How Python and Selenium are Used to Scrape Flight Prices?

Scraping Policy

  • Execute the entire code
  • Each flight’s output is a CSV file. The date and time of the scraping will be the name of the file.
  • The scraper will automatically identify all flights on the same path and place them in the relevant folder (the name of the route).

Data Fields

  • Departure time
  • Arrival time
  • Duration
  • Airline
  • Layovers
  • Airplane type
  • Arrival airport name
  • Price
  • Departure coach
  • The exact time of scraping
  • Stops


# Selenuim imports from selenium import webdriver from import By from selenium.webdriver.common.keys import Keys import selenium.common.exceptions as selexcept # Pandas imports using Pandas for structuring our data import pandas as pd from datetime import datetime import os.path import re import sys import glob # Time and date-time (mainly for using delays between clicks) import time # Change this to your own chromedriver path! chromedriver_path = 'Insert your own path' # This will open the Chrome window browser = webdriver.Chrome(executable_path=chromedriver_path) # Setting Round Trip type path return_ticket = "//label[@id='flight-type-roundtrip-label-hp-flight']"
def ticket_chooser(ticket): try: ticket_type = browser.find_element_by_xpath(ticket) except Exception as e: pass def more_details(details): try: details_type = browser.find_element_by_xpath(details) except Exception as e: pass def dep_country_chooser(dep_country): fly_from = browser.find_element_by_xpath("//input[@id='flight-origin-hp-flight']") time.sleep(3) fly_from.clear() time.sleep(3) fly_from.send_keys(' ' + dep_country) time.sleep(3) first_item = browser.find_element_by_xpath("//a[@id='aria-option-0']") time.sleep(3) def arrival_country_chooser(arrival_country): fly_to = browser.find_element_by_xpath("//input[@id='flight-destination-hp-flight']") time.sleep(3) fly_to.clear() time.sleep(3) fly_to.send_keys(' ' + arrival_country) time.sleep(3) first_item = browser.find_element_by_xpath("//a[@id='aria-option-0']") time.sleep(3) def dep_date_chooser(month, day, year): dep_date_button = browser.find_element_by_xpath("//input[@id='flight-departing-hp-flight']") dep_date_button.clear() dep_date_button.send_keys(str(month) + "/" + str(day) + "/" + str(year)) def return_date_chooser(month, day, year): return_date_button = browser.find_element_by_xpath("//input[@id='flight-returning-hp-flight']") for i in range(11): return_date_button.send_keys(Keys.BACKSPACE) return_date_button.send_keys(str(month) + "/" + str(day) + "/" + str(year))
def ProcessJourney(journeyDetails, i): ChooseFlight(journeyDetails, i) DataProcessing() AmendTimeAndDate() SaveDfToCsv(journeyDetails,i)
def DataProcessing(): global df df = df[0:0] number_element_to_ignore = 0 # departure times dep_times = browser.find_elements_by_xpath("//span[@data-test-id='departure-time']") dep_times_list = [value.text for value in dep_times] # arrival times arr_times = browser.find_elements_by_xpath("//span[@data-test-id='arrival-time']") arr_times_list = [value.text for value in arr_times] # airline name airlines = browser.find_elements_by_xpath("//span[@data-test-id='airline-name']") airlines_list = [value.text for value in airlines] # durations durations = browser.find_elements_by_xpath("//span[@data-test-id='duration']") durations_list = [value.text for value in durations] # stops stops = browser.find_elements_by_xpath("//span[@class='number-stops']") stops_list = [value.text for value in stops] # layovers layovers = browser.find_elements_by_xpath("//span[@data-test-id='layover-airport-stops']") layovers_list = [value.text for value in layovers] # prices prices = browser.find_elements_by_xpath("//span[@data-test-id='listing-price-dollars']") price_list = [value.text for value in prices] # last flight to scrape according to the price differences (here we choose multiply by 2) last_flight_index = CheckLastFlightIndexByPrice(price_list, 2) #Genrate flight to igonre according to the last_flight_index bad_indexes = GenerateBadIndex(dep_times_list, last_flight_index) # delete the non relevant flights for i in range(len(bad_indexes)): dep_times_list.pop(int(bad_indexes[i])) arr_times_list.pop(int(bad_indexes[i])) # Insert data to our DF for i in range(last_flight_index): try: df.loc[i, 'departure_time'] = dep_times_list[i] except Exception as e: pass try: df.loc[i, 'arrival_time'] = arr_times_list[i] except Exception as e: pass try: df.loc[i, 'airline'] = airlines_list[i] except Exception as e: pass try: df.loc[i, 'duration'] = durations_list[i] except Exception as e: pass try: df.loc[i, 'stops'] = stops_list[i] except Exception as e: pass try: df.loc[i, 'layovers'] = layovers_list[i] except Exception as e: pass try: df.loc[i, 'price'] = price_list[i] except Exception as e: pass try: # Adding flight details data number_element_to_ignore = AddingFlightDetails(i, stops_list, bad_indexes, number_element_to_ignore) except Exception as e: pass
def GetPathForExcelsOutPut(journeyDetails,i): dep_arr_name =[i,'dep_country_chooser'] + "_" \[i,'arrival_country_chooser'] sampleTime = "sampleTime_" str(pd.to_datetime('today').strftime("%d/%m/%Y")).replace("/", "_") dep_date = str("%02d" %[i,'dep_month']) "_" str("%02d" %[i,'dep_day']) "_" str([i,'dep_year']) arr_date = str("%02d" %[i,'arr_month']) "_" str("%02d" %[i,'arr_day']) "_" str([i,'arr_year']) conc_date = "depDate_" dep_date "_arrDate_" arr_date data_path = "Insert your data results folder here (the Parent folder that contain the all automatic subdir that will be created)" folderPath = os.path.join(data_path,dep_arr_name, conc_date, 'sampleTime\\') return [folderPath, conc_date] def SaveDfToCsv(journeyDetails,i): checkPathExist(journeyDetails,i) [pathForDepArrDate, nameOfFolder] = GetPathForExcelsOutPut(journeyDetails,i) df['departure_date'] = GetDepartDateUsingFolderName(nameOfFolder) # send file path to function df['arrival_date'] = GetReturnDateUsingFolderName(nameOfFolder) # send file path to function df.to_csv(str(GetPathForExcelsOutPut(journeyDetails,i)[0]) "_"'%y%m%d-%H%M%S') ".csv", index = False)



Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
iWeb Scraping Services

iWeb Scraping Services

Web Scraping services with iWeb Scraping Company is best Data scraping services provider in the USA, India, Australia, UAE, UK, and more countries at affordable