Introduction to Python Web Scraping

Web Scraping is often considered difficult because of the complexity involved with extracting data. While different programming languages are used for scraping, and each has a special benefit over others, Python is arguably the most preferred choice.

Based on our implemented research, Python has extensive libraries and integrations with other tools, making scraping tasks easier. Plus, as it is easy to learn and manage, it has been a go-to choice for beginners to pros.

This article will start with the basics of web scraping and its use cases, along with a list of the best Python web scraping libraries. Also, a small project has been included to get you started on web scraping using Python while mentioning the challenges and best practices to get you all covered.

What is Web Scraping?

Web Scraping is the process of extracting data from websites programmatically instead of manually copying and pasting information. Getting on the technical side, web scraping is done by sending HTTP requests to access a website’s content and parsing its HTML structure to extract meaningful and accurate data.

With web scraping, you get the flexibility to automate several tasks, allowing you to collect large volumes of data efficiently. Our data suggests that the data gathered through web scraping is often used for analysis, reporting, or integration into applications.

Use Cases of Web Scraping

According to the analysis aggregated by Ping Proxies, below are some of the crucial use cases of web scraping.

Market research

Businesses and individuals handle the automation tasks like collecting information from their competitors through web scraping. After analysis, the gaps are identified, and the latest trends are monitored to build marketing strategies that can put them at the top.

Dynamic Pricing

E-commerce sites rely on web scraping to scrape competitors’ prices in real-time and adjust the prices of their offerings. This advantage helps them set fair prices while being competitive, generating more sales, and winning more customers.

Ad Verification

Many ad agencies and brands use web scraping tools to track whether their ads are being displayed correctly on published websites. These tools are often used to verify ad placement, impressions, and compliance with advertising agreements and benefits by preventing potential fraud or misrepresentation.

Threat Intelligence

Security professionals monitor underground forums, paste sites, and social media platforms through web scraping. This procedure is repeated frequently to pick up signs of data breaches, phishing campaigns, or cyber threats.

According to the analysis aggregated by Ping Proxies, scraping dark web marketplaces helps identify stolen credentials or sensitive information. You can act before your personal information is exploited.

Travel Fare Aggregation

Travel platforms rely on web scraping to collect prices for different services, such as flights, hotels, and car rentals from multiple providers. With the data gathered, the prices are updated for a profitable yet fair deal for customers.

SERP Analysis

Many mass publishing websites and SEO professionals use SERP analysis to develop content strategies to achieve the best position in the competitive markets. Our data suggests that the scraped data is useful for analyzing keyword rankings, competitor performance, and search engine behavior.

AI Development

AI relies on training models with data, and the optimal way to gather large datasets is through web scraping. A huge collection of images, texts, user interactions, etc., from various websites is scraped to help develop AI systems, mostly with recommendation engines or natural language processing (NLP) tools.

Price Monitoring

Retailers and analysts use web scraping to track price changes across online stores and marketplaces. The gathered data is analyzed and put into use by predicting demand, identifying pricing trends, and making informed decisions about inventory and sales strategies.

Python Web Scraping Libraries

The best part of Python is you get to use a wide range of libraries for web scraping. Each library has its strengths, and understanding what they are and their uses can help you choose the right tool for your scraping project.

Requests library

The Requests library is a fundamental tool for web scraping and the best choice for sending HTTP requests. You can send GET and POST requests, handle cookies, and manage session data without going through the hassle of working on lower-level networking details.

It is ideal for scraping static web pages, where the content is readily available in the HTML source without requiring JavaScript rendering. Based on our implemented research, this library is ideal for small-scale projects such as retrieving data from blogs, news sites, or product pages.

To install the Requests library, use the following pip command:

pip install requests

Beautiful Soup

Beautiful Soup is ideal for parsing and extracting data from HTML and XML files. It works together with an HTML parser to navigate through the document’s structure and extract specific elements.

According to the analysis aggregated by Ping Proxies, it is best at handling irregular HTML structures, which are common on older or poorly maintained websites. It is often used with the Requests library and is especially effective for scraping structured content.

To install Beautiful Soup, use the following pip command:

Python

pip install beautifulsoup4

Selenium

Selenium is open source and an advanced library, preferred for performing automated interaction with web browsers. It isn’t limited to static pages, you can use it to scrape dynamic web pages by simulating user interactions on the selected browser.

Based on our implemented research, it is particularly effective for interacting with complex websites, such as those using AJAX or infinite scrolling. Plus, it is great at bypassing certain anti-bot measures.

To install Selenium, use the following pip command:

Python

pip install selenium

Scrapy

Scrapy is a Python framework designed specifically for web scraping and data extraction. It is preferred by many for its complete toolkit, which handles the entire scraping process, from sending requests to storing data.

The asynchronous architecture makes it efficient for scraping, and it is often used for large-scale projects requiring data extraction from multiple domains.

To install Scrapy, use the following pip command:

Python

pip install scrapy

Playwright

Playwright is known for its enhanced capabilities designed for automating web browsers. It supports multiple programming languages and offers cross-browser automation for Chrome, Firefox, and WebKit.

Based on our research, it is effective for scraping JavaScript-heavy websites with advanced rendering or security measures. The best part of it is you get to network interception and mocking that allows you to capture, modify, or simulate HTTP requests and responses without needing to parse HTTP.

To install Playwright, use the following pip command:

Python

pip install playwright

lxml

lxml is used for processing and extracting data from HTML and XML documents. With its support for XPath and CSS selectors, lxml lets you navigate and extract elements from web pages.

It is ideal for handling well-structured documents and extracting data efficiently. According to the analysis aggregated by Ping Proxies, it is one of the fastest libraries as it uses C-based parsers for handling large or complex datasets.

To install lxml, use the following pip command:

Python

pip install lxml

Python library Web scraping Comparison

Struggling to find the right scraping tool? Here are our 10 Essential Scraping Tools.

Sample Python Web Scraping Project

The purpose of the project below is to help you get started with web scraping using Python. It is performed on Windows with the help of PyCharm IDE. For libraries, BeautifulSoup and Request libraries are used, as static pages are considered for scraping.

Step 1. Install Python and Python IDE

Start by installing Python on your device. Visit Python's official download page, choose your operating system, select a version, and download the installer. We recommend you always go with the latest stable version to avoid potential deprecation and errors.

If you have downloaded an installer, the process arguably remains the same. We wouldn’t recommend leaving the options to default unless you’re aware. However, if you’re installing Python on Windows through the installer, make sure to check “Add python.exe to PATH”.

Doing this is important as it informs your command line what folder to look into in attempting to find the file. Plus, you don’t have to specify the full path to the Python executable every time you use it.

After successful installation, proceed with downloading the Python IDE of your choice (PyCharm, Visual Studio, Spyder, etc.). For this demonstration, we have used PyCharm Community Edition, a free version best for learning and trying web scraping.

Step 2. Set up Python IDE for Web Scraping

With PyCharm installed and loaded, click on (1) Pure Python. Next, (2) change your project name and check (3) “Create a welcome script” to access the main.py file.

In the main.py clear the entire script and open the terminal by pressing Alt + F12. You can also open it by clicking on the Terminal icon at the bottom left corner.

After the terminal is loaded, install requests and beautifulsoup libraries using the command - pip install requests beautifulsoup.

Note: The libraries used for scraping vary by website. For instance, the Selenium library is preferable if you’re scraping a dynamic website. Step 3. Import Required Libraries

Your PyCharm IDE is now ready for web scraping, as the required libraries are installed. To use them, you need to import them into the main.py file. For this project, the following libraries need to be imported.

• requests help fetch web pages.
• BeautifulSoup allows extracting specific information from the HTML.
• csv saves data into a structured file.

Python

import csv

import requests

from bs4 import BeautifulSoup

Step 4. Choose the Target Scraping Website

As you’ve started with Python web scraping, it is ideal to choose a static website for scraping. For the demonstration, we selected Books To Scrape. However, if you want to try another website and are confused, here is how to select a static webpage.

Open the webpage, right-click on an empty space, and then select (1) Inspect. Next, click on the (2) Network tab and then the (3) Fetch/XHR option.

As you can notice, the Fetch/XHR section is empty, making it a static website. In the case of dynamic websites, you would notice network activity.

Step 5. Setting Up the Web Scraping Environment

To begin scraping, we need to create a setup that allows the script to find the web pages' root (Base URL) and first state (Initial Page URL).

1. Define the Base URL: The base_url specifies the website's root address. It is essential for constructing full URLs when navigating between pages.
2. Set User-Agent Headers: Most websites block requests that appear to come from bots, so creating a User-Agent string mimics a real web browser.
3. Set the Initial Page URL: We specify page_url to point to the first page we want to scrape. It works as the starting point for the script to retrieve and process the website content.

Note: The below user agent header is for Google Chrome. You can find different user agents online based on your browser and operating system.

Step 6. Define a Function to Scrape Book Details

With the web scraping environment set, develop the script to collect book details from a web page and store them in a structured format. This is the crucial and confusing aspect of the entire scraping process, as adding the wrong tag and class can scrape inaccurate and unwanted data.

The main goal of this Python web scraping project is to scrape the book name, price, and availability on the targeted website. Here is a step-by-step process.

1. A list/array named books is created to store the details of all the books.
2. Now, create a function to handle the task of extracting book details from a single web page. This function takes two inputs: soup, which is the parsed HTML content of the page, and books, the list where book details will be stored.
3. The data we want to scrape is part of the book containers — article.product_pod. Hence, it is important to scrape all of them using the find_all() method. The parameters for this method are tag (article) and class_ (product_pod).
4. Develop a loop that goes through each book container to extract the required details (scrape the title, price, and availability).
5. The script is now capable of extracting the title, price, and availability. However, it hasn’t been added to the books list yet.

Before we further develop code for data extraction, it is important to understand the below soup methods.

• find() - Finds and returns the first matching element based on the condition you provide (e.g., tag name or class).
• find_all() - Finds and returns all matching elements as a list (e.g., all book entries).

Extraction Tags

• Extract the Title: The title of the book is retrieved from the <a> tag inside the <h3> tag, using the title attribute.
• Extract the Price: The price of the book is fetched from the <p> tag with the class price_color. The strip() method removes any unnecessary whitespace.
• Extract Availability: The availability status is extracted from the <p> tag with the class instock availability. It is also ideal to add the strip() method here.

Step 7. Scrape All Pages

So far, we have developed the script to extract the desired contents in a structured way. However, the script isn’t finished, as the requests and BeautifulSoup aren’t involved yet. This step puts the function scrape_books in action using a loop until all web pages of the target website are extracted.

1. Fetch the Page: A GET request is sent to page_url using the requests library. In return, you get a response that contains the HTML of the page.
2. Parse the HTML: The HTML page content is passed to BeautifulSoup to analyze. Once done it converts it into a structured format, making it easier to extract specific elements.
3. Scrape the Current Page: The scrape_books function is called with the parsed HTML and the list of books to extract book details from the page.
4. Find the "Next" Button: The script checks for the "Next" button on the page by searching for an <li> element with the class next.
5. Navigate to the Next Page: If the "Next" button exists, its relative URL is extracted and appended to the base_url to form the next page's full URL. However, if there’s no "Next" button, it means all pages have been scraped, and page_url is set to None to stop the loop.

Step 8. Present the Scraped Data as CSV

Moving to the last step of the Python web scraping project is to present the scraped data as CSV. Here are the steps for it.

1. Open the CSV File: The script creates (or overwrites if it already exists) a file named books.csv and opens it in write mode (w). It uses UTF-8 encoding to make sure special characters are correctly handled and specifies newline='' to prevent blank lines in the output.
2. Initialize the Writer: A csv.writer object is created to handle writing data to the CSV file.
3. Write the Header Row: The header row is written first to define the column names: Title, Price, and Availability. This row acts as a label for each column in the CSV file.
4. Write the Data Rows: The script loops through the books list, where each entry contains book details. For each book, the writerow() method is used to write the values (title, price, and availability) to a new row in the CSV file.
5. Completion Message: After all the data is written, a message is printed to the console using print(), showing the number of books saved and confirming the file creation.

Step 9. As the entire script is ready and our Python web scraping project is done, click the Run button or press Shift + F10. Now, you will find a CSV file created in the project directory with all website data (1000 books) organized.

The Code

Python

import csv

import requests

from bs4 import BeautifulSoup

# The base URL of the website

base_url = 'https://books.toscrape.com/catalogue/'

# Defining the User-Agent header

headers = {

   'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/107.0.0.0 Safari/537.36'

}

# Starting URL for the first page

page_url = 'https://books.toscrape.com/catalogue/page-1.html'

# Initializing the list to store book details

books = []

def scrape_books(soup, books):

   # Finding all the book containers on the page

   book_elements = soup.find_all('article', class_='product_pod')

   # Iterating over each book element

   for book_element in book_elements:

       # Extracting the title of the book

       title = book_element.find('h3').find('a')['title']

       # Extracting the price of the book

       price = book_element.find('p', class_='price_color').text.strip()

       # Extracting availability status

       availability = book_element.find('p', class_='instock availability').text.strip()

       # Appending book details to the list

       books.append(

           {

               'Title': title,

               'Price': price,

               'Availability': availability

           }

       )

# Loop to scrape all pages

while page_url:

   # Sending a GET request to the page

   page = requests.get(page_url, headers=headers)

   # Parsing the page content

   soup = BeautifulSoup(page.text, 'html.parser')

   # Scraping the current page

   scrape_books(soup, books)

   # Finding the "next" button to navigate to the next page

   next_page = soup.find('li', class_='next')

   if next_page:

       next_page_relative_url = next_page.find('a')['href']

       page_url = base_url + next_page_relative_url

   else:

       page_url = None

# Writing the scraped data to a CSV file

with open('books.csv', 'w', encoding='utf-8', newline='') as csv_file:

   writer = csv.writer(csv_file)

   # Writing the header

   writer.writerow(['Title', 'Price', 'Availability'])

   # Writing the book data

   for book in books:

       writer.writerow(book.values())

print(f"Scraped data for {len(books)} books and saved to 'books.csv'")

Common Scraping Challenges

While web scraping might feel easy when working with static web pages, the real complexity begins when you consider dynamic content or need to scale your scraping operations. These challenges make the process more difficult, requiring advanced tools and techniques.

Dynamic Content

Unlike static pages, where data is readily available in the HTML source, dynamic websites generate content on the client side using JavaScript. So if you send a request to the server, you won’t gather the required data, as it loads when the page is rendered by a browser.

In this scenario, we recommend using libraries like Selenium or Playwright. They can handle dynamic content by simulating browser environments. You can render pages, execute JavaScript, and interact with elements while having the ability to capture API responses directly.

Web Scraper Scaling

Scaling a web scraper can be scraping a few pages to handling a large collection of requests across multiple domains. As the scale grows, challenges like rate limiting, IP bans, and data management can become very difficult.

Also, resources are often depleted, and you can experience issues with anti-bot systems, which block your IP address and prevent access. Based on our implemented research, using rotating proxies and rotating user agents together is the best choice to overcome such issues, as you can send requests across multiple IP addresses and mimic real browser behavior.

Additional tips, such as adding delays and introducing random intervals between requests, further avoid detection. We recommend Scrapy for its asynchronous frameworks, as it is capable of handling multiple concurrent requests, even on large-scale operations.

Web scraping with Python best practices

If you’re new to scraping, following the practices below will help you create efficient, ethical projects. Plus, you can save time, prevent potential bans, and make your scraping efforts more productive.

• Respect the website’s robots.txt file and terms of service. While not legally binding, robots.txt indicates what the website allows, and ignoring it can lead to ethical or legal complications.
• Use rotating proxies and user agents to avoid detection. Scraping multiple pages with the same IP and headers increases the risk of being blocked. Rotating them mimics genuine user activity and reduces detection.
• Add random delays between requests to prevent overloading servers. Rapid requests can crash servers or trigger anti-bot systems. Random intervals make your scraper appear more natural.
• Test your scraper on smaller datasets before scaling up. This helps identify potential issues and make sure your code functions properly without straining the target server.
• Handle scraped data responsibly. Make sure sensitive or personal information is stored securely and comply with privacy laws to avoid serious consequences.

Conclusion

Python's powerful tools and libraries make web scraping accessible and efficient. This guide provides the essential knowledge and a sample project to help you get started with web scraping using Python. However, as your projects grow, scraping must be done with proxies to bypass restrictions and avoid IP bans.

Using ethically sourced proxies available at Ping Proxies ensures compliance and reliable performance. By integrating these proxies into your workflow, you can scrape effectively and responsibly, whether for small-scale projects or large-scale operations.