title: pandas - Extend Index of a DataFrame setting all columns for new rows to NaN?
body: <p>I have time-indexed data:</p>
  
  <pre><code>df2 = pd.DataFrame({ 'day': pd.Series([date(2012, 1, 1), date(2012, 1, 3)]), 'b' : pd.Series([0.22, 0.3]) })
  df2 = df2.set_index('day')
  df2
                 b
   day             
  2012-01-01  0.22
  2012-01-03  0.30
  </code></pre>
  
  <p>What is the best way to extend this data frame so that it has one row for every day in January 2012 (say), where all columns are set to <code>NaN</code> (here only <code>b</code>) where we don't have data?</p>
  
  <p>So the desired result would be:</p>
  
  <pre><code>               b
   day             
  2012-01-01  0.22
  2012-01-02   NaN
  2012-01-03  0.30
  2012-01-04   NaN
  ...
  2012-01-31   NaN
  </code></pre>
  
  <p>Many thanks!</p>