title: Python Interpolate Monthly value to Daily value (linear): Pandas
body: <p>I have a pandas dataframe with column of year month data(yyyymm). I am planning on interpolate data to daily &amp; weekly values. Here is my df below.</p>
  
  <pre><code>df:
      201301      201302      201303      ...    201709       201710  
  a   0.747711    0.793101    0.771819    ...    0.818161     0.812522 
  b   0.776537    0.759745    0.733673    ...    0.757496     0.765181 
  c   0.801699    0.847655    0.796586    ...    0.784537     0.763551 
  d   0.797942    0.687899    0.729911    ...    0.819887     0.772395 
  e   0.777472    0.799676    0.782947    ...    0.804533     0.791759 
  f   0.780933    0.750774    0.781056    ...    0.790846     0.773705 
  g   2.071699    2.261739    2.126915    ...    1.891780     2.098914 
  </code></pre>
  
  <p>As you can see that my df is in montly column data and I am hoping to change this to daily values. I am planning on using linear functions. Here is example. </p>
  
  <pre><code># (201302 - 201301)/31 (since January 2013 has 31 days)
  a = (0.793101-0.747711)/31
  # now a is the daily increasing (or decresing depends on values) value for a day.
  # 2013-01-01 value woud be
  0.747711
  # 2013-01-02 value woud be
  0.747711 + a
  # 2013-01-03 value woud be
  0.747711 + (a*2)
  # last day of January would be
  0.747711 + (a*30)
  # first day of Feb would be
  0.747711 + (a*31) which is 0.793101 (201302 value)
  </code></pre>
  
  <p>So my df_daily would have every day from 2013 to 2017 Oct(first day) and the values would be just like above. I am very week on working with timestamps so it would be great if there is any way to interpolate my value from month to day values. Thanks!</p>
  
  <p>Oh please let me know if my question is confusing...</p>