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Copy pathPlaneTrip_Traveloka_WithDef.py
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PlaneTrip_Traveloka_WithDef.py
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"""
@author : hhtrieu0108
github : https://github.com/hhtrieu0108
linkedin : https://www.linkedin.com/in/trieuhh
"""
import numpy as np
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.support import expected_conditions as EC
from time import sleep
import datetime
import pandas as pd
import glob
def get_url(places):
"""
:param places: list_of_place base on url of traveloka coach
:return: list of url
"""
url = []
date = []
today = datetime.datetime.today().date()
date.append(today)
for i in range(1,5):
date.append(today + datetime.timedelta(days=i))
for place in places:
for day in date:
url.append(f"https://www.traveloka.com/vi-vn/flight/fullsearch?ap={place}&dt={day.strftime(format='%d-%m-%Y')}.NA&ps=1.0.0&sc=ECONOMY")
return url
def crawl_planetrip(url_list):
"""
:param url_list: list of url
:return: dictionary with key is url and value is dataframe
"""
df_by_url = {}
driver = webdriver.Edge()
for url in url_list:
driver.get(url)
sleep(5)
wait = WebDriverWait(driver, 50)
df = pd.DataFrame(columns=['brand', 'price',
'start_time', 'start_day',
'end_day', 'end_time',
'trip_time', 'take_place', 'destination'])
initial_page_length = driver.execute_script("return document.body.scrollHeight")
start_time = []
end_time = []
start_day = []
end_day = []
trip_time = []
take_place = []
destination = []
price = []
brand = []
num_steps = 1000000
scroll_step = initial_page_length // 200
old_element = []
for i in range(num_steps):
scroll_position = scroll_step * (i + 1)
driver.execute_script(f"window.scrollTo(0, {scroll_position});")
position = driver.execute_script("return window.scrollY")
new_height = driver.execute_script("return document.body.scrollHeight")
if scroll_position >= new_height:
break
driver.execute_script("window.scrollTo(0, 0)")
elements = driver.find_elements(
By.XPATH,
"//div[@class='css-1dbjc4n r-9nbb9w r-otx420 r-1i1ao36 r-1x4r79x']"
)
price_elements = wait.until(EC.visibility_of_all_elements_located((
By.XPATH,
"//div[@class='css-1dbjc4n r-obd0qt r-eqz5dr r-1cmwbt1 r-knv0ih']" \
"//h3[@class='css-4rbku5 css-901oao r-t1w4ow r-ubezar r-b88u0q r-rjixqe r-fdjqy7']"
)))
brand_elements = wait.until(EC.visibility_of_all_elements_located((
By.XPATH,
"//div[@class='css-1dbjc4n r-1habvwh r-18u37iz r-1ssbvtb']//" \
"div[@class='css-901oao css-cens5h r-t1w4ow r-ubezar r-majxgm r-135wba7 r-fdjqy7']"
)))
detail = wait.until(EC.visibility_of_all_elements_located((
By.XPATH,
"//div[@class='css-1dbjc4n r-1awozwy r-1xr2vsu r-13awgt0 r-18u37iz r-1w6e6rj r-3mtglp r-1x4r79x']" \
"//div[@class='css-901oao r-t1w4ow r-1b43r93 r-majxgm r-rjixqe r-fdjqy7']"
)))
for i, j in zip(range(len(elements)), detail[::4]):
if elements[i] in old_element:
continue
ActionChains(driver).move_to_element(j).click().perform()
old_element.append(elements[i])
start_time_elements = wait.until(EC.visibility_of_element_located((
By.XPATH,
"//div[@class='css-1dbjc4n r-e8mqni r-1d09ksm r-1h0z5md r-ttb5dx']" \
"//div[@class='css-901oao r-t1w4ow r-1b43r93 r-majxgm r-rjixqe r-5oul0u r-fdjqy7']"
)))
start_time.append(start_time_elements.text)
end_time_elements = wait.until(EC.visibility_of_element_located((
By.XPATH,
"//div[@class='css-1dbjc4n r-e8mqni r-1d09ksm r-1h0z5md r-q3we1 r-ttb5dx']" \
"//div[@class='css-901oao r-t1w4ow r-1b43r93 r-majxgm r-rjixqe r-fdjqy7']"
)))
end_time.append(end_time_elements.text)
start_date_elements = wait.until(EC.visibility_of_element_located((
By.XPATH,
"//div[@class='css-1dbjc4n r-e8mqni r-1d09ksm r-1h0z5md r-ttb5dx']" \
"//div[@class='css-901oao r-t1w4ow r-1enofrn r-majxgm r-1cwl3u0 r-fdjqy7']"
)))
start_day.append(start_date_elements.text)
end_date_elements = wait.until(EC.visibility_of_element_located((
By.XPATH,
"//div[@class='css-1dbjc4n r-e8mqni r-1d09ksm r-1h0z5md r-q3we1 r-ttb5dx']" \
"//div[@class='css-901oao r-t1w4ow r-1enofrn r-majxgm r-1cwl3u0 r-fdjqy7']"
)))
end_day.append(end_date_elements.text)
trip_time_elements = wait.until(EC.visibility_of_element_located((
By.XPATH,
"//div[@class='css-901oao r-13awgt0 r-t1w4ow r-1enofrn r-majxgm r-1cwl3u0 r-fdjqy7']"
)))
trip_time.append(trip_time_elements.text)
destination_elements = wait.until(EC.visibility_of_element_located((
By.XPATH,
"//div[@class='css-1dbjc4n r-e8mqni r-1habvwh r-13awgt0 r-1h0z5md r-q3we1']"
)))
destination.append(destination_elements.text)
take_place_elements = wait.until(EC.visibility_of_element_located((
By.XPATH,
"//div[@class='css-1dbjc4n r-e8mqni r-1habvwh r-13awgt0 r-1h0z5md']"
)))
take_place.append(take_place_elements.text)
price.append(price_elements[i].text)
brand.append(brand_elements[i].text)
new_df = pd.DataFrame(list(zip(brand, price,
start_time,
start_day, end_time, end_day,
trip_time, take_place, destination)),
columns=['brand', 'price',
'start_time',
'start_day', 'end_time', 'end_day',
'trip_time', 'take_place', 'destination'])
df = pd.concat((df, new_df), axis=0, ignore_index=True)
start_time = []
end_time = []
start_day = []
end_day = []
trip_time = []
take_place = []
destination = []
price = []
brand = []
j.click()
sleep(1)
new_url = url[53:]
df.to_csv(f"Planetrip_{new_url}.csv", index=False)
df_by_url[new_url] = df
driver.quit()
return df_by_url
def preprocessing_data(df_by_url):
process_data = {}
for url, data in zip(df_by_url.keys(), df_by_url.values()):
new_data = data.copy()
new_data['price'] = new_data['price'].str.split(' ').str[0].str.replace('.', '').astype('int64')
new_data['end_day'] = new_data['end_day'].str.split(' ').str[0] + '-' + \
new_data['end_day'].str.split(' ').str[1].replace(
{"Jan": '01', "Feb": '02', "Mar": '03',
"Apr": '04', "May": '05', "Jun": '06',
"Jul": '07', "Aug": '08', "Sep": '09',
"Oct": '10', "Nov": '11', "Dec": '12'}) + '-' + str(datetime.datetime.today().year)
new_data['start_day'] = new_data['start_day'].str.split(' ').str[0] + '-' + \
new_data['start_day'].str.split(' ').str[1].replace(
{"Jan": '01', "Feb": '02', "Mar": '03',
"Apr": '04', "May": '05', "Jun": '06',
"Jul": '07', "Aug": '08', "Sep": '09',
"Oct": '10', "Nov": '11', "Dec": '12'}) + '-' + str(datetime.datetime.today().year)
new_data.to_csv(f"Processed_Data_PlaneTrip/PlaneTrip_{url}.csv", index=False)
process_data[url] = new_data
def last_processing():
"""
:return: Full data with full information
"""
csv_file = glob.glob("Processed_Data_PlaneTrip/*.csv")
full_data = pd.read_csv("Processed_Data_PlaneTrip/PlaneTrip_SGN.CXR&dt=20-05-2024.NA&ps=1.0.0&sc=ECONOMY.csv")
full_data['trip_to'] = 'Nha Trang'
today = datetime.datetime.today().date()
next_day = today + datetime.timedelta(days=4)
for data in csv_file[1:5]:
df = pd.read_csv(data)
df['trip_to'] = 'Nha Trang'
full_data = pd.concat((full_data, df), axis=0, ignore_index=True)
for data in csv_file[5:10]:
df = pd.read_csv(data)
df['trip_to'] = 'Đà Nẵng'
full_data = pd.concat((full_data, df), axis=0, ignore_index=True)
full_data['id'] = np.arange(1, len(full_data) + 1)
full_data.to_csv(f"Processed_Data_PlaneTrip/PlaneTrip_Full_{today.strftime(format='%d-%m-%Y')}_\
{next_day.strftime(format='%d-%m-%Y')}.csv",
index=False)
return full_data
if __name__ == "__main__":
places = ['SGN.DAD','SGN.CXR']
url_list = get_url(places)
df_by_url = crawl_planetrip(url_list)
process_data = preprocessing_data(df_by_url)
last_processing()