title: How can we detect inconsistency in pandas dataframe?
body: <p>I have the following dataframe for which I want to detect if the results are inconsistent: </p>
  
  <pre><code>&gt;&gt;&gt; import pandas as pd
  &gt;&gt;&gt; import numpy as np
  &gt;&gt;&gt; df = pd.DataFrame(np.array([[1, 2, 3, 1], [4, 5, 6, 0], [7, 8, 9, 0], [4, 5, 6, 1], [1, 2, 3, 1]]),
  ...                    columns=['a', 'b', 'c', 'result'])
  &gt;&gt;&gt; df
     a  b  c  result
  0  1  2  3       1
  1  4  5  6       0
  2  7  8  9       0
  3  4  5  6       1
  4  1  2  3       1
  </code></pre>
  
  <p>My goal is to drop those rows that show different results for the same values (id 1 and 3 should be dropped). 
  I know I can detect duplicates and loop over the results</p>
  
  <pre><code>&gt;&gt;&gt; df[df.duplicated(['a', 'b', 'c'], keep=False)]
     a  b  c  result
  0  1  2  3       1
  1  4  5  6       0
  3  4  5  6       1
  4  1  2  3       1
  </code></pre>
  
  <p>But I was wondering if there's a more pythonic way of getting (and dropping) those indexes. </p>