The Python selenium package<\/li>\n<\/ul>\nYou can install the package using the below command<\/p>\n
pip install selenium<\/pre>\nNow we start by importing an appropriate class for the browser from the selenium package. Then we create the object of the class, giving the path of the driver executable.<\/p>\n
After this, we will use the get() method to load any page in the browser, as shown below.<\/p>\n
Example of loading a page using Selenium:<\/strong><\/p>\nfrom selenium.webdriver import Chrome\r\ndriverObj = Chrome(executable_path='\/driver\/path\/on\/your\/device')\r\ndriverObj.get(\u2018https:\/\/data-flair.training\/\u2019)<\/pre>\nSelenium also allows us to use CSS selectors and XPath to extract data from the websites. Let see an example to get all the blog titles using CSS selectors.<\/p>\n
Example of getting all the blog titles using CSS selectors:<\/strong><\/p>\nblog_titles = driverObj.get_elements_by_css_selector(' h2.blog-card__content-title') \r\nfor title in blog_tiles: \r\n print(title.text) \r\ndriver.quit() # closing the browser\r\n\r\n<\/pre>\nBesides being able to handle dynamic websites, Selenium makes the web scraping process slow. And the reason is that it must first execute the JavaScript code for each page before making it available for parsing. And because of this, it is ideal for large-scale data extraction.<\/p>\n
Comparison between the three web scraping libraries<\/h3>\n
\n\n\n| <\/td>\n | Requests<\/b><\/td>\n | Beautiful Soup<\/b><\/td>\n | lxml<\/b><\/td>\n | Selenium<\/b><\/td>\n<\/tr>\n\n| Purpose<\/b><\/td>\n | Making the HTTP requests<\/span><\/td>\n | Parsing<\/span><\/td>\n | Parsing<\/span><\/td>\n | Making HTTP requests\u00a0<\/span><\/td>\n<\/tr>\n\n| Ease-of-use<\/b><\/td>\n | High<\/span><\/td>\n | High<\/span><\/td>\n | Medium<\/span><\/td>\n | Medium<\/span><\/td>\n<\/tr>\n\n| Speed<\/b><\/td>\n | Fast<\/span><\/td>\n | Fast<\/span><\/td>\n | Very fast<\/span><\/td>\n | Slow<\/span><\/td>\n<\/tr>\n\n| Ease of learning<\/b><\/td>\n | High<\/span><\/td>\n | High<\/span><\/td>\n | Medium<\/span><\/td>\n | Medium<\/span><\/td>\n<\/tr>\n\n| Documentation<\/b><\/td>\n | Very good<\/span><\/td>\n | Very good<\/span><\/td>\n | Good<\/span><\/td>\n | Good<\/span><\/td>\n<\/tr>\n\n| JavaScript Support<\/b><\/td>\n | None<\/span><\/td>\n | None<\/span><\/td>\n | None<\/span><\/td>\n | Yes<\/span><\/td>\n<\/tr>\n\n| CPU and Memory Usage<\/b><\/td>\n | Low<\/span><\/td>\n | Low<\/span><\/td>\n | Low<\/span><\/td>\n | High<\/span><\/td>\n<\/tr>\n\n| Size of Project Supported<\/b><\/td>\n | Large and small<\/span><\/td>\n | Large and small<\/span><\/td>\n | Large and small<\/span><\/td>\n | Small<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nPicking a web driver and browser<\/h3>\nEvery web scraper needs a browser to connect to the destination URL. We recommend that you use a regular browser (or not a headless one) for testing purposes, especially for newcomers. This would make the troubleshooting and debugging processes simpler by giving a better understanding of the entire process.<\/p>\n On the other hand, headless browsers can be used later on as they are more efficient for complex tasks. Here we will be using the Chrome browser. You can also use Firefox and download the web driver that matches your browser\u2019s version.<\/p>\n You can do this by selecting the requisite package, downloading it, and unzipping it. Then copy the driver\u2019s executable file to any easily accessible directory.<\/p>\n Selecting an appropriate URL<\/h3>\nPreviously, we saw how to inspect a website and get a better understanding of its structure. Here are some more tips for you to help you pick a URL:<\/p>\n \n- It is very important to ensure that you are scraping public data and are not falling into third-party rights issues. You can confirm it with the help of the robots.txt file for guidance.<\/li>\n
- Avoid data hidden in JavaScript elements, as these sometimes need to be scraped by performing specific actions and require a more sophisticated use of Python and its logic.<\/li>\n
- Avoid image scraping because you can easily download them directly with Selenium.<\/li>\n<\/ul>\n
Extracting information<\/h3>\nThere seems to be a lot of HTML content in the output. Don\u2019t worry, we also have methods to extract useful information, like the title<\/p>\n Example of extracting data from the website:<\/strong><\/p>\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\n# Making a GET request\r\nr = requests.get('https:\/\/data-flair.training\/')\r\n\r\n# check status code for response received\r\n# success code - 200\r\nsoup = BeautifulSoup(r.content, 'html.parser')\r\n\r\n#Extracting the title\r\npage_title = soup.title.text\r\n\r\n# Extract body of page\r\npage_body = soup.body\r\n\r\n# Extract head of page\r\npage_head = soup.head\r\n\r\n# print the result\r\nprint(page_title)\r\nprint(page_head)\r\nprint(page_body)\r\n<\/pre>\nOutput<\/strong><\/p>\nWe can also get the tags and details of it. Let\u2019s see how to get the details of the title and its tags.<\/p>\n Example of getting tags and tag information from the website:<\/strong><\/p>\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\n# Making a GET request\r\nr = requests.get('https:\/\/data-flair.training\/')\r\n\r\n# check status code for response received\r\n# success code - 200\r\nsoup = BeautifulSoup(r.content, 'html.parser')\r\n\r\n# Getting the title tag of the page\r\nprint(soup.title)\r\n \r\n# Getting the name of the title tag\r\nprint(soup.title.name)\r\n \r\n# Getting the name of parent tag of the title\r\nprint(soup.title.parent.name)\r\n<\/pre>\nOutput<\/strong><\/p>\n<title>Attention Required! | Cloudflare<\/title> \ntitle \nhead<\/div>\n Selecting with Beautiful Soup in Python<\/h3>\nBeautiful Soup has a select element that returns the list of elements that we would like to select, like headings, etc.
\nLet\u2019s see an example of selecting the heading 2\u2019s returning the 2nd one from the obtained list, respectively.<\/p>\n Example of selecting a heading from a website:<\/strong><\/p>\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\n# Making a GET request\r\nr = requests.get('https:\/\/www.amazon.in\/')\r\n\r\n# check status code for response received\r\n# success code - 200\r\nsoup = BeautifulSoup(r.content, 'html.parser')\r\n\r\nsecond_head = soup.select('h2')[1].text\r\nprint(second_head)\r\n<\/pre>\nOutput<\/strong><\/p>\nBig Savings for Everyone<\/div>\n Wondering what if we want to get all the headings or other components? Then, I would like to ask you which construct do we use to access multiple elements or run through different conditions?<\/p>\n Yes, you are correct, the looping statements. Let\u2019s now see how to get all the elements under a tag.<\/p>\n Example of selecting all headings from the website:<\/strong><\/p>\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\n# Making a GET request\r\nr = requests.get('https:\/\/data-flair.training\/')\r\n\r\nsoup = BeautifulSoup(r.content, 'html.parser')\r\nall_h1_tags = []\r\nfor element in soup.select('h1'):\r\n all_h1_tags.append(element.text)\r\nprint(all_h1_tags)<\/pre>\nOutput<\/strong><\/p>\n[‘One more step’, ‘Please turn JavaScript on and reload the page.’]<\/div>\n Finding components<\/h3>\nThis library also has other methods, like find, which is used for searching the required elements from the class. Let\u2019s dive deeper into it with some examples.<\/p>\n Finding by class<\/h3>\nLet’s search based on the class. In the example below, we search for a div tag with the class \u2018a-section\u2019. And then find all the heading 2s from the website and print them.<\/p>\n Example of finding elements by class:<\/strong><\/p>\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\n# Making a GET request\r\nr = requests.get('https:\/\/www.amazon.in\/')\r\n\r\nsoup = BeautifulSoup(r.content, 'html.parser')\r\ns = soup.find('div', class_='a-section') \r\ncontent = s.find_all('h2')\r\n \r\nprint(content)\r\n<\/pre>\nOutput<\/strong><\/p>\nFinding by Id<\/h3>\nWe know that when we add some components, we add class, id, etc., to add properties and add uniqueness to the elements. As we are done searching based on class, let\u2019s search based on ID. It\u2019s the same as the above example we saw, except that we use the id parameter in the find() function to search.<\/p>\n Example of finding elements by ID:<\/strong><\/p>\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\n\r\n# Making a GET request\r\nr = requests.get('https:\/\/data-flair.training\/')\r\n\r\n# Parsing the HTML\r\nsoup = BeautifulSoup(r.content, 'html.parser')\r\n\r\ns = soup.find('div',id=\"cf-wrapper\")\r\ncontent = s.find_all('p')\r\n\r\nprint(content)\r\n<\/pre>\nOutput<\/strong><\/p>\nExtracting Text from the tags<\/h3>\nIf you see the above outputs, we see that tags also got included along with the text. But when it comes to real-life applications, the important part is the content. We will take the above example and see how we remove the tags from it.<\/p>\n Example of getting text from the tags:<\/strong><\/p>\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\n\r\n# Making a GET request\r\nr = requests.get('https:\/\/data-flair.training\/')\r\n\r\n# Parsing the HTML\r\nsoup = BeautifulSoup(r.content, 'html.parser')\r\n\r\ns = soup.find('div',id=\"cf-wrapper\")\r\ncontent = s.find_all('p')\r\n\r\nfor line in content:\r\n print(line.text)\r\n<\/pre>\nOutput<\/strong><\/p>\nHere we find all the paragraphs and then run a loop to print all the found elements. This avoids the appearance of tags in the output.<\/p>\n Extracting Links<\/h3>\nIn many of the cases, links get attached to the content on the website. And this is done using the <a> tag by giving the link to be attached to the \u2018href\u2019 attribute of the tag. This information is what we will use along with the find_all() function to extract the links. Let\u2019s see an example.<\/p>\n Example of getting links from a website:<\/strong><\/p>\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\n\r\n# Making a GET request\r\nr = requests.get('https:\/\/data-flair.training\/')\r\n\r\n# Parsing the HTML\r\nsoup = BeautifulSoup(r.content, 'html.parser')\r\n\r\nfor link in soup.find_all('a'):\r\n print(link.get('href'))\r\n<\/pre>\nOutput<\/strong><\/p>\nhttps:\/\/www.cloudflare.com\/5xx-error-landing<\/div>\n Extracting Image Information using Python<\/h3>\nWe also have images attached to the website, and extracting these is what we see in the example below.<\/p>\n Example of extracting images from a website:<\/strong><\/p>\nimport requests\r\nfrom bs4 import BeautifulSoup\r\n\r\n# Making a GET request\r\nr = requests.get('https:\/\/www.amazon.in\/')\r\n\r\nsoup = BeautifulSoup(r.content, 'html.parser')\r\n\r\nimages_list = []\r\n \r\nimages = soup.select('img')\r\nfor image in images:\r\n src = image.get('src')\r\n alt = image.get('alt')\r\n images_list.append({\"src\": src, \"alt\": alt})\r\n \r\nfor image in images_list:\r\n print(image)<\/pre>\nOutput<\/strong><\/p>\nHere we saw that we select all the \u2018img\u2019 tags from the website and then get the information of \u2018src\u2019 and \u2018alt\u2019 attributes from each of the images and print them.<\/p>\n Scraping multiple Pages in Python<\/h3>\nScraping information from various elements on multiple websites can be tedious. Beautiful Soup can also scrape through multiple pages from the same website or from different URLs. We will see both cases.<\/p>\n When we use a website with multiple pages, we take a base URL and run a for loop to go through each of the websites. Let\u2019s say we have a website with 10 pages, and we want to extract information from each page, say the title.<\/p>\n Example of running through multiple pages:<\/strong><\/p>\nimport requests\r\nfrom bs4 import BeautifulSoup as bs\r\n\r\nURL = ''\r\n\r\nfor page in range(1, 10):\r\n\r\n req = requests.get(URL + str(page) + '\/')\r\n soup = bs(req.text, 'html.parser')\r\n print(soup.title.text)\r\n<\/pre>\nWe can follow the same process to run through multiple URLs by storing them in a list.<\/p>\n Example of running through multiple URLs:<\/strong><\/p>\nimport requests\r\nfrom bs4 import BeautifulSoup as bs\r\n\r\nURLs = ['https:\/\/www.amazon.in\/','https:\/\/www.flipkart.com\/']\r\n\r\nfor i in URLs:\r\n\r\n req = requests.get(i)\r\n soup = bs(req.text, 'html.parser')\r\n print(soup.title.text)\r\n<\/pre>\nOutput<\/strong><\/p>\nOnline Shopping site in India: Shop Online for Mobiles, Books, Watches, Shoes and More – Amazon.in \nOnline Shopping Site for Mobiles, Electronics, Furniture, Grocery, Lifestyle, Books & More. Best Offers!<\/div>\n Here we store all the URLs in a list and then run through each URL in the list using a loop to extract the required information from each website.<\/p>\n Saving Information in CSV<\/h3>\nWe can also save the information obtained from the website(s) in our device, for example, in the form of a CSV. In the example below, we run through different URLs and use a for loop to run through each website and get its title. Finally, save the title and the URL in the form of a CSV.<\/p>\n import requests\r\nfrom bs4 import BeautifulSoup as bs\r\nimport csv\r\n\r\nURLs = ['https:\/\/www.amazon.in\/gp\/bestsellers\/?ref_=nav_em_cs_bestsellers_0_1_1_2',\r\n 'https:\/\/www.amazon.in\/gp\/new-releases\/?ref_=nav_em_cs_newreleases_0_1_1_3',\r\n 'https:\/\/www.amazon.in\/gp\/movers-and-shakers\/?ref_=nav_em_ms_0_1_1_4',\r\n 'https:\/\/www.amazon.in\/finds?ref_=nav_em_sbc_desktop_foundit_0_1_1_27',\r\n 'https:\/\/www.flipkart.com\/mobile-phones-store?otracker=nmenu_sub_Electronics_0_Mobiles',\r\n 'https:\/\/www.flipkart.com\/laptops-store?otracker=nmenu_sub_Electronics_0_Laptops',\r\n 'https:\/\/www.flipkart.com\/books-store?otracker=nmenu_sub_Sports%2C%20Books%20%26%20More_0_Books',\r\n 'https:\/\/www.flipkart.com\/offers-store?otracker=nmenu_offer-zone'\r\n ]\r\n\r\ntitles_list = []\r\nfor i in URLs:\r\n\r\n req = requests.get(i)\r\n soup = bs(req.text, 'html.parser')\r\n d = {}\r\n d['URL'] = i\r\n d['Title Name'] = soup.title.text\r\n titles_list.append(d)\r\n\r\nfilename = r'C:\\Users\\Sai Siva Teja\\Downloads\\titles.csv'\r\nwith open(filename, 'w', newline='') as f:\r\n w = csv.DictWriter(f,['URL','Title Name'])\r\n w.writeheader()\r\n \r\n w.writerows(titles_list)\r\n<\/pre>\nPython Web Scraping Output<\/h3>\n![\"python](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2022\/06\/python-web-scraping-output.webp\") <\/a><\/p>\nConclusion<\/h3>\nHere we are at the end of the article on web scraping with Python. In this article, we were introduced with the concept of web scraping, sending HTTP requests, and extracting website data using Beautiful Soup in Python. Hope you enjoyed this article. Happy learning!<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Many of us would have come across situations when we need to extract information from websites. We generally copy the required information from the website. 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