00:00:00

Description

Editorial

Adaptive Product Price Unpivot by Active Store Set

HARD40 pts

A commerce data platform stores product prices in a wide table (one store channel per column), but downstream services consume row-level records. Each export batch also carries an active store list that controls which channels should be emitted.

Table: products

product_id: unique product identifier
store_north: nullable price for north region channel
store_south: nullable price for south region channel
store_east: nullable price for east region channel
store_west: nullable price for west region channel
store_online: nullable price for online channel
store_outlet: nullable price for outlet channel

Table: active_stores

store: enabled store column name for this export run

Business rules:

Keep only stores listed in active_stores.
active_stores can contain duplicate rows and must be handled correctly.
A NULL price means the product is unavailable in that store and should not produce output.

Task: Return normalized row-level records for active and available prices.

Output requirements:

Return exactly: product_id, store, price
Sort by product_id ascending, then store ascending

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

products:
| product_id | store_north | store_south | store_east | store_west | store_online | store_outlet |
|------------|-------------|-------------|------------|------------|--------------|--------------|
| 1          | 120         | null        | 100        | null       | 95           | null         |
| 2          | null        | 80          | null       | 70         | null         | 60           |

active_stores:
| store        |
|--------------|
| store_east   |
| store_online |
| store_outlet |

Output

[
  {"product_id":1,"store":"store_east","price":100},
  {"product_id":1,"store":"store_online","price":95},
  {"product_id":2,"store":"store_outlet","price":60}
]

Explanation

Only active stores are considered. Null store values are excluded.

Example

Input

products:
| product_id | store_north | store_south | store_east | store_west | store_online | store_outlet |
|------------|-------------|-------------|------------|------------|--------------|--------------|
| 10         | 15          | 20          | 25         | 30         | 35           | 40           |

active_stores:
| store       |
|-------------|
| store_west  |
| store_west  |
| store_north |

Output

[
  {"product_id":10,"store":"store_north","price":15},
  {"product_id":10,"store":"store_west","price":30}
]

Explanation

Duplicate active store rows should not duplicate output rows.

Example

Input

products:
[]

active_stores:
| store        |
|--------------|
| store_east   |
| store_online |

Output

[]

Explanation

No product rows means no output rows.

Accepted0/0·0% Acceptance

Constraints

0 <= number of rows in products <= 250
0 <= number of rows in active_stores <= 80
1 <= product_id <= 2000000000
product_id values are unique within products
Each non-null price is an integer in [1, 1000000]
active_stores.store is one of: store_north, store_south, store_east, store_west, store_online, store_outlet
active_stores may contain duplicate rows
Exclude rows where selected store price is NULL
Return exactly columns: product_id, store, price
Sort by product_id ascending, then store ascending
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with columns: product_id, store, price

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

products =

[{"product_id":1,"store_north":120,"store_south":null,"store_east":100,"store_west":null,"store_online":95,"store_outlet":null},{"product_id":3,"store_north":140,"store_south":130,"store_east":null,"store_west":null,"store_online":110,"store_outlet":90},{"product_id":2,"store_north":null,"store_south":80,"store_east":null,"store_west":70,"store_online":null,"store_outlet":60}]

active_stores =

[{"store":"store_east"},{"store":"store_online"},{"store":"store_outlet"}]

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00:00:00

Description

Editorial

Adaptive Product Price Unpivot by Active Store Set

HARD40 pts

Table: products

product_id: unique product identifier
store_north: nullable price for north region channel
store_south: nullable price for south region channel
store_east: nullable price for east region channel
store_west: nullable price for west region channel
store_online: nullable price for online channel
store_outlet: nullable price for outlet channel

Table: active_stores

store: enabled store column name for this export run

Business rules:

Keep only stores listed in active_stores.
active_stores can contain duplicate rows and must be handled correctly.
A NULL price means the product is unavailable in that store and should not produce output.

Task: Return normalized row-level records for active and available prices.

Output requirements:

Return exactly: product_id, store, price
Sort by product_id ascending, then store ascending

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

products:
| product_id | store_north | store_south | store_east | store_west | store_online | store_outlet |
|------------|-------------|-------------|------------|------------|--------------|--------------|
| 1          | 120         | null        | 100        | null       | 95           | null         |
| 2          | null        | 80          | null       | 70         | null         | 60           |

active_stores:
| store        |
|--------------|
| store_east   |
| store_online |
| store_outlet |

Output

[
  {"product_id":1,"store":"store_east","price":100},
  {"product_id":1,"store":"store_online","price":95},
  {"product_id":2,"store":"store_outlet","price":60}
]

Explanation

Only active stores are considered. Null store values are excluded.

Example

Input

products:
| product_id | store_north | store_south | store_east | store_west | store_online | store_outlet |
|------------|-------------|-------------|------------|------------|--------------|--------------|
| 10         | 15          | 20          | 25         | 30         | 35           | 40           |

active_stores:
| store       |
|-------------|
| store_west  |
| store_west  |
| store_north |

Output

[
  {"product_id":10,"store":"store_north","price":15},
  {"product_id":10,"store":"store_west","price":30}
]

Explanation

Duplicate active store rows should not duplicate output rows.

Example

Input

products:
[]

active_stores:
| store        |
|--------------|
| store_east   |
| store_online |

Output

[]

Explanation

No product rows means no output rows.

Accepted0/0·0% Acceptance

Constraints

0 <= number of rows in products <= 250
0 <= number of rows in active_stores <= 80
1 <= product_id <= 2000000000
product_id values are unique within products
Each non-null price is an integer in [1, 1000000]
active_stores.store is one of: store_north, store_south, store_east, store_west, store_online, store_outlet
active_stores may contain duplicate rows
Exclude rows where selected store price is NULL
Return exactly columns: product_id, store, price
Sort by product_id ascending, then store ascending
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with columns: product_id, store, price

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

products =

active_stores =

[{"store":"store_east"},{"store":"store_online"},{"store":"store_outlet"}]

Adaptive Product Price Unpivot by Active Store Set

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Adaptive Product Price Unpivot by Active Store Set

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