title: How to reindex a pandas DataFrame after concatenation
body: <p>Suppose I concatenate two DataFrames like so:</p>
  
  <pre><code>import numpy as np
  import pandas as pd
  
  array1 = np.random.randn(3,3)
  array2 = np.random.randn(3,3)
  
  df1 = pd.DataFrame(array1, columns=list('ABC'))
  df2 = pd.DataFrame(array2, columns=list('ABC'))
  
  df = pd.concat([df1, df2])
  </code></pre>
  
  <p>The resulting DataFrame <code>df</code> looks like this:</p>
  
  <pre><code>          A         B         C
  0  1.297362  0.745510 -0.206756
  1 -0.056807 -1.875149 -0.210556
  2  0.310837 -1.068873  2.054006
  0  1.163739 -0.678165  2.626052
  1 -0.557625 -1.448195 -1.391434
  2  0.222607 -0.334348  0.672643
  </code></pre>
  
  <p>Note that the indices are the same as in the original DataFrames. I'd like to re-index <code>df</code> such that the indices simply run from <code>0</code> to <code>5</code>. How can I do this?</p>
  
  <p>(I've tried <code>df = df.reindex(index = range(df.shape[0]))</code> but this gives <code>ValueError: cannot reindex from a duplicate axis</code>. This is because the original axis contains duplicates (two <code>0</code>s, two <code>1</code>s, etc.)).</p>