import pandas as pd
import numpy as np
import matplotlib
%matplotlib inline

data.groupby(['DATE','REVENUE']).sum().unstack()

import pandas as pd
import numpy as np
import matplotlib
%matplotlib inline

data.groupby(['DATE','REVENUE']).sum().unstack()

import pandas as pd
import numpy as np
import matplotlib
%matplotlib inline

data.groupby(['DATE','REVENUE']).sum().unstack()

# Do this first, if necessary.
df['Date'] = pd.to_datetime(df['Date'], errors='coerce')

(df.groupby([pd.Grouper(key='Date', freq='MS'), 'Description'])['Revenue']
   .sum()
   .reset_index())

        Date Description     Revenue
0 2010-01-01           A  197.379999
1 2010-01-01           B   79.040000
2 2010-02-01           A   79.040000
3 2010-02-01           B   39.520000
4 2010-02-01           C   39.520000
5 2010-02-01           D   39.520000