


My Steps to Scrape a Password-Protected Website:
- Capture the HTML form elements: username ID, password ID, and login button class
- - Use a tool like requests or Selenium to automate the login: fill username, wait, fill password, wait, click login
- - Store session cookies for authentication
- - Continue scraping the authenticated pages
Disclaimer: I’ve built an API for this specific use case at https://www.scrapewebapp.com/. So if you want to just get it done fast, use it, otherwise read on.
Let’s use this example: let’s say I want to scrape my own API key from my account at https://www.scrapewebapp.com/. It is on this page: https://app.scrapewebapp.com/account/api_key
1. The Login Page
First, you need to find the login page. Most websites will give you a redirect 303 if you try to access a page behind login, so if you try to scrape directly https://app.scrapewebapp.com/account/api_key, you will automatically get the login page https://app.scrapewebapp.com/login. So it is a good way to automate finding the login page if not provided already.
Ok, now that we have the login page, we need to find the place to add username or email as well as password and the actual sign-in button. The best way is to create a simple script that finds the ID of the inputs using their type “email”, “username”, “password” and finds the button with the type “submit”. I made a code for you below:
from bs4 import BeautifulSoup def extract_login_form(html_content: str): """ Extracts the login form elements from the given HTML content and returns their CSS selectors. """ soup = BeautifulSoup(html_content, "html.parser") # Finding the username/email field username_email = ( soup.find("input", {"type": "email"}) or soup.find("input", {"name": "username"}) or soup.find("input", {"type": "text"}) ) # Fallback to input type text if no email type is found # Finding the password field password = soup.find("input", {"type": "password"}) # Finding the login button # Searching for buttons/input of type submit closest to the password or username field login_button = None # First try to find a submit button within the same form if password: form = password.find_parent("form") if form: login_button = form.find("button", {"type": "submit"}) or form.find( "input", {"type": "submit"} ) # If no button is found in the form, fall back to finding any submit button if not login_button: login_button = soup.find("button", {"type": "submit"}) or soup.find( "input", {"type": "submit"} ) # Extracting CSS selectors def generate_css_selector(element, element_type): if "id" in element.attrs: return f"#{element['id']}" elif "type" in element.attrs: return f"{element_type}[type='{element['type']}']" else: return element_type # Generate CSS selectors with the updated logic username_email_css_selector = None if username_email: username_email_css_selector = generate_css_selector(username_email, "input") password_css_selector = None if password: password_css_selector = generate_css_selector(password, "input") login_button_css_selector = None if login_button: login_button_css_selector = generate_css_selector( login_button, "button" if login_button.name == "button" else "input" ) return username_email_css_selector, password_css_selector, login_button_css_selector def main(html_content: str): # Call the extract_login_form function and return its result return extract_login_form(html_content)
2. Using Selenium to Actually Log In
Now you need to create a selenium webdriver. We will use chrome headless to run it with Python. This is how to install it:
# Install selenium and chromium !pip install selenium !apt-get update !apt install chromium-chromedriver !cp /usr/lib/chromium-browser/chromedriver /usr/bin import sys sys.path.insert(0,'/usr/lib/chromium-browser/chromedriver')
Then actually log into our website and save the cookies. We will save all cookies, but you could only save the auth cookies if you wanted.
# Imports from selenium import webdriver from selenium.webdriver.common.by import By import requests import time # Set up Chrome options chrome_options = webdriver.ChromeOptions() chrome_options.add_argument('--headless') chrome_options.add_argument('--no-sandbox') chrome_options.add_argument('--disable-dev-shm-usage') # Initialize the WebDriver driver = webdriver.Chrome(options=chrome_options) try: # Open the login page driver.get("https://app.scrapewebapp.com/login") # Find the email input field by ID and input your email email_input = driver.find_element(By.ID, "email") email_input.send_keys("******@gmail.com") # Find the password input field by ID and input your password password_input = driver.find_element(By.ID, "password") password_input.send_keys("*******") # Find the login button and submit the form login_button = driver.find_element(By.CSS_SELECTOR, "button[type='submit']") login_button.click() # Wait for the login process to complete time.sleep(5) # Adjust this depending on your site's response time finally: # Close the browser driver.quit()
3. Store Cookies
It is as simple as saving them into a dictionary from the driver.getcookies() function.
def save_cookies(driver): """Save cookies from the Selenium WebDriver into a dictionary.""" cookies = driver.get_cookies() cookie_dict = {} for cookie in cookies: cookie_dict[cookie['name']] = cookie['value'] return cookie_dict
Save the cookies from the WebDriver
cookies = save_cookies(driver)
4. Get Data from Our Logged-in Session
In this part, we will use the simple library requests, but you could keep using selenium too.
Now we want to get the actual API from this page: https://app.scrapewebapp.com/account/api_key.
So we create a session from the requests library and add each cookie into it. Then request the URL and print the response text.
def scrape_api_key(cookies): """Use cookies to scrape the /account/api_key page.""" url = 'https://app.scrapewebapp.com/account/api_key' # Set up the session to persist cookies session = requests.Session() # Add cookies from Selenium to the requests session for name, value in cookies.items(): session.cookies.set(name, value) # Make the request to the /account/api_key page response = session.get(url) # Check if the request is successful if response.status_code == 200: print("API Key page content:") print(response.text) # Print the page content (could contain the API key) else: print(f"Failed to retrieve API key page, status code: {response.status_code}")
5. Get the Actual Data You Want (BONUS)
We got the page text we wanted, but there is a lot of data that we do not care about. We just want the api_key.
The best and easiest way to do that is to use AI like ChatGPT (GPT4o model).
Prompt the model like this: “You are an expert scraper and you will extract only the information asked from the context. I need the value of my api-key from {context}”
from bs4 import BeautifulSoup def extract_login_form(html_content: str): """ Extracts the login form elements from the given HTML content and returns their CSS selectors. """ soup = BeautifulSoup(html_content, "html.parser") # Finding the username/email field username_email = ( soup.find("input", {"type": "email"}) or soup.find("input", {"name": "username"}) or soup.find("input", {"type": "text"}) ) # Fallback to input type text if no email type is found # Finding the password field password = soup.find("input", {"type": "password"}) # Finding the login button # Searching for buttons/input of type submit closest to the password or username field login_button = None # First try to find a submit button within the same form if password: form = password.find_parent("form") if form: login_button = form.find("button", {"type": "submit"}) or form.find( "input", {"type": "submit"} ) # If no button is found in the form, fall back to finding any submit button if not login_button: login_button = soup.find("button", {"type": "submit"}) or soup.find( "input", {"type": "submit"} ) # Extracting CSS selectors def generate_css_selector(element, element_type): if "id" in element.attrs: return f"#{element['id']}" elif "type" in element.attrs: return f"{element_type}[type='{element['type']}']" else: return element_type # Generate CSS selectors with the updated logic username_email_css_selector = None if username_email: username_email_css_selector = generate_css_selector(username_email, "input") password_css_selector = None if password: password_css_selector = generate_css_selector(password, "input") login_button_css_selector = None if login_button: login_button_css_selector = generate_css_selector( login_button, "button" if login_button.name == "button" else "input" ) return username_email_css_selector, password_css_selector, login_button_css_selector def main(html_content: str): # Call the extract_login_form function and return its result return extract_login_form(html_content)
If you want all that in a simple and reliable API, please give a try to my new product https://www.scrapewebapp.com/
If you like this post, please give me claps and follow me. It does help a lot!
The above is the detailed content of How to Scrape Login-Protected Websites with Selenium (Step by Step Guide). For more information, please follow other related articles on the PHP Chinese website!

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

The article discusses popular Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Django, Flask, and Requests, detailing their uses in scientific computing, data analysis, visualization, machine learning, web development, and H

This tutorial builds upon the previous introduction to Beautiful Soup, focusing on DOM manipulation beyond simple tree navigation. We'll explore efficient search methods and techniques for modifying HTML structure. One common DOM search method is ex

This article guides Python developers on building command-line interfaces (CLIs). It details using libraries like typer, click, and argparse, emphasizing input/output handling, and promoting user-friendly design patterns for improved CLI usability.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

WebStorm Mac version
Useful JavaScript development tools

DVWA
Damn Vulnerable Web App (DVWA) is a PHP/MySQL web application that is very vulnerable. Its main goals are to be an aid for security professionals to test their skills and tools in a legal environment, to help web developers better understand the process of securing web applications, and to help teachers/students teach/learn in a classroom environment Web application security. The goal of DVWA is to practice some of the most common web vulnerabilities through a simple and straightforward interface, with varying degrees of difficulty. Please note that this software

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver Mac version
Visual web development tools

Notepad++7.3.1
Easy-to-use and free code editor
