Pandas: Split the specified dataframe into groups and calculate monthly purchase amount
11. Grouping by Month – Monthly Purchase Amount
Write a Pandas program to split the following dataframe into groups and calculate monthly 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("\nMonth wise purchase amount:")
result = df.set_index('ord_date').groupby(pd.Grouper(freq='M')).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
Month wise purchase amount:
purch_amt
ord_date
2012-04-30 3045.60
2012-05-31 215.76
2012-06-30 250.45
2012-07-31 2475.89
2012-08-31 110.50
2012-09-30 270.65
2012-10-31 11172.33
For more Practice: Solve these Related Problems:
- Write a Pandas program to convert order dates into monthly periods and then group by month to calculate the total purchase amount.
- Write a Pandas program to group a sales dataset by the month extracted from the order date and compute the sum of purchase amounts for each month.
- Write a Pandas program to group the sales data by month and then plot the total monthly purchase amounts.
- Write a Pandas program to group by month (from order date) and display the average, minimum, and maximum purchase amounts per month.
Python Code Editor:
Have another way to solve this solution? Contribute your code (and comments) through Disqus.
Previous: Write a Pandas program to split the following dataframe into groups based on customer id and create a list of order date for each group.
Next: 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.
What is the difficulty level of this exercise?
Test your Programming skills with w3resource's quiz.