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.
SELECT COUNT(*) AS missing_year
FROM products
WHERE year_added IS NULL;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”. |
--Created a CTE to obtain median values for WEIGHT and PRICE, decimals rounded to 2 decimal places
WITH MedianValues AS (
SELECT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(REPLACE(weight, ' grams', '') AS DECIMAL(10, 2))) AS median_weight,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY price) AS median_price
FROM products
)
-- Replaced null values in product_type
SELECT product_id, COALESCE(product_type, 'Unknown') AS product_type,
-- Replaced null values in 'brand'
REGEXP_REPLACE(brand, '-', 'Unknown', 'g') AS brand,
-- Converted string values in weight column to decimal
CASE WHEN weight LIKE '%grams' THEN
CAST(REPLACE(weight, ' grams', '') AS DECIMAL(10, 2))
ELSE CAST(weight AS DECIMAL(10, 2)) END
AS weight,
-- Replaced null values with median price as created in CTE
COALESCE(CAST(price AS DECIMAL(10, 2)), median_price) AS price,
-- Replaced null values with "0"
COALESCE(average_units_sold, '0') AS average_units_sold,
--Replace null values in year added with "2022"
COALESCE(year_added, '2022') AS year_added,
-- Replaced null values and capitalized Stock Location
INITCAP(COALESCE(stock_location, 'Unknown')) AS stock_location
FROM products
-- Joined Main Query to CTE
LEFT JOIN MedianValues ON 1=1;
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.
SELECT product_type,
MIN(price) AS min_price,
MAX(price) AS max_price
FROM products
GROUP BY product_type;Task 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.
SELECT product_id, price, average_units_sold
FROM products
WHERE (product_type = 'Meat' OR product_type = 'Dairy')
AND average_units_sold >10;