Leetcode MEDIUM 3055 - Top Percentile Fraud PERCENT_RANK() SQL - Explained by Everyday Data Science

Question: https://leetcode.com/problems/top-percentile-fraud/description/

SQL Schema:
Create table If Not Exists Fraud (policy_id int, state varchar(50), fraud_score decimal(5,2))
Truncate table Fraud
insert into Fraud (policy_id, state, fraud_score) values ('1', 'California', '0.92')
insert into Fraud (policy_id, state, fraud_score) values ('2', 'California', '0.68')
insert into Fraud (policy_id, state, fraud_score) values ('3', 'California', '0.17')
insert into Fraud (policy_id, state, fraud_score) values ('4', 'New York', '0.94')
insert into Fraud (policy_id, state, fraud_score) values ('5', 'New York', '0.81')
insert into Fraud (policy_id, state, fraud_score) values ('6', 'New York', '0.77')
insert into Fraud (policy_id, state, fraud_score) values ('7', 'Texas', '0.98')
insert into Fraud (policy_id, state, fraud_score) values ('8', 'Texas', '0.97')
insert into Fraud (policy_id, state, fraud_score) values ('9', 'Texas', '0.96')
insert into Fraud (policy_id, state, fraud_score) values ('10', 'Florida', '0.97')
insert into Fraud (policy_id, state, fraud_score) values ('11', 'Florida', '0.98')
insert into Fraud (policy_id, state, fraud_score) values ('12', 'Florida', '0.78')
insert into Fraud (policy_id, state, fraud_score) values ('13', 'Florida', '0.88')
insert into Fraud (policy_id, state, fraud_score) values ('14', 'Florida', '0.66')

Pandas Schema:
data = [[1, 'California', 0.92], [2, 'California', 0.68], [3, 'California', 0.17], [4, 'New York', 0.94], [5, 'New York', 0.81], [6, 'New York', 0.77], [7, 'Texas', 0.98], [8, 'Texas', 0.97], [9, 'Texas', 0.96], [10, 'Florida', 0.97], [11, 'Florida', 0.98], [12, 'Florida', 0.78], [13, 'Florida', 0.88], [14, 'Florida', 0.66]]
fraud = pd.DataFrame(data, columns=['policy_id', 'state', 'fraud_score']).astype({'policy_id':'Int64', 'state':'object', 'fraud_score':'Float64'})

#leetcodesolutions #datascience #sql

Видео Leetcode MEDIUM 3055 - Top Percentile Fraud PERCENT_RANK() SQL - Explained by Everyday Data Science канала Everyday Data Science