A web crawler, also known as a spider or bot, is a program designed to systematically browse the internet, indexing and gathering information from websites. The primary function of a web crawler is to traverse web pages, follow links, and extract data that can be used for various purposes, such as search engine indexing, data analysis, and content aggregation.

Although it is used for similar means, web crawling is different from web scraping. Web scraping involves extracting specific data from web pages, whereas web crawling refers to the process of navigating through the web, following links, and indexing content from multiple pages. While web scraping is targeted and precise, web crawling is broader and more comprehensive, often used to gather a large volume of data from numerous sources. For more information on web scraping, take a look at our dedicated article here.

Python is a popular choice of programming language for web crawling due to its simplicity, readability, and the extensive range of libraries available for handling HTTP requests, parsing HTML, and managing data. Libraries like Requests and BeautifulSoup, make Python an efficient and effective tool for building web crawlers.

Datamam, the global specialist data extraction company, works closely with customers to get exactly the data they need through developing and implementing bespoke web scraping solutions.


Datamam’s CEO and Founder, Sandro Shubladze, says: “Python is the go-to language for web crawling because of its versatility and the robustness of its libraries. One of the key benefits of using Python is its ability to handle dynamic content seamlessly.”

Why might an organization use a Python web crawler?

Python is often chosen for web crawling projects due to its ease of use, and powerful libraries. Using libraries such as Requests, BeautifulSoup and Selenium simplifies the process of sending HTTP requests, parsing HTML, and automating browser interactions when conducting a web crawling program. Python’s readability and vast community support make it accessible even to those who are new to programming. For more information on why Python is ideal for web crawling, refer to our Python article

There are a huge range of reasons organizations might want to utilize Python web crawlers. For example, search engines like Google use sophisticated web crawlers known as spiders to index the vast amount of information available on the internet and provide relevant search results to users. An online retailer might use a web crawler to scrape data on competitor pricing, or a university or research institution might deploy them to gather data from academic papers and publications and facilitate large-scale research projects.

Some of the common use cases for Python web crawlers include:

  • Content verification: Organizations use web crawlers to verify that their content is correctly displayed across different platforms and regions. For instance, an e-commerce site might crawl its own pages to ensure product descriptions and prices are accurate and consistent.
  • Large-scale data collection: Web crawlers are essential for gathering large datasets from the web. This data can include product prices, user reviews, social media posts, or any other information that can be used for analysis and decision-making.
  • Research: Researchers use web crawlers to gather data from academic journals, news websites, and other sources. This allows for the aggregation of large volumes of information that can be analyzed to identify trends, patterns, or new insights.
  • Lead generation: Web crawlers can automate the collection of potential leads by extracting contact information from directories, social media profiles, and other online resources.
  • Content exploring: Content creators and marketers use web crawlers to discover trending topics, gather ideas, and stay updated with the latest industry news. A blogger, for instance, might use a crawler to monitor competitor blogs and news sites to find inspiration for new content.
  • Competitor monitoring: Organizations use web crawlers to keep an eye on competitors. This includes tracking changes in product offerings, prices, and marketing strategies.

Python web crawlers offer organizations a powerful tool to automate the extraction of data from the web. Whether it’s for content verification, data collection, research, lead generation, content exploration, or competitor monitoring, the flexibility and power of Python make it a strong choice.

“Python’s rich ecosystem of libraries and tools makes it exceptionally powerful for web crawling,” says Sandro Shubladze. “One of the key advantages is the ability to handle both simple and complex crawling tasks efficiently.”

Is web crawling with Python legal?

Web crawling with Python is a powerful tool for gathering data from the internet, but it comes with legal and ethical considerations that must be understood to avoid potential pitfalls.

If the data being collected is publicly accessible, web crawling is generally considered legal. Public data includes information that is freely available on websites without any login requirements or paywalls. However, even when collecting public data, it’s important to respect the terms of service (ToS) of the website being crawled.

Data privacy is a major concern in web crawling. While public data is generally fair game, collecting personal data, such as user information or proprietary business data, can lead to legal issues. Always ensure that the data you are collecting does not infringe on the privacy rights of individuals or organizations.

Most websites have a robots.txt file that specifies which parts of the site can be crawled and which parts are off-limits. It’s essential to adhere to these guidelines to avoid legal repercussions and to show respect for the website’s policies. Additionally, rate limiting is crucial to avoid overloading a website’s server.

Sending too many requests in a short period can not only get your IP banned but can also disrupt the service for other users.

Engaging a scraping specialist can help mitigate potential risks associated with web crawling. Specialists are knowledgeable about the latest legal frameworks and best practices, ensuring that your data collection activities are compliant with all relevant laws and regulations. They can also help optimize your crawling strategy to minimize the load on target websites and reduce the risk of getting banned. For more guidance on ethical web scraping, click here.

Sandro Shubladze says: “While collecting publicly available data is generally acceptable, it’s crucial to navigate this space with caution. Always prioritize transparency, respect for website owners’ rights, and compliance with relevant laws and regulations.”

A step-by-step guide to web crawling with Python

1.    Planning and set-up

Before starting, it’s essential to plan your crawling strategy. Identify the target websites, the data you need, and any legal considerations such as the website’s robots.txt file, which specifies the pages that crawlers can or cannot access.

2.    Install libraries

To build a web crawler in Python, you’ll need to install several libraries. Commonly used libraries include Requests for sending HTTP requests and BeautifulSoup for parsing HTML.

3.    Send HTTP request

Use the Requests library to send an HTTP request to the target website and fetch the HTML content.

import requests

url = 'http://example.com'

response = requests.get(url)

html_content = response.content

4.    Parse the data

Parse the HTML content using BeautifulSoup to navigate the HTML structure and locate the data you need.

from bs4 import BeautifulSoup

soup = BeautifulSoup(html_content, 'html.parser')

5.    Extract the data

Extract specific data from the parsed HTML. For instance, you might want to extract all the links on a page.

links = soup.find_all('a')

for link in links:


6.    Use or store data

Finally, store the extracted data or use it for your intended purpose. You might save it to a file or a database for later analysis.

with open('data.txt', 'w', encoding=’utf-8) as file:

for link in links:

file.write(link.get('href') + '\n')

By following these steps, you can effectively create a web crawler in Python that automates the process of navigating through web pages and extracting valuable data. This not only saves time but also ensures you have access to up-to-date information from across the web.

What are the benefits and challenges of web crawling with Python?

Web crawling with Python offers numerous advantages, making it a popular choice for organizations looking to collect and analyze large volumes of data. However, it also presents certain challenges that must be addressed to ensure successful implementation.

Python’s extensive libraries, such as Requests and BeautifulSoup, streamline the web crawling process, making it quick and efficient. These libraries provide pre-built functions for sending HTTP requests, parsing HTML, and navigating web pages, reducing the time and effort required to build crawlers from scratch.

Web crawlers can systematically browse the internet and collect vast amounts of data from numerous sources. This capability is essential for applications such as search engine indexing, market research, and competitive analysis. For instance, a company can use a web crawler to gather pricing information from multiple competitors, allowing for real-time adjustments to their pricing strategy.

Python’s flexibility allows developers to customize web crawlers to meet specific needs. Whether it’s handling dynamic content with Selenium, managing cookies and sessions, or integrating with databases and data analysis tools, Python provides the versatility required to handle diverse web crawling tasks.

Python’s popularity ensures a robust community of developers who contribute to libraries, frameworks, and tools that enhance web crawling capabilities. This community support provides extensive resources, documentation, and forums where developers can seek help and share knowledge, facilitating the development and troubleshooting process.

Sounds great – but there are also some challenges to mitigate.

Building an effective web crawler can be complex and requires a significant amount of set-up. Developers need to install and configure various libraries, manage dependencies, and ensure compatibility with the target website’s structure and technology. This initial complexity can be a barrier for beginners or organizations with limited technical expertise.

While the ability to collect vast amounts of data is a benefit, it can also be overwhelming. Handling, storing, and processing large datasets requires robust infrastructure and efficient data management practices. Without proper planning, organizations can quickly find themselves overwhelmed by the volume of data and the associated storage and processing costs.

Web crawling activities must comply with legal and ethical guidelines. This includes respecting a website’s robots.txt file, avoiding overloading servers with excessive requests, and ensuring data privacy. Failure to adhere to these guidelines can lead to legal repercussions and damage to the organization’s reputation.

Websites often implement measures to detect and block automated crawling activities. These can include rate limiting, CAPTCHAs, and IP bans. Overcoming these obstacles requires sophisticated techniques such as IP rotation, user-agent switching, and the use of proxies, which add to the complexity of web crawling projects.

“Handling vast volumes of data is another critical challenge,” says Sandro Shubladze. “While Python makes data collection easier, storing and processing this data efficiently requires substantial infrastructure.”

Datamam can help mitigate the risks associated with web crawling. Our team of experts can provide guidance on best practices, legal compliance, and ethical considerations. We offer tailored solutions to handle complex crawling tasks, manage large volumes of data, and ensure that your web crawling activities are efficient and compliant with all relevant regulations.

By partnering with Datamam, organizations can leverage our expertise to streamline their web crawling projects and avoid common pitfalls. For more information on how we can assist with your web crawling needs, contact us.