Immediate food delivery i

# Table: Delivery
# 
# +-----------------------------+---------+
# | Column Name                 | Type    |
# +-----------------------------+---------+
# | delivery_id                 | int     |
# | customer_id                 | int     |
# | order_date                  | date    |
# | customer_pref_delivery_date | date    |
# +-----------------------------+---------+
# delivery_id is the primary key (column with unique values) of this table.
# The table holds information about food delivery to customers that make orders at some date and specify a preferred delivery date (on the same 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 called scheduled.
# 
# Write a solution to find the percentage of immediate orders in the table, rounded to 2 decimal places.
# 
# 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           | 5           | 2019-08-02 | 2019-08-02                  |
# | 3           | 1           | 2019-08-11 | 2019-08-11                  |
# | 4           | 3           | 2019-08-24 | 2019-08-26                  |
# | 5           | 4           | 2019-08-21 | 2019-08-22                  |
# | 6           | 2           | 2019-08-11 | 2019-08-13                  |
# +-------------+-------------+------------+-----------------------------+
# Output:
# +----------------------+
# | immediate_percentage |
# +----------------------+
# | 33.33                |
# +----------------------+
# Explanation: The orders with delivery id 2 and 3 are immediate while the others are scheduled.

import pandas as pd

def food_delivery(delivery: pd.DataFrame) -> pd.DataFrame:
  immediate = (delivery['order_date'] == delivery['customer_pref_delivery_date']).sum()
  total = len(delivery)
  return pd.DataFrame({'immediate_percentage': [round(immediate / total * 100, 2)]})