title: How to combine daily data with monthly data in Pandas?
body: <p>I have daily data, and also monthly numbers. I would like to normalize the daily data by the monthly number - so for example the first 31 days of 2017 are all divided by the number corresponding to January 2017 from another data set.</p>
  
  <pre><code>import pandas as pd
  import datetime as dt
  
  N=100
  start=dt.datetime(2017,1,1)
  df_daily=pd.DataFrame({"a":range(N)}, index=pd.date_range(start, start+dt.timedelta(N-1)))
  
  df_monthly=pd.Series([1, 2, 3], index=pd.PeriodIndex(["2017-1", "2017-2", "2017-3"], freq="M"))
  
  df_daily["a"] / df_monthly # ???
  </code></pre>
  
  <p>I was hoping the time series data would align in a one-to-many fashion and do the required operation, but instead I get a lot of NaN.</p>
  
  <p>How would you do this one-to-many data alignment correctly in Pandas?</p>
  
  <p>I might also want to concat the data, in which case I expect the monthly data to duplicate values within one month.</p>