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

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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

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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

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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

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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

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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

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a   np subtract a  0   dtype np float32

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

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