title: pandas group by, aggregate using multiple agg functions on input columns
body: <p>I am looking to do some aggregation on a pandas groupby dataframe, where I need to apply several different custom functions on multiple columns. This operation is very easy and customary in R (using <code>data.table</code> or <code>dplyr</code>), but I am surprised I'm finding it so difficult in pandas:</p>
  
  <pre><code>import pandas as pd
  data = pd.DataFrame({'A':[1,2,3,4,5,6],'B':[2,4,6,8,10,12],'C':[1,1,1,2,2,2]})
  
  #These work
  data.groupby('C').apply(lambda x: x.A.mean() - x.B.mean())
  data.groupby('C').agg(['mean','std'])
  
  #but this doesn't
  data.groupby('C').agg([lambda x: x.A.mean() - x.B.mean(),
                         lambda x: len(x.A)])
  </code></pre>
  
  <p>I want to calculate a statistic but also the sample size in each group, which seems like it should be a one or two line solution, but I also sometimes need to apply multiple functions on multiple columns of the grouped data frame.</p>