+-------------+---------+
| Column Name | Type |
+-------------+---------+
| user_id | int |
| song_id | int |
| day | date |
+-------------+---------+
This table may contain duplicate rows.
Each row of this table indicates that the user user_id listened to the song song_id on the day day.
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 the users user1_id and user2_id are friends.
Note that user1_id < user2_id.
Write a solution to report the similar friends of Leetcodify users. A user x
and user y are similar friends if:
Users x and y are friends, and
Users x and y listened to the same three or more different songs on the same day.
Return the result table in any order. Note that you must return the similar pairs of friends the same way they were represented in the input (i.e., always user1_id < user2_id).
Input:
Listens table:+---------+---------+------------+| user_id | song_id | day |+---------+---------+------------+|1|10|2021-03-15||1|11|2021-03-15||1|12|2021-03-15||2|10|2021-03-15||2|11|2021-03-15||2|12|2021-03-15||3|10|2021-03-15||3|11|2021-03-15||3|12|2021-03-15||4|10|2021-03-15||4|11|2021-03-15||4|13|2021-03-15||5|10|2021-03-16||5|11|2021-03-16||5|12|2021-03-16|+---------+---------+------------+Friendship table:+----------+----------+| user1_id | user2_id |+----------+----------+|1|2||2|4||2|5|+----------+----------+Output:
+----------+----------+| user1_id | user2_id |+----------+----------+|1|2|+----------+----------+Explanation:
Users 1 and 2 are friends, and they listened to songs 10,11, and 12 on the same day. They are similar friends.Users 1 and 3 listened to songs 10,11, and 12 on the same day, but they are not friends.Users 2 and 4 are friends, but they did not listen to the same three different songs.Users 2 and 5 are friends and listened to songs 10,11, and 12, but they did not listen to them on the same day.
We want to find pairs of friends who listened to at least three of the same songs on the same day. We can use a self-join on the Listens table to find such user pairs, group by user pairs and day, and count the number of common songs. Then, we filter to only those pairs that are friends.
WITH common_songs AS (
SELECT a.user_id AS user1, b.user_id AS user2, a.dayFROM Listens a
JOIN Listens b ON a.day= b.dayAND a.song_id = b.song_id AND a.user_id < b.user_id
GROUPBY a.user_id, b.user_id, a.day, a.song_id
),
user_pairs AS (
SELECT user1, user2, day, COUNT(*) AS cnt
FROM common_songs
GROUPBY user1, user2, dayHAVING cnt >=3)
SELECT f.user1_id, f.user2_id
FROM user_pairs u
JOIN Friendship f ON u.user1 = f.user1_id AND u.user2 = f.user2_id;
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
WITH common_songs AS (
SELECT a.user_id AS user1, b.user_id AS user2, a.dayFROM Listens a
JOIN Listens b ON a.day= b.dayAND a.song_id = b.song_id AND a.user_id < b.user_id
GROUPBY a.user_id, b.user_id, a.day, a.song_id
),
user_pairs AS (
SELECT user1, user2, day, COUNT(*) AS cnt
FROM common_songs
GROUPBY user1, user2, dayHAVINGCOUNT(*) >=3)
SELECT f.user1_id, f.user2_id
FROM user_pairs u
JOIN Friendship f ON u.user1 = f.user1_id AND u.user2 = f.user2_id;
1
2
3
4
5
6
7
8
9
10
11
12
13
14
defsimilar_friends(listens_df, friendship_df):
import pandas as pd
merged = listens_df.merge(listens_df, on=['day', 'song_id'])
merged = merged[merged['user_id_x'] < merged['user_id_y']]
grouped = merged.groupby(['user_id_x', 'user_id_y', 'day']).size().reset_index(name='cnt')
filtered = grouped[grouped['cnt'] >=3]
friends = set(tuple(x) for x in friendship_df[['user1_id', 'user2_id']].values)
recs = []
for _, row in filtered.iterrows():
u, v = row['user_id_x'], row['user_id_y']
if (u, v) in friends:
recs.append((u, v))
recs_df = pd.DataFrame(recs, columns=['user1_id', 'user2_id']).drop_duplicates()
return recs_df