Scraping Dynamic Websites with Python - 2025 Guide

Most web scraping data comes from dynamic websites, such as Amazon, and YouTube. These websites provide an interactive and responsive user experience based on user input. For instance, when you access your YouTube account, the video content presented is tailored to your input. As a result, web scraping dynamic sites can be more challenging since the data is subject to constant modifications from user interactions.

In order to scrape data from dynamic sites, you need to use advanced techniques that will simulate a user’s interaction with the website, navigate and select specific content that’s generated by JavaScript, and handle Asynchronous JavaScript and XML (AJAX) requests.

In this guide, you’ll learn how to scrape data from a dynamic website using an open source Python library called Selenium.

Scraping Data from a Dynamic Website with Selenium

Before you start scraping data from a dynamic site, you need to understand the Python package that you’ll be using: Selenium.

What Is Selenium?

Selenium is an open source Python package and automated testing framework that allows you to execute various operations or tasks on dynamic websites. These tasks include things like opening/closing dialogs, searching for particular queries on YouTube, or filling out forms, all in your preferred web browser.

When you utilize Selenium with Python, you can control your web browser and automatically extract data from dynamic websites by writing just a few lines of Python code with the Selenium Python package.

Now that you know how Selenium works, let’s get started.

Create a New Python Project

The first thing you need to do is create a new Python project. Create a directory named  data_scraping_project  where all the collected data and source code files will be stored. This directory will have two subdirectories:

scripts  will contain all the Python scripts that extract and collect data from the dynamic website.

data  is where all the data extracted from a dynamic website will be stored.

Install Python Packages

After you create the  data_scraping_project  directory, you need to install the following Python packages to help you scrape, collect, and save data from a dynamic website:

Selenium

Webdriver Manager  will manage binary drivers for different browsers. Webdriver gives you a set of APIs that lets you run different commands to interact with the sites, making it easier to parse, load, and change the content.

pandas  will save scraped data from a dynamic website into a simple CSV file.

You can install the Selenium Python package by running the following  pip  command in your terminal:

pip install selenium

Selenium will use the binary driver to control the web browser of your choosing. This Python package provides binary drivers for the following supported web browsers: Chrome, Chromium, Brave, Firefox, IE, Edge, and Opera.

Then run the following  pip  command in your terminal to install  webdriver-manager :

pip install webdriver-manager

To install pandas, run the following  pip  command:

pip install pandas

What You’ll Scrape

In this article, you’re going to scrape YouTube channel called Programming with Mosh and Hacker News:

Programming with Mosh YouTube channel

From the Programming with Mosh YouTube channel, you’ll scrape the following information:

The title of the video.

The link or URL of the video.

The link or URL of the image.

The number of views for the particular video.

The time when the video was published.

Comments from a particular YouTube video URL.

And from Hacker News, you’ll collect the following data:

The title of the article.

The link to the article.

Screenshot of Hacker News

Now that you know what you’ll scrape, let’s create a new Python script ( ie  data_scraping_project/scripts/youtube_videos_list.py).

Import Python Packages

First, you need to import the Python packages you’ll use to scrape, collect, and save data into a CSV file:

# import libraries
from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.common.by import By
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
import time
import pandas as pd

Instantiate the Webdriver

To instantiate Webdriver, you need to select the browser that Selenium will use (Chrome, in this instance) and then install the binary driver.

Chrome has developer tools to display the HTML code of the web page and identify HTML elements to scrape and collect the data. To display the HTML code, you need to right-click a web page in your Chrome web browser and select  Inspect Element .

To install a binary driver for Chrome, run the following code:

driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()))

The binary driver for Chrome will be installed on your machine and automatically instantiate Webdriver.

Scrape Data with Selenium

To scrape data with Selenium, you need to define the YouTube URL in a simple Python variable ( ie   url ). From this link, you collect all the data that was mentioned previously except for the comments from a particular YouTube URL:

# Define the URL
url = "https://www.youtube.com/@programmingwithmosh/videos"

# load the web page
driver.get(url)

# set maximum time to load the web page in seconds
driver.implicitly_wait(10)

Selenium automatically loads the YouTube link in the Chrome browser. Additionally, a time frame is specified ( ie  ten seconds) to make sure that the web page is fully loaded (including all HTML elements). This helps you scrape data that is rendered by JavaScript.

Scrape Data Using ID and Tags

One of the benefits of Selenium is that it can extract data using different elements presented on the web page, including the ID and tag.

For instance, you can use either the ID element ( ie   post-title ) or tags ( ie   h1  and  p ) to scrape the data:

<h1 id ="post-title">Introduction to data scrapping using Python</h1>
<p>You can use selenium python package to collect data from any dynamic website</p>

Or if you want to scrape data from the YouTube link, you need to use the ID presented on the web page. Open the YouTube URL in your web browser; then right-click and select  Inspect  to identify the ID. Then use your mouse to view the page and identify the ID that holds the list of videos presented on the channel:

Use Webdriver to scrape data that is within the ID identified. To find an HTML element by ID attribute, call the  find_element()  Selenium method and pass  By.ID  as the first argument and  ID  as the second argument.

To collect the video title and video link for each video, you need to use the  video-title-link  ID attribute. Since you’re going to collect multiple HTML elements with this ID attribute, you’ll need to use the  find_elements()  method:

# collect data that are withing the id of contents
contents = driver.find_element(By.ID, "contents")

#1 Get all the by video tite link using id video-title-link
video_elements = contents.find_elements(By.ID, "video-title-link")

#2 collect title and link for each youtube video
titles = []
links = []

for video in video_elements:

    #3 Extract the video title
    video_title = video.get_attribute("title")

    #4 append the video title
    titles.append(video_title)

    #5 Extract the video link
    video_link = video.get_attribute("href")

#6 append the video link
links.append(video_link)

This code performs the following tasks:

It collects data that is within the ID attribute of  contents .

It collects all HTML elements that have an ID attribute of  video-title-link  from the WebElement  contents  object.

It creates two lists to append titles and links.

It extracts the video title using the  get_attribute() method and passes the  title .

It appends the video title in the titles list.

It extracts the video link using the  get_atribute()  method and passes  href  as an argument.

It appends the video link in the links list.

At this point, all the video titles and links will be in two Python lists:  titles  and  links .

Next, you need to scrape the link of the image that is available on the web page before you click the YouTube video link to watch the video. To scrape this image link, you need to find all the HTML elements by calling the  find_elements()  Selenium method and passing  By.TAG_NAME  as the first argument and the name of the tag as the second argument:

#1 Get all the by Tag
img_elements = contents.find_elements(By.TAG_NAME, "img")

#2 collect img link and link for each youtube video
img_links = []

for img in img_elements:

    #3 Extract the img link
    img_link = img.get_attribute("src")
    if img_link:
        #4 append the img link
        img_links.append(img_link)

This code collects all the HTML elements with the  img  tag name from the WebElement object called  contents . It also creates a list to append the image links and extracts it using the  get_attribute()  method and passes  src  as an argument. Finally, it appends the image link to the  img_links  list.

You can also use the  ID  and the tag name to scrape more data for each YouTube video. On the web page of the YouTube URL, you should be able to see the number of views and the time published for each video listed on the page. To extract this data, you need to collect all the HTML elements that have an ID of  metadata-line  and then collect data from the HTML elements with a  span  tag name:

#1 find the element with the specific ID you want to scrape
meta_data_elements = contents.find_elements(By.ID, 'metadata-line')

#2 collect data from span tag
meta_data = []

for element in meta_data_elements:
    #3 collect span HTML element
    span_tags = element.find_elements(By.TAG_NAME, 'span')

    #4 collect span data
    span_data = []
    for span in span_tags:
        #5 extract data for each span HMTL element.
        span_data.append(span.text)
    #6 append span data to the list
    meta_data.append(span_data)

# print out the scraped data.
print(meta_data)

This code block collects all the HTML elements that have an ID attribute of  metadata-line  from the WebElement  contents  object and creates a list to append data from the  span  tag that will have the number of views and the time published.

It also collects all the HTML elements whose tag name is  span  from the WebElement object called  meta_data_elements  and creates a list with this span data. Then it extracts the text data from the span HTML element and appends it to the  span_data  list. Finally, it appends the data from the  span_data  list to the  meta_data .

The data extracted from the span HTML element will look like this:

Next, you need to create two Python lists and save the number of views and time published separately:

#1 Iterate over the list of lists and collect the first and second item of each sublist
views_list = []
published_list = []

for sublist in meta_data:
    #2 append number of views in the views_list
    views_list.append(sublist[0])

    #3 append time published in the published_list
    published_list.append(sublist[1])

Here, you create two Python lists that extract data from  meta_data , and you append the number of views for each sublist to  view_list  and the time published for each sublist to the  published_list .

At this point, you’ve scraped the title of the video, the URL of the video page, the URL of the image, the number of views, and the time the video was published. This data can be saved into a pandas DataFrame using the pandas Python package. Use the following code to save the data from the list of  titles ,  links ,  img_links ,  views_list , and  published_list  into the pandas DataFrame:

# save in pandas dataFrame
data = pd.DataFrame(

list(zip(titles, links, img_links, views_list, published_list)),

columns=['Title', 'Link', 'Img_Link', 'Views', 'Published']
)

# show the top 10 rows
data.head(10)

# export data into a csv file.
data.to_csv("../data/youtube_data.csv",index=False)

driver.quit()

This is what the scraped data should look like in the pandas DataFrame:

This saved data is exported from pandas to a CSV file called  youtube_data.csv  using  to_csv() .

Now you can run  youtube_videos_list.py , and make sure everything works correctly.

Scrape Data Using the CSS Selector

Selenium can also extract data based on the specific patterns in the HTML elements using the CSS selector on the web page. The CSS selector is applied to target specific elements according to their ID, tag name, class, or other attributes.

For example, here, the HTML page has some  div  elements, and one of them has a class name of  "inline-code" :

<html>
<body>
<p>Hello World!</p>
<div>Learn Data Scraping</div>
<div class="inline-code"> data scraping with Python code</div>
<div>Saving</div>
</body>
</html>

You can use a CSS selector to find the HTML element on a web page whose tag name is  div  and whose class name is “’inline-code”`. You can apply this same approach to extract comments from the comment section of YouTube videos.

Now, let’s use a CSS selector to collect comments posted on  this YouTube video .

The YouTube comments section is available under the following tag and class name:

<ytd-comment-thread-renderer class="style-scope ytd-item-section-renderer">...</tyd-comment-thread-renderer>

Let’s create a new script ( ie  data_scraping_project/scripts/youtube_video_ comments.py). Import all the necessary packages like before, and add the following code to automatically start the Chrome web browser, browse the URL of the YouTube video, and then scrape the comments using the CSS selector:

#1 instantiate chrome driver
driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()))

#2 Define the URL
url = "https://www.youtube.com/watch?v=hZB5bHDCmeY"

#3 Load the webpage
driver.get(url)

#4 define the CSS selector
comment_section = 'ytd-comment-thread-renderer.ytd-item-section-renderer’

#5 wait until element matching the given criteria to be found
try:
    element = WebDriverWait(driver, 10).until(
        EC.presence_of_element_located((By.CSS_SELECTOR, comment_section))
    )
except:
    driver.quit()

#6. collect HTML elements within the CSS selector
comment_blocks = driver.find_elements(By.CSS_SELECTOR,comment_section)

This code instantiates the Chrome driver and defines the YouTube video link to scrape the comments that have been posted. Then it loads the web page in the browser and waits ten seconds until the HTML elements matching the CSS selector are available.

Next, it collects all  comment  HTML elements using the CSS selector called  ytd-comment-thread-renderer.ytd-item-section-renderer  and saves all the comment elements in the  comment_blocks  WebElement object.

Then you can extract each author’s name using the ID  author-text  and the comment text using the ID  content-text  from each comment in the  comment_blocks  WebElement object:

#1 specify the id attribute for author and comment
author_id = 'author-text'
comment_id = 'content-text'

#2 Extract the text value for each comment and author in the list
comments = []
authors = []

for comment_element in comment_blocks:
    #3 collect author for each comment
    author = comment_element.find_element(By.ID, author_id)

    #4 append author name
    authors.append(author.text)

    #5 collect comments
    comment = comment_element.find_element(By.ID, comment_id)

    #6 append comment text
    comments.append(comment.text)

#7 save in pandas dataFrame
comments_df = pd.DataFrame(list(zip(authors, comments)), columns=['Author', 'Comment'])

#8 export data into a CSV file.
comments_df.to_csv("../data/youtube_comments_data.csv",index=False)

driver.quit()

This code specifies the ID for the author and comment. Then it creates two Python lists to append the author name and the comment text. It collects each HTML element that has the specified ID attributes from the WebElement object and appends the data to the Python lists.

Finally, it saves the scraped data into a pandas DataFrame and exports the data into a CSV file called  youtube_comments_data.csv .

This is what the authors and comments from the first ten rows will look like in a pandas DataFrame:

Scrape Data Using the Class Name

In addition to scraping data with the CSS selector, you can also extract data based on a specific class name. To find an HTML element by its class name attribute using Selenium, you need to call the  find_element()  method and pass  By.CLASS_NAME  as the first argument, and you need to find the class name as the second argument.

In this section, you’ll use the class name to collect the title and link of the articles posted on Hacker News. On this web page, the HTML element that has the title and link of each article has a  titleline  class name, as seen in the web page’s code:

<span class="titleline"><a href="https://mullvad.net/en/browser">The Mullvad Browser</a><span class="sitebit comhead"> (<a href="from?site=mullvad.net"><span class="sitestr">mullvad.net</span></a>)</span></span></td></tr><tr><td colspan="2"></td><td class="subtext"><span class="subline">

<span class="score" id="score_35421034">302 points</span> by <a href="user?id=Foxboron" class="hnuser">Foxboron</a> <span class="age" title="2023-04-03T10:11:50"><a href="item?id=35421034">2 hours ago</a></span> <span id="unv_35421034"></span> | <a href="hide?id=35421034&amp;auth=60e6bdf9e482441408eb9ca98f92b13ee2fac24d&amp;goto=news" class="clicky">hide</a> | <a href="item?id=35421034">119&nbsp;comments</a> </span>

Create a new Python script ( ie  data_scraping_project/scripts/hacker_news.py), import all the necessary packages, and add the following Python code to scrape the title and link for each article posted on the Hacker News page:

#1 define url
hacker_news_url = 'https://news.ycombinator.com/'

#2 instantiate chrome driver
driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()))

#3 load the web page
driver.get(hacker_news_url)

#4 wait until element matching the given criteria to be found
try:
    element = WebDriverWait(driver, 10).until(
        EC.presence_of_element_located((By.CLASS_NAME, 'titleline'))
    )
except:
    driver.quit()

#5 Extract the text value for each title and link in the list
titles= []
links = []

#6 Find all the articles on the web page
story_elements = driver.find_elements(By.CLASS_NAME, 'titleline')

#7 Extract title and link for each article
for story_element in story_elements:

    #8 append title to the titles list
    titles.append(story_element.text)

    #9 extract the URL of the article
    link = story_element.find_element(By.TAG_NAME, "a")

    #10 appen link to the links list
    links.append(link.get_attribute("href"))

driver.quit()

This code will define the URL of the web page, automatically start the Chrome web browser, and then browse the URL of Hacker News. It waits ten seconds until the HTML elements matching the  CLASS NAME  are available.

Then it creates two Python lists to append the title and link for each article. It also collects each HTML element that has a  titleline  class name from the WebElement driver object and extracts the title and link for each article represented in the  story_elements  WebElement object.

Finally, the code appends the title of the article to the titles list and collects the HTML element that has a tag name of  a  from the  story_element  object. It extracts the link using the  get_attribute()  method and appends the link to the links list.

Next, you need to use the  to_csv()  method from pandas to export the scraped data. You’ll be exporting both the titles and links into a  hacker_news_data.csv  CSV file and saving the data in the directory:

# save in pandas dataFrame
hacker_news = pd.DataFrame(list(zip(titles, links)),columns=['Title', 'Link'])

# export data into a csv file.
hacker_news.to_csv("../data/hacker_news_data.csv",index=False)

This is how the titles and links from the first five rows appear in a pandas DataFrame:

How to Handle Infinite Scrolls

Some dynamic web pages load additional content as you scroll to the bottom of the page. If you don’t navigate to the bottom, Selenium may only scrape the data that’s visible on your screen.

To scrape more data, you need to instruct Selenium to scroll to the bottom of the page, wait until new content loads, and then automatically scrape the data you want. For instance, the following Python script will scroll through the first forty results of Python books and extract their links:

#1 import packages
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
from selenium import webdriver  
from selenium.webdriver.common.by import By  
import time  
  
#2 Instantiate a Chrome webdriver  
driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()))  
  
#3 Navigate to the webpage  
driver.get("https://example.com/results?search_query=python+books")  
  
#4 instantiate a list to keep links  
books_list = []  
  
#5 Get the height of the current webpage  
last_height = driver.execute_script("return document.body.scrollHeight")  
  
#6 set target count  
books_count = 40  
  
#7 Keep scrolling down on the web page  
while books_count > len(books_list):  
    driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")  
  
    #8 Wait for the page to load  
    time.sleep(5)  
  
    #9 Calculate the new height of the page  
    new_height = driver.execute_script("return document.body.scrollHeight")  
  
    #10 Check if you have reached the bottom of the page  
    if new_height == last_height:  
        break  
    last_height = new_height  
  
    #11 Extract the data  
    links = driver.find_elements(By.TAG_NAME, "a")  
    for link in links:  
        #12 append extracted data  
        books_list.append(link.get_attribute("href"))  
  
#13 Close the webdriver  
driver.quit()

This code imports the Python packages that will be used and instantiates and opens Chrome. Then it navigates to the web page and creates a Python list to append the link for each of the search results.

It gets the height of the current page by running the  return document.body.scrollHeight  script and sets the number of links you want to collect. Then it continues to scroll down as long as the value of the  book_count  variable is greater than the length of the  book_list  and waits five seconds to load the page.

It calculates the new height of the web page by running the  return document.body.scrollHeight  script and checks if it’s reached the bottom of the page. If it does, the loop is terminated; otherwise, it will update the  last_height  and continue to scroll down. Finally, it collects the HTML element that has a tag name of  a  from the WebElement object and extracts and appends the link to the links list. After collecting the links, it will close the Webdriver.

Please note:  For your script to end at some point, you must set a total number of items to be scraped if the page has infinite scrolling. If you don’t, your code will continue to execute.

Please note:  For your script to end at some point, you must set a total number of items to be scraped if the page has infinite scrolling. If you don’t, your code will continue to execute.

Web Scraping with Bright Data

While it’s possible to scrape data with open source scrapers like Selenium, they tend to lack support. In addition, the process can be complicated and time-consuming. If you’re looking for a powerful and reliable web scraping solutions, you should consider Bright Data.

Bright Data controls the best proxy servers in the world, serving tens of Fortune 500 companies and over 20,000 customers. Its wide proxy network includes:

Datacenter proxies – Over 770,000 datacenter IPs. Residential proxies – Over 72M IPs from residential devices in more than 195 countries. ISP proxies – Over 700,000 ISP IPs. Mobile proxies – Over 7M mobile IPs.

Overall, this is one of the largest and most reliable scraping-oriented proxy networks on the market. But Bright Data is more than just a proxy provider. It also offers top-notch web scraping services, including a Scraping Browser (Puppeteer, Selenium, and Playwright), a Web Scraper IDE , and a SERP API .

If do not want to deal with scraping at all but are interested in web data, you can take advantage of its ready-to-use datasets .