title: Rolling mean (that extends its window) on grouped multi-index
body: <p>I have a multi-index based on dates and times (grouped by times). Data looks like this:</p>
  
  <pre><code>                             r2     var_v2x          _s
  date       time                                        
  2017-10-02 08:05:00  0.00000031  0.00008784  0.00353914
  2017-10-03 08:05:00  0.00000008  0.00009055  0.00085233
  2017-10-04 08:05:00  0.00000000  0.00018774  0.00000000
  2017-10-05 08:05:00  0.00000031  0.00007609  0.00409934
  </code></pre>
  
  <p>and for another time period:</p>
  
  <pre><code>                             r2     var_v2x          _s
  date       time                                        
  2017-10-02 08:30:00  0.00000000  0.00008784  0.00000000
  2017-10-03 08:30:00  0.00000008  0.00009055  0.00085375
  2017-10-04 08:30:00  0.00000008  0.00018774  0.00041180
  2017-10-05 08:30:00  0.00000000  0.00007609  0.00000000
  2017-10-10 08:30:00  0.00000008  0.00006900  0.00112288
  </code></pre>
  
  <p>Now I want to apply a rolling mean to column  <code>_s</code> with a self-extendable rolling window.
  This means for the first observation within a group the rolling window is 1 (so just the value, 2nd observation includes 1st and 2nd value of column <code>_s</code> and so on.</p>
  
  <p>In the end, I want to have the ungrouped <code>dataframe</code> like:</p>
  
  <pre><code>                             r2     var_v2x          _s      s_rolling
  date       time                                        
  2017-10-02 08:05:00  0.00000031  0.00008784  0.00353914     0.00353914
  2017-10-02 08:10:00  0.00000024  0.00008784  0.00249531     rolling(2)
  2017-10-02 08:15:00  0.00000024  0.00008784  0.00249531     rolling(3)
  2017-10-02 08:20:00  0.00000024  0.00008784  0.00249531     rolling(4)
  </code></pre>
  
  <p>So far I have this:</p>
  
  <pre><code>`file_name = r'E:\Model\ModelSpecific\Index_shat.txt'
  
   df = pd.read_csv(file_name, parse_dates=[0], index_col=None, sep=',')
  
   list_date = sorted(set(df['Date']))
   list_time = sorted(set(df['Time']))
   iterables = [list_date, list_time]
   indexed = pd.MultiIndex.from_product(iterables, names=['date', 'time'])
   df = df.set_index(indexed)
  
   df = df.dropna()
   df['_s'] = df['r2']/df['var_v2x']
  
   for date, new_df in enumerate(df.groupby(level=1)):
        new_df = pd.DataFrame(new_df)
        new_df['rolling_s'] = new_df.expanding().mean()
        print(new_df)
  </code></pre>
  
  <p>But returns: ValueError: Wrong number of items passed 3, placement implies 1.   </p>
  
  <p>I also tried <code>enumerate(df.groupby(level=1)):</code><br>
  But returns: AttributeError: 'tuple' object has no attribute 'rolling'</p>