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thatsabhishekthatsabhishek
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added sql queries 6 to 10
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‎groupingdata/sqlquery10.sql

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Information about the table:
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Table Customer:
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+---------+------------------+-----------------------+--------+-----------+---------+----------------+
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| cust_id | cname | Address | Gender | City | Pincode | TotalOrdersYet |
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+---------+------------------+-----------------------+--------+-----------+---------+----------------+
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| 546 | Rakesh Matam | Bongora, kamrup rural | M | Guwahati | 781015 | 3 |
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| 1111 | Kuldeep Ravaliya | Bongora, kamrup rural | M | Guwahati | 781015 | 7 |
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| 670 | Sugam Sehgal | Lajpat Nagar | M | Jalandhar | 144001 | 2 |
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| 1110 | Sumit Mishra | Bongora, kamrup rural | M | Guwahati | 781015 | 1 |
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| 890 | Lokesh Daga | Ashok Nagar | M | Jalandhar | 144003 | 4 |
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| 700 | Riya Gupta | Dilbagh Nagar | F | Jalandhar | 144002 | 5 |
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| 1251 | Ram Kumar | Dilbagh Nagar | M | Jalandhar | 144002 | 1 |
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| 1300 | Shayam Singh | Ludhiana H.O | M | Ludhiana | 141001 | 15 |
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| 245 | Neelabh Shukla | Ashok Nagar | M | Jalandhar | 144003 | 10 |
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| 210 | Barkha Singh | Dilbagh Nagar | F | Jalandhar | 144002 | 1 |
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| 500 | Rohan Arora | Ludhiana H.O | M | Ludhiana | 141001 | 7 |
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+---------+------------------+-----------------------+--------+-----------+---------+----------------+
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Problem Statement:
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List down all the addresses from Jalandhar city with the number of times the address appears.
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Note: Name the number of times the address appears as "Address_times" using Alias Keyword.
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Solution:
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SELECT address, count(address) AS Address_times FROM customer WHERE city='Jalandhar' GROUP BY address;
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Output:
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+---------------+---------------+
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| address | Address_times |
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+---------------+---------------+
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| Lajpat Nagar | 1 |
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| Ashok Nagar | 2 |
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| Dilbagh Nagar | 3 |
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+---------------+---------------+

‎groupingdata/sqlquery6.sql

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Information about the table:
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Table Customer:
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+---------+------------------+-----------------------+--------+-----------+---------+----------------+
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| cust_id | cname | Address | Gender | City | Pincode | TotalOrdersYet |
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+---------+------------------+-----------------------+--------+-----------+---------+----------------+
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| 546 | Rakesh Matam | Bongora, kamrup rural | M | Guwahati | 781015 | 3 |
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| 1111 | Kuldeep Ravaliya | Bongora, kamrup rural | M | Guwahati | 781015 | 7 |
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| 670 | Sugam Sehgal | Lajpat Nagar | M | Jalandhar | 144001 | 2 |
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| 1110 | Sumit Mishra | Bongora, kamrup rural | M | Guwahati | 781015 | 1 |
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| 890 | Lokesh Daga | Ashok Nagar | M | Jalandhar | 144003 | 4 |
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| 700 | Riya Gupta | Dilbagh Nagar | F | Jalandhar | 144002 | 5 |
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| 1251 | Ram Kumar | Dilbagh Nagar | M | Jalandhar | 144002 | 1 |
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| 1300 | Shayam Singh | Ludhiana H.O | M | Ludhiana | 141001 | 15 |
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| 245 | Neelabh Shukla | Ashok Nagar | M | Jalandhar | 144003 | 10 |
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| 210 | Barkha Singh | Dilbagh Nagar | F | Jalandhar | 144002 | 1 |
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| 500 | Rohan Arora | Ludhiana H.O | M | Ludhiana | 141001 | 7 |
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+---------+------------------+-----------------------+--------+-----------+---------+----------------+
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Problem Statement:
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List out the maximum number of orders made from a particular Pincode.
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Solution:
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SELECT pincode, MAX(totalordersyet) FROM customer GROUP BY pincode;
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Output:
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+---------+---------------------+
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| pincode | MAX(totalordersyet) |
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+---------+---------------------+
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| 781015 | 7 |
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| 144001 | 2 |
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| 144003 | 10 |
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| 144002 | 5 |
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| 141001 | 15 |
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+---------+---------------------+

‎groupingdata/sqlquery7.sql

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Information about the table:
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Table Customer:
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+---------+------------------+-----------------------+--------+-----------+---------+----------------+
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| cust_id | cname | Address | Gender | City | Pincode | TotalOrdersYet |
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+---------+------------------+-----------------------+--------+-----------+---------+----------------+
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| 546 | Rakesh Matam | Bongora, kamrup rural | M | Guwahati | 781015 | 3 |
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| 1111 | Kuldeep Ravaliya | Bongora, kamrup rural | M | Guwahati | 781015 | 7 |
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| 670 | Sugam Sehgal | Lajpat Nagar | M | Jalandhar | 144001 | 2 |
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| 1110 | Sumit Mishra | Bongora, kamrup rural | M | Guwahati | 781015 | 1 |
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| 890 | Lokesh Daga | Ashok Nagar | M | Jalandhar | 144003 | 4 |
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| 700 | Riya Gupta | Dilbagh Nagar | F | Jalandhar | 144002 | 5 |
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| 1251 | Ram Kumar | Dilbagh Nagar | M | Jalandhar | 144002 | 1 |
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| 1300 | Shayam Singh | Ludhiana H.O | M | Ludhiana | 141001 | 15 |
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| 245 | Neelabh Shukla | Ashok Nagar | M | Jalandhar | 144003 | 10 |
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| 210 | Barkha Singh | Dilbagh Nagar | F | Jalandhar | 144002 | 1 |
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| 500 | Rohan Arora | Ludhiana H.O | M | Ludhiana | 141001 | 7 |
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+---------+------------------+-----------------------+--------+-----------+---------+----------------+
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Problem Statement:
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List out the minimum number of orders made from a particular Gender.
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Solution:
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SELECT GENDER, MIN(TOTALORDERSYET) FROM CUSTOMER GROUP BY GENDER;
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Output:
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+--------+---------------------+
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| GENDER | MIN(TOTALORDERSYET) |
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+--------+---------------------+
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| M | 1 |
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| F | 1 |
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+--------+---------------------+

‎groupingdata/sqlquery8.sql

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Information about the table:
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Table Customer:
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+---------+------------------+-----------------------+--------+-----------+---------+----------------+
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| cust_id | cname | Address | Gender | City | Pincode | TotalOrdersYet |
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+---------+------------------+-----------------------+--------+-----------+---------+----------------+
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| 546 | Rakesh Matam | Bongora, kamrup rural | M | Guwahati | 781015 | 3 |
8+
| 1111 | Kuldeep Ravaliya | Bongora, kamrup rural | M | Guwahati | 781015 | 7 |
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| 670 | Sugam Sehgal | Lajpat Nagar | M | Jalandhar | 144001 | 2 |
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| 1110 | Sumit Mishra | Bongora, kamrup rural | M | Guwahati | 781015 | 1 |
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| 890 | Lokesh Daga | Ashok Nagar | M | Jalandhar | 144003 | 4 |
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| 700 | Riya Gupta | Dilbagh Nagar | F | Jalandhar | 144002 | 5 |
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| 1251 | Ram Kumar | Dilbagh Nagar | M | Jalandhar | 144002 | 1 |
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| 1300 | Shayam Singh | Ludhiana H.O | M | Ludhiana | 141001 | 15 |
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| 245 | Neelabh Shukla | Ashok Nagar | M | Jalandhar | 144003 | 10 |
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| 210 | Barkha Singh | Dilbagh Nagar | F | Jalandhar | 144002 | 1 |
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| 500 | Rohan Arora | Ludhiana H.O | M | Ludhiana | 141001 | 7 |
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+---------+------------------+-----------------------+--------+-----------+---------+----------------+
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Problem Statement:
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List out the Average number of orders made from each City.
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Solution:
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SELECT city, AVG(TOTALORDERSYET) FROM customer GROUP BY city;
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Output:
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+-----------+---------------------+
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| city | AVG(TOTALORDERSYET) |
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+-----------+---------------------+
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| Guwahati | 3.6667 |
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| Jalandhar | 3.8333 |
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| Ludhiana | 11.0000 |
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+-----------+---------------------+

‎groupingdata/sqlquery9.sql

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1+
Information about the table:
2+
Table Customer:
3+
4+
+---------+------------------+-----------------------+--------+-----------+---------+----------------+
5+
| cust_id | cname | Address | Gender | City | Pincode | TotalOrdersYet |
6+
+---------+------------------+-----------------------+--------+-----------+---------+----------------+
7+
| 546 | Rakesh Matam | Bongora, kamrup rural | M | Guwahati | 781015 | 3 |
8+
| 1111 | Kuldeep Ravaliya | Bongora, kamrup rural | M | Guwahati | 781015 | 7 |
9+
| 670 | Sugam Sehgal | Lajpat Nagar | M | Jalandhar | 144001 | 2 |
10+
| 1110 | Sumit Mishra | Bongora, kamrup rural | M | Guwahati | 781015 | 1 |
11+
| 890 | Lokesh Daga | Ashok Nagar | M | Jalandhar | 144003 | 4 |
12+
| 700 | Riya Gupta | Dilbagh Nagar | F | Jalandhar | 144002 | 5 |
13+
| 1251 | Ram Kumar | Dilbagh Nagar | M | Jalandhar | 144002 | 1 |
14+
| 1300 | Shayam Singh | Ludhiana H.O | M | Ludhiana | 141001 | 15 |
15+
| 245 | Neelabh Shukla | Ashok Nagar | M | Jalandhar | 144003 | 10 |
16+
| 210 | Barkha Singh | Dilbagh Nagar | F | Jalandhar | 144002 | 1 |
17+
| 500 | Rohan Arora | Ludhiana H.O | M | Ludhiana | 141001 | 7 |
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+---------+------------------+-----------------------+--------+-----------+---------+----------------+
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Problem Statement:
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List the cities in descending order of the number of customers residing in them.
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Note: Name the number of customers residing in them as "Number" using Alias Keyword.
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Solution:
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SELECT city, COUNT(cname) AS Number FROM customer GROUP BY CITY ORDER BY Number DESC;
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Output:
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+-----------+--------+
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| city | Number |
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+-----------+--------+
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| Jalandhar | 6 |
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| Guwahati | 3 |
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| Ludhiana | 2 |
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+-----------+--------+

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