title: picking out elements based on complement of indices in Python pandas
body: <p>I have a dataframe out of which I pick two subset dfs, <code>df_a</code> and <code>df_b</code>. For example in <code>iris</code> dataset:</p>
  
  <pre><code>df_a = iris[iris.Name == "Iris-setosa"]
  df_b = iris[iris.Name == "Iris-virginica"]
  </code></pre>
  
  <p>What's the best way to get all elements of <code>iris</code> that are neither in <code>df_a</code> nor in <code>df_b</code>? I prefer not to refer to the original conditions that defined <code>df_a</code> and <code>df_b</code>.  I just assume that <code>df_a</code> and <code>df_b</code> are subsets of <code>iris</code>, so I'd like to pull out elements from <code>iris</code> based on the indices of <code>df_a</code> and <code>df_b</code>.  Basically, assume that:</p>
  
  <pre><code>df_a = get_a_subset(iris)
  df_b = get_b_subset(iris)
  # retrieve the subset of iris that 
  # has all elements not in df_a or in df_b
  # ...
  </code></pre>
  
  <p><strong>EDIT:</strong> here is a solution that seems inefficient and inelegant and I'm sure pandas has a better way:</p>
  
  <pre><code># get subset of iris that is not in a nor in b
  df_rest = iris[map(lambda x: (x not in df_a.index) &amp; (x not in df_b.index), iris.index)]
  </code></pre>
  
  <p>And a second one:</p>
  
  <pre><code>df_rest = iris.ix[iris.index - df_a.index - df_b.index]
  </code></pre>
  
  <p>how can this be done most efficiently/elegantly in pandas? thanks.</p>