Users that actively request confirmation messages

# Table: Signups
# 
# +----------------+----------+
# | Column Name    | Type     |
# +----------------+----------+
# | user_id        | int      |
# | time_stamp     | datetime |
# +----------------+----------+
# user_id is the column with unique values for this table.
# Each row contains information about the signup time for the user with ID user_id.
# 
#  
# 
# Table: Confirmations
# 
# +----------------+----------+
# | Column Name    | Type     |
# +----------------+----------+
# | user_id        | int      |
# | time_stamp     | datetime |
# | action         | ENUM     |
# +----------------+----------+
# (user_id, time_stamp) is the primary key (combination of columns with unique values) for this table.
# user_id is a foreign key (reference column) to the Signups table.
# action is an ENUM (category) of the type ('confirmed', 'timeout')
# Each row of this table indicates that the user with ID user_id requested a confirmation message at time_stamp and that confirmation message was either confirmed ('confirmed') or expired without confirming ('timeout').
# 
#  
# 
# Write a solution to find the IDs of the users that requested a confirmation message twice within a 24-hour window. Two messages exactly 24 hours apart are considered to be within the window. The action does not affect the answer, only the request time.
# 
# Return the result table in any order.
# 
# The result format is in the following example.
#
# Example 1:
# Input:
# Signups table:
# +---------+---------------------+
# | user_id | time_stamp          |
# +---------+---------------------+
# | 3       | 2020-03-21 10:16:13 |
# | 7       | 2020-01-04 13:57:59 |
# | 2       | 2020-07-29 23:09:44 |
# | 6       | 2020-12-09 10:39:37 |
# +---------+---------------------+
# Confirmations table:
# +---------+---------------------+-----------+
# | user_id | time_stamp          | action    |
# +---------+---------------------+-----------+
# | 3       | 2021-01-06 03:30:46 | timeout   |
# | 3       | 2021-01-06 03:37:45 | timeout   |
# | 7       | 2021-06-12 11:57:29 | confirmed |
# | 7       | 2021-06-13 11:57:30 | confirmed |
# | 2       | 2021-01-22 00:00:00 | confirmed |
# | 2       | 2021-01-23 00:00:00 | timeout   |
# | 6       | 2021-10-23 14:14:14 | confirmed |
# | 6       | 2021-10-24 14:14:13 | timeout   |
# +---------+---------------------+-----------+
# Output:
# +---------+
# | user_id |
# +---------+
# | 2       |
# | 3       |
# | 6       |
# +---------+
# Explanation:
# User 2 requested two messages within exactly 24 hours of each other, so we include them.
# User 3 requested two messages within 6 minutes and 59 seconds of each other, so we include them.
# User 6 requested two messages within 23 hours, 59 minutes, and 59 seconds of each other, so we include them.
# User 7 requested two messages within 24 hours and 1 second of each other, so we exclude them from the answer.

import pandas as pd

def find_requesting_users(signups: pd.DataFrame, confirmations: pd.DataFrame) -> pd.DataFrame:
  confirmations = confirmations.sort_values(['user_id', 'time_stamp'])
  confirmations['prev_time'] = confirmations.groupby('user_id')['time_stamp'].shift(1)
  confirmations['diff'] = (confirmations['time_stamp'] - confirmations['prev_time']).dt.total_seconds()
  result = confirmations[confirmations['diff'] <= 24 * 3600][['user_id']].drop_duplicates()
  return result