Top percentile fraud

# Table: Fraud
# 
# +-------------+---------+
# | Column Name | Type    |
# +-------------+---------+
# | policy_id   | int     |
# | state       | varchar |
# | fraud_score | int     |
# +-------------+---------+
# policy_id is column of unique values for this table.
# This table contains policy id, state, and fraud score.
# 
# The Leetcode Insurance Corp has developed an ML-driven predictive model to detect the likelihood of fraudulent claims. Consequently, they allocate their most seasoned claim adjusters to address the top 5% of claims flagged by this model.
# 
# Write a solution to find the top 5 percentile of claims from each state.
# 
# Return the result table ordered by state in ascending order, fraud_score in descending order, and policy_id in ascending order.
# 
# The result format is in the following example.
#
# Example 1:
# Input:
# Fraud table:
# +-----------+------------+-------------+
# | policy_id | state      | fraud_score |
# +-----------+------------+-------------+
# | 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        |
# +-----------+------------+-------------+
# Output:
# +-----------+------------+-------------+
# | policy_id | state      | fraud_score |
# +-----------+------------+-------------+
# | 1         | California | 0.92        |
# | 11        | Florida    | 0.98        |
# | 4         | New York   | 0.94        |
# | 7         | Texas      | 0.98        |
# +-----------+------------+-------------+
# Explanation
# - For the state of California, only policy ID 1, with a fraud score of 0.92, falls within the top 5 percentile for this state.
# - For the state of Florida, only policy ID 11, with a fraud score of 0.98, falls within the top 5 percentile for this state.
# - For the state of New York, only policy ID 4, with a fraud score of 0.94, falls within the top 5 percentile for this state.
# - For the state of Texas, only policy ID 7, with a fraud score of 0.98, falls within the top 5 percentile for this state.
# Output table is ordered by state in ascending order, fraud score in descending order, and policy ID in ascending order.

import pandas as pd

def top_percentile_fraud(fraud: pd.DataFrame) -> pd.DataFrame:
  fraud['pct_rank'] = fraud.groupby('state')['fraud_score'].rank(pct=True, method='max')
  result = fraud[fraud['pct_rank'] >= 0.95][['policy_id', 'state', 'fraud_score']]
  return result.sort_values(['state', 'fraud_score', 'policy_id'], ascending=[True, False, True]).reset_index(drop=True)