title: How to convert numpy int to float with separate numpy array?
body: <p>I have a huge data of numpy memory error problem, 
  I try to use slicing to handle it like following
  <a href="https://stackoverflow.com/questions/31268998/how-to-merge-two-large-numpy-arrays-if-slicing-doesnt-resolve-memory-error">How to merge two large numpy arrays if slicing doesn&#39;t resolve memory error?</a></p>
  
  <p>Slicing is work for numpy.multiply, but it seems no way to convert numpy int to float with slicing. 
  Following is sample:</p>
  
  <pre><code>images = numpy.array([1,2,3,4,5,6,7,8,9,10])
  images[0:5] = images[0:5].astype(numpy.float32)
  print type(images[0])
  images = images.astype(numpy.float32)
  print type(images[0])
  
  &lt;type 'numpy.int32'&gt;
  &lt;type 'numpy.float32'&gt;
  </code></pre>
  
  <p>Once I use images.astype(numpy.float32), I got memory error(dtype is same). 
  Target memory is too small, and I may hard to use sparse matrix.</p>
  
  <p>Thanks for any suggestion...!</p>