title: NumPy min/max in-place assignment
body: <p>Is it possible to perform min/max in-place assignment with NumPy multi-dimensional arrays without an extra copy?</p>
  
  <p>Say, <code>a</code> and <code>b</code> are two 2D numpy arrays and I would like to have <code>a[i,j] = min(a[i,j], b[i,j])</code> for all <code>i</code> and <code>j</code>.</p>
  
  <p>One way to do this is:</p>
  
  <pre><code>a = numpy.minimum(a, b)
  </code></pre>
  
  <p>But according to the documentation, <code>numpy.minimum</code> creates and returns a new array:</p>
  
  <blockquote>
    <p>numpy.minimum(x1, x2[, out])<br>
    Element-wise minimum of array elements.<br>
    Compare two arrays and returns <strong>a new array</strong> containing the element-wise minima.</p>
  </blockquote>
  
  <p>So in the code above, it will create a new temporary array (min of <code>a</code> and <code>b</code>), then assign it to <code>a</code> and dispose it, right?</p>
  
  <p>Is there any way to do something like <code>a.min_with(b)</code> so that the min-result is assigned back to <code>a</code> in-place?</p>