Customer Referral Eligibility

Easy

You are maintaining a customer growth table called Customer. Each row contains: id: unique customer identifier name: customer display name referee_id: optional id of the customer who referred this customer Return the names of customers who are eligible under the following policy: Include customers whose referee_id is NULL (no recorded referrer). Include customers whose referee_id is not equal to 2. Exclude customers whose referee_id is exactly 2. Return exactly one column: name Row order is not semantically important unless your local checker enforces deterministic ordering. This problem should be solvable in both: SQL (SQLite 3.27.2) Pandas

Example 1

Input

Customer:
| id | name  | referee_id |
|----|-------|------------|
| 1  | Will  | null       |
| 2  | Jane  | null       |
| 3  | Alex  | 2          |
| 4  | Bill  | null       |
| 5  | Zack  | 1          |
| 6  | Mark  | 2          |

Output

[
  {"name":"Will"},
  {"name":"Jane"},
  {"name":"Bill"},
  {"name":"Zack"}
]

Explanation

Rows referred by customer 2 are excluded. NULL and non-2 referee rows remain.

Example 2

Input

Customer:
| id | name   | referee_id |
|----|--------|------------|
| 10 | Priya  | 7          |
| 11 | Omar   | 2          |
| 12 | Carla  | null       |
| 13 | Mei    | 999        |

Output

[
  {"name":"Priya"},
  {"name":"Carla"},
  {"name":"Mei"}
]

Explanation

Referrers other than 2 are allowed, even if that referrer id does not appear in the table.

Example 3

Input

Customer:
| id | name | referee_id |
|----|------|------------|
| 20 | Sam  | 2          |
| 21 | Sam  | null       |
| 22 | Sam  | 5          |

Output

[
  {"name":"Sam"},
  {"name":"Sam"}
]

Explanation

Eligibility is evaluated per row. Duplicate names can appear multiple times if multiple rows satisfy the rule.

Constraints

1 <= number of rows in Customer <= 50,000
1 <= id <= 2,147,483,647
name is a non-empty string
referee_id is NULL or an integer id
id is unique within each test case
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with column: name

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Customer Referral Eligibility

Easy

You are maintaining a customer growth table called Customer. Each row contains: id: unique customer identifier name: customer display name referee_id: optional id of the customer who referred this customer Return the names of customers who are eligible under the following policy: Include customers whose referee_id is NULL (no recorded referrer). Include customers whose referee_id is not equal to 2. Exclude customers whose referee_id is exactly 2. Return exactly one column: name Row order is not semantically important unless your local checker enforces deterministic ordering. This problem should be solvable in both: SQL (SQLite 3.27.2) Pandas

Example 1

Input

Customer:
| id | name  | referee_id |
|----|-------|------------|
| 1  | Will  | null       |
| 2  | Jane  | null       |
| 3  | Alex  | 2          |
| 4  | Bill  | null       |
| 5  | Zack  | 1          |
| 6  | Mark  | 2          |

Output

[
  {"name":"Will"},
  {"name":"Jane"},
  {"name":"Bill"},
  {"name":"Zack"}
]

Explanation

Rows referred by customer 2 are excluded. NULL and non-2 referee rows remain.

Example 2

Input

Customer:
| id | name   | referee_id |
|----|--------|------------|
| 10 | Priya  | 7          |
| 11 | Omar   | 2          |
| 12 | Carla  | null       |
| 13 | Mei    | 999        |

Output

[
  {"name":"Priya"},
  {"name":"Carla"},
  {"name":"Mei"}
]

Explanation

Referrers other than 2 are allowed, even if that referrer id does not appear in the table.

Example 3

Input

Customer:
| id | name | referee_id |
|----|------|------------|
| 20 | Sam  | 2          |
| 21 | Sam  | null       |
| 22 | Sam  | 5          |

Output

[
  {"name":"Sam"},
  {"name":"Sam"}
]

Explanation

Eligibility is evaluated per row. Duplicate names can appear multiple times if multiple rows satisfy the rule.

Constraints

1 <= number of rows in Customer <= 50,000
1 <= id <= 2,147,483,647
name is a non-empty string
referee_id is NULL or an integer id
id is unique within each test case
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with column: name