Travel data query

Travel Data Query

Dataset Schema

data/restaurants/clean_restaurant_2022.csv

Column	Type	Notes
(index)	int	Row number
Name	str	Restaurant name
City	str	City name
Cuisines	str	Comma-separated list
Average Cost	int	Cost per meal (USD)
Aggregate Rating	float	0.0–5.0

data/accommodations/clean_accommodations_2022.csv

Column	Type	Notes
(index)	int	Row number
NAME	str	Property name
room type	str	Private room / Entire home/apt / Shared room
price	float	Per night (USD)
minimum nights	float	Min booking nights
review rate number	float	1–5
house_rules	str	Restrictions (e.g. "No pets & No smoking")
maximum occupancy	int	Max guests
city	str	City name (lowercase key)

data/attractions/attractions.csv

Column	Type	Notes
Name	str	Attraction name
Latitude	float
Longitude	float
Address	str
Phone	str
Website	str
City	str	City name

data/googleDistanceMatrix/distance.csv

Column	Type	Notes
origin	str	Departure city
destination	str	Arrival city
cost	str	Usually empty
duration	str	e.g. "2 hours 8 mins"
distance	str	e.g. "228 km"

data/background/citySet_with_states.txt

Tab-separated: CityName\tStateName

Common Query Patterns

Find pet-friendly accommodations

import csv
with open('data/accommodations/clean_accommodations_2022.csv') as f:
    reader = csv.DictReader(f)
    results = [r for r in reader
               if r['city'] == 'Cleveland'
               and 'No pets' not in r['house_rules']
               and float(r['minimum nights']) <= 2]

Find restaurants by cuisine

import csv
with open('data/restaurants/clean_restaurant_2022.csv') as f:
    reader = csv.DictReader(f)
    results = [r for r in reader
               if r['City'] == 'Columbus'
               and any(c in r['Cuisines'] for c in ['American', 'Italian'])]

Look up driving distance

import csv
with open('data/googleDistanceMatrix/distance.csv') as f:
    reader = csv.DictReader(f)
    for row in reader:
        if row['origin'] == 'Minneapolis' and row['destination'] == 'Cleveland':
            print(row['duration'], row['distance'])

Verify city exists

with open('data/background/citySet_with_states.txt') as f:
    cities = [line.strip().split('\t') for line in f]
    ohio_cities = [c[0] for c in cities if len(c) > 1 and c[1] == 'Ohio']

Tips

• house_rules uses " & " as separator; check with 'No pets' not in row['house_rules']
• Cuisines is comma+space separated; simple substring search works
• Distance matrix is not symmetric — always check both directions if needed
• Some accommodations have minimum nights > 7; always filter for the planned stay length