travel-data-query

name	travel-data-query
description	Query and filter travel CSV datasets (restaurants, accommodations, attractions, distances) using Python. Use this skill whenever you need to extract specific records from the travel database files, check pet policies, filter by cuisine, look up driving times, or validate that a city exists in the dataset.

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