title: Merge daily and monthly Timeseries with Pandas
body: <p>I have two dataframes, "bio" with monthly data, where I only need the column bio['Bio_Avg'].
  And another one "tc" with daily data.
  Both with Datetime index.</p>
  
  <p>What I need is to merge both keeping daily granularity but adding monthly data repeating the value of the column bio['Bio_Avg'].</p>
  
  <pre><code>print(bio.head(5))
                          year  month   ncena    ...      contingency  D   Bio_Avg
              Date                              ...                              
              2019-12-01  2019   12       NaN    ...              NaN  1  38826.48
              2019-11-01  2019   11       NaN    ...              NaN  1  33867.68
              2019-10-01  2019   10       NaN    ...              NaN  1  31358.80
              2019-09-01  2019    9       NaN    ...              NaN  1  29583.84
              2019-08-01  2019    8       NaN    ...              NaN  1  27763.12
  </code></pre>
  
  <p>and </p>
  
  <pre><code>print(tc.head(5))
                                 exchg_rate
              2019-11-01            59.71
              2019-11-02            59.74
              2019-11-03            59.73
              2019-11-04            59.70
              2019-11-05            59.65
  </code></pre>
  
  <p>The expected result should be:</p>
  
  <pre><code>                               exchg_rate   Bio_Avg
              2019-11-01            59.71     33867.68
              2019-11-02            59.74     33867.68
              2019-11-03            59.73     33867.68
              2019-11-04            59.70     33867.68
              2019-11-05            59.65     33867.68
  </code></pre>
  
  <p>I've tried:</p>
  
  <pre><code>merge = pd.merge(tc, bio, left_index=True, right_index=True)
  print(merge.head(5))
  </code></pre>
  
  <p>And I get:</p>
  
  <pre><code>                               exchg_rate year month  ...      contingency  D   Bio_Avg
              2019-11-01            59.75  2019   11    ...              NaN  1  33867.68
              2019-12-01            59.94  2019   12    ...              NaN  1  38826.48
  </code></pre>
  
  <p>Off course it keeps only the exact match with the date, but I loose the days in between.
  What could be an elegant way to solve this without breaking all dataframes?</p>