title: Pandas: concatenating conditioned on unique values
body: <p>I am concatenating two Pandas dataframes as below.</p>
  
  <pre><code>part1 = pd.DataFrame({'id'    :[100,200,300,400,500], 
                     'amount': np.random.randn(5)
                      })
  
  part2 = pd.DataFrame({'id'    :[700,100,800,500,300], 
                     'amount': np.random.randn(5)
                      })
  
  concatenated = pd.concat([part1, part2], axis=0)
       amount   id
  0 -0.458653  100
  1  2.172348  200
  2  0.072494  300
  3 -0.253939  400
  4 -0.061866  500
  0 -1.187505  700
  1 -0.810784  100
  2  0.321881  800
  3 -1.935284  500
  4 -1.351507  300
  </code></pre>
  
  <p>How can I limit the operation so that a row in <code>part2</code> is only included in <code>concatenated</code> if the row <code>id</code> does not already appear in <code>part1</code>? In a way, I want to treat the <code>id</code> column like a set.</p>
  
  <p>Is it possible to do this during concat() or is this more a post-processing step?</p>
  
  <p>Desired output for this example would be:</p>
  
  <pre><code>concatenated_desired
       amount   id
  0 -0.458653  100
  1  2.172348  200
  2  0.072494  300
  3 -0.253939  400
  4 -0.061866  500
  0 -1.187505  700
  2  0.321881  800
  </code></pre>