Find all unique email domains

# Table: Emails
# 
# +-------------+---------+
# | Column Name | Type    |
# +-------------+---------+
# | id          | int     |
# | email       | varchar |
# +-------------+---------+
# id is the primary key (column with unique values) for this table.
# Each row of this table contains an email. The emails will not contain uppercase letters.
# 
# Write a solution to find all unique email domains and count the number of individuals associated with each domain. Consider only those domains that end with .com.
# 
# Return the result table orderd by email domains in ascending order.
# 
# The result format is in the following example.
#
# Example 1:
# Input:
# Emails table:
# +-----+-----------------------+
# | id  | email                 |
# +-----+-----------------------+
# | 336 | hwkiy@test.edu        |
# | 489 | adcmaf@outlook.com    |
# | 449 | vrzmwyum@yahoo.com    |
# | 95  | tof@test.edu          |
# | 320 | jxhbagkpm@example.org |
# | 411 | zxcf@outlook.com      |
# +----+------------------------+
# Output:
# +--------------+-------+
# | email_domain | count |
# +--------------+-------+
# | outlook.com  | 2     |
# | yahoo.com    | 1     |
# +--------------+-------+
# Explanation:
# - The valid domains ending with ".com" are only "outlook.com" and "yahoo.com", with respective counts of 2 and 1.
# Output table is ordered by email_domains in ascending order.

import pandas as pd

def find_unique_email_domains(emails: pd.DataFrame) -> pd.DataFrame:
  emails['email_domain'] = emails['email'].str.split('@').str[1]
  result = emails[emails['email_domain'].str.endswith('.com')].groupby('email_domain').size().reset_index(name='count')
  return result.sort_values('email_domain').reset_index(drop=True)