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

User · Accepted Answer

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])

User · Answer

Update  This function only avoids copy if it can  hence this is not the correct answer for this question  unutbu s answer is the right one     a   a astype numpy float32  copy False    numpy astype has a copy flag  Why shouldn t we use it

User · Answer

import numpy as np arr float   np arange 10  dtype np float32  arr int   arr float view np float32    use view   and parameter  dtype  to change the array in place

User · Answer

Use this   In  105   a Out 105    array   15  30  88  31  33           53  38  54  47  56           67   2  74  10  16           86  33  15  51  32           32  47  76  15  81    dtype int32   In  106   float32 a  Out 106    array    15    30    88    31    33             53    38    54    47    56             67     2    74    10    16             86    33    15    51    32             32    47    76    15    81     dtype float32

User · Answer

a   np subtract a  0   dtype np float32

User · Answer

You can change the array type without converting like this   a dtype   numpy float32   but first you have to change all the integers to something that will be interpreted as the corresponding float   A very slow way to do this would be to use python s struct module like this   def toi i       return struct unpack  i  struct pack  f  float i    0       applied to each member of your array   But perhaps a faster way would be to utilize numpy s ctypeslib tools  which I am unfamiliar with   - edit -  Since ctypeslib doesnt seem to work  then I would proceed with the conversion with the typical numpy astype method  but proceed in block sizes that are within your memory limits   a 0 10000    a 0 10000  astype  float32   view  int32        then change the dtype when done   Here is a function that accomplishes the task for any compatible dtypes  only works for dtypes with same-sized items  and handles arbitrarily-shaped arrays with user-control over block size   import numpy  def astype inplace a  dtype  blocksize 10000       oldtype   a dtype     newtype   numpy dtype dtype      assert oldtype itemsize is newtype itemsize     for idx in xrange 0  a size  blocksize           a flat idx idx   blocksize                  a flat idx idx   blocksize  astype newtype  view oldtype      a dtype   newtype  a   numpy random randint 100 size 100  reshape  10 10   print a astype inplace a   float32   print a

[python] In-place type conversion of a NumPy array

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