title: Assign new values to slice from MultiIndex DataFrame
body: <p>I would like to modify some values from a column in my DataFrame. At the moment I have  a <em>view</em> from select via the multi index of my original <code>df</code> (and modifying does change <code>df</code>).</p>
  
  <p>Here's an example: </p>
  
  <pre><code>In [1]: arrays = [np.array(['bar', 'bar', 'baz', 'qux', 'qux', 'bar']),
                    np.array(['one', 'two', 'one', 'one', 'two', 'one']),
                    np.arange(0, 6, 1)]
  In [2]: df = pd.DataFrame(randn(6, 3), index=arrays, columns=['A', 'B', 'C'])
  
  In [3]: df
                    A         B         C
  bar one 0 -0.088671  1.902021 -0.540959
      two 1  0.782919 -0.733581 -0.824522
  baz one 2 -0.827128 -0.849712  0.072431
  qux one 3 -0.328493  1.456945  0.587793
      two 4 -1.466625  0.720638  0.976438
  bar one 5 -0.456558  1.163404  0.464295
  </code></pre>
  
  <p>I try to modify a slice of <code>df</code> to a scalar value:</p>
  
  <pre><code>In [4]: df.ix['bar', 'two', :]['A']
  Out[4]:
  1    0.782919
  Name: A, dtype: float64
  
  In [5]: df.ix['bar', 'two', :]['A'] = 9999
  # df is unchanged
  </code></pre>
  
  <p>I really want to modify <em>several</em> values in the column (and since indexing returns a vector, not a scalar value, I think this would make more sense):</p>
  
  <pre><code>In [6]: df.ix['bar', 'one', :]['A'] = [999, 888]
  # again df remains unchanged
  </code></pre>
  
  <p>I'm using pandas 0.11. Is there is a simple way to do this?</p>
  
  <p><em>The current solution is to recreate df from a new one and modify values I want to. But it's not elegant and can be very heavy on complex dataframe. In my opinion the problem should come from .ix and .loc not returning a view but a copy.</em></p>