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Project: Analyzing and Formatting PostgreSQL Sales Data
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 | TIMESTAMP | |
ship_date | Date when order was shipped | TIMESTAMP | |
ship_mode | Shipping method used for the order | TEXT | |
customer_id | Unique identifier for each customer | TEXT | |
customer_name | Name of the customer | TEXT | |
segment | Customer segment | TEXT | |
city | City of the customer | TEXT | |
state | State or province of the customer | TEXT | |
country | Country of the customer | TEXT | |
postal_code | Postal code of the customer's address | TEXT | |
market | Market order 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 | INTEGER | |
discount | Discount applied for the Line Item | DOUBLE PRECISION | |
profit | Total Profit earned on the Line Item | DOUBLE PRECISION | |
shipping_cost | Cost of shipping the order | DOUBLE PRECISION | |
order_priority | Priority level of the order | TEXT |
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!
DataFrameas
top_five_products_each_category
variable
WITH top_five_products_each_category AS (
SELECT
p.category AS category,
p.product_name AS product_name,
ROUND(SUM(o.sales)::numeric, 2) AS product_total_sales,
ROUND(SUM(o.profit)::numeric, 2) AS product_total_profit,
RANK() OVER (PARTITION BY p.category ORDER BY SUM(o.sales) DESC) AS product_rank
FROM orders o
JOIN products p ON p.product_id = o.product_id
GROUP BY p.category, p.product_name
)
SELECT
category,
product_name,
product_total_sales,
product_total_profit,
product_rank
FROM top_five_products_each_category
WHERE product_rank <= 5
ORDER BY category ASC, product_total_sales DESC;
DataFrameas
salesperson_market_sales_details
variable
SELECT ppl.person,
ord.market,
CASE WHEN ord.sales >= 0 AND ord.sales < 100 THEN '0-100'
WHEN ord.sales >= 100 AND ord.sales < 500 THEN '100-500'
WHEN ord.sales >= 500 THEN '500+'
END AS sales_bin,
COUNT(DISTINCT ord.order_id) AS order_counts,
COUNT(ret.returned) AS orders_returned,
SUM(ord.sales) AS total_sales,
SUM(CASE WHEN ret.returned IS NULL THEN 0 ELSE ord.sales END) AS returned_sales
FROM orders as ord
INNER JOIN people AS ppl
ON ord.region = ppl.region
LEFT JOIN returned_orders AS ret
ON ord.order_id = ret.order_id AND ord.market = ret.market
GROUP BY sales_bin, ord.market, ppl.person
ORDER BY ppl.person, ord.market, sales_bin;DataFrameas
impute_missing_values
variable
-- impute_missing_values
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.sales / o.quantity)::NUMERIC AS unit_price
FROM orders o
RIGHT JOIN missing AS m
ON o.product_id = m.product_id
AND o.discount = m.discount
WHERE o.quantity IS NOT NULL
)
SELECT DISTINCT m.*,
ROUND(CAST(m.sales AS NUMERIC) / up.unit_price,0) AS calculated_quantity
FROM missing AS m
INNER JOIN unit_prices AS up
ON m.product_id = up.product_id;