00:00:00

Description

Editorial

Store Price Matrix by Product

EASY10 pts

A retail pricing warehouse stores one row per (product, store) offer. Analysts need a fixed-format report that places each store's price in its own column so downstream dashboards can compare stores side by side.

Table: products

product_id: unique product identifier
store: store label, always one of "store1", "store2", "store3"
price: listed price of that product in that store

Data guarantees:

(product_id, store) is unique.
A product can appear in one, two, or all three stores.
Missing store entries mean that product is not currently sold in that store.

Task:

Return one row per product_id.
Pivot store values into three fixed columns: store1, store2, store3.
If a product is absent in a store, return NULL for that store column.

Output requirements:

Return exactly these columns in this order: product_id, store1, store2, store3
Recommended deterministic ordering: product_id ascending.

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

products:
| product_id | store  | price |
|------------|--------|-------|
| 0          | store1 | 95    |
| 0          | store3 | 105   |
| 0          | store2 | 100   |
| 1          | store1 | 70    |
| 1          | store3 | 80    |

Output

[
  {"product_id":0,"store1":95,"store2":100,"store3":105},
  {"product_id":1,"store1":70,"store2":null,"store3":80}
]

Explanation

Product 0 has all three store prices. Product 1 has no store2 row, so store2 is null.

Example

Input

products:
| product_id | store  | price |
|------------|--------|-------|
| 11         | store2 | 45    |
| 11         | store1 | 30    |
| 12         | store3 | 90    |
| 13         | store2 | 0     |

Output

[
  {"product_id":11,"store1":30,"store2":45,"store3":null},
  {"product_id":12,"store1":null,"store2":null,"store3":90},
  {"product_id":13,"store1":null,"store2":0,"store3":null}
]

Explanation

Rows may be unordered, and a zero price is valid data that must be preserved.

Example

Input

products:
| product_id | store  | price |
|------------|--------|-------|
| (no rows)  |

Output

[]

Explanation

With no product-store rows, there are no products to report.

Accepted0/0·0% Acceptance

Constraints

0 <= number of rows in products <= 200000
0 <= product_id <= 1000000000
store is one of: store1, store2, store3
0 <= price <= 1000000000
(product_id, store) is unique
Each product contributes at most one value to each store column
Output must contain exactly four columns
Output column order must be: product_id, store1, store2, store3
For SQL submissions, assume SQLite 3.27.2 behavior
For Pandas submissions, return a DataFrame with the required columns

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

products =

[{"product_id":0,"store":"store3","price":105},{"product_id":1,"store":"store1","price":70},{"product_id":0,"store":"store1","price":95},{"product_id":0,"store":"store2","price":100},{"product_id":1,"store":"store3","price":80}]

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101

00:00:00

Description

Editorial

Store Price Matrix by Product

EASY10 pts

Table: products

product_id: unique product identifier
store: store label, always one of "store1", "store2", "store3"
price: listed price of that product in that store

Data guarantees:

(product_id, store) is unique.
A product can appear in one, two, or all three stores.
Missing store entries mean that product is not currently sold in that store.

Task:

Return one row per product_id.
Pivot store values into three fixed columns: store1, store2, store3.
If a product is absent in a store, return NULL for that store column.

Output requirements:

Return exactly these columns in this order: product_id, store1, store2, store3
Recommended deterministic ordering: product_id ascending.

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

products:
| product_id | store  | price |
|------------|--------|-------|
| 0          | store1 | 95    |
| 0          | store3 | 105   |
| 0          | store2 | 100   |
| 1          | store1 | 70    |
| 1          | store3 | 80    |

Output

[
  {"product_id":0,"store1":95,"store2":100,"store3":105},
  {"product_id":1,"store1":70,"store2":null,"store3":80}
]

Explanation

Product 0 has all three store prices. Product 1 has no store2 row, so store2 is null.

Example

Input

products:
| product_id | store  | price |
|------------|--------|-------|
| 11         | store2 | 45    |
| 11         | store1 | 30    |
| 12         | store3 | 90    |
| 13         | store2 | 0     |

Output

[
  {"product_id":11,"store1":30,"store2":45,"store3":null},
  {"product_id":12,"store1":null,"store2":null,"store3":90},
  {"product_id":13,"store1":null,"store2":0,"store3":null}
]

Explanation

Rows may be unordered, and a zero price is valid data that must be preserved.

Example

Input

products:
| product_id | store  | price |
|------------|--------|-------|
| (no rows)  |

Output

[]

Explanation

With no product-store rows, there are no products to report.

Accepted0/0·0% Acceptance

Constraints

0 <= number of rows in products <= 200000
0 <= product_id <= 1000000000
store is one of: store1, store2, store3
0 <= price <= 1000000000
(product_id, store) is unique
Each product contributes at most one value to each store column
Output must contain exactly four columns
Output column order must be: product_id, store1, store2, store3
For SQL submissions, assume SQLite 3.27.2 behavior
For Pandas submissions, return a DataFrame with the required columns

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

products =

Store Price Matrix by Product

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Store Price Matrix by Product

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