# Write a Pandas program to split the following dataframe into groups, group by month and year based on order date and find the total purchase amount year wise, month wise.  
 import pandas as pd
pd.set_option('display.max_rows', None)
#pd.set_option('display.max_columns', None)
df = pd.DataFrame({
'ord_no':[70001,70009,70002,70004,70007,70005,70008,70010,70003,70012,70011,70013],
'purch_amt':[150.5,270.65,65.26,110.5,948.5,2400.6,5760,1983.43,2480.4,250.45, 75.29,3045.6],
'ord_date': ['05-10-2012','09-10-2012','05-10-2013','08-17-2013','10-09-2013','07-27-2014','10-09-2012','10-10-2012','10-10-2012','06-17-2014','07-08-2012','04-25-2012'],
'customer_id':[3001,3001,3005,3001,3005,3001,3005,3001,3005,3001,3005,3005],
'salesman_id': [5002,5005,5001,5003,5002,5001,5001,5006,5003,5002,5007,5001]})
print("Original Orders DataFrame:")
print(df)
df['ord_date']= pd.to_datetime(df['ord_date']) 
print("\nYear wise Month wise purchase amount:")
result = df.groupby([df['ord_date'].dt.year, df['ord_date'].dt.month]).agg({'purch_amt':sum})
print(result)
