A Complete Guide for Web Crawlers Using Python

Web crawling isn’t new — it’s been around since 1994 when Brian Pinkerton developed the first web crawler to improve search engine performance. Known as “WebCrawler,” this full-text crawler-based Web search engine was a revolutionary breakthrough in the world of search engines. Today, it is one of the most reliable technologies used to extract data from websites. Read more about web crawling with python.

Web crawlers are an invaluable tool for collecting data from the internet and are popular among researchers, businesses, and developers. With crawlers, you can easily gather large amounts of data and use it for various purposes, like analytics, website automation and content evaluation. As a result, web crawlers are becoming increasingly important for businesses that want to stay competitive in the digital age. Nonetheless, it can be challenging to set up a web crawler, especially for users who are unfamiliar with coding. With Python, you can easily use and create web crawlers with a few lines of code.

This guide will provide an introduction to web crawlers and crawling the web with Python. You’ll learn about the different components of crawler systems and the key crawler web python libraries to use. Finally, you’ll learn how to use crawlers to extract valuable data from a website.

 

 

Web Crawling vs. Web Scraping

 

 

While web crawling is often used interchangeably with web scraping, they are actually two distinct processes. Web crawlers are used to explore websites by automatically visiting web pages and extracting links from the source code so that other pages can be discovered. This is often done to build a complete website map or collect data from multiple pages.

On the other hand, web scraping involves extracting specific data from a page and collecting it in an organized manner. Web scrapers are designed to identify and extract data based on a set of instructions. When compared, crawlers explore websites, while scrapers target specific pages and collect data from them.

How web crawlers work

The basic workflow of a web crawler is relatively straightforward and typically consists of the following four steps:

• Send an HTTP request to a web server to retrieve the page’s source code.
• Parse the HTML and extract all links from the page.
• Queue the links for further crawling or scrape the page for data.
• Store the scraped data and repeat the process with each new link.

Crawling the Web With Python

Python is one of the most popular programming languages for web crawlers due to its simplicity and rich ecosystem. Crawling the web with Python is easy. You just need to define the Python data crawler’s behavior and structure, set up a crawler object and launch the crawler. You can also use crawlers to automate website tasks like collecting data or checking for broken links.

Python crawlers are typically built using libraries like Scrapy, BeautifulSoup, and Selenium. All of these libraries are free to use and offer comprehensive crawler development features. Let’s discuss these libraries in detail so you can decide which one is right for your crawler project.

Scrapy

Scrapy is an open-source Python crawler framework that was designed for web scraping. Scrapy is easy to use and can be set up quickly with just a few lines of code. It is ideal for larger crawler projects and offers features like:

• Crawler scheduling
• Data extraction
• Crawler monitoring
• Crawler optimization

Pros

• It’s open source
• Scalable crawler framework
• Built-in crawler features
• Easy to debug and maintain crawlers

Cons

• Doesn’t support crawling with JavaScript (use Selenium instead)
• It is not ideal for small crawler projects because it contains heavier code
• Complex and hard-to-understand documentation for beginners

BeautifulSoup

BeautifulSoup is another widely used crawler web Python library. It is designed to parse HTML and XML documents and make crawler development more efficient. It can also be used to scrape data from web pages and extract specific information. Key features of BeautifulSoup include:

• Crawler management
• Data extraction
• HTTP authentication
• Crawler optimization

Pros

• Easy to learn and use
• Rich crawler features
• Lightweight crawler framework
• Accessible and easy-to-understand documentation for beginners

Cons

• It is not suitable for large crawler projects
• It cannot automatically execute crawlers

Selenium

Selenium is an open-source crawler web Python library designed for browser automation. It can be used to control a web browser and execute crawler scripts. Selenium is ideal for crawlers that require complex interactions with web pages. Its features include:

• Browser control
• Script execution
• Data extraction
• Crawler optimization

Pros

• It supports crawlers with JavaScript
• Accessible API for crawler development
• It can be used to automate complex interactions with web pages

Cons

• It can be difficult to learn and use
• It is not ideal for crawlers with large amounts of data

Environmental Preparation for Web Crawling

Before building crawlers using Python, you must set up the crawler’s environment. This involves the following steps:

• Creating a Virtual Environment: A virtual environment is a safe sandbox where crawlers can be tested and debugged without affecting other applications on the same computer. Creating a virtual crawler environment is easy and only takes a few minutes. All you need to do is set up a crawler folder, activate the environment and install crawler web python libraries.
• Install a Browser: Google Chrome is the most widely used and supports crawler automation. You can also use other browsers like Firefox, Edge or Safari.
• Installing Python: Next, you must install Python on your computer. Fortunately, installing Python is easy and only takes a few minutes. You can either install the latest version of Python or use an existing version on your computer.

Building a Web Crawler With Python and Scrapy

Scrapy is the most popular crawler web Python framework. With over 45K stars on GitHub, it is the go-to crawler library for many developers. One of its greatest features is its ability to schedule requests asynchronously. Scrapy crawlers can fetch thousands of pages per second without overloading the web server. Moreover, Scrapy crawlers are highly modular and easily customized depending on your crawler project’s requirements.

Here’s a step-by-step guide to creating crawlers with Scrapy. The project for this crawler web Python tutorial will crawl data from a news website.

1. Install Scrapy

Before you can start creating crawlers, you’ll need to install the Scrapy library. You can do so with a simple command as follows:

pip install scrapy

2. Create a crawler project

Once you’ve installed Scrapy, you can create crawler projects. Scrapy crawlers are built using spider classes and require only a few lines of code to get started. To set up a crawler project, type the following command in the terminal:

crapy startproject <project_name>

In this case, you should type:

scrapy startproject newswebsite

This will create a news_website directory with the following sub-directories:

newswebsite/spiders

newswebsite/items.py

newswebsite/middlewares.py

newswebsite/pipelines.py

newswebsite/settings.py

In the spider’s directory, you’ll need to create a file called newswebsite.py, where your crawler code will reside. Open it in a code editor and import all your Scrappy modules and the re-module for regular expression patterns.

3. Write crawler code

When using Python to crawl websites, you must define a spider class that crawls data from the news website. To create a spider, you’ll need to use the crawler web python libraries for Scrapy.

In this example, you’ll create a crawler that crawls the headlines from a news website. You can define your crawler as follows:

import scrapy

class newswebsitespider(scrapy.Spider):

name = ‘news_website’

start_urls = [‘http://example.com/’]

def parse(self, response):

for headline in response.css(‘h1’):

yield {‘Headline’: headline.css(‘h1 ::text’).extract_first()}

This crawler crawls all the headlines from the example.com website. While this crawler is ready to run, it’s not yet finished. You’ll need to add a crawler web python library for regular expressions to limit the crawler’s scope and prevent it from crawling unnecessary pages. To do this, add the following lines of code to your crawler web Python project:

import re

allowed_domains = [‘example.com’]

regex = re.compile(r’^/news/.*’)

start_urls = [‘http://example.com/news/’]

def parse(self, response):

for headline in response.css(‘h1’):

yield {‘Headline’: headline.css(‘h1 ::text’).extract_first()}

This crawler crawls only the headlines from http://example.com/news/* pages and ignores all other pages.

Additionally, you have to set the rules for crawler web python crawlers. To do this, you’ll need to add the following code to your crawler web Python project:

rules =(Rule(LinkExtractor(

allow=regex,

callback=’parse_items’,

follow=True))

This crawler web Python crawler will now crawl headlines from example.com/news/* pages and follow all the links on these pages.

4. Add a proxy

Adding a proxy to crawler web python spiders is essential for security and privacy. This is because crawlers often access large amounts of data, making them vulnerable to malicious users. Moreover, crawlers can get blocked if you use them to access the same web page too often. To prevent this, crawlers should use a proxy server that can provide them with a new IP address each time they access the same web page.

With so many types of proxies available, you’ll need to select one suitable for web Python crawlers. You can use a rotating ISP proxy for this crawler web Python tutorial. Rotating ISP proxies are ideal for web Python crawlers because they provide a new IP address on each request and are highly secure.

Rayobyte’s rotating ISP proxies are the perfect choice for crawler web Python projects. They’re fast, reliable and provide a new IP address with each request. Moreover, Rayobyte’s proxies have advanced features that provide your Python web crawler with authentication, country targeting, user-agent shielding and more.

5. Run the crawler

After setting up your crawler web python spider, you can now run it. To do so, type the following command in the terminal:

scrapy runspider crawler_name.py

In this case, you should type:

scrapy runspider newswebsite.py

This crawler web Python crawler will now start crawling data from the example.com website and store the scraped data in a CSV file.

6. Implementing the web crawler

For instance, you are crawling a news website such as BBC for a specific author name such as ‘John Doe.’ You can implement the web crawler as follows:

import scrapy

class BbcNewsSpider(scrapy.Spider):

name = ‘bbc_news’

allowed_domains = [‘bbc.com’]

start_urls = [‘https://www.bbc.com/news’]

def parse(self, response):

links = response.xpath(‘//a/@href’).extract()

for link in links:

if ‘author’ in link and ‘John Doe’ in link:

yield scrapy.Request(url=link, callback=self.parse_author)

def parse_author(self, response):

articles = response.xpath(‘//h3/a/text()’).extract()

for article in articles:

yield {‘title’: article}

This crawler will crawl the home page of BBC news and collect all the links related to author ‘John Doe.’ It will then request each link and parse the response for article titles. Finally, it will store the scraped data in a list and yield it to the output.

Building a Web Crawler With Python and Beautiful Soup

BeautifulSoup is another widely used crawler web Python library. It’s an HTML parser that can be used to parse HTML and XML documents. Unlike Scrapy, Beautiful Soup is designed for small crawlers and doesn’t offer as many features. Nevertheless, it can be used to build basic web crawlers quickly.

Here’s a step-by-step guide to building a web crawler with Python using Beautiful Soup. The crawler project for this tutorial will be a simple data-crawling Python crawler that extracts price information from a website such as Amazon.

1. Install BeautifulSoup and requests

Before you can start building crawlers, you’ll need to install the Beautiful Soup library. You can do so with a few simple commands as follows:

pip install beautifulsoup4

You’ll also need the requests library. To install it, type the following command in the terminal:

pip install requests

2. Write crawler code

Once you’ve installed the necessary libraries, you can write crawler web python code. You can start by importing all the necessary libraries as follows:

import requests

from bs4 import BeautifulSoup

You’ll also need to define a few variables for the crawler web Python project. For this tutorial, you’ll need to set a target URL and a selector element.

The target URL is the web page that you want to crawl, while the selector element identifies the data that you want to extract from the web page. For this tutorial, you’ll set the target URL as https://www.amazon.com/ and use the selector element .price-amount to extract the price information from the web page.

Now, you can write your crawler web Python code as follows:

url = ‘https://www.amazon.com/’

response = requests.get(url)

soup = BeautifulSoup(response.text, ‘html.parser’)

prices = soup.select(‘.price-amount’)

for price in prices:

print(price.text)

This crawler web Python code downloads the HTML source code from https://www.amazon.com and extracts its price information. It then displays the extracted data in the terminal.

3. Implementing the web crawler

Once you’ve created your crawler, you can use it in a crawler web Python project. For instance, if you want to crawl Amazon for price information for red sneakers, you can implement the crawler as follows:

import requests

from bs4 import BeautifulSoup

url = ‘https://www.amazon.com/s?k=red+sneakers’

response = requests.get(url)

soup = BeautifulSoup(response.text, ‘html.parser’)

prices = soup.select(‘.price-amount’)

for price in prices:

print(price.text)

This crawler web Python code downloads the HTML source code from https://www.amazon.com/s?k=red+sneakers and extracts the price information from it. It then prints out the extracted data in the terminal.

You’ll realize that the extracted text will be covered in HTML tags. If you want to get rid of the HTML tags, you can use various methods to strip them. For instance, after extracting the data, it may look like this: <span class=”price-amount”><span>$100.00</span></span>. To extract only the price, you can use various methods. For example, you can use regular expressions or the strip() method to strip the HTML tags.

You can use a regular expression pattern as follows:

import re

price_text = re.search(‘<span>.*?</span>’, price).group(0)

price = price_text.replace(‘<span>’, ”).replace(‘</span>’, ”)

print(price)

You can use the strip() method as follows:

price_text = price.strip(‘<span>’).strip(‘</span>’)

print(price_text)

Building a Python Web Crawler With JavaScript Support Using Selenium

Crawling JavaScript webpages can be difficult because data on these pages load dynamically. Moreover, these pages require users to perform actions like page scrolling, form filling, and button clicking to access the data. This can be difficult to do with traditional web python crawlers. Selenium is a crawler web Python library that can automate these tasks and allow you to crawl JavaScript webpages. This is because Selenium let crawlers control web browsers and perform any action a user can.

Here’s a step-by-step guide on how to build a Python web crawler with JavaScript support using Selenium. The crawler project for this crawler web Python tutorial will be a crawler that crawls data from Quora. Quora is an excellent example of a website that may require a Python web crawler with JavaScript support.

1. Install Selenium and WebDriver Manager

Installing Selenium is easy and only takes a few minutes. All you need to do is type the following command in the terminal:

pip install selenium

You’ll also need to install WebDriver Manager, which is a crawler web python library for managing web drivers. You can install it with the following command:

pip install webdriver-manager

2. Import modules

You’ll need to import the necessary modules for crawler web python crawlers. To do so, type the following code in your crawler web python project:

import pandas as pd

from selenium import webdriver

from selenium.webdriver.common.keys import Keys

import time

You’ll also need to add a few crawler optimization settings. For instance, to initialize a headless crawler, you can use the following code:

options = webdriver.ChromeOptions()

options.headless=True

driver = webdriver.Chrome(options=options)

3. Write crawler code

Once you’ve imported all the necessary modules and crawler settings, you can write crawler web python code. You can start by setting the target URL for crawlers as follows:

url = ‘https://www.quora.com/’

driver.get(url)

You’ll also need to define the crawler web python crawlers’ selector element. For this crawler project, you’ll use the selector class .answer to crawl data from Quora.

Now, you can write crawler web python code to crawl data from the Quora website. To do this, type the following code in your crawler web python project:

answers = driver.find_elements_by_class_name”answer”)

for answer in answers:

print(answer.text)

This crawler web python crawler will now crawl data from the Quora website and print it out in the terminal.

4. Implementing the web crawler

Now, let’s look at the actual webpage you need to crawl; in this case, it’s a Javascript-rendered page. For example, if you want to crawl Quora for celebrity questions, you can add the following crawler web python code to your crawler web python project:

query = driver.find_element_by_name(“q”)

query.send_keys(“celebrities”)

query.send_keys(Keys.RETURN)

time.sleep(5)

answers = driver.find_elements_by_class_name(“answer”)

for answer in answers:

print(answer.text)

Web Crawling With Python at Scale

When it comes to web crawling with Python, scaling is an important factor. Why? Because large websites like Google and others have millions of web pages. Plus, it’s impossible to crawl all of them in a reasonable amount of time while still being gentle and responsible on the website to avoid being blocked. You may need to adjust your crawler settings and use advanced scaling techniques to do this. Here are a few scaling settings you can apply when using Python to crawl websites:

• Regulate the number of concurrent requests.

By default, a single crawler can send up to 50 requests concurrently. This can stress the website and cause it to be unresponsive or even crash. To prevent this, you can limit the number of concurrent requests sent by the crawler web python spider. To do this, add the following settings to your Spider:

CONCURRENT_REQUESTS_PER_DOMAIN

CONCURRENT_REQUESTS_PER_IP

• Set a crawler delay time.

Crawler delay time is the time a crawler web python spider waits between requests. This setting is important for scaling since it prevents websites from being overloaded with too many requests. To adjust the crawler delay time, add the following settings to your spider:

DOWNLOAD_DELAY

• Specify the user agent.

The user agent setting specifies the browser type of your crawler. This is important for scaling because some websites may block certain browsers or IP addresses that send too many requests. To set the user agent, add the following setting to your Spider:

USER_AGENT

 

 

Build Python Web Crawler With Authentication With Rayobyte

While data crawling is a powerful tool for extracting data from websites, many websites have anti-scraping measures to prevent crawlers from accessing their data. Because of this, data-crawling Python spiders can be detected by websites and blocked or limited. You may, therefore, need to use proxies to prevent your crawlers from being blocked.

Rayobyte provides a range of proxies that can help you optimize web crawling with python, preventing your crawling from being blocked. Our proxies have advanced features that provide your crawlers with a secure and anonymous connection. Ready to get started? Sign up for a trial today!

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