Pandas: Split a specified datasets into groups on customer id and calculate the number of customers starting with 'C', the list of all products
23. Grouping by Customer ID with Multiple Aggregations
Write a Pandas program to split the following datasets into groups on customer id and calculate the number of customers starting with 'C', the list of all products and the difference of maximum purchase amount and minimum purchase amount.
Test Data:
ord_no purch_amt ord_date customer_id salesman_id
0 70001 150.50 05-10-2012 C3001 5002
1 70009 270.65 09-10-2012 C3001 5005
2 70002 65.26 05-10-2012 D3005 5001
3 70004 110.50 08-17-2012 D3001 5003
4 70007 948.50 10-09-2012 C3005 5002
5 70005 2400.60 07-27-2012 D3001 5001
6 70008 5760.00 10-09-2012 C3005 5001
7 70010 1983.43 10-10-2012 D3001 5006
8 70003 2480.40 10-10-2012 D3005 5003
9 70012 250.45 06-17-2012 C3001 5002
10 70011 75.29 07-08-2012 D3005 5007
11 70013 3045.60 04-25-2012 D3005 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':['C3001','C3001','D3005','D3001','C3005','D3001','C3005','D3001','D3005','C3001','D3005','D3005'],
'salesman_id': [5002,5005,5001,5003,5002,5001,5001,5006,5003,5002,5007,5001]})
print("Original Orders DataFrame:")
print(df)
def customer_id_C(x):
return (x.str[0] == 'C').sum()
result = df.groupby(['salesman_id'])\
.agg(customer_id_start_C = ('customer_id', customer_id_C),
customer_id_list = ('customer_id', lambda x: ', '.join(x)),
purchase_amt_gap = ('purch_amt', lambda x: x.max()-x.min())
)
print("\nNumber of customers starting with ‘C’, the list of all products and the difference of maximum purchase amount and minimum purchase amount:")
print(result)
Sample Output:
Original Orders DataFrame:
ord_no purch_amt ord_date customer_id salesman_id
0 70001 150.50 05-10-2012 C3001 5002
1 70009 270.65 09-10-2012 C3001 5005
2 70002 65.26 05-10-2012 D3005 5001
3 70004 110.50 08-17-2012 D3001 5003
4 70007 948.50 10-09-2012 C3005 5002
5 70005 2400.60 07-27-2012 D3001 5001
6 70008 5760.00 10-09-2012 C3005 5001
7 70010 1983.43 10-10-2012 D3001 5006
8 70003 2480.40 10-10-2012 D3005 5003
9 70012 250.45 06-17-2012 C3001 5002
10 70011 75.29 07-08-2012 D3005 5007
11 70013 3045.60 04-25-2012 D3005 5001
Number of customers starting with ‘C’, the list of all products and the difference of maximum purchase amount and minimum purchase amount:
customer_id_start_C customer_id_list purchase_amt_gap
salesman_id
5001 1 D3005, D3001, C3005, D3005 5694.74
5002 3 C3001, C3005, C3001 798.00
5003 0 D3001, D3005 2369.90
5005 1 C3001 0.00
5006 0 D3001 0.00
5007 0 D3005 0.00
Note: Run on Spyder Python 3.7.1
For more Practice: Solve these Related Problems:
- Write a Pandas program to group a sales dataset by customer_id and then count the number of customers whose id starts with 'C'.
- Write a Pandas program to group by customer_id and aggregate the list of all products purchased by each customer.
- Write a Pandas program to group the dataset by customer_id and then compute the difference between the maximum and minimum purchase amount.
- Write a Pandas program to group by customer_id and then display a summary with customer count, product list, and purchase range.
Go to:
Next: Write a Pandas program to split the following datasets into groups on customer_id to summarize purch_amt and calculate percentage of purch_amt in each group.
Python Code Editor:
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