title: Fill missing days in timeseries (with duplicate keys)
body: <p>Here is what I'm trying to do in Pandas:</p>
  
  <ul>
  <li>load CSV file containing information about stocks for certain days</li>
  <li>find the earliest and latest dates in the column <code>date</code></li>
  <li>create a new dataframe where all the days between the earliest and latest are filled (NaN or something like "missing" for all columns would be fine)</li>
  </ul>
  
  <p>Currently it looks like this: </p>
  
  <pre><code>import pandas as pd
  import dateutil
  
  df = pd.read_csv("https://dl.dropboxusercontent.com/u/84641/temp/berkshire_new.csv")
  df['date'] = df['date'].apply(dateutil.parser.parse)
  new_date_range = pd.date_range(df['date'].min(), df['date'].max())
  df = df.set_index('date')
  df.reindex(new_date_range)
  </code></pre>
  
  <p>Unfortunately this throws the following error which I don't quite understand:</p>
  
  <pre><code>ValueError: Shape of passed values is (3, 4825), indices imply (3, 4384)
  </code></pre>
  
  <p>I've tried a dozen variations of this - without any luck. Any help would be much appreciated.</p>
  
  <p><strong>Edit:</strong></p>
  
  <p>After investigating this further, it looks like the problem is caused by duplicate indexes. The CSV does contain several entries for each date, which is probably causing the errors.</p>
  
  <p>The question is still relevant though: How can I fill the gaps in between, although there are duplicate entries for each date?</p>