27Sep, 2023
    SQL: Difference Between ORDER BY and GROUP BY:
    The GROUP BY and ORDER BY clauses in SQL

    What is the GROUP BY clause in SQL?

    The GROUP BY clause in SQL is used to group rows in a table based on the values of one or more columns. This can be useful for summarizing data or identifying trends. For example, you could use the GROUP BY clause to group rows in a table of sales data by product category to see which categories are selling the best.

    Here is a simple example of how to use the GROUP BY clause in SQL:

    Create a table:

     
    CREATE TABLE `sales` (
  `product_category` VARCHAR(255) NOT NULL,
  `quantity` INT NOT NULL
);

    Insert data into the table:

    INSERT INTO `sales` (`product_category`, `quantity`) VALUES
  ('Electronics', 10),
  ('Electronics', 20),
  ('Clothing', 30),
  ('Clothing', 40),
  ('Food & Beverage', 50),
  ('Food & Beverage', 60);
    Use the GROUP BY clause to group the rows by product category and count the number of sales for each category: 
    SELECT product_category, COUNT(*) AS total_sales
FROM sales
GROUP BY product_category;

    example 1

    GROUP BY IN SQL

    The GROUP BY clause has grouped the rows in the sales table by product category and then counted the number of rows in each group. The results show that the Food & Beverage category has the most sales, followed by the Clothing category and then the Electronics category

    You can also use the GROUP BY clause to summarize the data in each group using aggregate functions, such as SUM(), AVG(), and MAX(). For example, the following query uses the SUM() function to calculate the total sales for each product category:

     

    example 2

    SELECT product_category, SUM(quantity) AS total_sales
FROM sales
GROUP BY product_category;

    As you can see, the GROUP BY clause is a powerful tool for organizing and summarizing data in SQL. By understanding how to use the GROUP BY clause correctly, you can write more efficient and effective SQL queries.

    ORDERD BY IN SQL

    The ORDER BY clause in SQL is used to sort the results of a SELECT statement in ascending or descending order. By default, the results are sorted in ascending order, but you can use the DESC keyword to sort in descending order.

    To use the ORDER BY clause, you simply list the column(s) that you want to sort by after the ORDER BY keyword. You can also specify whether you want to sort in ascending or descending order by using the ASC and DESC keywords, respectively.

    For example, the following query will select all of the rows from the sales table and sort the results by product category in ascending order:

    Example 1

    SELECT * FROM sales ORDER BY product_category ASC;
    ORDER BY IN SQL QUERY

    Exampe 2

    SELECT * FROM sales ORDER BY product_category ASC, quantity DESC;

    Grouby and orderby use together in sql

    SELECT product_category, SUM(quantity) AS total_quantity
FROM sales
GROUP BY product_category
ORDER BY total_quantity DESC;
    groupby and order by together

    You can use the GROUP BY and ORDER BY clauses together to generate a variety of reports and summaries. For example, you could use them to group customers by country and then sort the results by total spending, or to group products by category and then sort the results by profit margin.

    The GROUP BY and ORDER BY clauses are powerful tools for organizing and analyzing your SQL query results. By using them together, you can create custom reports and summaries that meet your specific needs.

    FAQ

    The GROUP BY clause is used to group rows in a table based on the values of one or more columns, while the ORDER BY clause is used to sort rows in a table in ascending or descending order based on the values of one or more columns

    You should use GROUP BY when you want to summarize data or identify trends. For example, you could use GROUP BY to group rows in a table of sales data by product category to see which categories are selling the best.

    You should use ORDER BY when you want to organize the results of a query in a specific order. For example, you could use ORDER BY to sort the rows in a table of customer data by name in ascending order.

    One common mistake is to use an unaggregated column in the SELECT clause that is not also included in the GROUP BY clause. This will result in an error.

    Yes, you can use GROUP BY and ORDER BY in the same query. However, it is important to remember that the GROUP BY clause must come before the ORDER BY clause.

    Yes, you can order by a column that is not included in the SELECT clause, but only if the column is also included in the GROUP BY clause.

    Yes, you can order by an aggregated column, but the aggregated column must not also be included in the GROUP BY clause.

    Primary Key vs. Foreign Key Distinctions SQL

    Call to Action: Take Your SQL Skills to the Next Level

    Explore Additional SQL Tutorials and Real-World Practice

    Now that you’ve mastered the LIKE operator and wildcards, it’s time to expand your knowledge and practice with more advanced SQL topics. The best way to improve your skills is through hands-on experience. Explore the following tutorials and practice on real-world datasets to take your SQL expertise to the next level:

    These resources will guide you through complex SQL concepts and offer practical tips for real-world applications. Keep learning and refining your skills, and don’t forget to practice on real datasets to solidify your knowledge.

    SQL COMMAND FOR MASTERING DATA ANALYTICS
    26May, 2023
    TOP 10 SQL COMMAND FOR MASTERING DATA ANALYTICS

    This blog shows the importance of data analytics and the prospect of improving analytical abilities by using the provided SQL commands.

    The core of data analytics is SQL, which enables experts to mine huge amounts of data for insightful information. The top 10 SQL commands for data analytics provide a potent toolkit that gives analysts the ability to precisely manipulate, filter, sort, aggregate, and compute data. While WHERE filters data based on specific criteria, SELECT permits the retrieval of specific columns or full datasets. Data can be sorted in ascending or descending order with the help of ORDER BY. Data can be grouped for aggregations using GROUP BY, and data from several tables can be combined using JOIN.

    DISTINCT finds singular values, COUNT calculates the number of rows or non-null values, and HAVING filters grouped data. The highest and lowest values are extracted, respectively, by MAX and MIN. Data analysts may maximize the value of their datasets, find significant trends, make wise decisions, and promote company success by understanding these top 10 SQL methods.

    SELECT:

    Data from a database table is retrieved using the SELECT query. You can choose which columns to retrieve or pick all columns by using the asterisk (*). SELECT * FROM sales in the example fetches every column and row from the “sales” table.

    WHERE

    Rows can be filtered using the WHERE clause based to certain criteria. You can define a requirement that must be satisfied in order for the rows to be included in the result set. In the given example, the statement SELECT * FROM sales WHERE category = ‘Electronics’ only returns rows with the word ‘Electronics’ in the “category” column.

    ORDER BY:

    The result set is sorted using the ORDER BY clause according to one or more columns, either ascending or descending. It always sorts in ascending order by default. In the above example, the statement SELECT * FROM sales ORDER BY price DESC arranges the result set according to the “price” column in decreasing order.

    GROUP BY

    Rows can be grouped based on one or more columns using the GROUP BY clause. Calculations on each group usually involve using it in combination with aggregate functions. In the given example, grouping the rows by the “category” column and computing the total quantity for each category is done by using the statements “SELECT category, SUM(quantity) as total_quantity FROM sales GROUP BY category.”

    JOIN

    In order to merge rows from two or more tables based on a shared column, use the JOIN clause. You can use it to get information from numerous tables with only one query. The statement JOIN categories c ON s.category = c.category_id joins the tables “sales” and “categories” based on the “category” and “category_id” columns in “sales” and “categories,” respectively.

    HAVING

    Groups produced by the GROUP BY clause are filtered using the HAVING clause. You can define a prerequisite that a group must meet in order to be included in the result set. According to the example, the groups are filtered such that only those with an average price higher than 100 are included.

    DISTINCT:

    To get distinct values from a column, use the DISTINCT keyword. Duplicate values are removed from the result set. In the given example, the statement SELECT DISTINCT category FROM sales finds unique values from the column labeled “category”.

    COUNT

    Count the number of rows or non-null values in a column using the COUNT function. To determine counts per group, it is frequently used in conjunction with the GROUP BY clause. In the given example, the number of rows for each category is counted using the SELECT category, COUNT(*) as count FROM sales GROUP BY category.

    MAX

    The MAX function is used to get a column’s highest value. It is frequently used with date-and-time or numerical columns. In the given example, selecting the maximum price from the “price” column using the command SELECT MAX(price) as max_price FROM sales.

    MIN:

    The MIN function is used to get a column’s lowest value. It is frequently used with date-and-time or numerical columns. The example finds the lowest price from the “price” column by using the statement SELECT MIN(price) as min_price FROM sales.

    Create a table and insert data

    -- Create the table
CREATE TABLE Books (
    BookID INT PRIMARY KEY,
    Title VARCHAR(255),
    Author VARCHAR(255),
    PublicationYear INT,
    Genre VARCHAR(50)
);

-- Insert data into the table
INSERT INTO Books (BookID, Title, Author, PublicationYear, Genre)
VALUES
    (1, 'Example Book 1', 'Author A', 2020, 'Fiction'),
    (2, 'Example Book 2', 'Author B', 2018, 'Mystery'),
    (3, 'Example Book 3', 'Author A', 2022, 'Science Fiction'),
    -- ... (more data)
    (10, 'Example Book 10', 'Author C', 2015, 'Thriller');

    ORDER BY

    SELECT * FROM Books ORDER BY PublicationYear DESC LIMIT 10;

    COUNT:

    SELECT Genre, COUNT(*) AS GenreCount FROM Books GROUP BY Genre;

    SUM

    SELECT Author, SUM(PublicationYear) AS TotalPublicationYears FROM Books GROUP BY Author;

    AVG:

    SELECT AVG(PublicationYear) AS AvgPublicationYear FROM Books;

    Min and Max

    SELECT MAX(PublicationYear) AS MaxPublicationYear, MIN(PublicationYear) AS MinPublicationYear FROM Books;

    Group by

    SELECT Genre, AVG(PublicationYear) AS AvgPublicationYear FROM Books GROUP BY Genre;

    DISTINCT

    SELECT DISTINCT Author FROM Books;

    LIKE

    SELECT * FROM Books WHERE Title LIKE '%Example%';

    Having

    SELECT Genre, AVG(PublicationYear) AS AvgPublicationYear FROM Books GROUP BY Genre HAVING AVG(PublicationYear) > 2000;