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Summarize

SQL Data Cleaning for Handling Missing Values and Duplicates

Data cleaning is a critical step in the data preprocessing pipeline. It involves identifying and rectifying missing values and duplicated entries to ensure the data’s quality and reliability. SQL, with its robust querying capabilities, provides effective tools for data cleaning tasks.

In this article, we’ll explore techniques to handle missing values and duplicate entries in SQL, along with practical code examples.

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1. Handling Missing Values

Missing values can disrupt data analysis and modeling. Let’s dive into various methods to deal with missing data in SQL.

  • Filtering Out Rows with Missing Values: To remove rows with missing values from a table, you can use the WHERE clause along with the IS NULL operator. For example:
SELECT order_id, order_status, order_delivered_carrier_date
FROM orders
WHERE order_delivered_carrier_date IS NULL;
  • Replacing Missing Values: To replace missing values with a default value, you can use the COALESCE or CASE statement. For instance:
SELECT order_id, order_status, COALESCE(order_delivered_carrier_date, 'Unknown') AS filled_column
FROM orders;

Handling Duplicated Values

Duplicate entries can distort analysis results. SQL provides ways to identify and handle duplicates effectively.

  • GROUP BY and HAVING: They are used to find duplicate records.
SELECT order_id, customer_id, COUNT(*)
FROM orders
GROUP BY order_id, customer_id
HAVING COUNT(*) > 1;
  • DISTINCT: It eliminates duplicate rows from the result set.
-- Select all the unique emails.

SELECT DISTINCT email
FROM Person;
  • DELETE FROM: It removes certain rows from the result set.
-- Delete all the duplicate emails, keeping only one unique email with the smallest id.

DELETE p1
FROM Person as p1, Person as p2
WHERE p1.email = p2.email AND p1.id > p2.id;

Conclusion

Data cleaning is an indispensable step in the data analysis process, and SQL provides powerful techniques to handle missing values and duplicate entries. The code examples provided in this article can serve as a foundation for your data cleaning endeavors.

Always remember that clean data leads to more accurate insights and better decision-making.

Thank you for your time!

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