Practical Exam: Grocery Store Sales
FoodYum is a grocery store chain that is based in the United States.
Food Yum sells items such as produce, meat, dairy, baked goods, snacks, and other household food staples.
As food costs rise, FoodYum wants to make sure it keeps stocking products in all categories that cover a range of prices to ensure they have stock for a broad range of customers.
Data
The data is available in the table products.
The dataset contains records of customers for their last full year of the loyalty program.
| Column Name | Criteria |
|---|---|
| product_id | Nominal. The unique identifier of the product. Missing values are not possible due to the database structure. |
| product_type | Nominal. The product category type of the product, one of 5 values (Produce, Meat, Dairy, Bakery, Snacks). Missing values should be replaced with “Unknown”. |
| brand | Nominal. The brand of the product. One of 7 possible values. Missing values should be replaced with “Unknown”. |
| weight | Continuous. The weight of the product in grams. This can be any positive value, rounded to 2 decimal places. Missing values should be replaced with the overall median weight. |
| price | Continuous. The price the product is sold at, in US dollars. This can be any positive value, rounded to 2 decimal places. Missing values should be replaced with the overall median price. |
| average_units_sold | Discrete. The average number of units sold each month. This can be any positive integer value. Missing values should be replaced with 0. |
| year_added | Nominal. The year the product was first added to FoodYum stock. Missing values should be replaced with 2022. |
| stock_location | Nominal. The location that stock originates. This can be one of four warehouse locations, A, B, C or D Missing values should be replaced with “Unknown”. |
Task 1
Last year (2022) there was a bug in the product system. For some products that were added in that year, the year_added value was not set in the data. As the year the product was added may have an impact on the price of the product, this is important information to have.
Write a query to determine how many products have the year_added value missing. Your output should be a single column, missing_year, with a single row giving the number of missing values.
-- This query counts the number of records in the 'products' table where the 'year_added' column is NULL.
SELECT
COUNT(*) FILTER (WHERE year_added IS NULL) AS missing_year
FROM
products;`# Task 2
Given what you know about the year added data, you need to make sure all of the data is clean before you start your analysis. The table below shows what the data should look like.
Write a query to ensure the product data matches the description provided. Do not update the original table.
| Column Name | Criteria |
|---|---|
| product_id | Nominal. The unique identifier of the product. Missing values are not possible due to the database structure. |
| product_type | Nominal. The product category type of the product, one of 5 values (Produce, Meat, Dairy, Bakery, Snacks). Missing values should be replaced with “Unknown”. |
| brand | Nominal. The brand of the product. One of 7 possible values. Missing values should be replaced with “Unknown”. |
| weight | Continuous. The weight of the product in grams. This can be any positive value, rounded to 2 decimal places. Missing values should be replaced with the overall median weight. |
| price | Continuous. The price the product is sold at, in US dollars. This can be any positive value, rounded to 2 decimal places. Missing values should be replaced with the overall median price. |
| average_units_sold | Discrete. The average number of units sold each month. This can be any positive integer value. Missing values should be replaced with 0. |
| year_added | Nominal. The year the product was first added to FoodYum stock. Missing values should be replaced with last year (2022). |
| stock_location | Nominal. The location that stock originates. This can be one of four warehouse locations, A, B, C or D Missing values should be replaced with “Unknown”. ` |
WITH median_values AS (
SELECT
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(REGEXP_REPLACE(weight, '[^0-9.]', '', 'g') AS numeric)) AS median_weight,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(price AS numeric)) AS median_price
FROM
products
WHERE
weight ~ '^\d+(\.\d+)?$' AND price IS NOT NULL
)
SELECT
CAST(product_id AS integer) AS product_id,
COALESCE(NULLIF(product_type, ''), 'Unknown') AS product_type,
CASE
WHEN brand = '-' OR brand IS NULL OR brand = '' THEN 'Unknown'
ELSE brand
END AS brand,
COALESCE(CAST(REGEXP_REPLACE(weight, '[^0-9.]', '', 'g') AS numeric(10,2)), (SELECT median_weight FROM median_values)) AS weight,
COALESCE(CAST(price AS numeric(10,2)), (SELECT median_price FROM median_values)) AS price,
COALESCE(average_units_sold, 0)::integer AS average_units_sold,
COALESCE(year_added, 2022)::integer AS year_added,
COALESCE(NULLIF(UPPER(stock_location), ''), 'Unknown') AS stock_location
FROM
products;
Task 3
To find out how the range varies for each product type, your manager has asked you to determine the minimum and maximum values for each product type.
Write a query to return the product_type, min_price and max_price columns.
-- This query calculates the minimum and maximum prices for each product type from the 'products' table.
SELECT
product_type, -- Selecting the product type
MIN(price) AS min_price, -- Calculating the minimum price for each product type
MAX(price) AS max_price -- Calculating the maximum price for each product type
FROM
products -- Selecting data from the 'products' table
GROUP BY
product_type; -- Grouping the results by product_type to calculate the minimum and maximum prices for each product typeTask 4
The team want to look in more detail at meat and dairy products where the average units sold was greater than ten.
Write a query to return the product_id, price and average_units_sold of the rows of interest to the team.
-- This query selects specific columns from the 'products' table based on certain conditions.
SELECT
product_id, -- Selecting the product identifier
price, -- Selecting the price of the product
average_units_sold -- Selecting the average units sold for the product
FROM
products -- Selecting data from the 'products' table
WHERE
product_type IN ('Meat', 'Dairy') -- Filtering for product types 'Meat' and 'Dairy'
AND average_units_sold > 10; -- Filtering for products with average units sold greater than 10