CREATE OR REPLACE FUNCTION clean_data() RETURNS VOID AS
$$
BEGIN
-- Step 1: Handling Missing Values
-- Example: Replace missing values in 'age' column with 0
UPDATE your_table SET age = 0 WHERE age IS NULL;
-- Step 2: Removing Duplicates
-- Example: Remove duplicate rows from the table
DELETE FROM your_table WHERE rowid NOT IN (SELECT MIN(rowid) FROM your_table GROUP BY column1, column2, ...);
-- Step 3: Standardizing Text Data
-- Example: Convert all text in 'name' column to lowercase
UPDATE your_table SET name = LOWER(name);
-- Step 4: Converting Data Types
-- Example: Convert 'age' column to integer
ALTER TABLE your_table ALTER COLUMN age TYPE INTEGER;
-- Step 5: Handling Outliers
-- Example: Identify and handle outliers (e.g., by setting a threshold)
UPDATE your_table SET value = threshold WHERE value > threshold;
-- Step 6: Dealing with Inconsistent Data
-- Example: Standardize 'category' column values
-- UPDATE your_table SET category = 'Standard Value' WHERE category IN ('Variation 1', 'Variation 2', ...);
-- Step 7: Handling Special Characters
-- Example: Remove special characters from 'description' column
UPDATE your_table SET description = REGEXP_REPLACE(description, '[^a-zA-Z0-9 ]', '');
-- Step 8: Normalizing or Scaling Data
-- This may require specific operations depending on your data.
-- Step 9: Handling Categorical Data
-- This often involves encoding categorical variables. The approach will depend on the specific encoding method.
-- Step 10: Handling Date and Time Data
-- Example: Convert date format to 'YYYY-MM-DD'
UPDATE your_table SET date_column = TO_CHAR(TO_DATE(date_column, 'DD-MON-YYYY'), 'YYYY-MM-DD');
-- Step 11: Creating Derived Variables
-- Example: Calculate percentage
SELECT column1, column2, (value1 / value2) * 100 AS percentage FROM your_table;
-- Step 12: Checking for Data Integrity
-- Example: Check if 'age' is non-negative
SELECT * FROM your_table WHERE age < 0;
-- Step 13: Documenting Changes
-- Consider maintaining a log table where you record the changes made to the data.
-- Step 14: Testing Data Quality
-- This involves running various queries and checks to ensure data quality. The specific tests will depend on your data and analysis requirements.
-- Step 15: Automating Data Cleansing
-- Consider using stored procedures or scheduling SQL jobs for routine data cleansing tasks.
END;
$$ LANGUAGE plpgsql;
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