title: Transform multiple columns data into one column for every groupby object
body: <p>I have a dataframe <code>df</code>:</p>
  
  <p><code>df = pd.DataFrame({'ID': ['1','1','2'], \
                     'diag1': ['C1.10', 'E10.40','F20.00'], \
                     'diag2': ['M30', '','O92.15'], \
                     'diag3': ['E15.34', 'H20.00','']})</code></p>
  
  <p>I want to create a new dataframe <code>df1</code> which should contain all the information present in <code>df['diag1']</code>, <code>df['diag2']</code> and <code>df['diag3']</code> columns based on every <code>ID</code> into one column of <code>df1['diag_all']</code>.   </p>
  
  <p>Should I need to use transform method in order to achieve this or is there any other function to do this? </p>
  
  <p>This is what I want to achieve:
  <code>
    ID diag_all
  0  1    C1.10
  1  1   E10.40
  2  1   F20.00
  3  1      M30
  4  1   O92.15
  5  2   E15.34
  6  2   H20.00</code></p>
  
  <p>Please remember that I have many other columns in the actual dataframe which I do not want to use during this transformation. So we need to specify columns in the <code>df</code> which we want to transform into <code>df1['diag_all']</code>.</p>