w3resource

Pandas: Split a dataset to group by two columns and count by each row

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-8 with Solution

Write a Pandas program to split a dataset to group by two columns and count by each row.

Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  2012-10-05         3005         5002
1    70009     270.65  2012-09-10         3001         5005
2    70002      65.26  2012-10-05         3002         5001
3    70004     110.50  2012-08-17         3009         5003
4    70007     948.50  2012-09-10         3005         5002
5    70005    2400.60  2012-07-27         3007         5001
6    70008    5760.00  2012-09-10         3002         5001
7    70010    1983.43  2012-10-10         3004         5006
8    70003    2480.40  2012-10-10         3009         5003
9    70012     250.45  2012-06-27         3008         5002
10   70011      75.29  2012-08-17         3003         5007
11   70013    3045.60  2012-04-25         3002         5001

Sample Solution:

Python Code :

import pandas as pd
pd.set_option('display.max_rows', None)
#pd.set_option('display.max_columns', None)
orders_data = 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': ['2012-10-05','2012-09-10','2012-10-05','2012-08-17','2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17','2012-04-25'],
'customer_id':[3005,3001,3002,3009,3005,3007,3002,3004,3009,3008,3003,3002],
'salesman_id': [5002,5005,5001,5003,5002,5001,5001,5006,5003,5002,5007,5001]})
print("Original Orders DataFrame:")
print(orders_data)
print("\nGroup by two columns and count by each row:")
result = orders_data.groupby(['salesman_id','customer_id']).size().reset_index().groupby(['salesman_id','customer_id'])[[0]].max()
print(result)

Sample Output:

Original Orders DataFrame:
    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  2012-10-05         3005         5002
1    70009     270.65  2012-09-10         3001         5005
2    70002      65.26  2012-10-05         3002         5001
3    70004     110.50  2012-08-17         3009         5003
4    70007     948.50  2012-09-10         3005         5002
5    70005    2400.60  2012-07-27         3007         5001
6    70008    5760.00  2012-09-10         3002         5001
7    70010    1983.43  2012-10-10         3004         5006
8    70003    2480.40  2012-10-10         3009         5003
9    70012     250.45  2012-06-27         3008         5002
10   70011      75.29  2012-08-17         3003         5007
11   70013    3045.60  2012-04-25         3002         5001

Group by two columns and count by each row:
                         0
salesman_id customer_id   
5001        3002         3
            3007         1
5002        3005         2
            3008         1
5003        3009         2
5005        3001         1
5006        3004         1
5007        3003         1

Python Code Editor:


Have another way to solve this solution? Contribute your code (and comments) through Disqus.

Previous: Write a Pandas program to split a dataset, group by one column and get mean, min, and max values by group. Using the following dataset find the mean, min, and max values of purchase amount (purch_amt) group by customer id (customer_id).
Next: Write a Pandas program to split a dataset to group by two columns and then sort the aggregated results within the groups.

What is the difficulty level of this exercise?

Test your Python skills with w3resource's quiz



Python: Tips of the Day

Returns True if there are duplicate values in a flat list, False otherwise

Example:

def tips_duplicates(lst):
  return len(lst) != len(set(lst))

x = [2, 4, 6, 8, 4, 2]
y = [1, 3, 5, 7, 9]
print(tips_duplicates(x))
print(tips_duplicates(y))

Output:

True
False