title: Columnwise Multiplication of Pandas Dataframe with Series (missmatched indices)
body: <p>I want to use python's pandas for some simple matrix calculation and encountered one problem when trying to do the following simple multiplication:</p>
  
  <pre><code>df = df.mul(column, axis=1)
  </code></pre>
  
  <p>df is one 3x3 dataframe and  looks like:</p>
  
  <pre><code>                    441090707  441090801  441090408
          441090408   0.255061   0.202650   0.689114
          441090707   0.287952   0.218351   0.116750
          441090801   0.456987   0.578998   0.194136
  </code></pre>
  
  <p>while column is one series which looks like:</p>
  
  <pre><code>            area
              441090707     179.727362
              441090801     698.939741
              441090408    1377.909774
              Name: Test, dtype: float64
  </code></pre>
  
  <p>The multiplication throws a RuntimeWarning ('&lt;' not supported between instances of 'str' and 'int', sort order is undefined for incomparable objects) and returns the following:</p>
  
  <pre><code>                       441090707  441090801    ...      441090801  441090408
          441090408        NaN        NaN    ...            NaN        NaN
          441090707        NaN        NaN    ...            NaN        NaN
          441090801        NaN        NaN    ...            NaN        NaN
  </code></pre>
  
  <p>As the documentation states "Mismatched indices will be unioned together", so there seems to be a problem with my index.
  But I can't figure out how to make the series-indices match the dataframe- columns.</p>
  
  <p>(The multiplication worked when I used "column.values", but this is not a solution because the order of the series might change)</p>