00:00:00

Description

Editorial

Product Quantity Rollup by SKU

EASY10 pts

You are given two related tables:

Sales: transactional sales rows with columns sale_id, product_id, year, quantity, and price
Product: product metadata with columns product_id and product_name

Task:

Compute total sold quantity per product_id across all rows in Sales.
Include only product_ids that actually appear in Sales.
Return one row per qualifying product_id.

Required output columns:

product_id
total_quantity

Notes:

Product metadata is provided for context and future extensions.
This task can be solved in both SQL and Pandas.

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

Sales:
| sale_id | product_id | year | quantity | price |
|---------|------------|------|----------|-------|
| 1       | 100        | 2008 | 10       | 5000  |
| 2       | 100        | 2009 | 12       | 5000  |
| 7       | 200        | 2011 | 15       | 9000  |

Product:
| product_id | product_name |
|------------|--------------|
| 100        | Phone-A      |
| 200        | Phone-B      |
| 300        | Phone-C      |

Output

[
  {"product_id":100,"total_quantity":22},
  {"product_id":200,"total_quantity":15}
]

Explanation

Product 100 appears twice and sums to 22. Product 200 sums to 15. Product 300 has no sales and is excluded.

Example

Input

Sales:
| sale_id | product_id | year | quantity | price |
|---------|------------|------|----------|-------|
| 11      | 10         | 2020 | 5        | 30    |
| 12      | 10         | 2020 | 7        | 28    |
| 13      | 11         | 2020 | 2        | 100   |
| 14      | 10         | 2021 | 3        | 32    |

Product:
| product_id | product_name |
|------------|--------------|
| 10         | Widget-X     |
| 11         | Widget-Y     |

Output

[
  {"product_id":10,"total_quantity":15},
  {"product_id":11,"total_quantity":2}
]

Explanation

Aggregation is by product_id across all years and rows. Price changes do not affect quantity totals.

Example

Input

Sales:
| sale_id | product_id | year | quantity | price |
|---------|------------|------|----------|-------|
| 50      | 5          | 2019 | 1        | 100   |
| 51      | 6          | 2019 | 4        | 90    |
| 52      | 6          | 2020 | 6        | 95    |
| 53      | 7          | 2020 | 8        | 120   |

Product:
| product_id | product_name |
|------------|--------------|
| 5          | Alpha        |
| 6          | Beta         |
| 7          | Gamma        |
| 8          | Delta        |

Output

[
  {"product_id":5,"total_quantity":1},
  {"product_id":6,"total_quantity":10},
  {"product_id":7,"total_quantity":8}
]

Explanation

Only products present in Sales are returned. Product 8 exists in Product but is not included because it has zero sales rows.

Accepted0/0·0% Acceptance

Constraints

1 <= number of rows in Sales <= 10000
1 <= number of rows in Product <= 10000
1 <= quantity <= 1000000
1 <= price <= 10000000
(sale_id, year) is unique
Sales.product_id references Product.product_id
Return one row per product_id appearing in Sales
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with columns product_id and total_quantity

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

sales =

[{"sale_id":7,"product_id":200,"year":2011,"quantity":15,"price":9000},{"sale_id":1,"product_id":100,"year":2008,"quantity":10,"price":5000},{"sale_id":2,"product_id":100,"year":2009,"quantity":12,"price":5000}]

product =

[{"product_id":100,"product_name":"Phone-A"},{"product_id":200,"product_name":"Phone-B"},{"product_id":300,"product_name":"Phone-C"}]

Loading problem...

101

00:00:00

Description

Editorial

Product Quantity Rollup by SKU

EASY10 pts

You are given two related tables:

Sales: transactional sales rows with columns sale_id, product_id, year, quantity, and price
Product: product metadata with columns product_id and product_name

Task:

Compute total sold quantity per product_id across all rows in Sales.
Include only product_ids that actually appear in Sales.
Return one row per qualifying product_id.

Required output columns:

product_id
total_quantity

Notes:

Product metadata is provided for context and future extensions.
This task can be solved in both SQL and Pandas.

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

Sales:
| sale_id | product_id | year | quantity | price |
|---------|------------|------|----------|-------|
| 1       | 100        | 2008 | 10       | 5000  |
| 2       | 100        | 2009 | 12       | 5000  |
| 7       | 200        | 2011 | 15       | 9000  |

Product:
| product_id | product_name |
|------------|--------------|
| 100        | Phone-A      |
| 200        | Phone-B      |
| 300        | Phone-C      |

Output

[
  {"product_id":100,"total_quantity":22},
  {"product_id":200,"total_quantity":15}
]

Explanation

Product 100 appears twice and sums to 22. Product 200 sums to 15. Product 300 has no sales and is excluded.

Example

Input

Sales:
| sale_id | product_id | year | quantity | price |
|---------|------------|------|----------|-------|
| 11      | 10         | 2020 | 5        | 30    |
| 12      | 10         | 2020 | 7        | 28    |
| 13      | 11         | 2020 | 2        | 100   |
| 14      | 10         | 2021 | 3        | 32    |

Product:
| product_id | product_name |
|------------|--------------|
| 10         | Widget-X     |
| 11         | Widget-Y     |

Output

[
  {"product_id":10,"total_quantity":15},
  {"product_id":11,"total_quantity":2}
]

Explanation

Aggregation is by product_id across all years and rows. Price changes do not affect quantity totals.

Example

Input

Sales:
| sale_id | product_id | year | quantity | price |
|---------|------------|------|----------|-------|
| 50      | 5          | 2019 | 1        | 100   |
| 51      | 6          | 2019 | 4        | 90    |
| 52      | 6          | 2020 | 6        | 95    |
| 53      | 7          | 2020 | 8        | 120   |

Product:
| product_id | product_name |
|------------|--------------|
| 5          | Alpha        |
| 6          | Beta         |
| 7          | Gamma        |
| 8          | Delta        |

Output

[
  {"product_id":5,"total_quantity":1},
  {"product_id":6,"total_quantity":10},
  {"product_id":7,"total_quantity":8}
]

Explanation

Only products present in Sales are returned. Product 8 exists in Product but is not included because it has zero sales rows.

Accepted0/0·0% Acceptance

Constraints

1 <= number of rows in Sales <= 10000
1 <= number of rows in Product <= 10000
1 <= quantity <= 1000000
1 <= price <= 10000000
(sale_id, year) is unique
Sales.product_id references Product.product_id
Return one row per product_id appearing in Sales
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with columns product_id and total_quantity

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

sales =

product =

[{"product_id":100,"product_name":"Phone-A"},{"product_id":200,"product_name":"Phone-B"},{"product_id":300,"product_name":"Phone-C"}]

Product Quantity Rollup by SKU

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