Leetcode 2990 - Loan Types - Solved by Everyday Data Science | GROUPBY, HAVING, COUNT DISTINCT

Question: https://leetcode.com/problems/loan-types/description/

SQL Schema:
Create Table if not Exists Loans ( loan_id int, user_id int, loan_type varchar(40))
Truncate table Loans
insert into Loans (loan_id, user_id, loan_type) values ('683', '101', 'Mortgage')
insert into Loans (loan_id, user_id, loan_type) values ('218', '101', 'AutoLoan')
insert into Loans (loan_id, user_id, loan_type) values ('802', '101', 'Inschool')
insert into Loans (loan_id, user_id, loan_type) values ('593', '102', 'Mortgage')
insert into Loans (loan_id, user_id, loan_type) values ('138', '102', 'Refinance')
insert into Loans (loan_id, user_id, loan_type) values ('294', '102', 'Inschool')
insert into Loans (loan_id, user_id, loan_type) values ('308', '103', 'Refinance')
insert into Loans (loan_id, user_id, loan_type) values ('389', '104', 'Mortgage')

Pandas Schema:
data = [[683, 101, 'Mortgage'], [218, 101, 'AutoLoan'], [802, 101, 'Inschool'], [593, 102, 'Mortgage'], [138, 102, 'Refinance'], [294, 102, 'Inschool'], [308, 103, 'Refinance'], [389, 104, 'Mortgage']]
loans = pd.DataFrame(data, columns=['loan_id', 'user_id', 'loan_type']).astype({'loan_id':'Int64', 'user_id':'Int64', 'loan_type':'object'})

#datasciencequestions #leetcodesolutions #dataengineering

Видео Leetcode 2990 - Loan Types - Solved by Everyday Data Science | GROUPBY, HAVING, COUNT DISTINCT канала Everyday Data Science