title: Row wise mean difference in a Pandas DataFrame
body: <p>Given a pandas dataframe, whats the most efficient way to do row wise difference of rolling mean. i.e. if we have a dataframe as:</p>
  <pre><code>np.random.seed(43)
  df = pd.DataFrame(np.random.randint(0,10,size=(6, 4)), columns=list('ABCD'))
  </code></pre>
  <p>which generated the dataframe as such:</p>
  <pre><code>   A  B  C  D
  0  4  0  1  5
  1  0  3  1  2
  2  7  0  3  2
  3  9  1  2  2
  4  3  5  4  4
  5  0  5  8  0
  </code></pre>
  <p>then the expected output is:</p>
  <pre><code>row_diff[0] = [row[0]['A'] - mean(row[1:]['A']), row[0]['B'] - mean(row[1:]['B']), row[0]['C'] - mean(row[1:]['C']), [row[0]['D'] - mean(row[1:]['D'])]]
  
  row_diff[1] = [row[1]['A'] - mean(row[2:]['A']+row[0]['A']), row[1]['B'] - mean(row[2:]['B']+row[0]['B']), row[1]['C'] - mean(row[2:]['C']+row[0]['C']), [row[0]['D'] - mean(row[2:]['D']+row[0]['D'])]]
  </code></pre>
  <p>and so on.</p>