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Extracting flight schedules and routes with Python and aiohttp
Scraping flight schedules and routes is a data-intensive task that benefits from asynchronous programming for faster performance. Using Python’s aiohttp library, you can fetch multiple pages simultaneously, making it an excellent choice for scraping airline websites. Most flight schedules are organized in tables or lists, making them easy to parse with libraries like BeautifulSoup. If the data is loaded dynamically via JavaScript, combining aiohttp with browser automation tools may be necessary. Additionally, analyzing network requests can help identify JSON endpoints for direct data retrieval.Here’s an example using aiohttp and BeautifulSoup to scrape flight schedules:
import aiohttp import asyncio from bs4 import BeautifulSoup async def fetch_flights(session, url): async with session.get(url) as response: html = await response.text() soup = BeautifulSoup(html, 'html.parser') flights = soup.find_all('div', class_='flight-item') for flight in flights: route = flight.find('span', class_='route').text.strip() time = flight.find('span', class_='time').text.strip() print(f"Route: {route}, Time: {time}") async def main(): urls = [f"https://example.com/flights?page={i}" for i in range(1, 6)] async with aiohttp.ClientSession() as session: tasks = [fetch_flights(session, url) for url in urls] await asyncio.gather(*tasks) asyncio.run(main()Managing proxies and handling retries for failed requests are critical for large-scale scraping. How do you optimize scraping flight data across multiple pages?
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5 Replies
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For dynamic content, I use Capybara with Selenium, which allows interacting with elements like dropdowns or infinite scrolling job lists.
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For multi-page scraping, I use asynchronous requests with aiohttp to fetch pages in parallel. This significantly reduces the time required to collect data.
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To handle layout changes, I use dynamic selectors based on attributes or patterns. This approach reduces the chances of the scraper breaking if class names or structures are modified.
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Storing user agent profiles in a database like PostgreSQL allows efficient querying and analysis, especially when tracking updates or comparing profiles across sessions.
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Storing flight data in a database helps track trends, such as price fluctuations or route availability, and makes it easier to query the data later.
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