Effortlessly Scrape Websites with Python

A Practical Introduction to Web Scraping in Python

Web scraping is the process of collecting and parsing raw data from the Web, and the Python community has come up with some pretty powerful web scraping tools.

The Internet hosts perhaps the greatest source of information on the planet. Many disciplines, such as data science, business intelligence, and investigative reporting, can benefit enormously from collecting and analyzing data from websites.

In this tutorial, you’ll learn how to:

• Parse website data using string methods and regular expressions
• Parse website data using an HTML parser
• Interact with forms and other website components

Note: This tutorial is adapted from the chapter “Interacting With the Web” in Python Basics: A Practical Introduction to Python 3. The book uses Python’s built-in IDLE editor to create and edit Python files and interact with the Python shell, so you’ll see occasional references to IDLE throughout this tutorial. However, you should have no problems running the example code from the editor and environment of your choice.

Scrape and Parse Text From Websites

Collecting data from websites using an automated process is known as web scraping. Some websites explicitly forbid users from scraping their data with automated tools like the ones that you’ll create in this tutorial. Websites do this for two possible reasons:

1. The site has a good reason to protect its data. For instance, Google Maps doesn’t let you request too many results too quickly.
2. Making many repeated requests to a website’s server may use up bandwidth, slowing down the website for other users and potentially overloading the server such that the website stops responding entirely.

Before using your Python skills for web scraping, you should always check your target website’s acceptable use policy to see if accessing the website with automated tools is a violation of its terms of use. Legally, web scraping against the wishes of a website is very much a gray area.

Important: Please be aware that the following techniques may be illegal when used on websites that prohibit web scraping.

Build Your First Web Scraper

The first step in web scraping is to retrieve the HTML content of a web page. Python provides several libraries for making HTTP requests and working with HTML content, but the requests library is one of the most popular and easy-to-use choices.

To install the requests library, you can use the following command in your terminal:

pip install requests

Once you have the requests library installed, you can import it into your Python script using the following line:

import requests

Now, let’s write a simple function to make a GET request to a web page and retrieve its HTML content:

def get_html_content(url):
    response = requests.get(url)
    return response.text

This function takes a URL as a parameter and uses the requests.get() function to make a GET request to the specified URL. It then returns the HTML content of the web page as a string.

To test this function, you can call it with the URL of the web page you want to scrape:

html_content = get_html_content(url)
print(html_content)

This will print the HTML content of the web page specified by the URL.

Extract Text From HTML With String Methods

Once you have the HTML content of a web page, the next step is to extract the desired data from it. One way to extract text from HTML is to use string methods like split() and find(). These methods allow you to locate specific parts of the HTML content and extract the text within them.

For example, let’s say you want to extract the title of a web page. The title is typically enclosed in <title> tags in the HTML. You can use the find() method to locate the starting and ending positions of the title tags, and then use string slicing to extract the text within them.

Here’s an example function that extracts the title of a web page using string methods:

def extract_title(html_content):
    start_index = html_content.find("<title>") + len("<title>")
    end_index = html_content.find("<https://codemdd.io/title>")
    title = html_content[start_index:end_index]
    return title.strip()

This function takes the HTML content of a web page as a parameter and uses find() to locate the positions of the <title> and <https://codemdd.io/title> tags. It then uses string slicing to extract the text between these positions, and finally, it calls the strip() method to remove any leading or trailing whitespace from the title.

To test this function, you can call it with the HTML content of a web page:

title = extract_title(html_content)
print(title)

This will print the title of the web page.

Get to Know Regular Expressions

Another way to extract text from HTML is to use regular expressions. Regular expressions, or regex, provide a powerful way to search for patterns in text and extract the desired information.

Python provides a built-in re module for working with regular expressions. To use this module, you need to import it into your Python script:

import re

Let’s say you want to extract all the links from a web page. The links are typically enclosed in <a> tags in the HTML. You can use a regex pattern to search for these tags and extract the URLs within them.

Here’s an example function that extracts all the links from a web page using regular expressions:

def extract_links(html_content):
    pattern = r"<a.*?href=['\"](.*?)['\"].*?>"
    links = re.findall(pattern, html_content)
    return links

This function takes the HTML content of a web page as a parameter and uses the findall() function from the re module to find all occurrences of the regex pattern. The pattern matches any <a> tag that contains an href attribute, and it captures the URL within the attribute.

To test this function, you can call it with the HTML content of a web page:

links = extract_links(html_content)
for link in links:
    print(link)

This will print all the links found in the web page.

Extract Text From HTML With Regular Expressions

In addition to extracting links, you can use regular expressions to extract other types of data from HTML. For example, you can extract the text within specific HTML tags, like <h1> or <p>, or you can extract data that follows a certain pattern, like phone numbers or email addresses.

Here’s an example function that extracts all the text within <h1> tags from a web page using regular expressions:

def extract_headings(html_content):
    pattern = r"<h1.*?>(.*?)<\https://codemdd.io/h1>"
    headings = re.findall(pattern, html_content)
    return headings

This function takes the HTML content of a web page as a parameter and uses the findall() function from the re module to find all occurrences of the regex pattern. The pattern matches any <h1> tag and captures the text within it.

To test this function, you can call it with the HTML content of a web page:

headings = extract_headings(html_content)
for heading in headings:
    print(heading)

This will print all the headings found in the web page.

Check Your Understanding

Now that you’ve learned the basics of web scraping in Python, it’s time to check your understanding. Here are a few questions to test your knowledge:

1. What is web scraping, and why is it useful?
2. What are some ethical considerations to keep in mind when web scraping?
3. What are some commonly used Python libraries for web scraping?
4. Name two methods for extracting text from HTML using string methods.
5. How can regular expressions be used for web scraping?

Take some time to answer these questions on your own, and then compare your answers with the explanations provided. This will help reinforce what you’ve learned and identify areas that may need further study.

Use an HTML Parser for Web Scraping in Python

While using string methods and regular expressions can be effective for simple web scraping tasks, they can quickly become complex and error-prone when dealing with more complex HTML structures. To handle such cases, it’s often better to use an HTML parser.

Python provides several libraries for parsing HTML, but one of the most popular and widely used is Beautiful Soup. Beautiful Soup makes it easy to extract data from HTML and XML documents by transforming them into a nested data structure, called a parse tree, that can be navigated and searched using Python code.

To install Beautiful Soup, you can use the following command in your terminal:

pip install beautifulsoup4

Once you have Beautiful Soup installed, you can import it into your Python script using the following line:

from bs4 import BeautifulSoup

Now, let’s write a function that takes the HTML content of a web page and creates a BeautifulSoup object:

def create_soup(html_content):
    soup = BeautifulSoup(html_content, "html.parser")
    return soup

This function takes the HTML content as a parameter and uses the BeautifulSoup class from the bs4 module to create a BeautifulSoup object. The second parameter, "html.parser", specifies the parser to be used (in this case, the built-in HTML parser).

To test this function, you can call it with the HTML content of a web page:

soup = create_soup(html_content)

This will create a BeautifulSoup object representing the structure of the web page.

Use a BeautifulSoup Object

Once you have a BeautifulSoup object, you can use it to navigate and search the HTML structure of the web page. BeautifulSoup provides a wide range of methods and attributes for this purpose.

For example, let’s say you want to extract the text of all the <p> tags in the web page. You can use the find_all() method to find all the occurrences of a particular tag and then use the text attribute to access the text within each tag.

Here’s an example function that extracts all the text within <p> tags using Beautiful Soup:

def extract_paragraphs(soup):
    paragraphs = soup.find_all("p")
    texts = [p.text for p in paragraphs]
    return texts

This function takes a BeautifulSoup object as a parameter and uses the find_all() method to find all the <p> tags in the web page. It then extracts the text within each tag using the text attribute and returns a list of the texts.

To test this function, you can call it with the BeautifulSoup object:

paragraphs = extract_paragraphs(soup)
for paragraph in paragraphs:
    print(paragraph)

This will print all the paragraphs found in the web page.

Check Your Understanding

Now that you’ve learned how to use an HTML parser for web scraping in Python, take some time to check your understanding. Here are a few questions to test your knowledge:

1. What is an HTML parser, and why is it useful for web scraping?
2. What library in Python provides an HTML parser?
3. How can you create a BeautifulSoup object from the HTML content of a web page?
4. How can you use a BeautifulSoup object to navigate and search the HTML structure of a web page?
5. Name two methods in Beautiful Soup for finding tags in a web page.

Answer these questions on your own, and then compare your answers with the explanations provided. This will help reinforce what you’ve learned and identify areas that may need further study.

Interact With HTML Forms

In addition to extracting data from web pages, you may also need to interact with forms and other components of websites. Python provides several libraries for interacting with HTML forms, but one of the most popular and convenient is MechanicalSoup.

MechanicalSoup is a Python library that makes it easy to automate browser actions, such as submitting forms, clicking buttons, and navigating between pages. It combines the power of the Requests library for making HTTP requests with the BeautifulSoup library for parsing HTML.

To install MechanicalSoup, you can use the following command in your terminal:

pip install MechanicalSoup

Once you have MechanicalSoup installed, you can import it into your Python script using the following line:

import mechanicalsoup

Now, let’s write a function that creates a MechanicalSoup browser object:

def create_browser():
    browser = mechanicalsoup.StatefulBrowser()
    return browser

This function creates a MechanicalSoup browser object using the StatefulBrowser class. The browser object can be used to simulate browser actions, such as submitting forms and clicking buttons.

To test this function, you can call it to create a browser object:

browser = create_browser()

This will create a MechanicalSoup browser object that you can use to interact with HTML forms.

Submit a Form With MechanicalSoup

Once you have a browser object, you can use it to submit HTML forms on web pages. MechanicalSoup provides several methods for this purpose, but one of the most commonly used is the submit_selected() method.

To submit a form, you’ll need to know the URL of the web page containing the form and the names and values of the form fields. You can use the select_form() method to select the form you want to submit and then use the `set()“ method to set the values of the form fields.

Here’s an example function that submits a form using MechanicalSoup:

def submit_form(browser, url, form_data):
    browser.open(url)
    browser.select_form()
    for field_name in form_data:
        browser.set(field_name, form_data[field_name])
    browser.submit_selected()

This function takes a browser object, a URL, and a dictionary of form data as parameters. It uses the open() method of the browser object to open the specified URL, the select_form() method to select the form on the web page, the set() method to set the values of the form fields, and finally, the submit_selected() method to submit the form.

To test this function, you can call it with a browser object, the URL of a web page containing a form, and a dictionary of form data:

url = "https:https://codemdd.io/example.comhttps://codemdd.io/login"
form_data = {
    "username": "john",
    "password": "secret"
}
submit_form(browser, url, form_data)

This will simulate the submission of the form with the specified data.

Check Your Understanding

Now that you’ve learned how to interact with HTML forms using MechanicalSoup, it’s time to check your understanding. Here are a few questions to test your knowledge:

1. What is MechanicalSoup, and why is it useful for interacting with HTML forms?
2. What library in Python provides a convenient way to automate browser actions?
3. How can you create a browser object using MechanicalSoup?
4. How can you submit an HTML form using a browser object from MechanicalSoup?
5. What method in MechanicalSoup can be used to set the value of a form field?

Answer these questions on your own, and then compare your answers with the explanations provided. This will help reinforce what you’ve learned and identify areas that may need further study.

Interact With Websites in Real Time

In addition to scraping data from web pages, you may also need to interact with websites in real time, for example, to extract data from dynamic web pages or to perform actions on behalf of a user.

Python provides several libraries for interacting with websites in real time, but one of the most powerful and flexible is Selenium WebDriver. WebDriver allows you to automate browser actions, such as clicking elements, filling out forms, and navigating between pages, as if you were interacting with a website manually.

To use Selenium WebDriver, you’ll need to install the appropriate WebDriver for the browser you want to control. WebDriver supports several browsers, including Chrome, Firefox, and Safari.

Once you have the WebDriver installed, you’ll need to import the appropriate class from the Selenium library into your Python script. For example, if you’re using Chrome, you can import the Chrome class as follows:

from selenium.webdriver import Chrome

Now, let’s write a function that creates a Selenium WebDriver object:

def create_driver():
    driver = Chrome()
    return driver

This function creates a Selenium WebDriver object using the Chrome class. You can replace Chrome with the appropriate class for the browser you want to control.

To test this function, you can call it to create a WebDriver object:

driver = create_driver()

This will create a Selenium WebDriver object that you can use to interact with websites.

Check Your Understanding

Now that you’ve learned how to use Selenium WebDriver to interact with websites in real time, it’s time to check your understanding. Here are a few questions to test your knowledge:

1. What is Selenium WebDriver, and why is it useful for interacting with websites in real time?
2. What library in Python provides a convenient way to automate browser actions?
3. How can you create a WebDriver object using Selenium WebDriver?
4. How can you simulate clicking an element on a web page using a WebDriver object?
5. What method in Selenium WebDriver can be used to fill out a form field?

Answer these questions on your own, and then compare your answers with the explanations provided. This will help reinforce what you’ve learned and identify areas that may need further study.

Conclusion

Web scraping is a powerful technique for collecting and analyzing data from the Web. Python provides several libraries and tools for web scraping, including string methods, regular expressions, HTML parsers like Beautiful Soup, and browser automation tools like MechanicalSoup and Selenium WebDriver.

In this tutorial, you learned how to:

• Parse website data using string methods and regular expressions
• Parse website data using an HTML parser
• Interact with HTML forms and other website components
• Interact with websites in real time

By combining these techniques, you can automate the collection and analysis of data from websites, enabling you to extract valuable insights and make informed decisions.

Additional Resources

If you want to learn more about web scraping in Python, here are some additional resources to explore:

• Beautiful Soup Documentation: This is the official documentation for the Beautiful Soup library, which provides detailed information and examples on how to use Beautiful Soup for web scraping.
• MechanicalSoup Documentation: This is the official documentation for the MechanicalSoup library, which provides detailed information and examples on how to use MechanicalSoup for interacting with HTML forms.
• Selenium Documentation: This is the official documentation for Selenium WebDriver, which provides detailed information and examples on how to use WebDriver for interacting with websites in real time.