datetime.datetime(2018, 4, 1, 15, 34)]], dtype=object)

就像a.astype(object)它已经“拆箱”了日期一样。

但是，如果我一一分配元素：

In [62]: for i in range(5):
    ...:     d[i,1]=a[i,0]
    ...:     
In [63]: d
Out[63]: 
array([[0, numpy.datetime64('2018-04-01T15:30:00')],
       [1, numpy.datetime64('2018-04-01T15:31:00')],
       [2, numpy.datetime64('2018-04-01T15:32:00')],
       [3, numpy.datetime64('2018-04-01T15:33:00')],
       [4, numpy.datetime64('2018-04-01T15:34:00')]], dtype=object)

但是这样一个数组的价值是什么？

我可以将 timedelta 添加到原始时间数组：

In [67]: a + np.array(10, 'timedelta64[m]')
Out[67]: 
array([['2018-04-01T15:40:00'],
       ['2018-04-01T15:41:00'],
       ['2018-04-01T15:42:00'],
       ['2018-04-01T15:43:00'],
       ['2018-04-01T15:44:00']], dtype='datetime64[s]')

但我不能对对象数组列做同样的事情：

In [68]: d[:,1] + np.array(10, 'timedelta64[m]')
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-68-f82827d3d355> in <module>()
----> 1 d[:,1] + np.array(10, 'timedelta64[m]')

TypeError: ufunc add cannot use operands with types dtype('O') and dtype('<m8[m]')

我必须明确地迭代对象：

In [70]: for i in range(5):
    ...:     d[i,1] += np.array(i*10, 'timedelta64[m]')
    ...:     
In [71]: d
Out[71]: 
array([[0, numpy.datetime64('2018-04-01T15:30:00')],
       [1, numpy.datetime64('2018-04-01T15:41:00')],
       [2, numpy.datetime64('2018-04-01T15:52:00')],
       [3, numpy.datetime64('2018-04-01T16:03:00')],
       [4, numpy.datetime64('2018-04-01T16:14:00')]], dtype=object)

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