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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!
DataFrameas
top_five_products_each_category
variable
-- Goal: Top_five_products_each_by_category
-- Subquery to rank products in each category
SELECT * FROM (
-- Select category, product name, total sales, total profit, and rank products
SELECT products.category,
products.product_name,
ROUND(SUM(CAST(ord.sales AS NUMERIC)), 2) AS product_total_sales,
ROUND(SUM(CAST(ord.profit AS NUMERIC)), 2) AS product_total_profit,
RANK() OVER(PARTITION BY products.category ORDER BY SUM(ord.sales) DESC) AS product_rank
FROM orders AS ord
INNER JOIN products
ON ord.product_id = products.product_id
GROUP BY products.category, products.product_name
) AS tmp
-- Filter to get the top 5 products in each category
WHERE product_rank < 6;
DataFrameas
df
variable
-- Select missing values
SELECT product_id,
discount,
market,
region,
sales,
quantity
FROM orders
WHERE quantity IS NULL;DataFrameas
df1
variable
-- Calculate unit prices for products with non-NULL quantity
SELECT o.product_id,
CAST(o.sales / o.quantity AS NUMERIC) AS unit_price
FROM orders o
WHERE o.quantity IS NOT NULL;DataFrameas
impute_missing_values
variable
-- Goal: Impute missing values
-- Create a CTE to select products with NULL quantity
WITH missing AS (
SELECT product_id,
discount,
market,
region,
sales,
quantity
FROM orders
WHERE quantity IS NULL
),
-- Create a CTE to calculate unit prices for products with non-NULL quantity
unit_prices AS (
SELECT o.product_id,
CAST(o.sales / o.quantity AS NUMERIC) AS unit_price
FROM orders o
-- Perform RIGHT JOIN to ensure all rows from 'missing' are included
RIGHT JOIN missing AS m
ON o.product_id = m.product_id
AND o.discount = m.discount
WHERE o.quantity IS NOT NULL
)
-- Select rows from 'missing' and calculate the missing quantities
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;
DataFrameas
products
variable
SELECT * FROM public.productsDataFrameas
df2
variable
SELECT * FROM orders where quantity is null;