00:00:00

Description

Editorial

Multi-Signal Above-Benchmark Businesses

MEDIUM20 pts

You are analyzing a business telemetry table that tracks how often each business triggers different event categories.

Table: events

business_id: unique business identifier
event_type: activity category name
occurrences: observed count for this business and event type

Data property:

(business_id, event_type) is unique.

Definitions:

Benchmark for an event_type = average occurrences across all businesses that have that event_type.
A business is considered active when it beats this benchmark (strictly greater than average) in more than one event_type.

Task: Return all active businesses.

Output requirements:

Return exactly one column: business_id
One row per qualifying business
Sort by business_id ascending

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

events:
| business_id | event_type  | occurrences |
|-------------|-------------|-------------|
| 1           | reviews     | 7           |
| 2           | reviews     | 3           |
| 3           | reviews     | 5           |
| 1           | ads         | 11          |
| 2           | ads         | 7           |
| 3           | ads         | 6           |
| 1           | page_views  | 9           |
| 2           | page_views  | 12          |
| 3           | page_views  | 6           |

Output

[
  {"business_id":1}
]

Explanation

Business 1 is above average in reviews and ads. It is above benchmark in two event types, so it qualifies.

Example

Input

events:
| business_id | event_type     | occurrences |
|-------------|----------------|-------------|
| 10          | search_clicks  | 50          |
| 11          | search_clicks  | 20          |
| 12          | search_clicks  | 30          |
| 10          | support_tickets| 9           |
| 11          | support_tickets| 3           |
| 12          | support_tickets| 3           |
| 10          | shares         | 5           |
| 11          | shares         | 8           |
| 12          | shares         | 8           |
| 11          | wishlists      | 18          |
| 12          | wishlists      | 6           |

Output

[
  {"business_id":10},
  {"business_id":11}
]

Explanation

Business 10 beats averages for search_clicks and support_tickets. Business 11 beats averages for shares and wishlists.

Example

Input

events:
| business_id | event_type | occurrences |
|-------------|------------|-------------|
| 101         | visits     | 20          |
| 102         | visits     | 20          |
| 101         | calls      | 10          |
| 102         | calls      | 10          |

Output

[]

Explanation

All values equal their event averages, and strict inequality is required.

Accepted0/0·0% Acceptance

Constraints

1 <= number of rows in events <= 10,000
1 <= business_id <= 1,000,000,000
1 <= occurrences <= 1,000,000,000
1 <= length of event_type <= 64
(business_id, event_type) is unique
A business qualifies only if it beats event averages in more than one event type
Comparison must be strictly greater than average, not greater-or-equal
Output must contain one row per qualifying business_id
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with column [business_id]

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

events =

[{"business_id":1,"event_type":"reviews","occurrences":7},{"business_id":2,"event_type":"reviews","occurrences":3},{"business_id":3,"event_type":"reviews","occurrences":5},{"business_id":1,"event_type":"ads","occurrences":11},{"business_id":2,"event_type":"ads","occurrences":7},{"business_id":3,"event_type":"ads","occurrences":6},{"business_id":1,"event_type":"page_views","occurrences":9},{"business_id":2,"event_type":"page_views","occurrences":12},{"business_id":3,"event_type":"page_views","occurrences":6}]

Loading problem...

101

00:00:00

Description

Editorial

Multi-Signal Above-Benchmark Businesses

MEDIUM20 pts

You are analyzing a business telemetry table that tracks how often each business triggers different event categories.

Table: events

business_id: unique business identifier
event_type: activity category name
occurrences: observed count for this business and event type

Data property:

(business_id, event_type) is unique.

Definitions:

Benchmark for an event_type = average occurrences across all businesses that have that event_type.
A business is considered active when it beats this benchmark (strictly greater than average) in more than one event_type.

Task: Return all active businesses.

Output requirements:

Return exactly one column: business_id
One row per qualifying business
Sort by business_id ascending

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

events:
| business_id | event_type  | occurrences |
|-------------|-------------|-------------|
| 1           | reviews     | 7           |
| 2           | reviews     | 3           |
| 3           | reviews     | 5           |
| 1           | ads         | 11          |
| 2           | ads         | 7           |
| 3           | ads         | 6           |
| 1           | page_views  | 9           |
| 2           | page_views  | 12          |
| 3           | page_views  | 6           |

Output

[
  {"business_id":1}
]

Explanation

Business 1 is above average in reviews and ads. It is above benchmark in two event types, so it qualifies.

Example

Input

events:
| business_id | event_type     | occurrences |
|-------------|----------------|-------------|
| 10          | search_clicks  | 50          |
| 11          | search_clicks  | 20          |
| 12          | search_clicks  | 30          |
| 10          | support_tickets| 9           |
| 11          | support_tickets| 3           |
| 12          | support_tickets| 3           |
| 10          | shares         | 5           |
| 11          | shares         | 8           |
| 12          | shares         | 8           |
| 11          | wishlists      | 18          |
| 12          | wishlists      | 6           |

Output

[
  {"business_id":10},
  {"business_id":11}
]

Explanation

Business 10 beats averages for search_clicks and support_tickets. Business 11 beats averages for shares and wishlists.

Example

Input

events:
| business_id | event_type | occurrences |
|-------------|------------|-------------|
| 101         | visits     | 20          |
| 102         | visits     | 20          |
| 101         | calls      | 10          |
| 102         | calls      | 10          |

Output

[]

Explanation

All values equal their event averages, and strict inequality is required.

Accepted0/0·0% Acceptance

Constraints

1 <= number of rows in events <= 10,000
1 <= business_id <= 1,000,000,000
1 <= occurrences <= 1,000,000,000
1 <= length of event_type <= 64
(business_id, event_type) is unique
A business qualifies only if it beats event averages in more than one event type
Comparison must be strictly greater than average, not greater-or-equal
Output must contain one row per qualifying business_id
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with column [business_id]

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

events =

Multi-Signal Above-Benchmark Businesses

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