#expediadataextraction

🌍 Unlock the Secrets Behind Hotel Pricing Trends with Real-Time Data! 🏨💡

Ever wondered how hotel prices fluctuate between Expedia, Booking.com, and other platforms? 🤔

In today’s data-driven travel industry, real-time hotel pricing data extraction isn’t just about monitoring numbers — it’s about decoding market behavior, competitor tactics, and consumer patterns.

Here’s how scraping Expedia & Booking.com hotel data empowers businesses to make smarter travel and pricing decisions 👇

🔹 1️⃣ Competitive Pricing Intelligence – Track room rates across destinations and spot market fluctuations instantly.

🔹 2️⃣ Dynamic Pricing Analysis – Understand how events, seasons, and occupancy rates impact real-time pricing.

🔹 3️⃣ Customer Behavior Insights – Analyze trends in booking preferences, location demand, and stay durations.

🔹 4️⃣ Profit Optimization – Align your pricing strategy with competitor benchmarks and maximize revenue.

🔹 5️⃣ Strategic Market Forecasting – Use predictive insights to anticipate demand shifts across global markets.

📊 “Travel companies leveraging real-time pricing analytics see up to 28% higher conversion and 20% better rate accuracy.”

At iWeb Data Scraping, we empower businesses with real-time hotel pricing and market insights that fuel data-driven decision-making — helping brands stay competitive, agile, and informed.

👉 Read full insights here:

🔗 https://www.iwebdatascraping.com/expedia-real-time-hotel-price-trends.php

💬 Let’s talk travel intelligence!

When done manually, gathering travel data for planes is a massive undertaking. There are various possible combinations of airports, routes, times, and costs, all of which are always changing. Ticket rates fluctuate on a daily (or even hourly) basis, and there are numerous flights available each day. Web scraping is one method for keeping track of this information. In this Blog, we'll scrape Expedia , a popular vacation booking site, to get flight information. The flight schedules and pricing for a sender and the receiver pair will be extracted by our scraper.

Data Fields that will be extracted:

Arrival Airport

Arrival Time

Departure Airport

Departure Time

Flight Name

Flight Duration

Ticket Price

No. Of Stops

Airline

Below shown is the screenshot of the data fields that we will be extracting:

Scraping Code:

1. Create the URL of the search results from Expedia for instance, we will check the available flights listed from New York to Miami:

https://www.expedia.com/Flights-Search?trip=oneway&leg1=from:New%20York,%20NY%20(NYC-All%20Airports),to:Miami,%20Florida,departure:04/01/2017TANYT&passengers=children:0,adults:1,seniors:0,infantinlap:Y&mode=search

2. Using Python Requests, download the HTML of the search result page.

3. Parse the webpage with LXML — LXML uses Xpaths to browse the HTML Tree Structure. The XPaths for the details we require in the code have already been defined.

4. Save the information in a JSON file. You can change this later to write to a database.

Requirements

We'll need several libraries for obtaining and parsing HTML for this Python 3 web scraping tutorial. The requirements for the package are shown below.

Install Python 3 and Pip

Install Packages

The code is explanatory

You can check the code from the link here.

Executing the Expedia Scraper

Let's say the script's name is expedia.py. In a command prompt or terminal, input the script name followed by a -h.

usage: expedia.py [-h] source destination date positional arguments: source            Source airport code destination       Destination airport code date              MM/DD/YYYY optional arguments: -h, --help show this help message and exit

The input and output arguments are the airline codes for the source and destination airports, respectively. The date parameter must be in the form MM/DD/YYYY

For example, to get flights from New York to Miami, we would use the following arguments:

python3 expedia.py nyc mia 04/01/2017

The nyc-mia-flight-results.json file will be created as a result of this. json, which will be saved in the same directory as the script.

This is what the output file will look like:

{ "arrival": "Miami Intl., Miami", "timings": [ { "arrival_airport": "Miami, FL (MIA-Miami Intl.)", "arrival_time": "12:19a", "departure_airport": "New York, NY (LGA-LaGuardia)", "departure_time": "9:00p" } ], "airline": "American Airlines", "flight duration": "1 days 3 hours 19 minutes", "plane code": "738", "plane": "Boeing 737-800", "departure": "LaGuardia, New York", "stops": "Nonstop", "ticket price": "1144.21" }, { "arrival": "Miami Intl., Miami", "timings": [ { "arrival_airport": "St. Louis, MO (STL-Lambert-St. Louis Intl.)", "arrival_time": "11:15a", "departure_airport": "New York, NY (LGA-LaGuardia)", "departure_time": "9:11a" }, { "arrival_airport": "Miami, FL (MIA-Miami Intl.)", "arrival_time": "8:44p", "departure_airport": "St. Louis, MO (STL-Lambert-St. Louis Intl.)", "departure_time": "4:54p" } ], "airline": "Republic Airlines As American Eagle", "flight duration": "0 days 11 hours 33 minutes", "plane code": "E75", "plane": "Embraer 175", "departure": "LaGuardia, New York", "stops": "1 Stop", "ticket price": "2028.40" },

You can download the code at:

import json import requests from lxml import html from collections import OrderedDict import argparse def parse(source,destination,date): for i in range(5): try: url = "https://www.expedia.com/Flights-Search?trip=oneway&leg1=from:{0},to:{1},departure:{2}TANYT&passengers=adults:1,children:0,seniors:0,infantinlap:Y&options=cabinclass%3Aeconomy&mode=search&origref=www.expedia.com".format(source,destination,date) headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.77 Safari/537.36'} response = requests.get(url, headers=headers, verify=False) parser = html.fromstring(response.text) json_data_xpath = parser.xpath("//script[@id='cachedResultsJson']//text()") raw_json =json.loads(json_data_xpath[0] if json_data_xpath else '') flight_data = json.loads(raw_json["content"]) flight_info  = OrderedDict() lists=[] for i in flight_data['legs'].keys(): total_distance =  flight_data['legs'][i].get("formattedDistance",'') exact_price = flight_data['legs'][i].get('price',{}).get('totalPriceAsDecimal','') departure_location_airport = flight_data['legs'][i].get('departureLocation',{}).get('airportLongName','') departure_location_city = flight_data['legs'][i].get('departureLocation',{}).get('airportCity','') departure_location_airport_code = flight_data['legs'][i].get('departureLocation',{}).get('airportCode','') arrival_location_airport = flight_data['legs'][i].get('arrivalLocation',{}).get('airportLongName','') arrival_location_airport_code = flight_data['legs'][i].get('arrivalLocation',{}).get('airportCode','') arrival_location_city = flight_data['legs'][i].get('arrivalLocation',{}).get('airportCity','') airline_name = flight_data['legs'][i].get('carrierSummary',{}).get('airlineName','') no_of_stops = flight_data['legs'][i].get("stops","") flight_duration = flight_data['legs'][i].get('duration',{}) flight_hour = flight_duration.get('hours','') flight_minutes = flight_duration.get('minutes','') flight_days = flight_duration.get('numOfDays','') if no_of_stops==0:    stop = "Nonstop" else:    stop = str(no_of_stops)+' Stop' total_flight_duration = "{0} days {1} hours {2} minutes".format(flight_days,flight_hour,flight_minutes) departure = departure_location_airport+", "+departure_location_city arrival = arrival_location_airport+", "+arrival_location_city carrier = flight_data['legs'][i].get('timeline',[])[0].get('carrier',{}) plane = carrier.get('plane','') plane_code = carrier.get('planeCode','') formatted_price = "{0:.2f}".format(exact_price) if not airline_name:    airline_name = carrier.get('operatedBy','') timings = [] for timeline in  flight_data['legs'][i].get('timeline',{}):    if 'departureAirport' in timeline.keys():        departure_airport = timeline['departureAirport'].get('longName','')        departure_time = timeline['departureTime'].get('time','')        arrival_airport = timeline.get('arrivalAirport',{}).get('longName','')        arrival_time = timeline.get('arrivalTime',{}).get('time','')        flight_timing = {                            'departure_airport':departure_airport,                            'departure_time':departure_time,                            'arrival_airport':arrival_airport,                            'arrival_time':arrival_time        }        timings.append(flight_timing) flight_info={'stops':stop,    'ticket price':formatted_price,    'departure':departure,    'arrival':arrival,    'flight duration':total_flight_duration,    'airline':airline_name,    'plane':plane,    'timings':timings,    'plane code':plane_code } lists.append(flight_info)    sortedlist = sorted(lists, key=lambda k: k['ticket price'],reverse=False)    return sortedlist except ValueError:    print ("Rerying...")     return {"error":"failed to process the page",} if __name__=="__main__": argparser = argparse.ArgumentParser() argparser.add_argument('source',help = 'Source airport code') argparser.add_argument('destination',help = 'Destination airport code') argparser.add_argument('date',help = 'MM/DD/YYYY') args = argparser.parse_args() source = args.source destination = args.destination date = args.date print ("Fetching flight details") scraped_data = parse(source,destination,date) print ("Writing data to output file") with open('%s-%s-flight-results.json'%(source,destination),'w') as fp: json.dump(scraped_data,fp,indent = 4)

Unless the page structure changes dramatically, this scraper should be able to retrieve most of the flight details present on Expedia. This scraper is probably not going to work for you if you want to scrape the details of thousands of pages at very short intervals.

Contact iWeb Scraping for extracting Expedia using Python and LXML or ask for a free quote!