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SQL Essentials: Replacing Queries, Cheat Sheets, and More

SQL Essentials: Replacing Queries, Cheat Sheets, and More

SQL is a powerful tool in data analytics that lets you extract, manipulate, and transform data with ease. One of its essential functions is replacing queries. Whether you're new to SQL or just need a refresher, this article will guide you through the basics of using the Replace Query in SQL, provide some useful cheat sheets, and explore its crucial role in data analytics.

What is SQL Replace Query?

The Replace Query in SQL is a built-in function that lets you change or replace specific characters or substrings within a column or table. It's useful when you need to update existing data in a database or when you want to standardize or format your data.


The syntax of the Replace Query in SQL is straightforward and easy to use. Here's a basic example:

UPDATE table_name
SET column_name = REPLACE(column_name, 'old_value', 'new_value')
WHERE condition;

In this example, table_name is the name of the table you want to update, column_name is the name of the column you want to update, old_value is the value you want to replace, new_value is the value with which you want to replace it, and condition is an optional statement that specifies which rows to update.


Let's say you have a table called employee that contains the following columns: id, name, salary, and hire_date. You want to update the salary column for all employees who have a hire_date before January 1, 2021. Here's what your SQL query would look like:

UPDATE employee
SET salary = REPLACE(salary, '$', '')
WHERE hire_date < '2021-01-01';

This query would remove the '$' from the salary column for all employees who were hired before January 1, 2021.

SQL Cheat Sheet

If you're new to SQL or need some quick reference, a cheat sheet can be handy. Here are some Cheat sheets that can help you with your SQL Replace Query:

SQL Replace Query in Data Analytics

In data analytics, SQL can help you transform and clean your data, making it ready for analysis. One of the most common ways to use SQL Replace Query is to remove unwanted characters or strings from your data.

For example, let's say your data contains a column called product_description, and the values include the manufacturer's name, such as "Apple iPhone 13". However, you only want to analyze the model number. Using the Replace Query in SQL, you can eliminate the manufacturer's name and leave only the model number.

SELECT REPLACE(product_description, 'Apple iPhone ', '')
FROM product_table;

This SQL query would return the model number of all iPhones in the product_description column in the product_table table.

Using the Replace Query in SQL for Excel Formulas

In some cases, you might want to perform a Replace Query in SQL directly into your Excel formulas. For example, let's say you have an Excel sheet that contains a list of products with their prices and discounts. You want to calculate the final price for each item after the discount is applied.

Here's how you can do it using the Replace Query in SQL in an Excel formula:

  1. Select the cell where you want to enter the final price.
  2. Type =PRICE- DISCOUNT into the cell.
  3. Replace PRICE with the cell reference of the original price.
  4. Replace DISCOUNT with the cell reference of the discount.
  5. Use the Excel SUBSTITUTE function with the REPLACE Query in SQL to remove the currency symbol from the price and discount fields.

Here's an example formula:

=(SUBSTITUTE(SUBSTITUTE(PRICE,"$", ""),SUBSTITUTE(Discount,"%",""), "")*(100-SUBSTITUTE(Discount,"%","")))/100

This formula would calculate the final price after the discount is applied, removing the $ and % symbols in the process.


In conclusion, the Replace Query in SQL is a powerful tool that can help you update or standardize your data effortlessly. Whether you're working in data analytics or need to manipulate Excel data, knowing how to use the Replace Query in SQL is essential. Follow our tutorial, reference our cheat sheets, and explore the other functions SQL has to offer to transform your data effectively.

Remember, data is gold, and it's up to you to refine it!