title: Panda mean calculation groupby 2 columns - How to ignore the order?
body: <p>Suppose I have a dataframe containing 3 columns : C1,C2 and Value.
  My C1 and C2 columns both contains values ranging from 0 to 3. </p>
  
  <pre><code>| C1 | C2 | Value |
  |----|----|-------|
  | 1  | 3  |   45  |
  | 2  | 1  |  -50  |
  | 3  | 1  |  200  |
  | 1  | 2  |  -10  |
  | 3  | 1  |   18  |
  | 2  | 3  |    2  |
  | 1  | 3  |   25  |
  </code></pre>
  
  <p>I want to get the mean over the values in 'value' column for a given index tuple (x,y) with x in C1 and y in C2. However, I would like to treat the tuples (x,y) and (y,x) as being the same ie. not taking the ordering into consideration. 
  I'll try to clarify my problem with an example : 
  Assume C1 = 1 and C1 = 3, in that case, I would like to take the average of 45, 200, 18 and 25. 
  I'd like to get the result into a dataframe or serie with both ordering as index. In the example above, the mean is 72. I'd like to have in my result the entries (1,3) and (3,1) both having for value 72. (The goal is to then be able to make bar plots of the means.)</p>
  
  <p>I hope my description is sufficiently clear.</p>
  
  <p>I've used panda groupby operator to find the means but I can't find how to deal with the ordering problem.</p>
  
  <p>Here is the code I have for computing the means :</p>
  
  <pre><code>python results = df.groupby(['C1','C2'])['value'].mean()
  </code></pre>
  
  <p>I'd have to take the mean over both ordering and then save the result back into both tuple ordering but I can't find a  proper way to do that.
  Thanks for your help !</p>