Game play analysis ii

# Table: Activity
# 
# +--------------+---------+
# | Column Name  | Type    |
# +--------------+---------+
# | player_id    | int     |
# | device_id    | int     |
# | event_date   | date    |
# | games_played | int     |
# +--------------+---------+
# (player_id, event_date) is the primary key (combination of columns with unique values) of this table.
# This table shows the activity of players of some games.
# Each row is a record of a player who logged in and played a number of games (possibly 0) before logging out on someday using some device.
# 
#  
# 
# Write a solution to report the device that is first logged in for each player.
# 
# Return the result table in any order.
# 
# The result format is in the following example.
#
# Example 1:
# Input:
# Activity table:
# +-----------+-----------+------------+--------------+
# | player_id | device_id | event_date | games_played |
# +-----------+-----------+------------+--------------+
# | 1         | 2         | 2016-03-01 | 5            |
# | 1         | 2         | 2016-05-02 | 6            |
# | 2         | 3         | 2017-06-25 | 1            |
# | 3         | 1         | 2016-03-02 | 0            |
# | 3         | 4         | 2018-07-03 | 5            |
# +-----------+-----------+------------+--------------+
# Output:
# +-----------+-----------+
# | player_id | device_id |
# +-----------+-----------+
# | 1         | 2         |
# | 2         | 3         |
# | 3         | 1         |
# +-----------+-----------+

import pandas as pd

def game_analysis(activity: pd.DataFrame) -> pd.DataFrame:
  idx = activity.groupby('player_id')['event_date'].idxmin()
  return activity.loc[idx, ['player_id', 'device_id']]