title: pandas: Fill missing dates when keeping duplicates
body: <p>I have a simple <code>pandas</code> series:</p>
  
  <pre><code>import pandas as pd
  
  quantities = [1, 14, 14, 11, 12, 13, 14]
  timestamps = [pd.Timestamp(2015, 4, 1), pd.Timestamp(2015, 4, 1), pd.Timestamp(2015, 4, 2), pd.Timestamp(2015, 4, 3), pd.Timestamp(2015, 4, 4), pd.Timestamp(2015, 4, 5), pd.Timestamp(2015, 4, 8)]
  series = pd.Series(quantities, index=timestamps)
  </code></pre>
  
  <p>which looks as follows:</p>
  
  <pre><code>2015-04-01     1
  2015-04-01    14
  2015-04-02    14
  2015-04-03    11
  2015-04-04    12
  2015-04-05    13
  2015-04-08    14
  dtype: int64
  </code></pre>
  
  <p>I would like to fill the missing dates i.e. <code>2015-04-06 = NaN</code> and <code>2015-04-07 = NaN</code> but keep the series as is, i.e.:</p>
  
  <pre><code>2015-04-01     1
  2015-04-01    14
  2015-04-02    14
  2015-04-03    11
  2015-04-04    12
  2015-04-05    13
  2015-04-06    NaN
  2015-04-07    NaN
  2015-04-08    14
  dtype: int64
  </code></pre>
  
  <p>I've tried:</p>
  
  <pre><code>series = series.asfreq('D')
  </code></pre>
  
  <p>but get the following error: <strong>ValueError: cannot reindex from a duplicate axis</strong>. This error happens because of the duplicate timestamp values.</p>
  
  <p><strong>Is there any way on Earth to achieve this?</strong></p>
  
  <p>Thanks for any help.</p>