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!
-- top_five_products_each_category
Select *
From (Select p.category, p.product_name, Round(CAST(Sum(o.sales)as numeric),2)as product_total_sales, Round(Cast(Sum(o.profit)as numeric), 2)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)as tmp
Where product_rank<6-- salesperson_market_sales_details
Select p.person, o.market,
Case When o.sales>0 and o.sales<100 Then '0-100'
When o.sales>=100 and o.sales<500 Then '100-500'
When o.sales>=500 Then '500+' End As sales_bin,
Count(DISTINCT o.order_id)As order_counts, Count(ro.returned)as orders_returned,
Sum(o.sales)As total_sales, Sum(Case When ro.returned is null Then 0 Else o.sales End)As returned_sales
From public.orders as o
Inner Join public.people as p On o.region=p.region
Left Join public.returned_orders as ro On o.order_id=ro.order_id and o.market=ro.market
Group By sales_bin, p.person, o.market
Order By p.person, o.market, sales_bin-- impute_missing_values
With missing As (Select product_id, discount, market, region, sales, quantity From public.orders Where public.orders.quantity is null),
unit_prices As (Select o.product_id, Cast(o.sales/o.quantity as numeric)as unit_price
From public.orders as o right join missing as m On m.product_id=o.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)As calculated_quantity
From missing as m Inner join unit_prices as up On m.product_id=up.product_id