Web Crawling: Techniques and Frameworks for Collecting Web Data

One of the most powerful techniques to collect data from the Web is web crawling, which involves finding all the URLs for one or more domains. Python has several popular Web crawling libraries and frameworks available- We will first introduce different Web crawling, strategies, and use cases, then see simple Web crawling with Python using libraries: requests, Beautiful Soup and Scrapy. Next, we’ll see why it’s better to use a Web crawling framework like Crawlbase (formerly ProxyCrawl).

A web crawler, also known as a web spider or web robot, automatically searches the Internet for content. The term crawler comes from the Web Crawler, the Internet’s original search engine, and Search engine bots are the most well-known crawler. Search engines use web bots to index the contents of web pages all over the Internet so that they can appear in search engine results.
Web crawlers collect data, including a website’s URL, meta tag information, Web page content, page links, and the destinations of those links. To prevent repeatedly downloading the same page, they maintain a note of previously downloaded URLs. It also checks for errors in HTML code and hyperlinks.

Web crawling searches websites for information and retrieves documents to create a searchable index. The crawl begins on a website page and proceeds through the links towards other sites until all of them have been scanned.

Crawlers can automate tasks such as:
• Archiving old copies of websites as static HTML files.
• Extracting and displaying content from websites in spreadsheets.
• Identifying broken links and the pages that contain them that need to be fixed.
• Comparing old and modern versions of websites.
• Extracting information from page meta tags, body content, headlines, and picture descriptive alt-tags

Use Cases

Monitoring of Competitor Prices

Retail and businesses can acquire a more comprehensive understanding of how specific entities or consumer groups feel about their price tactics and their competitors’ pricing strategies by employing advanced web crawling techniques. By leveraging and acting on this information, they may better align pricing and promotions with market and customer objectives.

Monitoring the Product Catalogue

Businesses can also use web crawling to collect product catalogues and listings. Brands can address customer issues and fulfil their needs regarding product specifications, accuracy, and design by monitoring and analysing large volumes of product data available on various sites. This can help firms better target their audiences with individualised solutions, resulting in higher customer satisfaction.

Social media and news monitoring

The web crawler can track what’s being said about you and your competitors on news sites, social media sites, forums, as well as other places. This piece of data can be handy for your marketing team to monitor your brand image through sentiment analysis. This could help you understand more about your customers’ impressions of you and how you compare to your competition.

Web Crawling using Beautifulsoup

Beautiful Soup is a popular Python library that aids in parsing HTML or XML documents into a tree structure so that data may be found and extracted. This library has a simple interface with automated encoding conversion to make website data more accessible.
This library includes basic methods and Pythonic idioms for traversing, searching, and changing a parse tree, as well as automated Unicode and UTF-8 conversions for incoming and outgoing texts.

Installing Beautiful Soup 4

pip install beautifulsoup4

Installing Third-party libraries

pip install requests
pip install html5lib
pip install bs4

Accessing the HTML content from the webpage

import requests
URL = "https://www.theverge.com/tech"
r = requests.get(URL)
print(r.content)

Parsing the HTML content

import requests
from bs4 import BeautifulSoup

URL = "http://www.theverge.com/tech"
r = requests.get(URL)

soup = BeautifulSoup(r.content,
'html5lib')
print(soup.prettify())

Web Crawling with Python using Scrapy

Scrapy is a Python framework for web crawling with Python on a large scale. It provides you with all of the features you need to extract data from websites easily, analyse it as needed, and save it in the structure and format of your choice.
Scrapy is compatible with Python 2 and 3. When you’re using Anaconda, you may download the package from the conda-forge channel, which has up-to-date packages for Linux, Windows, and Mac OS X.
To install Scrapy using conda, run:

conda install -c conda-forge scrapy

If you’re using Linux or Mac OS X, you can install scrapy through

pip install scrapy

In order to execute the crawler in the shell, enter:

fetch("https://www.reddit.com")

Scrapy produces a “response” object containing the downloaded data when you crawl something with it. Let’s have a look at what the crawler has gotten.

view(response)
print response.text

Web Crawling with Python using Crawlbase (formerly ProxyCrawl)

Crawling the web might be difficult and frustrating because some websites can block your requests and even restrict your IP address. Writing a simple crawler in Python may not be sufficient without using proxies. To properly crawl relevant data on the web, you’ll require Crawlbase (formerly ProxyCrawl)’s Crawling API, which lets you scrape most web pages without dealing with banned requests or CAPTCHAs.
Let’s demonstrate how to use Crawlbase (formerly ProxyCrawl)’s Crawling API to create your crawling tool.
the requirements for our basic scraping tool:

1. Crawlbase (formerly ProxyCrawl) account
2. Python 3.x
3. Crawlbase (formerly ProxyCrawl) Python Library

Take note of your Crawlbase (formerly ProxyCrawl) token, which will be the authentication key when using the Crawling API. Let’s begin by downloading and installing the library we’ll use for this project. On your console, type the following command:

pip install crawlbase

The next step is to import the Crawlbase (formerly ProxyCrawl) API

from crawlbase import CrawlingAPI

Next, after initialising the API, enter your authentication token as follows:

api = CrawlingAPI({'token': 'USER_TOKEN'})

Enter your target URL or any other website you wish to crawl. We’ll use Amazon as an example in this demonstration.

targetURL = 'https://www.amazon.com/AMD-Ryzen-3800XT-16-Threads-Processor/dp/B089WCXZJC'

The following section of our code will enable us to download the URL’s whole HTML source code and, if successful, will show the result on your console or terminal:

response = api.get(targetURL)
if response['status_code'] == 200:
print(response['body'])

We’ve now built a crawler. Crawlbase (formerly ProxyCrawl) responds to every request it receives. If the status is 200 or successful, our code will show you the crawled HTML. Any other result, such as 503 or 404, indicates that the web crawler was unsuccessful. On the other hand, the API employs thousands of proxies around the world, ensuring that the best data are obtained.
One of the best features of the Crawling API is that you can use the built-in data scrapers for supported sites, which fortunately includes Amazon. Send the data scraper as a parameter in our GET request to use it. Our complete code should now seem as follows:

from crawlbase import CrawlingAPI

api = CrawlingAPI({'token': 'USER_TOKEN'})

targetURL = 'https://www.amazon.com/AMD-Ryzen-3800XT-16-Threads-Processor/dp/B089WCXZJC'

response = api.get(targetURL, {'autoparse': 'true'})
if response['status_code'] == 200:
print(response['body'])

If everything works properly, you will receive a response similar to the one below:

Conclusion

Using a web crawling framework like Proxycarwl will make crawling very simple compared to other crawling solutions for any scale of crawling, and the crawling tool will be complete in just a few lines of code. You won’t have to worry about website restrictions or CAPTCHAs with the Crawling API will ensure that your scraper will stay effective and reliable at all times allowing you to focus on what matters most to your project or business.