Problem
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.
Examples
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.
Solution
Method 1 - Using Division and Round
Code
SQL
SELECT ROUND(100*AVG(order_date = customer_pref_delivery_date), 2) AS immediate_percentage
FROM Delivery;
Pandas
import pandas as pd
def food_delivery(delivery: pd.DataFrame) -> pd.DataFrame:
return pd.DataFrame({'immediate_percentage': [
round(delivery[delivery['order_date'] == delivery['customer_pref_delivery_date']].size / delivery.size * 100, 2)
]})