title: set difference for pandas
body: <p>A simple pandas question: </p>
  
  <p>Is there a <code>drop_duplicates()</code> functionality to drop every row involved in the duplication? </p>
  
  <p>An equivalent question is the following: Does pandas have a set difference for dataframes? </p>
  
  <p>For example:</p>
  
  <pre><code>In [5]: df1 = pd.DataFrame({'col1':[1,2,3], 'col2':[2,3,4]})
  
  In [6]: df2 = pd.DataFrame({'col1':[4,2,5], 'col2':[6,3,5]})
  
  In [7]: df1
  Out[7]: 
     col1  col2
  0     1     2
  1     2     3
  2     3     4
  
  In [8]: df2
  Out[8]: 
     col1  col2
  0     4     6
  1     2     3
  2     5     5
  </code></pre>
  
  <p>so maybe something like <code>df2.set_diff(df1)</code> will produce this:</p>
  
  <pre><code>   col1  col2
  0     4     6
  2     5     5
  </code></pre>
  
  <p>However, I don't want to rely on indexes because in my case, I have to deal with dataframes that have distinct indexes.</p>
  
  <p>By the way, I initially thought about an extension of the current <code>drop_duplicates()</code> method, but now I realize that the second approach using properties of set theory would be far more useful in general. Both approaches solve my current problem, though.</p>
  
  <p>Thanks!</p>