title: Converting PANDAS dataframe from monthly to daily
body: <p>I have a data frame with monthly data for 2014 for a series of 317 stock tickers (317 tickers x 12 months = 3,804 rows in DF). I would like to convert it to a daily dataframe (317 tickers x 365 days = 115,705 rows). So, I believe I need to upsample or reindex while spreading the monthly values over every day in the month, but I can't get it to work properly.</p>
  
  <p>The dataframe is currently in this format:</p>
  
  <pre><code>&gt;&gt;&gt; df
  month    ticker   b    c
  2014-1   AAU      10   .04     #different values every month for each ticker
  2014-2   AAU      20   .03
  2014-3   AAU      13   .06
  .
  2014-12  AAU      11   .03
  .
  .
  .
  2014-1   ZZY      11   .11
  2014-2   ZZY      6    .03
  .
  2014-12  ZZY      17   .09
  </code></pre>
  
  <p>And this is what I'd like:</p>
  
  <pre><code>&gt;&gt;&gt; df
  day          ticker   b    c
  2014-01-01   AAU      10   .04  #same values every day in month for each ticker
  2014-01-02   AAU      10   .04
  2014-01-03   AAU      10   .04
  .
  2014-01-31   AAU      10   .04
  2014-02-01   AAU      20   .03
  2014-02-02   AAU      20   .03
  .
  2014-02-28   AAU      20   .03
  .
  .
  .
  2014-12-30   ZZY      17   .09 
  2014-12-31   ZZY      17   .09 
  </code></pre>
  
  <p>I have tried doing a groupby combined with resampling by day, but the updated dataframe will start with the date '2014-01-13' rather than January 1st, and end with '2014-12-01' rather than December 31st. I have also tried to change the month values from, for instance, '2014-1' to '2014-01-01', etc., but the resampled dataframe still ends on '2014-01-01'. There has to be an easier way to go about this, so I'd appreciate any help. I've been going around in circles all day on this. </p>