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:
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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.
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Find current directory and file's directory:
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To get the current working directory use
import os cwd = os.getcwd()
Documentation references for the modules, constants and functions used above:
- The os and os.path modules.
- The __file__ constant
- os.path.realpath(path) (returns "the canonical path of the specified filename, eliminating any symbolic links encountered in the path")
- os.path.dirname(path) (returns "the directory name of pathname path")
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