Handling JavaScript with Python

Mar 22, 2023

Handling JavaScript with Python

Python is a versatile language that can be used for a wide variety of tasks, including web scraping. When scraping websites, you will often encounter JavaScript that renders content dynamically. This can make scraping more challenging, as the HTML retrieved with a simple GET request will not include the dynamically generated content. However, Python provides some powerful libraries that allow you to handle JavaScript when scraping websites.

The two main approaches for handling JavaScript with Python when web scraping are:

  1. Using a headless browser like Selenium or Playwright

  2. Analyzing network traffic to find API endpoints that return the desired data

Using a Headless Browser

A headless browser is a web browser without a graphical user interface. It can load web pages, execute JavaScript, and interact with the page programmatically. This makes it very useful for web scraping, as it allows you to retrieve the fully-rendered HTML after all the JavaScript has executed.

Two popular headless browsers that can be controlled with Python are Selenium and Playwright.


Selenium is an established tool that supports multiple languages and browsers. Here's an example of using Selenium with Python to scrape a page with JavaScript:

from selenium import webdriver

from selenium.webdriver.chrome.service import Service

service = Service('/path/to/chromedriver')

driver = webdriver.Chrome(service=service)


# Wait for the dynamically loaded content to appear

element = driver.find_element(By.CSS_SELECTOR, '#dynamic-content')



This code launches a Chrome browser, loads the specified URL, waits for a specific element containing dynamically loaded content to appear, and then prints the text of that element.


Playwright is a newer headless browser automation library that supports modern rendering engines like Chromium, Firefox, and WebKit. It has a user-friendly API and can be used asynchronously. Here's an example:

from playwright.async_api import async_playwright

async def main():

async with async_playwright() as p:

browser = await p.chromium.launch()

page = await browser.new_page()

await page.goto('https://example.com')

# Wait for the dynamically loaded content

await page.wait_for_selector('#dynamic-content')

element = await page.query_selector('#dynamic-content')

print(await element.inner_text())

await browser.close()


This asynchronous code launches a Chromium browser, navigates to the URL, waits for the dynamic content to load, and prints the text.

Analyzing Network Traffic

Another approach is to monitor the network traffic when the page loads to identify API endpoints returning the data that is used to render the dynamic content. You can then make requests directly to those endpoints to retrieve the data, bypassing the need to execute JavaScript.

Tools like your browser's Developer Tools or an HTTP proxy debugger like Fiddler or Charles can help you analyze the network traffic. Look for XHR/Fetch requests that return JSON data and inspect that data to see if it contains the content you want to scrape.

Once you've found the API endpoint, you can use Python's requests library to retrieve the data. For example:

import requests

url = 'https://example.com/api/data'

response = requests.get(url)

data = response.json()


This sends a GET request to the API endpoint and parses the JSON response.


When scraping websites with JavaScript, you have two main options in Python:

  1. Use a headless browser like Selenium or Playwright to load the page, execute the JavaScript, and extract the fully-rendered HTML.

  2. Analyze the network traffic to find API endpoints returning the desired data, and make requests to those endpoints directly.

The best approach depends on the specific website and your scraping requirements. Using a headless browser provides a more robust solution that closely mimics a real user, but it can be slower and more resource-intensive. Directly accessing API endpoints is faster but may be less reliable if the site changes. With these techniques, you can effectively handle JavaScript when web scraping using Python.

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