title: t test for multiple columns after groupby pandas
body: <p>I want to calculate and test the mean of two different groups of multiple columns in pandas, I can work the calculate part out, but no good solution so far for the test part. Below are a toy sample and the result I want.</p>
  
  <pre><code>df = pd.DataFrame(np.random.randint(0,100,size=(100, 2)), columns=['col_1','col_2'])
  df['group'] = ['A']*50 + ['B']*50
  
  df.groupby('group').agg({"col_1":"mean","col_2":"mean"})
  
         col_1  col_2
  group              
  A      52.26  56.58
  B      53.04  49.18
  </code></pre>
  
  <p>What I want to have:</p>
  
  <pre><code>       col_1  t_col_1  col_2 t_col_2
  group              
  A      52.26  4.3***   56.58 0.8
  B      53.04  4.3***   49.18 0.8
  </code></pre>
  
  <p>In which <code>t_col_1</code> is t statistics of the difference of means of <code>col_1</code> in group A and group B, i.e. <code>t.test(df.loc[df['group'].isin(['B'])][col_1], df.loc[df['group'].isin(['A'])][col_1])</code>. The stars are not necessary but wouldb be great if they can be there.</p>
  
  <p>Any suggestions on how to do this?</p>