Pandas: Split a specified dataframe into groups and calculate quarterly purchase amount
21. Quarterly Purchase Amount Calculation
Write a Pandas program to split the following dataframe into groups and calculate quarterly purchase amount.
Test Data:
ord_no purch_amt ord_date customer_id salesman_id 0 70001 150.50 05-10-2012 3001 5002 1 70009 270.65 09-10-2012 3001 5005 2 70002 65.26 05-10-2012 3005 5001 3 70004 110.50 08-17-2012 3001 5003 4 70007 948.50 10-09-2012 3005 5002 5 70005 2400.60 07-27-2012 3001 5001 6 70008 5760.00 10-09-2012 3005 5001 7 70010 1983.43 10-10-2012 3001 5006 8 70003 2480.40 10-10-2012 3005 5003 9 70012 250.45 06-17-2012 3001 5002 10 70011 75.29 07-08-2012 3005 5007 11 70013 3045.60 04-25-2012 3005 5001
Sample Solution:
Python Code :
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','05-10-2012','08-17-2012','10-09-2012','07-27-2012','10-09-2012','10-10-2012','10-10-2012','06-17-2012','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("\nQuartly purchase amount:")
result = df.set_index('ord_date').groupby(pd.Grouper(freq='Q')).agg({'purch_amt':sum})
print(result)
Sample Output:
Original Orders DataFrame: ord_no purch_amt ord_date customer_id salesman_id 0 70001 150.50 05-10-2012 3001 5002 1 70009 270.65 09-10-2012 3001 5005 2 70002 65.26 05-10-2012 3005 5001 3 70004 110.50 08-17-2012 3001 5003 4 70007 948.50 10-09-2012 3005 5002 5 70005 2400.60 07-27-2012 3001 5001 6 70008 5760.00 10-09-2012 3005 5001 7 70010 1983.43 10-10-2012 3001 5006 8 70003 2480.40 10-10-2012 3005 5003 9 70012 250.45 06-17-2012 3001 5002 10 70011 75.29 07-08-2012 3005 5007 11 70013 3045.60 04-25-2012 3005 5001 Quartly purchase amount: purch_amt ord_date 2012-06-30 3511.81 2012-09-30 2857.04 2012-12-31 11172.33
For more Practice: Solve these Related Problems:
- Write a Pandas program to convert order dates into quarterly periods and then group by quarter to calculate total purchase amount.
- Write a Pandas program to group the sales dataset by quarter and then sum the purchase amounts for each quarter.
- Write a Pandas program to group a sales dataset by quarterly periods and then compare the quarterly totals.
- Write a Pandas program to extract quarterly time periods from order dates and then aggregate the purchase amount per quarter.
Python Code Editor:
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