title: Converting Pandas groupby.groups result into dataframe, using index tuple value as row and columns name of dataframe
body: <p>the resuts of my groupby.groups return : </p>
  
  <pre><code>{(2014, 36): [2], (2014, 41): [3, 4], (2013, 10): [0], (2014, 48): [5], (2014, 37): [1]}
  </code></pre>
  
  <p>i want to convert it into a dataframe that will looks like :</p>
  
  <pre><code>      2013 2014
  10    1    0
  36    0    1
  37    0    1
  41    0    2
  48    0    1 
  </code></pre>
  
  <p>-- adds --
  here is my workflow to the groupby.groups results :</p>
  
  <pre><code>def tr_epoch(epoch):
         y,wn,dn = epoch.isocalendar()
         return y, wn
  
  d = [1362826800, 1410260400, 1409828400, 1412766000, 1412769600, 1417262400 ] 
  l = map(lambda x:  tr_epoch(datetime.datetime.fromtimestamp(x)), d)
  df = pd.DataFrame(l, columns=['year','week_idx'])
  res = df.groupby(['year','week_idx']).groups
  </code></pre>
  
  <p>-- adds -- 
  in pythonic way, using iteration,  i will do :</p>
  
  <pre><code>def  to_dict(k,v):
      yr, wk = k
      return {'week': wk, yr: len(v)}
  
  data =  map(lambda(k,v): to_dict(k,v), res.iteritems())
  df = pd.DataFrame.from_records(data, index='week').fillna(0).sort()
  </code></pre>
  
  <p>But i am sure, there is a pandas way to do.</p>