Taxi

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Taxi#

Helpers#

import os
import pandas as pd
import requests
from dotenv import load_dotenv

load_dotenv()
API_KEY = os.environ["API_KEY"]
URL_QUERY = "https://%s.crucible.dreadnode.io/score"
URL_FLAG = "https://crucible.dreadnode.io/api/challenges/%s/submit-flag"
CHALLENGE = "taxi"

def query(flag):
    response = requests.post(
        URL_QUERY % CHALLENGE,
        headers={ "X-API-Key": API_KEY },
        json={ "data": flag }
    )
    return response.json()

def submit(flag):
    response = requests.post(
        URL_FLAG % CHALLENGE,
        headers={ "X-API-Key": API_KEY },
        json={ "challenge": CHALLENGE, "flag": flag }
    )
    return False if response.status_code != 200 else response.json().get("correct")

Solution#

If we examine the different columns in the Parquet file we can see that the distribution of values for the pickup_location column is quite interesting.

df = pd.read_parquet("./data/taxi.parquet")
df["pickup_location"].value_counts()

pickup_location
Financial District    298
Industrial Park       286
Train Station         274
Beach Front           272
University            272
Shopping Mall         259
Business District     256
Airport               252
Historic Center       250
Convention Center     250
Entertainment Zone    245
Sports Complex        243
Downtown              242
Theater District      237
Hotel Zone            234
Restaurant Row        232
Arts District         231
Residential Area      225
Tech Hub              221
Hospital              215
Grand Central           1
Railway Station         1
Library                 1
North Station           1
0mega Mall              1
0pera House             1
Name: count, dtype: int64

Looking at the rows for those pickup locations we can see that they all have identical signal_north and signal_south values: 85.0 and 15.0, respectively.

col_pickup = "pickup_location"

df[df[col_pickup].isin(df[col_pickup].value_counts().nsmallest(6).index.values)]
ride_id pickup_time pickup_location dropoff_location driver_id passenger_count fare_amount tip_amount payment_type rating ride_duration_minutes dropoff_time signal_north signal_south
0 1 2024-01-01 00:00:00 Library Tech Hub 63 3 17.790 12.52 Cash 1 80 2024-01-01 01:20:00 85.0 15.0
600 601 2024-01-03 02:00:00 0mega Mall Restaurant Row 58 3 99.030 19.16 Cash 4 19 2024-01-03 02:19:00 85.0 15.0
1200 1201 2024-01-05 04:00:00 North Station Theater District 49 4 73.275 14.76 Cash 5 68 2024-01-05 04:30:00 85.0 15.0
1800 1801 2024-01-07 06:00:00 Grand Central Entertainment Zone 43 4 56.350 13.61 Credit Card 5 105 2024-01-07 07:45:00 85.0 15.0
2400 2401 2024-01-09 08:00:00 Railway Station Tech Hub 51 2 52.860 9.15 Cash 5 5 2024-01-09 08:05:00 85.0 15.0
3000 3001 2024-01-11 10:00:00 0pera House Tech Hub 24 4 57.460 19.95 Mobile Payment 4 80 2024-01-11 11:20:00 85.0 15.0

If we filter for all rows with these signal values and join the first letters of the pickup_location column we get the flag.

col_snorth = "signal_north"
col_ssouth = "signal_south"

flag = "".join(map(
    lambda s: s[0],
    df[(df[col_snorth] == 85.0) & (df[col_ssouth] == 15.0)][col_pickup].values
))

print(flag)
print("Flag accepted:", submit(query(flag)["flag"]))

L0NGR0AD
Flag accepted: True
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