Page recommendations

# Table: Friendship
# 
# +---------------+---------+
# | Column Name   | Type    |
# +---------------+---------+
# | user1_id      | int     |
# | user2_id      | int     |
# +---------------+---------+
# (user1_id, user2_id) is the primary key (combination of columns with unique values) for this table.
# Each row of this table indicates that there is a friendship relation between user1_id and user2_id.
# 
#  
# 
# Table: Likes
# 
# +-------------+---------+
# | Column Name | Type    |
# +-------------+---------+
# | user_id     | int     |
# | page_id     | int     |
# +-------------+---------+
# (user_id, page_id) is the primary key (combination of columns with unique values) for this table.
# Each row of this table indicates that user_id likes page_id.
# 
#  
# 
# Write a solution to recommend pages to the user with user_id = 1 using the pages that your friends liked. It should not recommend pages you already liked.
# 
# Return result table in any order without duplicates.
# 
# The result format is in the following example.
#
# Example 1:
# Input:
# Friendship table:
# +----------+----------+
# | user1_id | user2_id |
# +----------+----------+
# | 1        | 2        |
# | 1        | 3        |
# | 1        | 4        |
# | 2        | 3        |
# | 2        | 4        |
# | 2        | 5        |
# | 6        | 1        |
# +----------+----------+
# Likes table:
# +---------+---------+
# | user_id | page_id |
# +---------+---------+
# | 1       | 88      |
# | 2       | 23      |
# | 3       | 24      |
# | 4       | 56      |
# | 5       | 11      |
# | 6       | 33      |
# | 2       | 77      |
# | 3       | 77      |
# | 6       | 88      |
# +---------+---------+
# Output:
# +------------------+
# | recommended_page |
# +------------------+
# | 23               |
# | 24               |
# | 56               |
# | 33               |
# | 77               |
# +------------------+
# Explanation:
# User one is friend with users 2, 3, 4 and 6.
# Suggested pages are 23 from user 2, 24 from user 3, 56 from user 3 and 33 from user 6.
# Page 77 is suggested from both user 2 and user 3.
# Page 88 is not suggested because user 1 already likes it.

import pandas as pd

def page_recommendations(friendship: pd.DataFrame, likes: pd.DataFrame) -> pd.DataFrame:
  # Find friends of user 1
  friends1 = friendship[friendship['user1_id'] == 1]['user2_id']
  friends2 = friendship[friendship['user2_id'] == 1]['user1_id']
  friends = pd.concat([friends1, friends2]).unique()

  # Pages liked by friends
  friend_pages = likes[likes['user_id'].isin(friends)]['page_id'].unique()

  # Pages already liked by user 1
  my_pages = likes[likes['user_id'] == 1]['page_id'].unique()

  # Recommend pages not already liked
  recommended = set(friend_pages) - set(my_pages)
  return pd.DataFrame({'recommended_page': sorted(recommended)})