NumPy数组的就地类型转换 [英] In-place type conversion of a NumPy array

问题描述

给出一个int32的NumPy数组，如何将它转换为float32 ?所以基本上，我想做

Given a NumPy array of int32, how do I convert it to float32 in place? So basically, I would like to do

a = a.astype(numpy.float32)

，不复制数组.很大.

之所以这样做，是因为我有两种算法来计算a.其中一个返回一个数组int32，另一个返回一个数组float32(这是两种不同算法所固有的).所有进一步的计算都假定a是float32的数组.

The reason for doing this is that I have two algorithms for the computation of a. One of them returns an array of int32, the other returns an array of float32 (and this is inherent to the two different algorithms). All further computations assume that a is an array of float32.

当前，我在通过ctypes调用的C函数中进行转换.有没有办法在Python中做到这一点?

Currently I do the conversion in a C function called via ctypes. Is there a way to do this in Python?

推荐答案

您可以创建具有不同dtype的视图，然后就地复制到该视图中:

You can make a view with a different dtype, and then copy in-place into the view:

import numpy as np
x = np.arange(10, dtype='int32')
y = x.view('float32')
y[:] = x

print(y)

收益

array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.], dtype=float32)

要显示转换已就位，请注意将从 x复制到y会更改x:

To show the conversion was in-place, note that copying from x to y altered x:

print(x)

打印

array([         0, 1065353216, 1073741824, 1077936128, 1082130432,
       1084227584, 1086324736, 1088421888, 1090519040, 1091567616])

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