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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 | 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!
Find the top 5 products from each category based on highest total sales. The output should be sorted by category in ascending order and by sales in descending order within each category, i.e. within each category product with highest margin should sit on the top.
DataFrameas
top_five_products_each_category
variable
-- top_five_products_each_category
WITH ranked_products AS (
SELECT
p.category,
p.product_name,
SUM(o.sales) AS product_total_sales,
SUM(o.profit) AS product_total_profit,
RANK() OVER(PARTITION BY p.category ORDER BY SUM(o.sales) DESC) AS product_rank
FROM public.orders AS o
INNER JOIN public.products AS p ON o.product_id = p.product_id
GROUP BY p.category, p.product_name
)
SELECT
category,
product_name,
product_total_sales,
product_total_profit,
product_rank
FROM ranked_products
WHERE product_rank <= 5
ORDER BY category ASC, product_total_sales DESC;Calculate the quantity for orders with missing values in the quantity column by determining the unit price for each product_id using available order data, considering relevant pricing factors such as discount, market, or region. Then, use this unit price to estimate the missing quantity values.
DataFrameas
impute_missing_values
variable
-- impute_missing_values
WITH unit_price AS (
SELECT
market,
region,
product_id,
SUM(sales) AS sales,
SUM(quantity) AS quantity,
SUM(sales) / SUM(quantity) AS unit_price,
discount
FROM public.orders
GROUP BY market, region, product_id, discount
),
discounted AS (
SELECT
market,
region,
product_id,
sales,
quantity,
discount,
unit_price,
unit_price * (1 - discount) AS discounted
FROM unit_price
),
missing AS (
SELECT
market,
region,
product_id,
sales,
quantity,
discount
FROM orders
WHERE quantity IS NULL
)
SELECT
m.*,
d.discount,
d.unit_price,
d.discounted,
ROUND(CAST(m.sales AS NUMERIC) / CAST(d.discounted AS NUMERIC), 0) AS calculated_quantity
FROM missing AS m
INNER JOIN discounted AS d
ON m.market = d.market
AND m.region = d.region
AND m.product_id = d.product_id
AND m.discount = d.discount;