HeroCTF - DataScience

Mon, 28 Oct 2024 00:00:00 +0000

In this challenge, we had to sort data and calculate specific values. After several attempts, I arrived at the following code:

import pandas as pd 
import math 

df = pd.read_csv("orders.csv", parse_dates=["date"]) 

df = df[df["date"] < "2023-01-01"] 

initial_balance = 10000 
user_balances = {} 

unique_users = pd.concat([df["buyer_id"], df["seller_id"]]).unique() 
user_balances = {}
for user_id in unique_users:
 user_balances[user_id] = initial_balance

total_discount_spared = 0 

for _, row in df.iterrows(): 
 buyer_id = row["buyer_id"] 
 seller_id = row["seller_id"] 
 price = row["price"] 
 discount = row["discount"] 

 discounted_price = price * (1 - discount / 100) 
 amount_spared = price - discounted_price 
 total_discount_spared += amount_spared 

 user_balances[buyer_id] -= discounted_price 
 user_balances[seller_id] += discounted_price 

# resp 1 
richest_user = max(user_balances, key=user_balances.get) 

# resp 2 
total_discount_spared = math.floor(total_discount_spared) 

# resp 3 
negative_balance_count = 0
for balance in user_balances.values():
 if balance < 0:
 negative_balance_count += 1

#print the flag 
flag = f"Hero{{{richest_user}_{total_discount_spared}_{negative_balance_count}}}" 
print(flag)

Hero{732669_188098001_3468}

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HeroCTF - DataScience