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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
-- top_five_products_each_category
with prod_ord as (
select p.category, p.product_name, sum(ROUND(o.sales::numeric, 2)) as product_total_sales, sum(ROUND(o.profit::numeric, 2)) as product_total_profit
from orders o LEFT JOIN products p using (product_id)
group by p.category, p.product_name
),
ranked_prod_ord as (
select *, rank() over (partition by category order by product_total_sales desc) as product_rank
from prod_ord
order by category asc
),
top_five_products_each_category as(
select *
from ranked_prod_ord
where product_rank <= 5
order by category asc)
select *
from top_five_products_each_category;DataFrameas
impute_missing_values
variable
-- impute_missing_values
with emp as (
select *
from orders
where quantity IS NULL
),
--region Africa
afr as (
select *
from orders
where region = 'Africa'
),
--region EMEA
emea as (
select *
from orders
where region = 'EMEA'
),
--region West
west as (
select *
from orders
where region = 'West'
),
-- afs_c as(
-- select *,case when product_id = 'TEC-STA-10003330' and quantity is null then COALESCE(quantity, sales/253.32)
-- when product_id = 'TEC-STA-10004542' and quantity is null then COALESCE(quantity, sales/40.08) end as calculated_quantity
-- from afr
-- where afr.product_id IN (select emp.product_id from emp)),
-- emea_c as (
-- select *,case when product_id = 'FUR-ADV-10000571' and quantity is null then COALESCE(quantity, sales/109.74)
-- when product_id = 'FUR-ADV-10004395' and quantity is null then COALESCE(quantity, sales/42.06) end as calculated_quantity
-- from emea
-- where emea.product_id IN (select emp.product_id from emp)),
-- west_c as (
-- select *,case when product_id = 'FUR-BO-10001337' and quantity is null then COALESCE(quantity, sales/102.833) end as calculated_quantity
-- from west
-- where west.product_id IN (select emp.product_id from emp))
impute_missing_values as (
select product_id, discount, market, region, sales, quantity,
case when product_id = 'TEC-STA-10003330' and quantity is null then COALESCE(quantity, sales/253.32)
when product_id = 'TEC-STA-10004542' and quantity is null then COALESCE(quantity, sales/40.08)
when product_id = 'FUR-ADV-10000571' and quantity is null then COALESCE(quantity, sales/109.74)
when product_id = 'FUR-ADV-10004395' and quantity is null then COALESCE(quantity, sales/42.06)
when product_id = 'FUR-BO-10001337' and quantity is null then COALESCE(quantity, sales/102.833)
end as calculated_quantity
from emp)
select *
from impute_missing_values;