Leetcode MEDIUM 2686 - Immediate Food Delivery III - Complete SQL Explained by Everyday Data Science
Question: https://leetcode.com/problems/immediate-food-delivery-iii/description/
SQL Schema:
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', '2019-08-01', '2019-08-01')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('3', '1', '2019-08-01', '2019-08-01')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('4', '3', '2019-08-02', '2019-08-13')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('5', '3', '2019-08-02', '2019-08-02')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('6', '2', '2019-08-02', '2019-08-02')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('7', '4', '2019-08-03', '2019-08-03')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('8', '1', '2019-08-03', '2019-08-03')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('9', '5', '2019-08-04', '2019-08-18')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('10', '2', '2019-08-04', '2019-08-18')
Pandas Schema:
data = [[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', '2019-08-18'], [10, 2, '2019-08-04', '2019-08-18']]
delivery = pd.DataFrame(data, 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]'})
#leetcodesolutions #datascience #sql
Видео Leetcode MEDIUM 2686 - Immediate Food Delivery III - Complete SQL Explained by Everyday Data Science канала Everyday Data Science
SQL Schema:
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', '2019-08-01', '2019-08-01')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('3', '1', '2019-08-01', '2019-08-01')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('4', '3', '2019-08-02', '2019-08-13')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('5', '3', '2019-08-02', '2019-08-02')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('6', '2', '2019-08-02', '2019-08-02')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('7', '4', '2019-08-03', '2019-08-03')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('8', '1', '2019-08-03', '2019-08-03')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('9', '5', '2019-08-04', '2019-08-18')
insert into Delivery (delivery_id, customer_id, order_date, customer_pref_delivery_date) values ('10', '2', '2019-08-04', '2019-08-18')
Pandas Schema:
data = [[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', '2019-08-18'], [10, 2, '2019-08-04', '2019-08-18']]
delivery = pd.DataFrame(data, 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]'})
#leetcodesolutions #datascience #sql
Видео Leetcode MEDIUM 2686 - Immediate Food Delivery III - Complete SQL Explained by Everyday Data Science канала Everyday Data Science
data science data analysis data science interview prep LeetCode solutions everyday data science data science portfolio projects data structures and algorithms sql window function data enegineering sql tutorial solved answers learn sql fast how to learn sql practice sql questions step by step solution data analytics best sql video window function joins in sql leetcode 2686 learn to earn leetcode medium sql interview immediate food delivery iii
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