# Unleashing the Power of SQL for Data Analysis

The Power of SQL: Extracting and Grouping Data by Year

If you are a data analyst or a business owner, you need to have a way to extract and analyze data easily. Structured Query Language (SQL) is a robust tool that allows you to extract data and perform calculations based on the data obtained.

One essential component that you may often need to analyze is time. In this article, we will explore two ways to extract and group data by the year column using SQL.

## Extracting Year from a Date Column

Suppose you have a table with sales records, and each record contains a date column. Suppose you want to group the sales data according to the year that it took place.

In that case, we will explore two methods of achieving that goal. Solution 1: Grouping Data by Year and Calculating Total Money Earned

One solution is to group the data by the year column and calculate the total amount of money earned for that year.

The SQL YEAR() function extracts the year from the date column. Here is the SQL code to achieve this:

“`

SELECT YEAR(date_column), SUM(money_earned)

## FROM sales_record

GROUP BY YEAR(date_column);

“`

The GROUP BY statement groups the sales records by the year column. We then use the YEAR() function to extract the year from the date column and SUM() function to calculate the total money earned for each year.

Solution 2: Displaying Complete Date, Year, and Money Earned in Corresponding Year

Another solution is to display the complete date, year, and money earned for each sales record in the corresponding year. We can use the SQL window function along with the PARTITION BY statement to achieve this.

## Here is the SQL code:

“`

SELECT date_column, YEAR(date_column) AS year, money_earned,

## SUM(money_earned) OVER (PARTITION BY YEAR(date_column)) AS total_money_earned

FROM sales_record;

“`

The window function SUM() over(), along with the PARTITION BY statement, calculates the total money earned for all sales records in each year. We can then use the year column to group the sales records by year.

## Grouping Data by a Year Column

Suppose you have a table where each row contains a year column, and you want to extract and group data by the year column. In that case, we will explore two methods to accomplish that goal.

Solution 1: Calculating Total Money Earned for Each Year

One solution is to group the data by the year column and calculate the total amount of money earned for that year. Here is the SQL code:

“`

SELECT year_column, SUM(money_earned)

## FROM sales_record

GROUP BY year_column;

“`

The GROUP BY statement groups the sales records by the year column, and the SUM() function calculates the total money earned for each year. Solution 2: Displaying Year, Month, Day, and Money Earned in Corresponding Year

Another solution is to display the year, month, day, and money earned in each sale record for the corresponding year.

## Here is the SQL code:

“`

SELECT year_column, MONTH(date_column), DAY(date_column), money_earned

## FROM sales_record

WHERE year_column = ‘2021’;

“`

The WHERE clause filters the sales records by the year column and selects only those records that have the desired year. We then display the year, month, day and money earned in each sales record.

## Conclusion

In conclusion, SQL is a powerful tool for extracting and grouping data. By using the YEAR() function and GROUP BY statement, we can extract and group data by the year column in a sales record.

We can also use the window function and PARTITION BY statement to display data with corresponding year, month, day and money earned. SQL provides endless opportunities for us to analyze data and gain valuable insights.

## 3) Using Aggregate Functions and Group By

In databases, aggregate functions are used for computation involving multiple rows. These functions are often used along with the GROUP BY statement.

Grouping data allows for data analysis at a higher level of granularity, enabling users to extract useful insights.

## Explanation of GROUP BY

The GROUP BY statement is used to group a result set by one or more columns. It allows users to summarize a large amount of data by grouping rows based on a specified column or columns.

The GROUP BY statement is used with aggregate functions such as SUM, COUNT, and AVG to perform calculations on the grouped rows.

For example, suppose we have a table called ‘orders’ and we want to group the orders by the customer’s name and calculate the total amount spent by each customer.

## The SQL query would be as follows:

“`

SELECT customer_name, SUM(order_total_amount)

## FROM orders

GROUP BY customer_name;

“`

In this example, the GROUP BY statement groups the orders by the customer’s name column, and the SUM function is used to calculate the total amount spent by each customer.

## Use of Aggregate Functions in Group By

Aggregate functions are used with the GROUP BY statement to perform computations on grouped rows. Here are some common aggregate functions used in conjunction with the GROUP BY statement:

– COUNT: returns the number of rows in a group

– SUM: calculates the sum of a column in a group

– AVG: calculates the average value of a column in a group

– MIN: returns the minimum value of a column in a group

– MAX: returns the maximum value of a column in a group

For example, suppose we have a table called ’employees’ that stores information about the company’s employees.

We want to group the employees by their department and calculate the average salary paid to employees in each department. The SQL query would be as follows:

“`

SELECT department, AVG(salary)

## FROM employees

GROUP BY department;

“`

In this example, the GROUP BY statement groups the employees by their department column, and the AVG function is used to calculate the average salary for each department.

## 4) Using Subqueries and Joins

Subqueries and joins are advanced SQL techniques that allow users to manipulate and analyze data in sophisticated ways.

Solution 1: Using Subquery to Filter Data

Subqueries are a powerful tool to extract data that meets certain conditions.

A subquery is a query nested inside another query. The subquery returns a dataset that the outer query can then use for further analysis.

Subqueries can be used in various contexts like the WHERE clause or the FROM clause.

For example, suppose we have a table called ‘products’ that stores information about the company’s products.

We want to view products that have a higher price than the average price of all the products. The SQL query would be as follows:

“`

SELECT product_name, price

## FROM products

WHERE price > (SELECT AVG(price)

FROM products);

“`

In this example, the subquery calculates the average price of all the products and returns a single value. The outer query uses this value to filter products whose price is higher than the average price.

Solution 2: Using Inner Join to Combine Data from Multiple Tables

Joins are used to combine data from multiple tables into a single result set. The INNER JOIN is the most commonly used type of join in SQL.

The INNER JOIN returns all rows from both tables where the join condition is true.

For example, suppose we have two tables, ‘orders’ and ‘order_details,’ that store information about company orders and the details of each order.

We want to combine the two tables to view the customer name, order date, and total amount spent for each order placed. The SQL query would be as follows:

“`

SELECT customers.customer_name, orders.order_date, SUM(order_details.order_total)

## FROM orders

INNER JOIN customers ON orders.customer_id = customers.customer_id

INNER JOIN order_details ON orders.order_id = order_details.order_id

GROUP BY orders.order_id;

“`

In the above example, INNER JOIN is used to link the ‘orders’ and ‘customers’ tables and the ‘orders’ and ‘order_details’ tables. The GROUP BY statement groups the result set by the order_id column, and the SUM function is used to calculate the total amount spent for each order.

## Conclusion:

In summary, SQL is a powerful tool that allows users to analyze and manipulate data effectively. Aggregate functions and the GROUP BY statement can be used to perform calculations on grouped rows.

Subqueries and joins are more advanced SQL techniques that allow users to filter data and combine data from multiple tables, respectively. By using these techniques, users can extract valuable insights and make data-driven decisions.

## 5) Using CASE Statements

CASE statements provide a powerful way to manipulate data in SQL using conditional logic. They are often used to categorize data or transform data in a query.

CASE statements can be used in various clauses such as SELECT, WHERE, and GROUP BY.

Solution 1: Grouping Data into Categories with CASE Statements

CASE statements can be used with the GROUP BY statement to categorize data into groups.

This is useful for data analysis when you want to group data into categories such as age groups or income brackets. Here is an example of how to group data into categories using CASE statements and the GROUP BY statement:

“`

## SELECT

CASE

WHEN age <= 18 THEN 'Under 18'

WHEN age > 18 AND age <= 30 THEN '18-30'

WHEN age > 30 AND age <= 50 THEN '31-50'

ELSE ‘Over 50’

END AS age_group,

COUNT(*) AS count

## FROM customers

GROUP BY age_group;

“`

In this example, we are grouping the customers into age groups using CASE statements. We then use the GROUP BY statement to group the data by the age_group column.

Solution 2: Using CASE Statements in WHERE Clause

CASE statements can also be used in the WHERE clause to filter data based on different conditions. Here is an example of how to use CASE statements in the WHERE clause:

“`

SELECT customer_name, credit_rating

## WHERE

CASE

WHEN credit_rating = ‘Good’ THEN 1

WHEN credit_rating = ‘Fair’ THEN 2

ELSE 3

END <= 2;

“`

In this example, we are filtering customers based on their credit rating using CASE statements. We then use the WHERE clause to filter data where the CASE statement is less than or equal to 2, which represents customers with a good or fair credit rating.

## 6) Using Ranking Functions

Ranking functions are used to rank rows based on their values in a specified column or columns. Ranking functions are helpful in data analysis when you need to retrieve the top or bottom-performing records from a result set.

SQL provides several built-in ranking functions such as ROW_NUMBER(), RANK(), DENSE_RANK(), and NTILE().

Solution 1: Ranking Rows Based on a Column

The ROW_NUMBER() function assigns a unique sequential number to each row in the resultset based on a specified column’s values.

Here is an example of how to use the ROW_NUMBER() function to rank rows based on a specified column:

“`

SELECT customer_name, order_date, order_total,

ROW_NUMBER() OVER (ORDER BY order_total DESC) AS row_num

## FROM orders

“`

In this example, we are using the ROW_NUMBER() function to rank rows in the ‘orders’ table based on their total order amount in descending order. The OVER clause defines the order and partitioning of the rows.

In this case, we are ordering the rows by the order_total column in descending order. Solution 2: Ranking Rows Based on Multiple Columns

Ranking functions can also be used to rank rows based on multiple columns.

In this case, we can use the ORDER BY clause to define the order in which the columns are ranked. Here is an example of how to rank rows based on multiple columns:

“`

SELECT customer_name, order_date, order_total,

RANK() OVER (ORDER BY order_total DESC, order_date ASC) AS rank_num

## FROM orders

“`

In this example, we are using the RANK() function to rank rows in the ‘orders’ table based on two columns: order_total in descending order and order_date in ascending order. The ORDER BY clause specifies the order in which the columns are ranked.

## Conclusion:

In conclusion, SQL provides several powerful tools for manipulating and analyzing data. CASE statements allow us to categorize data or transform data based on different conditions.

Ranking functions provide a powerful way to rank rows based on a specified column or columns. By using these SQL functions, we can extract valuable insights from our data and make data-driven decisions.

In summary, SQL is a powerful tool for extracting and manipulating data. On the topic of data analysis, this article covered several essential SQL techniques, including grouping data by the year column, using aggregate functions and group by, using subqueries and joins, using CASE statements, and using ranking functions.

By using these techniques in SQL, users can manipulate, categorize, and analyze data effectively. The main takeaway is that understanding and utilizing these SQL techniques can provide valuable insights for data-driven decisions and improve business operations.

Knowing how to use SQL can help individuals and organizations glean insights from their data and improve overall performance.