Enterprise User Email Policy Filter

EASY10 pts

A platform trust team is auditing account emails against a strict signup policy.

Table: users

user_id: unique account identifier
name: account display name
mail: submitted email address

An email is considered policy-compliant when all rules hold:

The domain is exactly '@leetcode.com' (case-sensitive).
The prefix before the domain starts with a letter (A-Z or a-z).
Every character in the prefix is one of:
- letters (A-Z, a-z)
- digits (0-9)
- underscore (_)
- period (.)
- dash (-)

Task: Return all rows from users whose mail is policy-compliant.

Output requirements:

Return exactly three columns in this order:
- user_id
- name
- mail
Result rows may be returned in any order.

Supported submission environments:

SQL (SQLite 3.27.2)
Pandas (Python)

Example

Input

users:
| user_id | name      | mail                      |
|---------|-----------|---------------------------|
| 1       | Winston   | winston@leetcode.com      |
| 2       | Jonathan  | jonathanisgreat           |
| 3       | Annabelle | bella-@leetcode.com       |
| 4       | Sally     | sally.come@leetcode.com   |
| 5       | Marwan    | quarz#2020@leetcode.com   |
| 6       | David     | david69@gmail.com         |
| 7       | Shapiro   | .shapo@leetcode.com       |

Output

[
  {"user_id":1,"name":"Winston","mail":"winston@leetcode.com"},
  {"user_id":3,"name":"Annabelle","mail":"bella-@leetcode.com"},
  {"user_id":4,"name":"Sally","mail":"sally.come@leetcode.com"}
]

Explanation

Rows 1, 3, and 4 satisfy all policy rules. Other rows fail due to missing domain, invalid characters, wrong domain, or invalid starting character.

Example

Input

users:
| user_id | name    | mail                     |
|---------|---------|--------------------------|
| 10      | Asha    | A_1-2.x@leetcode.com     |
| 11      | Ben     | 0ben@leetcode.com        |
| 12      | Cia     | cia@LeetCode.com         |
| 13      | Dori    | dori@leetcode.com        |
| 14      | Erin    | erin__@leetcode.com      |

Output

[
  {"user_id":10,"name":"Asha","mail":"A_1-2.x@leetcode.com"},
  {"user_id":13,"name":"Dori","mail":"dori@leetcode.com"},
  {"user_id":14,"name":"Erin","mail":"erin__@leetcode.com"}
]

Explanation

User 11 fails because the prefix starts with a digit. User 12 fails because the domain must match exact lowercase '@leetcode.com'.

Example

Input

users:
| user_id | name   | mail                   |
|---------|--------|------------------------|
| 20      | Nila   | @leetcode.com          |
| 21      | Omar   | -omar@leetcode.com     |
| 22      | Pia    | pia@leetcode.co        |
| 23      | Quinn  | q+in@leetcode.com      |

Output

[]

Explanation

All rows violate at least one rule: empty prefix, non-letter prefix start, wrong domain, or disallowed '+' in prefix.

Accepted0/0·0% Acceptance

Constraints

0 <= number of rows in users <= 200000
1 <= user_id <= 1000000000
user_id is unique
name and mail are non-null text values
mail length is at least 1 and at most 320 characters
Domain check must be exact and case-sensitive: @leetcode.com
Prefix must start with [A-Za-z]
Allowed prefix characters: [A-Za-z0-9_.-]
Return exactly columns: user_id, name, mail
Rows may be returned in any order
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with columns: user_id, name, mail

Code

Visualizer

Solutions

14px

Test Cases3

Results

Submissions

users =

[{"user_id":1,"name":"Winston","mail":"winston@leetcode.com"},{"user_id":2,"name":"Jonathan","mail":"jonathanisgreat"},{"user_id":3,"name":"Annabelle","mail":"bella-@leetcode.com"},{"user_id":4,"name":"Sally","mail":"sally.come@leetcode.com"},{"user_id":5,"name":"Marwan","mail":"quarz#2020@leetcode.com"},{"user_id":6,"name":"David","mail":"david69@gmail.com"},{"user_id":7,"name":"Shapiro","mail":".shapo@leetcode.com"}]

Enterprise User Email Policy Filter

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Enterprise User Email Policy Filter

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