00:00:00

Description

Editorial

Daily Distinct Product Rollup

EASY10 pts

A retail analytics pipeline records one row per observed sale event.

Table: activities

sell_date: calendar day of the sale event
product: sold product name
There is no primary key; duplicate rows are allowed

Task: For each sell_date, build a compact daily summary with:

num_sold: number of distinct products sold on that date
products: comma-separated list of distinct product names in lexicographic order

Output requirements:

Return exactly these columns in this order:
- sell_date
- num_sold
- products
Sort the final result by sell_date ascending.

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

activities:
| sell_date  | product    |
|------------|------------|
| 2024-05-30 | Headphone  |
| 2024-06-01 | Pencil     |
| 2024-06-02 | Mask       |
| 2024-05-30 | Basketball |
| 2024-06-01 | Bible      |
| 2024-06-02 | Mask       |
| 2024-05-30 | TShirt     |

Output

[
  {"sell_date":"2024-05-30","num_sold":3,"products":"Basketball,Headphone,TShirt"},
  {"sell_date":"2024-06-01","num_sold":2,"products":"Bible,Pencil"},
  {"sell_date":"2024-06-02","num_sold":1,"products":"Mask"}
]

Explanation

Duplicate rows do not increase num_sold. Product names are deduplicated and listed in lexicographic order per day.

Example

Input

activities:
| sell_date  | product |
|------------|---------|
| 2025-01-10 | alpha   |
| 2025-01-10 | alpha   |
| 2025-01-10 | alpha   |
| 2025-01-11 | beta    |
| 2025-01-11 | gamma   |
| 2025-01-11 | beta    |

Output

[
  {"sell_date":"2025-01-10","num_sold":1,"products":"alpha"},
  {"sell_date":"2025-01-11","num_sold":2,"products":"beta,gamma"}
]

Explanation

Each day is computed independently. Repeated appearances of the same product on one day are counted once.

Example

Input

activities:
| sell_date  | product |
|------------|---------|
| 2023-09-04 | p2      |
| 2023-09-04 | p10     |
| 2023-09-04 | p1      |
| 2023-09-03 | x       |

Output

[
  {"sell_date":"2023-09-03","num_sold":1,"products":"x"},
  {"sell_date":"2023-09-04","num_sold":3,"products":"p1,p10,p2"}
]

Explanation

Lexicographic sorting is string-based, so p10 appears between p1 and p2.

Accepted0/0·0% Acceptance

Constraints

1 <= number of rows in activities <= 500000
sell_date is a valid YYYY-MM-DD date
1 <= length of product <= 100
activities may contain duplicate rows
A product can appear multiple times on the same date
Distinct counting is per sell_date
products must include each distinct daily product exactly once
products must be lexicographically ordered within each date
Return exactly three columns: sell_date, num_sold, products
Order final rows by sell_date ascending
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with columns: sell_date, num_sold, products

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

activities =

[{"sell_date":"2024-06-02","product":"Mask"},{"sell_date":"2024-06-02","product":"Mask"},{"sell_date":"2024-06-01","product":"Pencil"},{"sell_date":"2024-05-30","product":"Headphone"},{"sell_date":"2024-05-30","product":"TShirt"},{"sell_date":"2024-06-01","product":"Bible"},{"sell_date":"2024-05-30","product":"Basketball"}]

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101

00:00:00

Description

Editorial

Daily Distinct Product Rollup

EASY10 pts

A retail analytics pipeline records one row per observed sale event.

Table: activities

sell_date: calendar day of the sale event
product: sold product name
There is no primary key; duplicate rows are allowed

Task: For each sell_date, build a compact daily summary with:

num_sold: number of distinct products sold on that date
products: comma-separated list of distinct product names in lexicographic order

Output requirements:

Return exactly these columns in this order:
- sell_date
- num_sold
- products
Sort the final result by sell_date ascending.

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

activities:
| sell_date  | product    |
|------------|------------|
| 2024-05-30 | Headphone  |
| 2024-06-01 | Pencil     |
| 2024-06-02 | Mask       |
| 2024-05-30 | Basketball |
| 2024-06-01 | Bible      |
| 2024-06-02 | Mask       |
| 2024-05-30 | TShirt     |

Output

[
  {"sell_date":"2024-05-30","num_sold":3,"products":"Basketball,Headphone,TShirt"},
  {"sell_date":"2024-06-01","num_sold":2,"products":"Bible,Pencil"},
  {"sell_date":"2024-06-02","num_sold":1,"products":"Mask"}
]

Explanation

Duplicate rows do not increase num_sold. Product names are deduplicated and listed in lexicographic order per day.

Example

Input

activities:
| sell_date  | product |
|------------|---------|
| 2025-01-10 | alpha   |
| 2025-01-10 | alpha   |
| 2025-01-10 | alpha   |
| 2025-01-11 | beta    |
| 2025-01-11 | gamma   |
| 2025-01-11 | beta    |

Output

[
  {"sell_date":"2025-01-10","num_sold":1,"products":"alpha"},
  {"sell_date":"2025-01-11","num_sold":2,"products":"beta,gamma"}
]

Explanation

Each day is computed independently. Repeated appearances of the same product on one day are counted once.

Example

Input

activities:
| sell_date  | product |
|------------|---------|
| 2023-09-04 | p2      |
| 2023-09-04 | p10     |
| 2023-09-04 | p1      |
| 2023-09-03 | x       |

Output

[
  {"sell_date":"2023-09-03","num_sold":1,"products":"x"},
  {"sell_date":"2023-09-04","num_sold":3,"products":"p1,p10,p2"}
]

Explanation

Lexicographic sorting is string-based, so p10 appears between p1 and p2.

Accepted0/0·0% Acceptance

Constraints

1 <= number of rows in activities <= 500000
sell_date is a valid YYYY-MM-DD date
1 <= length of product <= 100
activities may contain duplicate rows
A product can appear multiple times on the same date
Distinct counting is per sell_date
products must include each distinct daily product exactly once
products must be lexicographically ordered within each date
Return exactly three columns: sell_date, num_sold, products
Order final rows by sell_date ascending
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with columns: sell_date, num_sold, products

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

activities =

Daily Distinct Product Rollup

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Daily Distinct Product Rollup

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