Immediate food delivery iii

# Table: Delivery
# 
# +-----------------------------+---------+
# | Column Name                 | Type    |
# +-----------------------------+---------+
# | delivery_id                 | int     |
# | customer_id                 | int     |
# | order_date                  | date    |
# | customer_pref_delivery_date | date    |
# +-----------------------------+---------+
# delivery_id is the column with unique values of this table.
# Each row contains information about food delivery to a customer that makes an order at some date and specifies a preferred delivery date (on the order date or after it).
# 
# If the customer's preferred delivery date is the same as the order date, then the order is called immediate, otherwise, it is scheduled.
# 
# Write a solution to find the percentage of immediate orders on each unique order_date, rounded to 2 decimal places. 
# 
# Return the result table ordered by order_date in ascending order.
# 
# The result format is in the following example.
#
# Example 1:
# Input:
# Delivery table:
# +-------------+-------------+------------+-----------------------------+
# | delivery_id | customer_id | order_date | customer_pref_delivery_date |
# +-------------+-------------+------------+-----------------------------+
# | 1           | 1           | 2019-08-01 | 2019-08-02                  |
# | 2           | 2           | 2019-08-01 | 2019-08-01                  |
# | 3           | 1           | 2019-08-01 | 2019-08-01                  |
# | 4           | 3           | 2019-08-02 | 2019-08-13                  |
# | 5           | 3           | 2019-08-02 | 2019-08-02                  |
# | 6           | 2           | 2019-08-02 | 2019-08-02                  |
# | 7           | 4           | 2019-08-03 | 2019-08-03                  |
# | 8           | 1           | 2019-08-03 | 2019-08-03                  |
# | 9           | 5           | 2019-08-04 | 2019-08-08                  |
# | 10          | 2           | 2019-08-04 | 2019-08-18                  |
# +-------------+-------------+------------+-----------------------------+
# Output:
# +------------+----------------------+
# | order_date | immediate_percentage |
# +------------+----------------------+
# | 2019-08-01 | 66.67                |
# | 2019-08-02 | 66.67                |
# | 2019-08-03 | 100.00               |
# | 2019-08-04 | 0.00                 |
# +------------+----------------------+
# Explanation:
# - On 2019-08-01 there were three orders, out of those, two were immediate and one was scheduled. So, immediate percentage for that date was 66.67.
# - On 2019-08-02 there were three orders, out of those, two were immediate and one was scheduled. So, immediate percentage for that date was 66.67.
# - On 2019-08-03 there were two orders, both were immediate. So, the immediate percentage for that date was 100.00.
# - On 2019-08-04 there were two orders, both were scheduled. So, the immediate percentage for that date was 0.00.
# order_date is sorted in ascending order.

import pandas as pd

def immediate_delivery(delivery: pd.DataFrame) -> pd.DataFrame:
  delivery['is_immediate'] = (delivery['order_date'] == delivery['customer_pref_delivery_date']).astype(int)
  result = delivery.groupby('order_date')['is_immediate'].mean().reset_index()
  result['immediate_percentage'] = (result['is_immediate'] * 100).round(2)
  return result[['order_date', 'immediate_percentage']].sort_values('order_date')