Intermediate SQL

Data Science for Everyone

Limiting number of rows.

• LIMIT n limits how many rows we return

Find the first 10 rows in the jobs table

SELECT * 
FROM ____
LIMIT 10;

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Find the first 10 rows in the employees table

SELECT * 
FROM ___
LIMIT ___;

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CASE statements

• Contains a WHEN , THEN , and ELSE statement

• CASE WHEN x = 1 THEN ‘a’

• WHEN x = 2 THEN ‘b’

• ELSE ‘c’ END AS new_column

• create a new column from the employee table such that if salary is less than 10000 it is categorized as less_10000 , if it is between 10000 and 30000 then it is categorised as 10000_30000 otherwise it is categorized as above_30000 and call the column salary_range.

• select salary as well

SELECT salary ,
      CASE WHEN salary < '___' THEN '___'
           WHEN salary BETWEEN '___' AND '___' THEN '10000_30000'
           ELSE 'above_30000'
           END AS '___'
FROM employees;

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Grouping and Aggregating

• allows you to aggregate data based on a certain grouping

Find the MIN() OF salary per each job_id then per each department_id from the employees department ALIASE as min_salary

SELECT job_id , MIN(__) 
FROM employees
GROUP BY job_id;

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Find the MAX() OF salary per each job_id then per each department_id from the employees department aliase as max_salary

SELECT job_id , ___(__) AS ___
FROM employees
GROUP BY ___;

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now do it for each department_id

SELECT department_id, ___(__) 
FROM employees
GROUP BY ___;

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SELECT department_id , ___(__) 
FROM employees
GROUP BY ___;

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Find the AVG() OF salary per each job_id then per each department_id from the employees department

SELECT department_id , AVG(__) AS avg_salary
FROM ___
GROUP BY ___;

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Use COUNT(*) to find number of employees in each department department_id .

SELECT department_id ,COUNT(*) AS num_employees
FROM ___
GROUP BY ___;

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Querying A join

generally the following step show how a join is done in SQL

• SELECT t1.comon_column1,t1.common_column2,…
• FROM table1 AS t1
• <type> JOIN table2 AS t2
• ON t1.common_unique_column = t2.common_unique_column;

Inner Join

When talking about inner joins, we are only going to keep an observation if it is found in all of the tables we’re combining. Here, we’re combining the tables based on the ArtistId column. In our dummy example, there are only two artists that are found in both tables. These are highlighted in green and will be the rows used to join the two tables. Then, once the inner join happens, only these artists’ data will be included after the inner join.

when doing an inner join, data from any observation found in all the tables being joined are included in the output. Here, ArtistIds “1” and “2” are in both the artists and albums tables. Thus, those will be the only ArtistIds in the output from the inner join.

SELECT * 
FROM employees
INNER JOIN jobs
USING(job_id)

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we note from the above that only the colours that are common to both tables are returned. however instead of using USING we can use ON table1.columnname=table2.columnname where columnname is the column that tables to be matched by and in our example this is column job_id . when using this method it is important to give each table an ALIAS which is a shorthand for our tables

SELECT * 
FROM ___ AS emp
___ JOIN ___ AS jo
ON emp.___=jo.____;            

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Window functions

• A window function performs an aggregate-like operation on a set of query rows.

However, whereas an aggregate operation groups query rows into a single result row, a window function produces a result for each query row:

Anatomy of a window function

• FUNCTION_NAME() OVER()
• ORDER BY
• PARTITION BY
• ROWS/RANGE PRECEDING/FOLLOWING/UNBOUNDED

Lets get started

• The row for which function evaluation occurs is called the current row.
• The query rows related to the current row over which function evaluation occurs comprise the window for the current row.

ROW_NUMBER() OVER()

• use it if maybe you have duplicates or intend to create rowids for your data

SELECT ROW_NUMBER() OVER () AS row_id ,first_name, last_name,email, salary
FROM employees;

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PARTITION BY

• This splits the table into partitions based on a column’s unique values and results aren’t rolled into one column

therefore ROW_NUMBER() With PARTITION BY produces the row number of each row within its partition. In this case, rows are numbered per country. By default, partition rows are unordered and row numbering is nondeterministic.

• partition by job_id in employees table

SELECT ROW_NUMBER() OVER(PARTITION BY ____) AS row_num1, salary, job_id,
       first_name, last_name,email
FROM _____ ;

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RANK()

• ROW_NUMBER assigns different numbers to profits with the same amount, so it’s not a useful ranking function; if profits are the same , they should have the same rank.

in such a case we use RANK()

• rank by salary

SELECT RANK() OVER(ORDER BY ____ DESC) AS RANK, 
     first_name, last_name,email, salary
FROM employees;

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DENSE_RANK()

we can use DENSE_RANK() if we need ranks to be ordered or require a further partition

• partition by job_id and order by salary

SELECT DENSE_RANK() OVER(PARTITION BY __ ORDER BY __ DESC) AS RANK, job_id,salary
     first_name, last_name,email
FROM employees;

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now we have ranked our salary by job starting with the highest salary for each job

Aggregations

• window functions are similar to GROUP BY aggregate function but all rows stay in the output

using the employees information table, these two queries perform aggregate operations that produce a single global average for all rows taken as a group, and averages grouped per job:

SELECT AVG(___) AS avg_salary
FROM ____;

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lets us see a GROUP BY in action

SELECT job_id, AVG(___) AS avg_salary
FROM employees
GROUP BY job_id;

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On the other hand , window operations do not collapse groups of query rows to a single output row. Instead, they produce a result for each row. Like the preceding queries, the following query uses SUM(), but this time as a window function:

SELECT
first_name, last_name,email, salary,
         AVG(__) OVER() AS avg_salary,
         AVG(__) OVER(PARTITION BY ___) AS avg_per_job_salary
FROM ___
ORDER BY first_name, last_name,email, salary;         

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Note

we have included OVER clause that specifies how to partition query rows into groups for processing by the window function:

• The first OVER clause is empty, which treats the entire set of query rows as a single partition. The window function thus produces a global sum, but does so for each row.
• The second OVER clause partitions rows by country, producing a sum per partition (per country). The function produces this sum for each partition row.

Window functions are permitted only in the select list and ORDER BY clause. Query result rows are determined from the FROM clause, after WHERE, GROUP BY, and HAVING processing, and windowing execution occurs before ORDER BY, LIMIT, and SELECT DISTINCT.