(Python Example for Beginners)
Write a Pandas program to split a dataset to group by two columns and then sort the aggregated results within the groups.
In the following dataset group on ‘customer_id’, ‘salesman_id’ and then sort sum of purch_amt within the groups
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 :
Sample Output:
Original Orders DataFrame: ord_no purch_amt ord_date customer_id salesman_id 0 70001 150.50 2012-10-05 3001 5002 1 70009 270.65 2012-09-10 3001 5005 2 70002 65.26 2012-10-05 3005 5001 3 70004 110.50 2012-08-17 3001 5003 4 70007 948.50 2012-09-10 3005 5002 5 70005 2400.60 2012-07-27 3001 5001 6 70008 5760.00 2012-09-10 3005 5001 7 70010 1983.43 2012-10-10 3001 5006 8 70003 2480.40 2012-10-10 3005 5003 9 70012 250.45 2012-06-27 3001 5002 10 70011 75.29 2012-08-17 3005 5007 11 70013 3045.60 2012-04-25 3005 5001 Group on 'customer_id', 'salesman_id' and then sort sum of purch_amt within the groups: customer_id salesman_id 3001 5001 2400.60 5006 1983.43 5002 400.95 5005 270.65 5003 110.50 3005 5001 8870.86 5003 2480.40 5002 948.50 5007 75.29 Name: purch_amt, dtype: float64
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Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
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