<jupyter_start><jupyter_text>2.Scrape the details team India’s international fixtures from bcci.tv.Url = https://www.bcci.tv/You need to find following details:A) Match title (I.e. 1st ODI)B) SeriesC) PlaceD) DateE) TimeNote: - From bcci.tv home page you have reach to the international fixture page through code.<jupyter_code>#Connect to web driver
driver=webdriver.Chrome(r"D://chromedriver.exe")  #r converts string to raw string
#If not r, we can use executable_path = "C:/path name"
#Getting the website to driver
driver.get('https://www.bcci.tv/')

#When we run this line, automatically the webpage will be opened
#Clicking international fixture section
international=driver.find_element_by_xpath("//ul[@class='navigation__list showMoreEnabled']/li[1]/div[2]/div/ul/li[1]/a")
driver.get(international.get_attribute('href'))
#Creating empty lists for storing the scrapped data
Match=[]
Series=[]
Place=[]
Date=[]
Time=[]

#Scrapping the data having the match name
name=driver.find_elements_by_xpath("//strong[@class='fixture__name fixture__name--with-margin']")
for i in name:
    Match.append(i.text)

#Scrapping the data having the series name
series=driver.find_elements_by_xpath("//div[@class='fixture__format-strip']/span[2]")
for i in series:
    Series.append(i.text)
    
#Scrapping the data having the place of match
place=driver.find_elements_by_xpath("//p[@class='fixture__additional-info']/span")
for i in place:
    Place.append(i.text)
    
#Scrapping the data having the match date
date=driver.find_elements_by_xpath("//div[@class='fixture__datetime desktop-only']/div/span")  #Only date
month=driver.find_elements_by_xpath("//div[@class='fixture__datetime desktop-only']/div/div/span[1]")  #Month
for i in range(len(date)):
    Date.append(date[i].text + month[i].text)  #Appending date and month texts together
    
#Scrapping the data having match time
time=driver.find_elements_by_xpath("//div[@class='fixture__full-date']/div/span[2]")
for i in time:
    Time.append(i.text)
#Checking the length of scrapped data
print(len(Match),len(Series),len(Place),len(Date),len(Time))
#Creating a Dataframe for the scrapped data
fixtures=pd.DataFrame({})
fixtures['Match name']=Match
fixtures['Series']=Series
fixtures['Place']=Place
fixtures['Date']=Date
fixtures['Time']=Time
fixtures
#Closing the driver
driver.close()<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '200209', 'license': 'MIT', 'n_tokens_mistral': 798, 'n_tokens_neox': 778, 'n_words': 199, 'path': 'Webscraping/Cricket/IndiaFixtures_Webscraping.ipynb', 'repo_name': 'Jerish7/FlipRoboTechnologies-Internship', 'size': '2365'}