Follower Base Size by User

Easy

A social graph analytics service records follow relationships as directed edges. Product and growth teams need a compact user-level snapshot of audience size. Table: followers user_id: account being followed follower_id: account following user_id Data guarantees: (user_id, follower_id) is unique. Every row represents one active follow relationship. Task: For each user_id present in the table, return how many followers that user has. Output requirements: Return exactly two columns: user_id followers_count Sort rows by user_id in ascending order. Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

followers:
| user_id | follower_id |
|---------|-------------|
| 0       | 1           |
| 1       | 0           |
| 2       | 0           |
| 2       | 1           |

Output

[
  {"user_id":0,"followers_count":1},
  {"user_id":1,"followers_count":1},
  {"user_id":2,"followers_count":2}
]

Explanation

User 0 is followed by {1}, user 1 by {0}, and user 2 by {0,1}.

Example 2

Input

followers:
| user_id | follower_id |
|---------|-------------|
| 50      | 10          |
| 50      | 11          |
| 50      | 12          |
| 90      | 40          |
| 90      | 42          |

Output

[
  {"user_id":50,"followers_count":3},
  {"user_id":90,"followers_count":2}
]

Explanation

Counts are computed per user_id; each row contributes exactly one follower to that target user.

Example 3

Input

followers:
| user_id | follower_id |
|---------|-------------|
| (no rows) |

Output

[]

Explanation

With no follow edges, no user has a measurable follower count.

Constraints

0 <= number of rows in followers <= 400000
0 <= user_id <= 1000000000
0 <= follower_id <= 1000000000
(user_id, follower_id) is unique
Return exactly two columns: user_id, followers_count
Sort by user_id ascending
For SQL submissions, use SQLite 3.27.2 semantics
For Pandas submissions, return a DataFrame with columns: user_id, followers_count