title: Setting values in a pandas MultiIndex series
body: <p>I'm trying to set a series into another one, at a multi-index value. I cannot find a way to do it in Pandas without complex hacks. </p>
  
  <p>My original series: </p>
  
  <pre><code>one  1    0.522764
       3    0.362663
       7    0.963108
  two  2    0.717855
       4    0.004645
       5    0.077471
  </code></pre>
  
  <p>The data I want to concatenate, at level <code>three</code>: </p>
  
  <pre><code>2    0.8
  7    0.9
  8    0.7
  </code></pre>
  
  <p>The desired output:  </p>
  
  <pre><code>one    1    0.522764
         3    0.362663
         7    0.963108
  two    2    0.717855
         4    0.004645
         5    0.077471
  three  2    0.800000
         7    0.900000
         8    0.700000
  </code></pre>
  
  <p>I cannot figure out an elegant way to do this in pandas. All I've been able to do is the following hack: </p>
  
  <pre><code># imports
  import numpy as np
  import pandas as pd 
  
  # to replicate the Series: 
  np.arrays = [['one','one','one','two','two','two'],[1,3,7,2,4,5]]
  my_series = pd.Series([np.random.random() for i in range(6)],
                 index=pd.MultiIndex.from_tuples(list(zip(*np.arrays))))
  
  # the new data I need to add: 
  new_data = pd.Series({1: .9, 2: .7, 3: .8})
  </code></pre>
  
  <p>Here is how I'm currently solving it: </p>
  
  <pre><code># rename the index so that I can call it later 
  new_data.index.name = 'level_1' 
  
  # turn it into temporary a dataframe so that I can add a new column 
  temp = pd.DataFrame(new_data) 
  
  # create a new column with the desired name for first index level 
  temp['level_0'] = 'three'   
  
  # reset index, set the new index, turn into Series again
  temp = temp.reset_index().set_index(['level_0', 'level_1'])[0]                              
  
  # append it to the larger dataframe 
  my_series = my_series.append(temp)                  
  </code></pre>
  
  <p>This yields the desired output. </p>
  
  <p><strong>Question: Is there a simple, elegant way to do this in Pandas?</strong></p>