title: how to compute mean absolute deviation row wise in pandas
body: <p>snippet of the dataframe is as follows. but actual dataset is 200000 x 130.</p>
  <pre><code>ID 1-jan 2-jan 3-jan 4-jan 
  1.  4      5    7    8     
  2.  2      0    1    9     
  3.  5      8    0    1     
  4.  3      4    0    0   
  </code></pre>
  <p>I am trying to compute Mean Absolute Deviation for each row value like this.</p>
  <pre><code>ID 1-jan 2-jan 3-jan 4-jan mean
  1.    4      5    7    8   12.5 
  1_MAD 8.5   7.5 5.5  4.5
  2.    2      0    1    9    6  
  2_MAD.4      6    5    3
  .
  . 
      
  </code></pre>
  <p>I tried this,</p>
  <pre><code>new_df = pd.DataFrame()
  for rows in (df['ID']):
      new_df[str(rows) + '_mad'] = mad(df3.loc[row_value][1:])
      new_df.T
  </code></pre>
  <p>where <code>mad</code> is a function that compares the mean to each value.</p>
  <p>But, this is very time consuming since i have a large dataset and i need to do in a quickest way possible.</p>