title: Modify describe function to remove and add columns
body: <p>I have a data frame with 3 columns. Each column contains <code>yes</code>,<code>no</code>, or <code>nan</code>.
  I am trying to find the frequency of each column based on column <code>a</code>. I was able to do this with <code>describe()</code>.  </p>
  
  <pre><code>import pandas as pd, numpy as np
  
  df2 = pd.DataFrame({'a':['yes','yes','no','yes','no','yes'],
                          'b':['no','yes','no','yes','no','no'],
                          'c':['yes','yes','yes','no','no', np.nan]})
  
  df2.groupby('a').describe().transpose()
  
  a    no                   yes                 
    count unique top freq count unique  top freq
  b     2      1  no    2     4      2   no    2
  c     2      2  no    1     3      2  yes    2
  </code></pre>
  
  <p>I am having trouble selecting the <code>describe</code> columns I want. Below is an example of how I would like it to look. The <code>freq/total_count</code> column is the <code>freq</code> over total <code>freq</code> of the row. For example, <code>b</code> &amp; <code>no</code> is <code>2/6</code>. </p>
  
  <pre><code>a    no                                      yes                
    count top freq freq/total_count   count top freq freq/total_count
  b     2  no    2     33%             4    no    2     33% 
  c     2  no    1     20%             3   yes    2     40%
  </code></pre>
  
  <p>Please let me know if more information is needed.</p>