{ "data": { "question": { "questionId": "1292", "questionFrontendId": "1174", "boundTopicId": null, "title": "Immediate Food Delivery II", "titleSlug": "immediate-food-delivery-ii", "content": "

Table: Delivery

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\n+-----------------------------+---------+\n| Column Name                 | Type    |\n+-----------------------------+---------+\n| delivery_id                 | int     |\n| customer_id                 | int     |\n| order_date                  | date    |\n| customer_pref_delivery_date | date    |\n+-----------------------------+---------+\ndelivery_id is the column of unique values of this table.\nThe 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).\n
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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.

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The first order of a customer is the order with the earliest order date that the customer made. It is guaranteed that a customer has precisely one first order.

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Write a solution to find the percentage of immediate orders in the first orders of all customers, rounded to 2 decimal places.

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The result format is in the following example.

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Example 1:

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\nInput: \nDelivery table:\n+-------------+-------------+------------+-----------------------------+\n| delivery_id | customer_id | order_date | customer_pref_delivery_date |\n+-------------+-------------+------------+-----------------------------+\n| 1           | 1           | 2019-08-01 | 2019-08-02                  |\n| 2           | 2           | 2019-08-02 | 2019-08-02                  |\n| 3           | 1           | 2019-08-11 | 2019-08-12                  |\n| 4           | 3           | 2019-08-24 | 2019-08-24                  |\n| 5           | 3           | 2019-08-21 | 2019-08-22                  |\n| 6           | 2           | 2019-08-11 | 2019-08-13                  |\n| 7           | 4           | 2019-08-09 | 2019-08-09                  |\n+-------------+-------------+------------+-----------------------------+\nOutput: \n+----------------------+\n| immediate_percentage |\n+----------------------+\n| 50.00                |\n+----------------------+\nExplanation: \nThe customer id 1 has a first order with delivery id 1 and it is scheduled.\nThe customer id 2 has a first order with delivery id 2 and it is immediate.\nThe customer id 3 has a first order with delivery id 5 and it is scheduled.\nThe customer id 4 has a first order with delivery id 7 and it is immediate.\nHence, half the customers have immediate first orders.\n
\n", "translatedTitle": null, "translatedContent": null, "isPaidOnly": false, "difficulty": "Medium", "likes": 523, "dislikes": 89, "isLiked": null, "similarQuestions": "[]", "exampleTestcases": "{\"headers\":{\"Delivery\":[\"delivery_id\",\"customer_id\",\"order_date\",\"customer_pref_delivery_date\"]},\"rows\":{\"Delivery\":[[1,1,\"2019-08-01\",\"2019-08-02\"],[2,2,\"2019-08-02\",\"2019-08-02\"],[3,1,\"2019-08-11\",\"2019-08-12\"],[4,3,\"2019-08-24\",\"2019-08-24\"],[5,3,\"2019-08-21\",\"2019-08-22\"],[6,2,\"2019-08-11\",\"2019-08-13\"],[7,4,\"2019-08-09\",\"2019-08-09\"]]}}", "categoryTitle": "Database", "contributors": [], "topicTags": [ { "name": "Database", "slug": "database", "translatedName": null, "__typename": "TopicTagNode" } ], "companyTagStats": null, "codeSnippets": [ { "lang": "MySQL", "langSlug": "mysql", "code": "# Write your MySQL query statement below\n", "__typename": "CodeSnippetNode" }, { "lang": "MS SQL Server", "langSlug": "mssql", "code": "/* Write your T-SQL query statement below */\n", "__typename": "CodeSnippetNode" }, { "lang": "Oracle", "langSlug": "oraclesql", "code": "/* Write your PL/SQL query statement below */\n", "__typename": "CodeSnippetNode" }, { "lang": "Pandas", "langSlug": "pythondata", "code": "import pandas as pd\n\ndef immediate_food_delivery(delivery: pd.DataFrame) -> pd.DataFrame:\n ", "__typename": "CodeSnippetNode" }, { "lang": "PostgreSQL", "langSlug": "postgresql", "code": "-- Write your PostgreSQL query statement below\n", "__typename": "CodeSnippetNode" } ], "stats": "{\"totalAccepted\": \"80.5K\", \"totalSubmission\": \"158.4K\", \"totalAcceptedRaw\": 80518, \"totalSubmissionRaw\": 158449, \"acRate\": \"50.8%\"}", "hints": [], "solution": null, "status": null, "sampleTestCase": "{\"headers\":{\"Delivery\":[\"delivery_id\",\"customer_id\",\"order_date\",\"customer_pref_delivery_date\"]},\"rows\":{\"Delivery\":[[1,1,\"2019-08-01\",\"2019-08-02\"],[2,2,\"2019-08-02\",\"2019-08-02\"],[3,1,\"2019-08-11\",\"2019-08-12\"],[4,3,\"2019-08-24\",\"2019-08-24\"],[5,3,\"2019-08-21\",\"2019-08-22\"],[6,2,\"2019-08-11\",\"2019-08-13\"],[7,4,\"2019-08-09\",\"2019-08-09\"]]}}", "metaData": "{\"mysql\": [\"Create table If Not Exists Delivery (delivery_id int, customer_id int, order_date date, customer_pref_delivery_date date)\"], \"mssql\": [\"Create table Delivery (delivery_id int, customer_id int, order_date date, customer_pref_delivery_date date)\"], \"oraclesql\": [\"Create table Delivery (delivery_id int, customer_id int, order_date date, customer_pref_delivery_date date)\", \"ALTER SESSION SET nls_date_format='YYYY-MM-DD'\"], \"database\": true, \"name\": \"immediate_food_delivery\", \"pythondata\": [\"Delivery = pd.DataFrame([], columns=['delivery_id', 'customer_id', 'order_date', 'customer_pref_delivery_date']).astype({'delivery_id':'Int64', 'customer_id':'Int64', 'order_date':'datetime64[ns]', 'customer_pref_delivery_date':'datetime64[ns]'})\"], \"postgresql\": [\"\\nCreate table If Not Exists Delivery (delivery_id int, customer_id int, order_date date, customer_pref_delivery_date date)\"], \"database_schema\": {\"Delivery\": {\"delivery_id\": \"INT\", \"customer_id\": \"INT\", \"order_date\": \"DATE\", \"customer_pref_delivery_date\": \"DATE\"}}}", "judgerAvailable": true, "judgeType": "large", "mysqlSchemas": [ "Create table If Not Exists Delivery (delivery_id int, customer_id int, order_date date, customer_pref_delivery_date date)", "Truncate table Delivery", "insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('1', '1', '2019-08-01', '2019-08-02')", "insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('2', '2', '2019-08-02', '2019-08-02')", "insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('3', '1', '2019-08-11', '2019-08-12')", "insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('4', '3', '2019-08-24', '2019-08-24')", "insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('5', '3', '2019-08-21', '2019-08-22')", "insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('6', '2', '2019-08-11', '2019-08-13')", "insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('7', '4', '2019-08-09', '2019-08-09')" ], "enableRunCode": true, "enableTestMode": false, "enableDebugger": false, "envInfo": "{\"mysql\": [\"MySQL\", \"

MySQL 8.0.

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mssql server 2019.

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Oracle Sql 11.2.

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Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0

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PostgreSQL 16

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