00:00:00

Description

Editorial

Daily Immediate Delivery Share

MEDIUM20 pts

A logistics analytics team tracks whether customers request same-day fulfillment when placing orders.

Table: Delivery

delivery_id: unique order event identifier
customer_id: customer identifier
order_date: date when the order was placed
customer_pref_delivery_date: customer's preferred delivery date

Order classification:

immediate order: customer_pref_delivery_date is the same day as order_date
scheduled order: customer_pref_delivery_date is later than order_date

Task: For each distinct order_date, compute the percentage of immediate orders among all orders placed on that date.

Metric definition per order_date: immediate_percentage = (immediate_orders / total_orders) * 100

Output requirements:

Return exactly two columns: order_date, immediate_percentage
Round immediate_percentage to 2 decimal places
Return rows sorted by order_date in ascending order
If Delivery is empty, return an empty table

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

Delivery:
| delivery_id | customer_id | order_date  | customer_pref_delivery_date |
|-------------|-------------|-------------|-----------------------------|
| 1           | 11          | 2024-03-01  | 2024-03-01                  |
| 2           | 12          | 2024-03-01  | 2024-03-03                  |
| 3           | 13          | 2024-03-01  | 2024-03-01                  |
| 4           | 12          | 2024-03-02  | 2024-03-02                  |
| 5           | 14          | 2024-03-02  | 2024-03-05                  |
| 6           | 15          | 2024-03-03  | 2024-03-03                  |

Output

[
  {"order_date":"2024-03-01","immediate_percentage":66.67},
  {"order_date":"2024-03-02","immediate_percentage":50.0},
  {"order_date":"2024-03-03","immediate_percentage":100.0}
]

Explanation

On 2024-03-01, 2 of 3 orders are immediate. On 2024-03-02, 1 of 2 is immediate. On 2024-03-03, 1 of 1 is immediate.

Example

Input

Delivery:
| delivery_id | customer_id | order_date  | customer_pref_delivery_date |
|-------------|-------------|-------------|-----------------------------|
| 100         | 8           | 2024-01-10  | 2024-01-15                  |
| 101         | 9           | 2024-01-10  | 2024-01-20                  |
| 102         | 9           | 2024-01-11  | 2024-01-11                  |
| 103         | 7           | 2024-01-11  | 2024-01-12                  |

Output

[
  {"order_date":"2024-01-10","immediate_percentage":0.0},
  {"order_date":"2024-01-11","immediate_percentage":50.0}
]

Explanation

The first date has no immediate requests. The second date has one immediate order out of two total orders.

Example

Input

Delivery:
[]

Output

[]

Explanation

There are no order dates to report, so the result is empty.

Accepted0/0·0% Acceptance

Constraints

0 <= number of rows in Delivery <= 200000
delivery_id values are unique
1 <= delivery_id <= 2147483647
1 <= customer_id <= 2147483647
order_date and customer_pref_delivery_date are valid YYYY-MM-DD dates
For every row: customer_pref_delivery_date >= order_date
Return exactly columns: order_date, immediate_percentage
Return one row per distinct order_date
Round immediate_percentage to exactly 2 decimal places
Sort the output by order_date ascending
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with columns: order_date, immediate_percentage

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

Delivery =

[{"delivery_id":1,"customer_id":11,"order_date":"2024-03-01","customer_pref_delivery_date":"2024-03-01"},{"delivery_id":2,"customer_id":12,"order_date":"2024-03-01","customer_pref_delivery_date":"2024-03-03"},{"delivery_id":3,"customer_id":13,"order_date":"2024-03-01","customer_pref_delivery_date":"2024-03-01"},{"delivery_id":4,"customer_id":12,"order_date":"2024-03-02","customer_pref_delivery_date":"2024-03-02"},{"delivery_id":5,"customer_id":14,"order_date":"2024-03-02","customer_pref_delivery_date":"2024-03-05"},{"delivery_id":6,"customer_id":15,"order_date":"2024-03-03","customer_pref_delivery_date":"2024-03-03"}]

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Daily Immediate Delivery Share (Medium) — Practice with Code Visualizer | OneNoughtOne

101

00:00:00

Description

Editorial

Daily Immediate Delivery Share

MEDIUM20 pts

A logistics analytics team tracks whether customers request same-day fulfillment when placing orders.

Table: Delivery

delivery_id: unique order event identifier
customer_id: customer identifier
order_date: date when the order was placed
customer_pref_delivery_date: customer's preferred delivery date

Order classification:

immediate order: customer_pref_delivery_date is the same day as order_date
scheduled order: customer_pref_delivery_date is later than order_date

Task: For each distinct order_date, compute the percentage of immediate orders among all orders placed on that date.

Metric definition per order_date: immediate_percentage = (immediate_orders / total_orders) * 100

Output requirements:

Return exactly two columns: order_date, immediate_percentage
Round immediate_percentage to 2 decimal places
Return rows sorted by order_date in ascending order
If Delivery is empty, return an empty table

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

Delivery:
| delivery_id | customer_id | order_date  | customer_pref_delivery_date |
|-------------|-------------|-------------|-----------------------------|
| 1           | 11          | 2024-03-01  | 2024-03-01                  |
| 2           | 12          | 2024-03-01  | 2024-03-03                  |
| 3           | 13          | 2024-03-01  | 2024-03-01                  |
| 4           | 12          | 2024-03-02  | 2024-03-02                  |
| 5           | 14          | 2024-03-02  | 2024-03-05                  |
| 6           | 15          | 2024-03-03  | 2024-03-03                  |

Output

[
  {"order_date":"2024-03-01","immediate_percentage":66.67},
  {"order_date":"2024-03-02","immediate_percentage":50.0},
  {"order_date":"2024-03-03","immediate_percentage":100.0}
]

Explanation

On 2024-03-01, 2 of 3 orders are immediate. On 2024-03-02, 1 of 2 is immediate. On 2024-03-03, 1 of 1 is immediate.

Example

Input

Delivery:
| delivery_id | customer_id | order_date  | customer_pref_delivery_date |
|-------------|-------------|-------------|-----------------------------|
| 100         | 8           | 2024-01-10  | 2024-01-15                  |
| 101         | 9           | 2024-01-10  | 2024-01-20                  |
| 102         | 9           | 2024-01-11  | 2024-01-11                  |
| 103         | 7           | 2024-01-11  | 2024-01-12                  |

Output

[
  {"order_date":"2024-01-10","immediate_percentage":0.0},
  {"order_date":"2024-01-11","immediate_percentage":50.0}
]

Explanation

The first date has no immediate requests. The second date has one immediate order out of two total orders.

Example

Input

Delivery:
[]

Output

[]

Explanation

There are no order dates to report, so the result is empty.

Accepted0/0·0% Acceptance

Constraints

0 <= number of rows in Delivery <= 200000
delivery_id values are unique
1 <= delivery_id <= 2147483647
1 <= customer_id <= 2147483647
order_date and customer_pref_delivery_date are valid YYYY-MM-DD dates
For every row: customer_pref_delivery_date >= order_date
Return exactly columns: order_date, immediate_percentage
Return one row per distinct order_date
Round immediate_percentage to exactly 2 decimal places
Sort the output by order_date ascending
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with columns: order_date, immediate_percentage

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

Delivery =

Daily Immediate Delivery Share

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Daily Immediate Delivery Share

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