Introduction
SQL window functions are powerful tools that allow you to perform calculations across a set of table rows related to the current row. These functions are crucial for detailed analysis and offer more advanced capabilities than standard SQL aggregate functions. In cities that are technical learning hubs, such as Bangalore, advanced technical courses are available where one can learn about cutting-edge tools such as SQL window functions. A Data Science Course in Bangalore targeting scientists and researchers, will, for instance, cover such sophisticated tools.
SQL Window Functions and Their Uses
Here are some key SQL window functions and their uses. Do note that there are several other functions too apart from these. To learn about them, enrol in any of the Data Scientist Classes that offer advanced courses for data analysts and scientists.
1. ROW_NUMBER()
Assigns a unique number to each row, starting at 1 for the first row in each partition.
SELECT
employee_id,
department,
salary,
ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS row_num
FROM
employees;
2. RANK()
Assigns a rank to each row within the partition of a result set, with gaps in the ranking sequence when there are ties.
SELECT
employee_id,
department,
salary,
RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS rank
FROM
employees;
3. DENSE_RANK()
Similar to RANK(), but without gaps in the ranking sequence.
SELECT
employee_id,
department,
salary,
DENSE_RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS dense_rank
FROM
employees;
4. NTILE(n)
Divides rows in an ordered partition into a specified number of roughly equal groups, or buckets.
SELECT
employee_id,
department,
salary,
NTILE(4) OVER (PARTITION BY department ORDER BY salary DESC) AS quartile
FROM
employees;
5. LAG() and LEAD()
Accesses data from a subsequent or preceding row in the same result set without the use of a self-join.
SELECT
employee_id,
department,
salary,
LAG(salary, 1) OVER (PARTITION BY department ORDER BY salary) AS previous_salary,
LEAD(salary, 1) OVER (PARTITION BY department ORDER BY salary) AS next_salary
FROM
employees;
6. FIRST_VALUE() and LAST_VALUE()
Returns the first or last value in an ordered set of values.
SELECT
employee_id,
department,
salary,
FIRST_VALUE(salary) OVER (PARTITION BY department ORDER BY salary DESC) AS highest_salary,
LAST_VALUE(salary) OVER (PARTITION BY department ORDER BY salary ASC) AS lowest_salary
FROM
employees;
7. CUME_DIST()
Calculates the cumulative distribution of a value in a group of values.
SELECT
employee_id,
department,
salary,
CUME_DIST() OVER (PARTITION BY department ORDER BY salary DESC) AS cum_dist
FROM
employees;
8. PERCENT_RANK()
Calculates the relative rank of a row within a group of rows.
SELECT
employee_id,
department,
salary,
PERCENT_RANK() OVER (PARTITION BY department ORDER BY salary DESC) AS percent_rank
FROM
employees;
Use Cases and Benefits
Most Data Scientist Classes that impart practice-oriented training demonstrate the benefits of SQL window functions by showcasing examples drawn from case studies. While a number of such case studies are available for reference on the Internet, here is a list of the key benefits that SQL window functions offer.
- Detailed Reporting: Window functions enable detailed reporting and analytics without the need for complex subqueries or self-joins.
- Comparative Analysis: Functions like LAG() and LEAD() help compare current row values with previous or next row values, useful in time-series data analysis.
- Ranking and Distribution: Functions like RANK(), DENSE_RANK(), and NTILE() help in ranking data and dividing data into segments or buckets.
- Cumulative Metrics: CUME_DIST() and PERCENT_RANK() are beneficial for cumulative metrics, showing the relative standing of each row within a partition.
Conclusion
SQL window functions are essential for anyone looking to perform advanced and detailed data analysis directly within SQL queries. They provide a flexible and efficient way to calculate and compare values across rows, making them invaluable for detailed analytical tasks. If you are a data analyst seeking to acquire skills in advanced data analysis techniques, enrol for an advanced Data Science Course in Bangalore, Mumbai, Chennai, and such cities where you can learn specific tools and advanced methods used in data analysis.
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