title: pandas groupby apply on multiple columns
body: <p>I am trying to apply the same function to multiple columns of a groupby object, such as:</p>
  
  <pre><code>In [51]: df
  Out[51]: 
             a         b group
  0   0.738628  0.242605  grp1
  1   0.411315  0.340703  grp1
  2   0.328785  0.780767  grp1
  3   0.059992  0.853132  grp1
  4   0.041380  0.368674  grp1
  5   0.181592  0.632006  grp1
  6   0.427660  0.292086  grp1
  7   0.582361  0.239835  grp1
  8   0.158401  0.328503  grp2
  9   0.430513  0.540628  grp2
  10  0.436652  0.085609  grp2
  11  0.164037  0.381844  grp2
  12  0.560781  0.098178  grp2
  
  In [52]: df.groupby('group')['a'].apply(pd.rolling_mean, 2, min_periods = 2)
  Out[52]: 
  0          NaN
  1     0.574971
  2     0.370050
  3     0.194389
  4     0.050686
  5     0.111486
  6     0.304626
  7     0.505011
  8          NaN
  9     0.294457
  10    0.433582
  11    0.300345
  12    0.362409
  dtype: float64
  
  In [53]: 
  </code></pre>
  
  <p>However, if I try <code>df.groupby('group')['a', 'b'].apply(pd.rolling_mean, 2, min_periods = 2)</code> or <code>df.groupby('group')[['a', 'b']].apply(pd.rolling_mean, 2, min_periods = 2)</code>, both will give me <code>ValueError: could not convert string to float: grp1</code>. 
  What is the correct way to apply the function to multiple columns at once?</p>