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GeoIp2\Model\City Object ( [raw:protected] => Array ( [city] => Array ( [geoname_id] => 4509177 [names] => Array ( [de] => Columbus [en] => Columbus [es] => Columbus [fr] => Columbus [ja] => コロンバス [pt-BR] => Columbus [ru] => Колумбус [zh-CN] => 哥伦布 ) ) [continent] => Array ( [code] => NA [geoname_id] => 6255149 [names] => Array ( [de] => Nordamerika [en] => North America [es] => Norteamérica [fr] => Amérique du Nord [ja] => 北アメリカ [pt-BR] => América do Norte [ru] => Северная Америка [zh-CN] => 北美洲 ) ) [country] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [location] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [postal] => Array ( [code] => 43215 ) [registered_country] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [subdivisions] => Array ( [0] => Array ( [geoname_id] => 5165418 [iso_code] => OH [names] => Array ( [de] => Ohio [en] => Ohio [es] => Ohio [fr] => Ohio [ja] => オハイオ州 [pt-BR] => Ohio [ru] => Огайо [zh-CN] => 俄亥俄州 ) ) ) [traits] => Array ( [ip_address] => 216.73.216.165 [prefix_len] => 22 ) ) [continent:protected] => GeoIp2\Record\Continent Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [code] => NA [geoname_id] => 6255149 [names] => Array ( [de] => Nordamerika [en] => North America [es] => Norteamérica [fr] => Amérique du Nord [ja] => 北アメリカ [pt-BR] => América do Norte [ru] => Северная Америка [zh-CN] => 北美洲 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => code [1] => geonameId [2] => names ) ) [country:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [locales:protected] => Array ( [0] => en ) [maxmind:protected] => GeoIp2\Record\MaxMind Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [validAttributes:protected] => Array ( [0] => queriesRemaining ) ) [registeredCountry:protected] => GeoIp2\Record\Country Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 6252001 [iso_code] => US [names] => Array ( [de] => USA [en] => United States [es] => Estados Unidos [fr] => États Unis [ja] => アメリカ [pt-BR] => EUA [ru] => США [zh-CN] => 美国 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names ) ) [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isInEuropeanUnion [3] => isoCode [4] => names [5] => type ) ) [traits:protected] => GeoIp2\Record\Traits Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [ip_address] => 216.73.216.165 [prefix_len] => 22 [network] => 216.73.216.0/22 ) [validAttributes:protected] => Array ( [0] => autonomousSystemNumber [1] => autonomousSystemOrganization [2] => connectionType [3] => domain [4] => ipAddress [5] => isAnonymous [6] => isAnonymousProxy [7] => isAnonymousVpn [8] => isHostingProvider [9] => isLegitimateProxy [10] => isp [11] => isPublicProxy [12] => isResidentialProxy [13] => isSatelliteProvider [14] => isTorExitNode [15] => mobileCountryCode [16] => mobileNetworkCode [17] => network [18] => organization [19] => staticIpScore [20] => userCount [21] => userType ) ) [city:protected] => GeoIp2\Record\City Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 4509177 [names] => Array ( [de] => Columbus [en] => Columbus [es] => Columbus [fr] => Columbus [ja] => コロンバス [pt-BR] => Columbus [ru] => Колумбус [zh-CN] => 哥伦布 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => names ) ) [location:protected] => GeoIp2\Record\Location Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [accuracy_radius] => 20 [latitude] => 39.9625 [longitude] => -83.0061 [metro_code] => 535 [time_zone] => America/New_York ) [validAttributes:protected] => Array ( [0] => averageIncome [1] => accuracyRadius [2] => latitude [3] => longitude [4] => metroCode [5] => populationDensity [6] => postalCode [7] => postalConfidence [8] => timeZone ) ) [postal:protected] => GeoIp2\Record\Postal Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [code] => 43215 ) [validAttributes:protected] => Array ( [0] => code [1] => confidence ) ) [subdivisions:protected] => Array ( [0] => GeoIp2\Record\Subdivision Object ( [record:GeoIp2\Record\AbstractRecord:private] => Array ( [geoname_id] => 5165418 [iso_code] => OH [names] => Array ( [de] => Ohio [en] => Ohio [es] => Ohio [fr] => Ohio [ja] => オハイオ州 [pt-BR] => Ohio [ru] => Огайо [zh-CN] => 俄亥俄州 ) ) [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array ( [0] => en ) [validAttributes:protected] => Array ( [0] => confidence [1] => geonameId [2] => isoCode [3] => names ) ) ) )
country : United States
city : Columbus
US
Array ( [as_domain] => amazon.com [as_name] => Amazon.com, Inc. [asn] => AS16509 [continent] => North America [continent_code] => NA [country] => United States [country_code] => US )
In this tutorial, we'll explore how to utilize Python and Selenium to scrape airfare prices from Wizz Air, one of the world's leading and well-known Online Travel Agencies (OTAs). By combining Python 3.5+ with Selenium, a widely-used web browser automation package, we can automate web interactions and extract valuable flight price data from Wizz Air's website.
Ensure that Python 3.5 or a newer version is installed on your system. You can download Python from the official website.
Use the pip package manager to install Selenium by executing the following command in your terminal or command prompt:
Download the appropriate version of ChromeDriver that matches your installed Google Chrome browser version. ChromeDriver can be downloaded from the official Selenium website.
Before proceeding with the Python script, let's understand the scraping policy and the data input strategy for the flight price scraper.
1.1 Ethical Scraping: We will conduct web scraping in an ethical and responsible manner, adhering to the terms of service of the websites we are scraping. We will not engage in any activities that violate website policies, disrupt website performance, or compromise user data.
1.2 Data Usage: The scraped flight price data will be used solely for personal purposes, such as travel planning and analysis. We will not distribute, sell, or share the data with any third parties without proper authorization.
1.3 API Usage: Whenever possible, we will prioritize using official APIs provided by airlines or travel websites for accessing flight prices data. APIs are the recommended and authorized method for obtaining data.
1.4 Respectful Request Frequency: We will set appropriate delays between requests to avoid overwhelming the website's servers. Excessive scraping and high request rates can lead to IP blocking or other restrictive measures.
2.1 CSV File Format: We will create a CSV (Comma-Separated Values) file to input the desired flight routes and dates for scraping. Each row of the CSV file will represent a different roundtrip route, and the columns will contain the necessary details.
2.2 Required Columns: The CSV file must include the following columns with their respective data:
Departure City
Destination City
Departure Date
Return Date
2.3 Multiple Routes: You are free to add as many routes as you like to the CSV file. Each row represents a single roundtrip route.
By following these scraping policies and adhering to the data input strategy, we can conduct a responsible and efficient scraping process while accessing flight price data from Wizz Air or any other travel website. Let's now proceed to write the Python script based on this policy and strategy.
We can also scrape data like:
When executing the code, each flight's output is saved as a CSV file with the date and time of the scraping as the file name. The scraper automatically identifies all flights on the same path and places them in the relevant folder (named after the route).
Find scraped information for Athens-Abu Dhabi routes in individual CSV files, each named after its execution date and time. Each CSV file represents a scrape sample for the Athens-Abu Dhabi route with a title indicating the date and time of execution.
The flight scraper can extract various data fields including Departure Time, Arrival Time, Duration, Airline, Layovers, Airplane Type, Arrival Airport Name, Price, Departure Coach, Stops, and the Exact Time of Scraping. For flights with connections, additional information will also be provided.
Here's the script to start a web scraper for round-trip flights by importing necessary libraries and specifying the Chrome driver:
Using Selenium tools, you will construct functions to locate specific characteristics on the webpage based on the function names.
For each flight in the CSV routes file, the following step is repeated:
It is time to retrieve data from the internet and append it to the Pandas DataFrame.
You have the ability to save data in various formats, including CSV, JSON, and more.
Actowiz Solutions offers a comprehensive web scraping service for extracting flight price data using Python and Selenium. Our team of experts is skilled in developing efficient web scrapers that gather accurate and up-to-date information from various airline websites.
With our web scraping service, you can access essential flight details such as departure time, arrival time, duration, airline, layovers, airplane type, arrival airport name, price, departure coach, the exact time of scraping, and stops for both direct and connecting flights.
Actowiz Solutions can tailor our service to meet your needs, whether you require one-time data extraction or continuous updates. We ensure that our web scraping process adheres to ethical guidelines and respects the terms of service of the websites we scrape.
Contact Actowiz Solutions today to discuss your flight price data extraction requirements and benefit from our reliable and efficient web scraping service. Our dedicated team is ready to assist you in obtaining the flight data you need to make informed decisions.
You can also reach us for all your mobile app scraping, instant data scraper and web scraping service requirements.
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