﻿ Formatting Output - Exercises, Practice, Solution

Formatting Output - Exercises, Practice, Solution

SQL [10 exercises with solution]

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1. 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. 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. 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. 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.

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. 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. 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:

Nick Rimando	New York	100
Jozy Altidor	Moscow		200
Fabian Johnson	Paris		300
Graham Zusi	California	200
Julian Green	London		300
Geoff Cameron	Berlin		100

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7. 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. 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:

Fabian Johnson	Paris		300
Julian Green	London		300
Jozy Altidor	Moscow		200
Graham Zusi	California	200
Nick Rimando	New York	100
Geoff Cameron	Berlin		100

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9. 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. 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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Practice Online

More to Come !

Query visualizations are generated using Postgres Explain Visualizer (pev).

Do not submit any solution of the above exercises at here, if you want to contribute go to the appropriate exercise page.

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