title: Aggregate daily data by month and an additional column
body: <p>I've got a DataFrame storing daily-based data which is as below:</p>
  
  <pre>
     Date        Product Number  Description        Revenue
  2010-01-04       4219-057       Product A        39.299999    
  2010-01-04       4219-056       Product A        39.520000
  2010-01-04       4219-100       Product B        39.520000
  2010-01-04       4219-056       Product A        39.520000
  2010-01-05       4219-059       Product A        39.520000
  2010-01-05       4219-056       Product A        39.520000
  2010-01-05       4219-056       Product B        39.520000
  2010-02-08       4219-123       Product A        39.520000
  2010-02-08       4219-345       Product A        39.520000
  2010-02-08       4219-456       Product B        39.520000
  2010-02-08       4219-567       Product C        39.520000
  2010-02-08       4219-789       Product D        39.520000</pre>
  
  <p>(Product number is just to give an idea) 
  What I intend to do is to merge it into Monthly-based data.
  Something like: </p>
  
  <pre>Date        Description        Revenue
  2010-01-01    Product A        157.85000 (Sum of all Product A in Month 01)    
                Product B        79.040000
                Product C        00.000000
                Product D        00.000000
  2010-02-01    Product A        39.299999 (Sum of all Product A in Month 02)   
                Product B        39.520000
                Product C        39.520000
                Product D        39.520000  
  
  </pre>
  
  <p>The problem is I have 500+ products for every month</p>
  
  <p>I am new to python and don't know how to implement it. Currently, I am using </p>
  
  <pre><code>import pandas as pd
  import numpy as np
  import matplotlib
  %matplotlib inline
  
  data.groupby(['DATE','REVENUE']).sum().unstack()
  </code></pre>
  
  <p>but not grouping it with the Products.</p>
  
  <p>How can I implement this? </p>