Back to blog

What is Web Scraping & How to Scrape Data from a Website?

Iveta Vistorskyte

2024-10-098 min read

The internet is full of invaluable public data that can assist companies in achieving their goals. A challenge lies in getting website data without a dedicated team manually collecting the required information around the clock.

The concept of data scraping is becoming familiar to every modern company aiming to base its decisions on valuable data. This article will explain web scraping and how to effectively incorporate it into your business, whether through scraping tools or data extraction software.

For your convenience, we also have this topic covered in a video format:

What is web scraping?

Web scraping, also known as internet scraping or website scraping, refers to the automated process of collecting publicly available data from a website. Instead of gathering data manually, simple scraper tools can acquire vast amounts of information in a few minutes.

You can use web scraping to extract data from various websites, depending on the goals of the scraping project. For instance, e-commerce businesses leverage web scraping to compare prices, monitor competitors, and refine their strategies by collecting public pricing data, customer reviews, product descriptions, and more. Meanwhile, cybersecurity companies employ web scraping to monitor threats across the web, and brand monitoringhelps track mentions and sentiment about a company.

Is web scraping legal?

The web scraping legality is a frequently discussed topic and it’s especially important for businesses. Therefore, there are some things you need to know before starting web data scraping:

Even if you're gathering publicly available data, ensure that you’re not breaching laws that may apply to such data, e.g., downloading copyrighted data.
Avoid logging in to websites to get the required information because by doing that you must accept Terms of Service (ToS) (or other legal agreement) and that may forbid automated scraping content.
Data for personal usage should also be collected cautiously, according to websites’ policy.

Before engaging in web scraping activities of any kind, we advise you to seek legal consultation to ensure you’re not violating any laws.

Web scraping vs. web crawling

Simply put, web crawling means navigating the web to index content, while web scraping is focused on extracting public data from a target website. Both scraping and crawling complement each other in the overall public data gathering process, often done sequentially – one following the other. Learn more about web scraping vs. web crawling by reading our in-depth article about this topic.

Web scraping vs. web crawling

The process of web scraping

To clearly define what web scraping is, it's crucial to explain the basic web scraping process. Here’s a step-by-step guide on how to extract data:

1. Identify target websites

Depending on your objective, the initial step in any scraping project involves identifying the specific web pages from which you intend to gather relevant information.

For a quick web scraping demonstration, let’s crawl and scrape this e-commerce website demo. To keep it simple, we’ll showcase the steps for Python, which you can download from the official website. Once you have it ready, open your computer’s terminal and install the required Python libraries for this project using pip:

python -m pip install requests beautifulsoup4 pandas

Depending on your setup, you may want to use the python3 keyword:

python3 -m pip install requests beautifulsoup4 pandas

The requests library will send web requests to the target server to retrieve HTML documents, while Beautiful Soup will help you extract data from each scraped HTML file.

2. Collect target page URLs

To make your web scraping process more efficient, collecting specific URLs can help you save your resources. You'll collect only the needed data without vast amounts of irrelevant information from multiple pages.

Let’s say your goal is to extract data from each product listing. To do this, you need to collect all the product URLs from each search page. If you scroll down the first page to the bottom, you can see there are a total of 94 pages:

Since each page URL has a page parameter, for example, https://sandbox.oxylabs.io/products?page=1, you can easily generate URLs for all 94 pages using Python. As the purpose of this guide is to showcase the steps, let’s just scrape the first five pages:

# === url_crawler.py === #
import requests
from bs4 import BeautifulSoup


urls = []

for page in range(1,6):
    print(f"Scraping page {page}.")
    response = requests.get(f"https://sandbox.oxylabs.io/products?page={page}")

Next, convert raw HTML to a Beautiful Soup object for parsing:

    html_doc = BeautifulSoup(response.text, "html.parser")

Then, open up your browser’s Developer Tools by right-clicking and selecting Inspect. This way, you can view the HTML code of a page and determine the CSS or XPath selectors for extracting the public data you need. You can see that each product card is in a <div> tag with a class set to “product-card”, while the link of each product is inside the <a> tag’s href attribute:

Using this information, you can form the URL extraction logic and save all product URLs into a .txt file, as shown in the Python code below:

# === url_crawler.py === #
import requests
from bs4 import BeautifulSoup


urls = []

for page in range(1,6):
    print(f"Scraping page {page}.")
    response = requests.get(f"https://sandbox.oxylabs.io/products?page={page}")
    html_doc = BeautifulSoup(response.text, "html.parser")

    for product in html_doc.find_all("div", class_="product-card"):
        link = product.select_one("a")
        urls.append("https://sandbox.oxylabs.io" + link.get("href"))

with open("urls.txt", "w") as f:
    for url in urls:
        f.write(url + "\n")
print("URLs saved to a file.")

3. Make requests to get the HTML of the page

This critical step is where the essence of the entire project unfolds. By making requests, you retrieve the HTML of the desired pages containing all the necessary website elements.

In a new Python file, let’s build a scraper that reads the urls.txt file and makes a request for each URL:

# === scraper.py === #
import requests
import pandas as pd
from bs4 import BeautifulSoup


with open("urls.txt", "r") as f:
    urls = f.readlines()

product_data = []
for url in urls:
    print(f"Scraping: {url.strip()}")
    response = requests.get(url.strip())

4. Navigate & extract information from the HTML

Following the acquisition of HTML code, the scraper navigates through it to extract specific unstructured data, presenting it in the structured format specified by the user. Here’s how you can achieve this in Python:

    html_doc = BeautifulSoup(response.text, "html.parser")

    product_data.append({
        "title": html_doc.select_one("h2").get_text(),
        "price": html_doc.select_one(".price").get_text(),
        "developer": html_doc.select_one(".developer").get_text().replace("Developer: ", ""),
        "link": url.strip()
    })

5. Store scraped data

This is the final step of the whole web scraping process. The extracted data needs to be stored in CSV, JSON format, or in any database for further usage.

For this short guide, let’s use the pandas library to save the parsed product data to a CSV file:

df = pd.DataFrame(product_data)
df.to_csv("product_data.csv", index=False)

In the end, you should have a basic code that looks like this:

# === scraper.py === #
import requests
import pandas as pd
from bs4 import BeautifulSoup


with open("urls.txt", "r") as f:
    urls = f.readlines()

product_data = []
for url in urls:
    print(f"Scraping: {url.strip()}")
    response = requests.get(url.strip())
    html_doc = BeautifulSoup(response.text, "html.parser")

    product_data.append({
        "title": html_doc.select_one("h2").get_text(),
        "price": html_doc.select_one(".price").get_text(),
        "developer": html_doc.select_one(".developer").get_text().replace("Developer: ", ""),
        "link": url.strip()
    })

df = pd.DataFrame(product_data)
df.to_csv("product_data.csv", index=False)

You can open the CSV file in a program that supports the CSV format. For example, here’s a snippet of the saved file in Google Sheets:

Methods and tools for web scraping

When it comes to what is web scraping and how to scrape data from a website, there are multiple approaches you can take, each with its own benefits and limitations. Choosing the right solution depends on your specific goals, resources, and technical skills, including coding knowledge.

No-code and low-code web scraping solutions

For beginners or those who want a fast, easy way to gather web data without heavy coding, no-code and low-code options are ideal:

Manual copy and paste: The most straightforward method involves manually copying data from a website, though it's time-consuming for large-scale data extraction.
Browser developer tools: Most modern browsers include built-in tools, such as the "Inspect" feature, that allow you to view a site's source code and extract elements directly. Learn more about how to scrape images from a website using browser tools.
Browser extensions: Some extensions can automate simple scraping tasks by identifying and capturing patterns on web pages, requiring minimal setup.
RSS feeds: Some websites provide structured data through RSS feeds, which is a simple way to gather updated content without the need for coding.
Web scraping services: Many platforms offer data scraping as a service, automating the process with little or no technical input required.
Data mining software: Several software suites offer integrated scraping alongside data analytics tools, providing a low-code approach to managing both collection and analysis, often used in data science applications.

Third-party web scrapers

For users seeking quicker implementation without the need for custom development, third-party scraper APIs can be a practical solution:

Scraper APIs: These pre-built tools allow you to collect data without extensive coding, often through easy-to-use interfaces and documentation. Despite a few drawbacks associated with third-party solutions, such as limited customization, cost considerations, and the challenge of finding a reliable provider, there are decent options available that can cater to your specific needs. For instance, you may explore our Web Scraper API to address your requirements.

Additionally, many third-party proxy scrapers integrate seamlessly with other platforms to enhance functionality. For example, you can explore proxy integration with ParseHub, or consider scraping automation through Apify integration. For more advanced users, Scrapy proxy integration is another valuable option.

Advanced web scraping techniques

For those requiring more robust and scalable solutions, advanced methods involving coding can provide greater control and flexibility:

APIs: Many websites offer their own APIs to access structured data, allowing efficient data extraction if you are familiar with data formats like JSON or XML. You can learn more about how to crawl a website without getting blocked while scraping data.
Scrapy: Python’s Scrapy framework is a robust option for handling larger-scale or more complicated web data. It’s well-suited for experienced users who need advanced functionality.
Headless browsers: Tools like Selenium or Puppeteer let you control browsers using code, making them useful for scraping JavaScript-heavy sites.
Web crawling frameworks: For large-scale scraping, frameworks like Apache Nutch can automate data collection across vast numbers of web pages, although this requires significant technical expertise.

Building an in-house web scraper

For complete control over your web scraping projects, building a custom in-house scraper may be the best option. This method offers:

Customization and control: You can tailor your data scraping solution to your exact needs, ensuring it fits specific use cases and scales with your projects.
Tech stack: Python and JavaScript are the most common programming languages for web scraping. Libraries like Beautiful Soup and Scrapy can simplify the process.
Proxies: For any projects based on web scraping, it’s necessary to use and maintain proxies. Using proxies is crucial for gathering vast amounts of valuable data without being blocked by targeted websites, as they help rotate IP addresses.

However, this approach requires a dedicated development team and significant resources to maintain and manage the scraper effectively.

Datasets as an alternative

If you’re wondering how to scrape data from a website or whether web scraping is a suitable solution for you, datasets, even if they're not a type of web scraper, can serve as an excellent alternative for acquiring the required valuable data. You don’t need to manage the whole web scraping process with datasets because you get ready-to-use data in a preferred format. However, it's important to note that maintaining the relevance of datasets can pose challenges, particularly in dynamic fields where information is constantly changing, especially with the advent of large language models being trained on vast datasets.

Choosing the right web data extraction solution or datasets always depends on your needs. Before making any decision, you should think of what you expect from it now and, of course, in the future.

Choosing the right solution depends on your needs

Web scraping challenges

Web scraping poses various challenges that can make the web data extraction process complex. The primary hurdles include the risk of being blocked by target websites, issues related to scalability, unique HTML site structures, and the ongoing need for infrastructure maintenance.

Getting blocked by target websites

Websites commonly employ strategies to regulate incoming traffic, including measures such as CAPTCHAs, rate limiting, IP addresses blocking, browser fingerprinting, etc. Using proxies from reputable providers and managing user agents may help you overcome this challenge.

Scalability

Building a highly scalable web scraping infrastructure is challenging due to the required resources and knowledge. Choosing pre-built web scraping tools that support a high volume of requests will help you save time to achieve your goals.

Website structure changes

Websites are constantly improving their user experience, meaning their design, features, or layout might change occasionally. These changes can impact a web scraping process, meaning your web scraping tool needs to be constantly updated.

We suggest checking our blog post dedicated to main web scraping challenges and how to overcome them.

Web scraping use cases

Web scraping is a powerful technique used across various industries to gather, analyze, and utilize data from the web. Below are some of the most common and impactful web scraping applications:

1. Price monitoring and comparison

Retailers and e-commerce businesses scrape competitor websites to track pricing, enabling dynamic pricing strategies and competitive positioning.

2. Data in finance

Financial analysts scrape stock market websites for data on stock prices, company financials, and news, helping to forecast trends and inform investment decisions.

3. Real estate listings

Real estate agencies scrape property websites to monitor listings, prices, and market trends, providing clients with up-to-date information.

4. Lead generation

Businesses scrape websites to gather contact information for potential customers, helping them build marketing lists and generate sales leads.

5. SEO and competitive analysis

SEO professionals scrape search engine results to track keyword rankings and analyze competitors’ SEO strategies, enhancing their search visibility.

6. Social media marketing

Marketers scrape social media platforms to monitor brand mentions, track sentiment analysis, and gain insights into customer behavior for targeted marketing campaigns.

Wrapping it up

There isn’t any hesitation that web scraping is a crucial process for businesses that make data-driven decisions. Whether companies build their own web scraping tools or use third-party solutions, implementing business automations with data scraping in their daily tasks is a definite improvement and a step forward.

If you want to perform web scraping right away, try our Web Scraper API for free with up to 5K results, or check various tutorials on our blog for more information.

Frequently asked questions

What is web scraping used for?

Web scraping is used for extracting data from a website for various purposes, such as data analysis, research, monitoring, and content aggregation.

What is an example of web scraping?

A great example of web scraping is extracting product prices and reviews from e-commerce websites to analyze market trends and make informed business decisions.

Can you get banned for web scraping?

Yes, web scraping can lead to being banned or blocked by websites, as it may violate terms of service and policies. This is the reason why seeking legal consultation to ensure you’re not violating any laws before engaging in web scraping activities is important.

Is it legal to scrape data from websites?

The legality of web scraping varies depending on factors such as the website’s terms of service, the type of data being scraped, and local regulations. Generally, scraping publicly accessible data is legal, but scraping private, copyrighted, or sensitive information without permission could violate laws like the Computer Fraud and Abuse Act (CFAA) in the U.S. It’s crucial to review the terms of service of the website and ensure compliance with relevant legal guidelines in your jurisdiction.

How do I extract raw data from a website?

To extract raw data from a website, there are several approaches based on your technical knowledge. A common method is to use browser developer tools, such as the "Inspect" feature, to view the website’s source code and manually extract the data. For more automated solutions, you can use web scraping tools or programming libraries like Python’s Beautiful Soup or Scrapy to scrape the data programmatically. Additionally, third-party services and APIs provide a streamlined way to extract raw data with minimal coding.

Can Excel scrape data from a website?

Yes, Excel can be used to scrape data from websites by utilizing the Web Query tool. This feature allows you to pull data from a web page directly into your Excel spreadsheet. By entering the website's URL into Web Query, Excel can retrieve and structure the data for you to work with. It's a straightforward way to extract data from a website without needing advanced programming skills.

How do I scrape data from a website in Chrome?

To scrape data from a website using Chrome, you can leverage the browser’s built-in developer tools or install browser extensions. One option is to right-click on the webpage, select "Inspect," and use the developer tools to locate and extract specific elements of the page. Alternatively, Chrome extensions like Data Miner or Web Scraper allow you to automate the scraping process by selecting data directly from the webpage and exporting it in a usable format. For smaller datasets, manually copying and pasting data from the website is another option.

About the author

Iveta Vistorskyte

Lead Content Manager

Iveta Vistorskyte is a Lead Content Manager at Oxylabs. Growing up as a writer and a challenge seeker, she decided to welcome herself to the tech-side, and instantly became interested in this field. When she is not at work, you'll probably find her just chillin' while listening to her favorite music or playing board games with friends.

Learn more about Iveta Vistorskyte

All information on Oxylabs Blog is provided on an "as is" basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website's terms of service or receive a scraping license.