travel-database-parser

name	travel-database-parser
description	Parse and search travel-related databases including cities, accommodations, restaurants, attractions, and distances. Use this skill whenever building a travel itinerary that requires querying real-world data from CSV files or text databases. This skill helps extract relevant POIs, lodging, dining, and routing information from structured datasets.

Travel Database Parser

This skill provides techniques for searching, parsing, and querying travel-related databases that store information about cities, accommodations, restaurants, attractions, and distance matrices.

Database File Locations

The travel databases are typically organized as:

• Cities: background/citySet_with_states.txt - Contains city names and state information
• Accommodations: accommodations/clean_accommodations_2022.csv - Pet-friendly lodging options
• Restaurants: restaurants/clean_restaurant_2022.csv - Dining establishments by cuisine type
• Attractions: attractions/attractions.csv - Tourist attractions and POIs
• Distances: googleDistanceMatrix/distance.csv - Travel distances and times between cities

Parsing Strategy

1. Load and Index Databases

Before searching, load each database into memory:

import csv
import json

# Load cities
cities = {}
with open('background/citySet_with_states.txt', 'r') as f:
    for line in f:
        parts = line.strip().split(',')
        city_name = parts[0].strip()
        state = parts[1].strip() if len(parts) > 1 else ''
        cities[city_name] = state

# Load accommodations
accommodations = []
with open('accommodations/clean_accommodations_2022.csv', 'r') as f:
    reader = csv.DictReader(f)
    for row in reader:
        accommodations.append(row)

# Load restaurants
restaurants = []
with open('restaurants/clean_restaurant_2022.csv', 'r') as f:
    reader = csv.DictReader(f)
    for row in reader:
        restaurants.append(row)

# Load attractions
attractions = []
with open('attractions/attractions.csv', 'r') as f:
    reader = csv.DictReader(f)
    for row in reader:
        attractions.append(row)

# Load distances
distances = {}
with open('googleDistanceMatrix/distance.csv', 'r') as f:
    reader = csv.DictReader(f)
    for row in reader:
        key = (row['from'], row['to'])
        distances[key] = row

2. Search by City

To find accommodations, restaurants, and attractions for a specific city:

def find_accommodations(city, pet_friendly=False):
    results = [r for r in accommodations if r.get('city', '').lower() == city.lower()]
    if pet_friendly:
        results = [r for r in results if r.get('pet_friendly') == '1' or 'pet' in r.get('amenities', '').lower()]
    return results

def find_restaurants(city, cuisine_type=None):
    results = [r for r in restaurants if r.get('city', '').lower() == city.lower()]
    if cuisine_type:
        results = [r for r in results if cuisine_type.lower() in r.get('cuisine', '').lower()]
    return results

def find_attractions(city):
    results = [a for a in attractions if a.get('city', '').lower() == city.lower()]
    return results

3. Query Distances

Look up travel distances and times between cities:

def get_distance(from_city, to_city):
    key = (from_city, to_city)
    if key in distances:
        return distances[key]
    return None

4. Filter by Constraints

When building an itinerary, apply filters for:

• Pet-friendly: Check accommodation amenities or pet policies
• Cuisine types: Match restaurants to dietary/preference requirements
• Budget: Filter accommodations and restaurants by price ranges
• Distance/Travel time: Verify feasibility of daily routes

Key Considerations

• Field names vary by dataset: Always check CSV headers; common variations include City, city, CITY
• Pet-friendly fields: Check for boolean flags, amenity lists, or policy descriptions
• Cuisine categories: May be comma-separated or single-valued; normalize to consistent format
• Pricing: Some databases use price ranges (e.g., "$$$"), others use exact costs; extract numeric values where available
• Attraction descriptions: May be brief or detailed; concatenate multiple attractions with semicolons for clarity

Example: Building a City Summary

def summarize_city(city_name, num_accommodations=3, num_restaurants=3, num_attractions=3):
    accomodations = find_accommodations(city_name, pet_friendly=True)[:num_accommodations]
    restaurants = find_restaurants(city_name)[:num_restaurants]
    attractions = find_attractions(city_name)[:num_attractions]

    return {
        'city': city_name,
        'accommodations': accommodations,
        'restaurants': restaurants,
        'attractions': attractions
    }

When using these functions in a real itinerary builder, always verify that the data exists and handle empty result sets gracefully.