Web Crawling with Python

Web crawling is a powerful technique for collecting data from the web by finding and following links to discover new pages. Python has several popular libraries and frameworks that make it easy to build web crawlers. In this article, we'll take an in-depth look at what web crawling is, common use cases, and how to build a web crawler using Python.

What is Web Crawling?

Web crawling is the process of programmatically visiting web pages to collect data. A web crawler starts with a list of seed URLs to visit, finds links in the HTML of those pages, and adds them to a queue of pages to crawl next. As the crawler visits each page, it extracts the desired data from the HTML. Web crawling is often used in conjunction with web scraping, which is the process of extracting data from the downloaded HTML pages.

Some common use cases for web crawling include:

• Building search engines by indexing a large portion of the web

• Collecting data for data mining, machine learning, and analytics

• Monitoring websites for changes or new content

• Archiving websites for historical preservation

Web Crawling Strategies

Web crawlers typically only visit a subset of pages on a website based on a crawl budget, which can be a maximum number of pages, a maximum depth from the seed URLs, or a maximum crawl time.

Many websites provide a robots.txt file that specifies which pages should not be crawled. Well-behaved crawlers will respect these rules. Some websites also provide a sitemap.xml file that lists all the pages that should be crawled.

Building a Web Crawler with Python

Let's walk through building a basic web crawler in Python using the popular Scrapy framework. Scrapy is a powerful and extensible framework that handles many of the challenges of web crawling, such as respecting robots.txt, throttling requests, and handling errors.

Setup

First, install Scrapy using pip:

pip install scrapy

Then create a new Scrapy project:

scrapy startproject example_crawler

This will create a directory structure for the project with a Python module for the crawler code.

Defining the Crawler

The core component of a Scrapy crawler is the Spider class. This defines the starting URLs for the crawler, and parsing logic for handling downloaded pages.

Here's a basic spider that starts from the Quotes to Scrape website and extracts the text and author of each quote:

import scrapy

class QuoteSpider(scrapy.Spider):

name = 'quote-spider'

start_urls = ['https://quotes.toscrape.com']

def parse(self, response):

QUOTE_SELECTOR = '.quote'

TEXT_SELECTOR = '.text::text'

AUTHOR_SELECTOR = '.author::text'

for quote in response.css(QUOTE_SELECTOR):

yield {

'text': quote.css(TEXT_SELECTOR).extract_first(),

'author': quote.css(AUTHOR_SELECTOR).extract_first(),

}

The start_urls list defines the initial pages the crawler will visit. The parse method is called with the downloaded content of each page.

CSS selectors are used to find the desired elements on the page. For each quote, we extract the text and author and yield them as a Python dict. Yielding the dict results in it being collected by the crawler.

Crawling Multiple Pages

To crawl additional pages, we need to find the links to those pages and instruct the crawler to follow them. On Quotes to Scrape, there is a "Next" link at the bottom of the page that goes to the next page of quotes.

We can update our spider to find and follow this link:

import scrapy

class QuoteSpider(scrapy.Spider):

name = 'quote-spider'

start_urls = ['https://quotes.toscrape.com']

def parse(self, response):

QUOTE_SELECTOR = '.quote'

TEXT_SELECTOR = '.text::text'

AUTHOR_SELECTOR = '.author::text'

NEXT_SELECTOR = '.next a::attr("href")'

for quote in response.css(QUOTE_SELECTOR):

yield {

'text': quote.css(TEXT_SELECTOR).extract_first(),

'author': quote.css(AUTHOR_SELECTOR).extract_first(),

}

next_page = response.css(NEXT_SELECTOR).extract_first()

if next_page:

yield scrapy.Request(response.urljoin(next_page))

After extracting data from the page, we check for a "Next" link. If found, we use scrapy.Request to schedule the linked page to be downloaded and parsed by the crawler.

The crawler will continue to follow "Next" links until it doesn't find anymore, effectively crawling all pages of quotes on the website.

Running the Crawler

To run the crawler:

scrapy runspider spider.py

This will output the extracted quotes to the console. You can also write them to a file using:

scrapy runspider spider.py -o quotes.jl

Conclusion

Web crawling is a powerful technique for collecting data from websites. Python and Scrapy make it easy to build robust, extensible crawlers.

Some key concepts to remember:

• Respect robots.txt and be nice to web servers

• Use CSS or XPath selectors to extract data from downloaded HTML

• Follow links to crawl additional pages

• Adjust concurrency, throttling, and depth settings as needed

With these fundamentals, you can build crawlers to collect data for a wide variety of applications. Happy crawling!