Connection Invite Acceptance Ratio

MEDIUM15 pts

You are given two event tables for a social networking system:

friend_request(sender_id, send_to_id, request_date)
request_accepted(requester_id, accepter_id, accept_date)

Both tables may contain duplicate rows.

Definitions:

Unique request: distinct pair (sender_id, send_to_id)
Unique acceptance: distinct pair (requester_id, accepter_id)

Compute one metric: accept_rate = unique_acceptance_count / unique_request_count

Rules:

Count each directed pair once, even if it appears multiple times.
Acceptances are counted even if the corresponding request pair is missing.
If unique_request_count is zero, return 0.00.
Round the final ratio to 2 decimal places.

Return exactly one row with one column:

accept_rate

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

friend_request:
| sender_id | send_to_id | request_date |
|-----------|------------|--------------|
| 1         | 2          | 2024-01-01   |
| 1         | 3          | 2024-01-01   |
| 1         | 4          | 2024-01-01   |
| 2         | 3          | 2024-01-02   |
| 3         | 4          | 2024-01-09   |

request_accepted:
| requester_id | accepter_id | accept_date |
|--------------|-------------|-------------|
| 1            | 2           | 2024-01-03  |
| 1            | 3           | 2024-01-08  |
| 2            | 3           | 2024-01-08  |
| 3            | 4           | 2024-01-09  |
| 3            | 4           | 2024-01-10  |

Output

[
  {"accept_rate":0.8}
]

Explanation

There are 5 unique request pairs and 4 unique acceptance pairs, so the rounded acceptance rate is 0.80.

Example

Input

friend_request:
[]

request_accepted:
| requester_id | accepter_id | accept_date |
|--------------|-------------|-------------|
| 9            | 10          | 2024-04-11  |
| 9            | 10          | 2024-04-12  |

Output

[
  {"accept_rate":0.0}
]

Explanation

When no requests exist, denominator is zero and the answer must be 0.00.

Example

Input

friend_request:
| sender_id | send_to_id | request_date |
|-----------|------------|--------------|
| 11        | 12         | 2024-02-01   |
| 12        | 13         | 2024-02-02   |
| 13        | 14         | 2024-02-03   |
| 14        | 15         | 2024-02-04   |

request_accepted:
| requester_id | accepter_id | accept_date |
|--------------|-------------|-------------|
| 11           | 12          | 2024-02-05  |
| 12           | 13          | 2024-02-05  |
| 13           | 14          | 2024-02-05  |
| 40           | 41          | 2024-02-06  |
| 41           | 42          | 2024-02-06  |
| 50           | 51          | 2024-02-07  |

Output

[
  {"accept_rate":1.5}
]

Explanation

Accepted pairs can include pairs that never appeared in request events, so the ratio can exceed 1.00.

Accepted0/0·0% Acceptance

Constraints

0 <= number of rows in friend_request <= 100000
0 <= number of rows in request_accepted <= 100000
sender_id, send_to_id, requester_id, accepter_id are positive integers
Tables may contain duplicates and have no guaranteed primary key
Directed pairs matter: (u, v) is different from (v, u)
If there are zero unique requests, return 0.00
Round final metric to two decimal places
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with column: accept_rate

Code

Visualizer

Solutions

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Test Cases3

Results

Submissions

friend_request =

[]

request_accepted =

[{"requester_id":9,"accepter_id":10,"accept_date":"2024-06-11"},{"requester_id":11,"accepter_id":12,"accept_date":"2024-06-12"},{"requester_id":9,"accepter_id":10,"accept_date":"2024-06-13"}]

Connection Invite Acceptance Ratio

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Connection Invite Acceptance Ratio

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