Cleaning a PostgreSQL Database
In this project, you will work with data from a hypothetical Super Store to challenge and enhance your SQL skills in data cleaning. This project will engage you in identifying top categories based on the highest profit margins and detecting missing values, utilizing your comprehensive knowledge of SQL concepts.
Data Dictionary:
orders:
orders:| Column | Definition | Data type | Comments |
|---|---|---|---|
row_id | Unique Record ID | INTEGER | |
order_id | Identifier for each order in table | TEXT | Connects to order_id in returned_orders table |
order_date | Date when order was placed | TEXT | |
market | Market order_id belongs to | TEXT | |
region | Region Customer belongs to | TEXT | Connects to region in people table |
product_id | Identifier of Product bought | TEXT | Connects to product_id in products table |
sales | Total Sales Amount for the Line Item | DOUBLE PRECISION | |
quantity | Total Quantity for the Line Item | DOUBLE PRECISION | |
discount | Discount applied for the Line Item | DOUBLE PRECISION | |
profit | Total Profit earned on the Line Item | DOUBLE PRECISION |
returned_orders:
returned_orders:| Column | Definition | Data type |
|---|---|---|
returned | Yes values for Order / Line Item Returned | TEXT |
order_id | Identifier for each order in table | TEXT |
market | Market order_id belongs to | TEXT |
people:
people:| Column | Definition | Data type |
|---|---|---|
person | Name of Salesperson credited with Order | TEXT |
region | Region Salesperson in operating in | TEXT |
products:
products:| Column | Definition | Data type |
|---|---|---|
product_id | Unique Identifier for the Product | TEXT |
category | Category Product belongs to | TEXT |
sub_category | Sub Category Product belongs to | TEXT |
product_name | Detailed Name of the Product | TEXT |
As you can see in the Data Dictionary above, date fields have been written to the orders table as TEXT and numeric fields like sales, profit, etc. have been written to the orders table as Double Precision. You will need to take care of these types in some of the queries. This project is an excellent opportunity to apply your SQL skills in a practical setting and gain valuable experience in data cleaning and analysis. Good luck, and happy querying!
-- What are the top 5 products from each category based on highest total sales?
SELECT *
FROM (
SELECT
category,
product_name,
ROUND(SUM(sales)::NUMERIC, 2) AS product_total_sales,
ROUND(SUM(profit)::NUMERIC, 2) AS product_total_profit,
RANK() OVER (PARTITION BY category ORDER BY SUM(sales) DESC) AS product_rank
FROM orders AS o
INNER JOIN products AS p
USING(product_id)
GROUP BY category, product_name
) AS sq
WHERE product_rank <= 5
ORDER BY category, product_rank;-- Impute missing data by quantity of products per order.
WITH missing AS (
SELECT product_id, discount, market, region, sales, quantity
FROM orders
WHERE quantity IS NULL
), unit_prices AS (
SELECT
o.product_id,
o.discount,
CAST(o.sales / o.quantity AS NUMERIC) AS unit_price
FROM orders AS o
INNER JOIN missing AS m
USING(product_id, discount)
)
SELECT DISTINCT m.product_id, m.discount, m.market, m.region, m.sales, m.quantity,
ROUND(CAST(m.sales / unit_price AS NUMERIC), 0) AS calculated_quantity
FROM missing AS m
INNER JOIN unit_prices AS u
USING(product_id)
WHERE CAST(m.sales / unit_price AS NUMERIC) IS NOT NULL