title: Expanding mean over multiple series in pandas
body: <p>I have a groupby object I apply expanding mean to. However I want that calculation over another series/group at the same time. Here is my code:</p>
  
  <pre><code>d = { 'home' : ['A', 'B', 'B', 'A', 'B', 'A', 'A'], 
   'away' : ['B', 'A','A', 'B', 'A', 'B', 'B'],
  'aw' : [1,0,0,0,1,0,np.nan],
  'hw' : [0,1,0,1,0,1, np.nan]}
  
  
  df2 = pd.DataFrame(d, columns=['home', 'away', 'hw', 'aw'])
  df2['tie'] = np.where(df2.hw == df2.aw, 1, 0)
  df2.index = range(1,len(df2) + 1)
  
  avgcol = ['hw','tie','aw']
  homenames = ['home_win_at_home', 'home_tie_at_home', 'home_loss_at_home']
  awaynames = ['away_win_at_away', 'away_tie_at_away', 'away_loss_at_away']
  
  
  def win_at_venue(df, venuecol, avgcol, name):
      df[name] = df.groupby('away')[avgcol].apply(lambda x:pd.expanding_mean(x).shift())
  
  win_at_venue(df2, 'home', avgcol, homenames)
  win_at_venue(df2, 'away', avgcol[::-1], awaynames)
  </code></pre>
  
  <p>How can I use <code>pd.expanding_mean</code> in a <code>groupby</code> object that will average over the <code>'home'</code> and <code>'away'</code> columns so I see their average wins/ties/losses over all venues? Now it just gives the prior win average for a team playing at home or away, not both home &amp; away.</p>
  
  <p>I've been trying different levels and df.stack() and reindexing but no luck.</p>
  
  <p>Any help appreciated in getting there.</p>
  
  <p>Here is what the correct result for just home wins at home and home wins all venues:</p>
  
  <pre><code>  home away  hw  aw  homewin_at_home  homewins_all_venues
  0    A    B   0   1              NaN                  NaN
  1    B    A   1   0              NaN                 1.00
  2    B    A   0   0         1.000000                 1.00
  3    A    B   1   0         0.000000                 0.00
  4    B    A   0   1         0.500000                 0.50
  5    A    B   1   0         0.500000                 0.40
  6    A    B NaN NaN         0.666667                 0.50
  </code></pre>