python - In-place type conversion of a NumPy array

Question

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)

without copying the array. It is big.

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.

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

Answer 1

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)

yields

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

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

print(x)

prints

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