Low quality problems

# Table: Problems
# 
# +-------------+------+
# | Column Name | Type |
# +-------------+------+
# | problem_id  | int  |
# | likes       | int  |
# | dislikes    | int  |
# +-------------+------+
# In SQL, problem_id is the primary key column for this table.
# Each row of this table indicates the number of likes and dislikes for a LeetCode problem.
# 
#  
# 
# Find the IDs of the low-quality problems. A LeetCode problem is low-quality if the like percentage of the problem (number of likes divided by the total number of votes) is strictly less than 60%.
# 
# Return the result table ordered by problem_id in ascending order.
# 
# The result format is in the following example.
#
# Example 1:
# Input:
# Problems table:
# +------------+-------+----------+
# | problem_id | likes | dislikes |
# +------------+-------+----------+
# | 6          | 1290  | 425      |
# | 11         | 2677  | 8659     |
# | 1          | 4446  | 2760     |
# | 7          | 8569  | 6086     |
# | 13         | 2050  | 4164     |
# | 10         | 9002  | 7446     |
# +------------+-------+----------+
# Output:
# +------------+
# | problem_id |
# +------------+
# | 7          |
# | 10         |
# | 11         |
# | 13         |
# +------------+
# Explanation: The like percentages are as follows:
# - Problem 1: (4446 / (4446 + 2760)) * 100 = 61.69858%
# - Problem 6: (1290 / (1290 + 425)) * 100 = 75.21866%
# - Problem 7: (8569 / (8569 + 6086)) * 100 = 58.47151%
# - Problem 10: (9002 / (9002 + 7446)) * 100 = 54.73006%
# - Problem 11: (2677 / (2677 + 8659)) * 100 = 23.61503%
# - Problem 13: (2050 / (2050 + 4164)) * 100 = 32.99002%
# Problems 7, 10, 11, and 13 are low-quality problems because their like percentages are less than 60%.

import pandas as pd

def low_quality_problems(problems: pd.DataFrame) -> pd.DataFrame:
  problems['like_pct'] = problems['likes'] / (problems['likes'] + problems['dislikes']) * 100
  result = problems[problems['like_pct'] < 60][['problem_id']].sort_values('problem_id')
  return result