Cleaning Data SQL Project

SELECT DISTINCT industry
FROM world_layoffs.layoffs_staging2
ORDER BY industry;

SELECT *
FROM world_layoffs.layoffs_staging2
WHERE industry IS NULL 
OR industry = ''
ORDER BY industry;

SELECT *
FROM world_layoffs.layoffs_staging2
WHERE company LIKE 'Bally%';

SELECT *
FROM world_layoffs.layoffs_staging2
WHERE company LIKE 'airbnb%';

UPDATE world_layoffs.layoffs_staging2
SET industry = NULL
WHERE industry = '';

SELECT *
FROM world_layoffs.layoffs_staging2
WHERE industry IS NULL 
OR industry = ''
ORDER BY industry;

UPDATE layoffs_staging2 t1
JOIN layoffs_staging2 t2
ON t1.company = t2.company
SET t1.industry = t2.industry
WHERE t1.industry IS NULL
AND t2.industry IS NOT NULL;

SELECT *
FROM world_layoffs.layoffs_staging2
WHERE industry IS NULL 
OR industry = ''
ORDER BY industry;

SELECT DISTINCT industry
FROM world_layoffs.layoffs_staging2
ORDER BY industry;

UPDATE layoffs_staging2
SET industry = 'Crypto'
WHERE industry IN ('Crypto Currency', 'CryptoCurrency');

SELECT DISTINCT industry
FROM world_layoffs.layoffs_staging2
ORDER BY industry;

SELECT *
FROM world_layoffs.layoffs_staging2;

SELECT DISTINCT country
FROM world_layoffs.layoffs_staging2
ORDER BY country;

UPDATE layoffs_staging2
SET country = TRIM(TRAILING '.' FROM country);

SELECT DISTINCT country
FROM world_layoffs.layoffs_staging2
ORDER BY country;

SELECT *
FROM world_layoffs.layoffs_staging2;

UPDATE layoffs_staging2
SET `date` = STR_TO_DATE(`date`, '%m/%d/%Y');

ALTER TABLE layoffs_staging2
MODIFY COLUMN `date` DATE;

SELECT *
FROM world_layoffs.layoffs_staging2;

SELECT *
FROM world_layoffs.layoffs_staging2
WHERE total_laid_off IS NULL;

SELECT *
FROM world_layoffs.layoffs_staging2
WHERE total_laid_off IS NULL
AND percentage_laid_off IS NULL;

DELETE FROM world_layoffs.layoffs_staging2
WHERE total_laid_off IS NULL
AND percentage_laid_off IS NULL;

SELECT * 
FROM world_layoffs.layoffs_staging2;

ALTER TABLE layoffs_staging2
DROP COLUMN row_num;


SELECT * 
FROM world_layoffs.layoffs_staging2;