title: In-place row-wise operation on pandas DataFrame
body: <p>Suppose I have this:</p>
  
  <pre><code>&gt;&gt;&gt; x = pandas.DataFrame([[1.0, 2.0, 3.0], [3, 4, 5]], columns=["A", "B", "C"])
  &gt;&gt;&gt; print x
     A  B  C
  0  1  2  3
  1  3  4  5
  </code></pre>
  
  <p>Now I want to normalize <code>x</code> by row --- that is, divide each row by its sum.  As described in <a href="https://stackoverflow.com/questions/18594469/normalizing-a-pandas-dataframe-by-row">this question</a>, this can be achieved with <code>x = x.div(x.sum(axis=1), axis=0)</code>.  However, this creates a <em>new</em> DataFrame.  If my DataFrame is large, a lot of memory can be consumed in creating this new DataFrame, even though I immediately assign it to the original name.</p>
  
  <p>Is there an efficient way to perform this operation in place?  I want something like <code>x.idiv()</code> that provides the <code>axis</code> option of <code>div</code> but updates <code>x</code> in place.  For this specific case I need the division, but sometimes it would also be nice to have similar in-place versions for all the basic operations.</p>
  
  <p>(I can update it in place by iterating over it row-wise and assigning each normalized row back into the original, but this is slow, and I'm looking for a more efficient solution.)</p>