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Akash Soni/

Associate data analyst

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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 NameCriteria
product_idNominal. The unique identifier of the product.
Missing values are not possible due to the database structure.
product_typeNominal. The product category type of the product, one of 5 values (Produce, Meat, Dairy, Bakery, Snacks).
Missing values should be replaced with “Unknown”.
brandNominal. The brand of the product. One of 7 possible values.
Missing values should be replaced with “Unknown”.
weightContinuous. 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.
priceContinuous. 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_soldDiscrete. The average number of units sold each month. This can be any positive integer value.
Missing values should be replaced with 0.
year_addedNominal. The year the product was first added to FoodYum stock.
Missing values should be replaced with 2022.
stock_locationNominal. 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.

Unknown integration
DataFrameavailable as
missing_year
variable
-- Write your query for task 1 in this cell

SELECT COUNT(*) as missing_year
FROM products
WHERE year_added is NULL;
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

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 NameCriteria
product_idNominal. The unique identifier of the product.
Missing values are not possible due to the database structure.
product_typeNominal. The product category type of the product, one of 5 values (Produce, Meat, Dairy, Bakery, Snacks).
Missing values should be replaced with “Unknown”.
brandNominal. The brand of the product. One of 7 possible values.
Missing values should be replaced with “Unknown”.
weightContinuous. 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.
priceContinuous. 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_soldDiscrete. The average number of units sold each month. This can be any positive integer value.
Missing values should be replaced with 0.
year_addedNominal. The year the product was first added to FoodYum stock.
Missing values should be replaced with last year (2022).
stock_locationNominal. 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”.
Unknown integration
DataFrameavailable as
clean_data
variable
-- Write your query for task 2 in this cell

SELECT
    product_id,
    COALESCE(product_type, 'Unknown') AS product_type,
	COALESCE(NULLIF(brand, '-'), 'Unknown') AS brand,
	COALESCE(
        CAST(CASE WHEN weight LIKE '%grams' THEN SUBSTRING(weight, 1, LENGTH(weight) - 5) ELSE weight END AS DECIMAL),
        (SELECT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY CAST(CASE WHEN weight LIKE '%grams' THEN SUBSTRING(weight, 1, LENGTH(weight) - 5) ELSE weight END AS DECIMAL)) AS median_weight FROM products)
    ) AS weight,
	CASE 
WHEN price IS NULL THEN CAST((SELECT PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY price) FROM products) AS NUMERIC(10,2))
        ELSE CAST(price AS NUMERIC(10,2))
    END AS price,
	COALESCE(average_units_sold, 0) AS average_units_sold,
	COALESCE(year_added, 2022) AS year_added,
	CASE 
        WHEN UPPER(stock_location) = 'A' THEN 'A'
        WHEN UPPER(stock_location) = 'B' THEN 'B'
        WHEN UPPER(stock_location) = 'C' THEN 'C'
        WHEN UPPER(stock_location) = 'D' THEN 'D'
        ELSE 'Unknown'
    END AS stock_location
FROM products;



This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

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.

Unknown integration
DataFrameavailable as
min_max_product
variable
-- Write your query for task 3 in this cell

SELECT product_type,min(price) as min_price,max(price) as max_price
FROM products 
GROUP BY product_type
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.

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.

Unknown integration
DataFrameavailable as
average_price_product
variable
-- Write your query for task 4 in this cell

SELECT product_id,price,average_units_sold
FROM products 
where average_units_sold > 10 and product_type in ('Meat','Dairy')
This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
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