title: Count of unique rows based on preceding row - Pandas
body: <p>I want to return unique rows from multiple columns in a <code>df</code>. The issue is I want to include the same set of values if they don't appear in the previous row. This is a little hard to explain so I'll display it;</p>
  
  <pre><code>df = pd.DataFrame({   
          'Time' : ['2019-08-02 09:50:10.1','2019-08-02 09:50:10.2','2019-08-02 09:50:10.3','2019-08-02 09:50:10.4','2019-08-02 09:50:10.5','2019-08-02 09:50:10.6','2019-08-02 09:50:10.7','2019-08-02 09:50:10.8','2019-08-02 09:50:10.9','2019-08-02 09:50:11.0'],
          'Code1' : ['A','A','B','B','C','C','A','A','B','B'],    
          'Code2' : ['B','B','A','A','B','B','B','B','A','A'],    
          'Code3' : [np.nan,np.nan,'C','C','A','A','C','C','C','C'],                                   
          })
  
  
  df = df[df.iloc[:, 1:].shift().ne(df.iloc[:, 1:]).any(axis=1)].reset_index(drop = True)
  </code></pre>
  
  <p>Intended Output:</p>
  
  <pre><code>                    Time Code1 Code2 Code3
  0  2019-08-02 09:50:10.1     A     B   NaN
  1  2019-08-02 09:50:10.3     B     A     C
  2  2019-08-02 09:50:10.5     C     B     A
  3  2019-08-02 09:50:10.7     A     B     C
  4  2019-08-02 09:50:10.9     B     A     C
  </code></pre>