Cleaning Superstore Data With SQL

A step-by-step approach

Photo by Claudio Schwarz on Unsplash

Efficient data cleaning is the foundation of any successful data analysis project.

With this in mind, I wanted to go back to basics and look at essential data cleaning techniques to enhance data integrity, before starting the analysis.

I selected a Superstore dataset from Kaggle and used SQL for the data cleaning process. The steps are listed below:

1. Initializing Database and Preliminary Checks

The first step involved was creating a dedicated database and importing the dataset into MySQL Workbench. This helped confirm the successful import of data and allowed a quick review of the structure and content.

CREATE DATABASE superstore_db;
USE superstore_db;
SHOW TABLES;
SELECT * 
FROM `superstore.csv` 
LIMIT 10;

Image by the author.

2. Creating a Clean Version of the Table

To preserve the original dataset, I created a copy for the data cleaning process.

CREATE TABLE superstore_cleaned AS 
SELECT * 
FROM `superstore.csv`;