Handling Missing Data in SQL: 8 Strategies for Data Imputation

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Handling missing data is a common challenge in data analysis, and SQL provides several techniques to manage missing values. Here are some strategies for handling missing data in SQL:

1. Identifying Missing Data:

Before addressing missing values, it’s essential to identify them. Use the IS NULL or IS NOT NULL conditions in the WHERE clause to filter rows with missing or non-missing values, respectively.

-- Rows with missing values
SELECT * FROM your_table WHERE column_name IS NULL;

-- Rows with non-missing values
SELECT * FROM your_table WHERE column_name IS NOT NULL;

2. Removing Rows with Missing Values:

One straightforward approach is to remove rows containing missing values using the DELETE statement.

DELETE FROM your_table WHERE column_name IS NULL;

However, this strategy might not be suitable if missing values are present in critical columns or if it leads to substantial data loss.

3. Replacing Missing Values:

Replace missing values with a default value or a specific value using the COALESCE or CASE statement.

-- Replace NULL with a default value…