Formatting Output - Exercises, Practice, Solution
This resource offers a total of 60 SQL Formatting Output problems for practice. It includes 10 main exercises, each accompanied by solutions, detailed explanations, and five related problems.
[An Editor is available at the bottom of the page to write and execute the scripts.]
1. Salespeople with Percent Commission
From the following table, write a SQL query to select all the salespeople. Return salesman_id, name, city, commission with the percent sign (%).
Sample table: salesman
salesman_id | name | city | commission
-------------+------------+----------+------------
5001 | James Hoog | New York | 0.15
5002 | Nail Knite | Paris | 0.13
5005 | Pit Alex | London | 0.11
5006 | Mc Lyon | Paris | 0.14
5007 | Paul Adam | Rome | 0.13
5003 | Lauson Hen | San Jose | 0.12
Sample Output:
salesman_id name city ?column? ?column? 5001 James Hoog New York % 15.00 5002 Nail Knite Paris % 13.00 5005 Pit Alex London % 11.00 5006 Mc Lyon Paris % 14.00 5007 Paul Adam Rome % 13.00 5003 Lauson Hen San Jose % 12.00
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2. Daily Order Count Format
From the following table, write a SQL query to find the number of orders booked for each day. Return the result in a format like "For 2001-10-10 there are 15 orders".".
Sample table: orders
ord_no purch_amt ord_date customer_id salesman_id ---------- ---------- ---------- ----------- ----------- 70001 150.5 2012-10-05 3005 5002 70009 270.65 2012-09-10 3001 5005 70002 65.26 2012-10-05 3002 5001 70004 110.5 2012-08-17 3009 5003 70007 948.5 2012-09-10 3005 5002 70005 2400.6 2012-07-27 3007 5001 70008 5760 2012-09-10 3002 5001 70010 1983.43 2012-10-10 3004 5006 70003 2480.4 2012-10-10 3009 5003 70012 250.45 2012-06-27 3008 5002 70011 75.29 2012-08-17 3003 5007 70013 3045.6 2012-04-25 3002 5001
Sample Output:
?column? ord_date ?column? count ?column? For 2012-04-25 ,there are 1 orders. For 2012-06-27 ,there are 1 orders. For 2012-07-27 ,there are 1 orders. For 2012-08-17 ,there are 2 orders. For 2012-09-10 ,there are 3 orders. For 2012-10-05 ,there are 2 orders. For 2012-10-10 ,there are 2 orders.
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3. All Orders Sorted by Order Number
From the following table, write a SQL query to find all the orders. Sort the result-set in ascending order by ord_no. Return all fields.
Sample table: orders
ord_no purch_amt ord_date customer_id salesman_id ---------- ---------- ---------- ----------- ----------- 70001 150.5 2012-10-05 3005 5002 70009 270.65 2012-09-10 3001 5005 70002 65.26 2012-10-05 3002 5001 70004 110.5 2012-08-17 3009 5003 70007 948.5 2012-09-10 3005 5002 70005 2400.6 2012-07-27 3007 5001 70008 5760 2012-09-10 3002 5001 70010 1983.43 2012-10-10 3004 5006 70003 2480.4 2012-10-10 3009 5003 70012 250.45 2012-06-27 3008 5002 70011 75.29 2012-08-17 3003 5007 70013 3045.6 2012-04-25 3002 5001
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4. Orders Sorted by Date Descending
From the following table, write a SQL query to find all the orders. Sort the result-set in descending order by ord_date. Return all fields.
Sample table: orders
ord_no purch_amt ord_date customer_id salesman_id ---------- ---------- ---------- ----------- ----------- 70001 150.5 2012-10-05 3005 5002 70009 270.65 2012-09-10 3001 5005 70002 65.26 2012-10-05 3002 5001 70004 110.5 2012-08-17 3009 5003 70007 948.5 2012-09-10 3005 5002 70005 2400.6 2012-07-27 3007 5001 70008 5760 2012-09-10 3002 5001 70010 1983.43 2012-10-10 3004 5006 70003 2480.4 2012-10-10 3009 5003 70012 250.45 2012-06-27 3008 5002 70011 75.29 2012-08-17 3003 5007 70013 3045.6 2012-04-25 3002 5001
Sample Output:
ord_no purch_amt ord_date customer_id salesman_id 70010 1983.43 2012-10-10 3004 5006 70003 2480.40 2012-10-10 3009 5003 70002 65.26 2012-10-05 3002 5001 70001 150.50 2012-10-05 3005 5002 70009 270.65 2012-09-10 3001 5005 70008 5760.00 2012-09-10 3002 5001 70007 948.50 2012-09-10 3005 5002 70011 75.29 2012-08-17 3003 5007 70004 110.50 2012-08-17 3009 5003 70005 2400.60 2012-07-27 3007 5001 70012 250.45 2012-06-27 3008 5002 70013 3045.60 2012-04-25 3002 5001
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5. Orders Sorted by Date & Amount Descending
From the following table, write a SQL query to find all the orders. Sort the result-set in descending order by ord_date and purch_amt. Return all fields.
Sample table: orders
ord_no purch_amt ord_date customer_id salesman_id ---------- ---------- ---------- ----------- ----------- 70001 150.5 2012-10-05 3005 5002 70009 270.65 2012-09-10 3001 5005 70002 65.26 2012-10-05 3002 5001 70004 110.5 2012-08-17 3009 5003 70007 948.5 2012-09-10 3005 5002 70005 2400.6 2012-07-27 3007 5001 70008 5760 2012-09-10 3002 5001 70010 1983.43 2012-10-10 3004 5006 70003 2480.4 2012-10-10 3009 5003 70012 250.45 2012-06-27 3008 5002 70011 75.29 2012-08-17 3003 5007 70013 3045.6 2012-04-25 3002 5001
Sample Output:
ord_no purch_amt ord_date customer_id salesman_id 70013 3045.60 2012-04-25 3002 5001 70012 250.45 2012-06-27 3008 5002 70005 2400.60 2012-07-27 3007 5001 70004 110.50 2012-08-17 3009 5003 70011 75.29 2012-08-17 3003 5007 70008 5760.00 2012-09-10 3002 5001 70007 948.50 2012-09-10 3005 5002 70009 270.65 2012-09-10 3001 5005 70001 150.50 2012-10-05 3005 5002 70002 65.26 2012-10-05 3002 5001 70003 2480.40 2012-10-10 3009 5003 70010 1983.43 2012-10-10 3004 5006
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6. Customers Sorted by ID
From the following table, write a SQL query to find all the customers. Sort the result-set by customer_id. Return cust_name, city, grade.
Sample table: customer
customer_id | cust_name | city | grade | salesman_id
-------------+----------------+------------+-------+-------------
3002 | Nick Rimando | New York | 100 | 5001
3007 | Brad Davis | New York | 200 | 5001
3005 | Graham Zusi | California | 200 | 5002
3008 | Julian Green | London | 300 | 5002
3004 | Fabian Johnson | Paris | 300 | 5006
3009 | Geoff Cameron | Berlin | 100 | 5003
3003 | Jozy Altidor | Moscow | 200 | 5007
3001 | Brad Guzan | London | | 5005
Sample Output:
cust_name city grade Brad Guzan London Nick Rimando New York 100 Jozy Altidor Moscow 200 Fabian Johnson Paris 300 Graham Zusi California 200 Brad Davis New York 200 Julian Green London 300 Geoff Cameron Berlin 100
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7. Maximum Purchase per Salesperson per Date
From the following table, write a SQL query that calculates the maximum purchase amount generated by each salesperson for each order date. Sort the result-set by salesperson id and order date in ascending order. Return salesperson id, order date and maximum purchase amount.
Sample table: orders
ord_no purch_amt ord_date customer_id salesman_id ---------- ---------- ---------- ----------- ----------- 70001 150.5 2012-10-05 3005 5002 70009 270.65 2012-09-10 3001 5005 70002 65.26 2012-10-05 3002 5001 70004 110.5 2012-08-17 3009 5003 70007 948.5 2012-09-10 3005 5002 70005 2400.6 2012-07-27 3007 5001 70008 5760 2012-09-10 3002 5001 70010 1983.43 2012-10-10 3004 5006 70003 2480.4 2012-10-10 3009 5003 70012 250.45 2012-06-27 3008 5002 70011 75.29 2012-08-17 3003 5007 70013 3045.6 2012-04-25 3002 5001
Sample Output:
salesman_id ord_date max 5001 2012-04-25 3045.60 5001 2012-07-27 2400.60 5001 2012-09-10 5760.00 5001 2012-10-05 65.26 5002 2012-06-27 250.45 5002 2012-09-10 948.50 5002 2012-10-05 150.50 5003 2012-08-17 110.50 5003 2012-10-10 2480.40 5005 2012-09-10 270.65 5006 2012-10-10 1983.43 5007 2012-08-17 75.29
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8. Customers Sorted by Third Field
From the following table, write a SQL query to find all the customers. Sort the result-set in descending order on 3rd field. Return customer name, city and grade.
Sample table: customer
customer_id | cust_name | city | grade | salesman_id
-------------+----------------+------------+-------+-------------
3002 | Nick Rimando | New York | 100 | 5001
3007 | Brad Davis | New York | 200 | 5001
3005 | Graham Zusi | California | 200 | 5002
3008 | Julian Green | London | 300 | 5002
3004 | Fabian Johnson | Paris | 300 | 5006
3009 | Geoff Cameron | Berlin | 100 | 5003
3003 | Jozy Altidor | Moscow | 200 | 5007
3001 | Brad Guzan | London | | 5005
Sample Output:
cust_name city grade Brad Guzan London Fabian Johnson Paris 300 Julian Green London 300 Brad Davis New York 200 Jozy Altidor Moscow 200 Graham Zusi California 200 Nick Rimando New York 100 Geoff Cameron Berlin 100
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9. Customer Unique Orders and Maximum Purchase
From the following table, write a SQL query that counts the unique orders and the highest purchase amount for each customer. Sort the result-set in descending order on 2nd field. Return customer ID, number of distinct orders and highest purchase amount by each customer.
Sample table: orders
ord_no purch_amt ord_date customer_id salesman_id ---------- ---------- ---------- ----------- ----------- 70001 150.5 2012-10-05 3005 5002 70009 270.65 2012-09-10 3001 5005 70002 65.26 2012-10-05 3002 5001 70004 110.5 2012-08-17 3009 5003 70007 948.5 2012-09-10 3005 5002 70005 2400.6 2012-07-27 3007 5001 70008 5760 2012-09-10 3002 5001 70010 1983.43 2012-10-10 3004 5006 70003 2480.4 2012-10-10 3009 5003 70012 250.45 2012-06-27 3008 5002 70011 75.29 2012-08-17 3003 5007 70013 3045.6 2012-04-25 3002 5001
Sample Output:
customer_id count max 3002 3 5760.00 3009 2 2480.40 3005 2 948.50 3004 1 1983.43 3001 1 270.65 3007 1 2400.60 3008 1 250.45 3003 1 75.29
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10. Daily Totals with Commission Calculation
From the following table, write a SQL query to calculate the summation of purchase amount, total commission (15% for all salespeople) by each order date. Sort the result-set on order date. Return order date, summation of purchase amount and commission.
Sample table : orders
ord_no purch_amt ord_date customer_id salesman_id ---------- ---------- ---------- ----------- ----------- 70001 150.5 2012-10-05 3005 5002 70009 270.65 2012-09-10 3001 5005 70002 65.26 2012-10-05 3002 5001 70004 110.5 2012-08-17 3009 5003 70007 948.5 2012-09-10 3005 5002 70005 2400.6 2012-07-27 3007 5001 70008 5760 2012-09-10 3002 5001 70010 1983.43 2012-10-10 3004 5006 70003 2480.4 2012-10-10 3009 5003 70012 250.45 2012-06-27 3008 5002 70011 75.29 2012-08-17 3003 5007 70013 3045.6 2012-04-25 3002 5001
Sample Output:
ord_date sum ?column? 2012-04-25 3045.60 456.8400 2012-06-27 250.45 37.5675 2012-07-27 2400.60 360.0900 2012-08-17 185.79 27.8685 2012-09-10 6979.15 1046.8725 2012-10-05 215.76 32.3640 2012-10-10 4463.83 669.5745
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