The Best Websites to Master Your Web Scraping Skills

As a data crawling expert with over 10 years of experience using proxies for large-scale web scraping, I‘ve scraped my fair share of websites. In the process, I’ve learned where the best opportunities are to hone different web scraping skills safely and legally.

In this guide, I’ll share the top websites I recommend for practitioners at all levels to level up their abilities, along with the exact skills you’ll build at each one. I’ll also provide tips on tools and proxies based on my own workflows, so you can scrape like a pro!

Getting Started with Web Scraping

For beginners unfamiliar with web scraping, let me quickly overview how the process works.

Web scraping involves writing automated programs to extract data from websites – any data that’s publicly visible on the web can potentially be scraped. Popular use cases are pulling pricing data, analyzing reviewer sentiment, aggregating news articles, and more.

The scraped data can then be stored and analyzed in spreadsheet apps, dashboards, or fed into machine learning models.

Common web scraping steps include:

Sending HTTP requests to download web page content
Parsing the raw HTML to identify key data elements
Extracting the target data points based on HTML tags and CSS selectors
Structuring and exporting the scraped data to JSON, CSV, etc

There are many open-source libraries in Python that handle these steps for you – Requests manages HTTP requests, Beautiful Soup parses HTML, and pandas helps export structured records.

More advanced setups might use proxy rotation services to avoid blocks, headless Selenium browser automation to render dynamic JavaScript content, and distributed scraping frameworks like Scrapy to scale data collection.

We’ll explore tools for both beginner and advanced scraping as we go through practice sites.

Scraping Safely – Mind your Manners!

However, before we dive into the code, it’s crucial we discuss ethics when it comes to web scraping.

Respecting a few ground rules will ensure your scraper doesn’t cause any trouble:

Review Robots.txt: The robots.txt file states if a site allows scrapers or not. Fortunately most sites we’ll cover are permissive.
Limit request rates: Don’t rush to pull everything at once! Space out requests over longer durations to avoid traffic spikes.
Use proxies: Proxies rotate your IP address so you avoid dreaded IP blocks. We’ll cover proxies more later.
Obey opt-outs: Stop scraping a site if asked directly, and don’t collect private/personal data without consent.

If you keep these courtesies in mind, both you and the sites should co-exist just fine! Now let’s explore some friendly pages to scrape…

Choosing Your Web Scraping Arsenal

There are a variety of developer tools that can be used for web scraping tasks:

Library-Based Scrapers

Beautiful Soup and Requests are very beginner friendly Python libraries for basic web scraping. Beautiful Soup makes parsing HTML easy, while Requests handles sending and receiving HTTP responses.

Together they allow extracting simple data from static sites, perfect for getting started!

Scrapy, also Python-based, is an extremely popular web crawling framework optimized for large scale scraping operations. It abstracts away networking, parsing, exporting details so you can focus on extraction logic.

If you’re familiar with Python and need to scale, I’d definitely check Scrapy out.

Browser Automation & Headless Scraping

Selenium allows programmatically driving a real web browser like Chrome or Firefox via automation scripts.

This means your scripts can navigate pages, click elements, scroll down in a realistic fashion to render full rich AJAX-heavy sites.

Headless browser modes hide the visible browser while retaining functionality. They’re perfect for robust JavaScript scraping unseen!

I leverage headless Selenium with Python for many dynamic scraping projects.

Commercial Scraping Services

Beyond open libraries, commercial services like ScrapeStack and ProxyCrawl simplify scraping via turn-key APIs requiring no coding.

They offer handy browser automation, proxies, CAPTCHA solving and more. I recommend them if you just need basic scraping fast without programming skill.

Now that you know the common tools, let‘s explore useful sites to practice web scraping!

Entry-Level Practice: Books to Scrape

If you’re just getting started with web scraping, Books to Scrape is the best place to get your feet wet.

The site is a mock bookstore with catalog pages chock-full of product listings just begging to be scraped. The pages are completely static – no JavaScript tricks here!

Let’s look at key skills Books to Scrape can teach:

Extracting basic text and attributes – book titles, prices, rating
Downloading images – book thumbnails
Handling pagination across multiple catalog pages
Basic HTML parsing with Beautiful Soup
Exporting structured data to CSV/JSON

Here‘s a simple Python scraping script to extract just book titles across paginated listings:

import requests
from bs4 import BeautifulSoup

base_url = ‘http://books.toscrape.com/catalogue/‘

titles = []
for page in range(1, page_count+1):
    print(f‘Scraping page {page}‘)  
    res = requests.get(f‘{base_url}page-{page}.html‘)
    soup = BeautifulSoup(res.text, ‘html.parser‘)

    for title in soup.select(‘.product_pod h3 a‘): 
        titles.append(title.text.strip())

print(f‘Total titles extracted: {len(titles)}‘)

This script iterates through each page, parses the HTML with Beautiful Soup, then extracts just the text from title links. Finally it prints out the total number of books scraped!

With the basics covered, let‘s level up…

Adding Proxy Rotation

Now one problem – if you actually ran the Books to Scrape scraper at scale, there‘s a good chance they would block your IP address!

This is where proxies come in very handy for reliable web scraping. Proxies act as middlemen for requests, allowing you to hide your real IP and avoid blocks.

Here‘s a popular proxy service I use, BrightData, with over 72 million IPs available. Plans start around $500/month.

To integrate proxies, we just modify our requests flow to use BrightData creds:

import requests
from brightdata import BrightData

proxy_user = ‘BRIGHTDATA_USER‘
proxy_pass = ‘BRIGHTDATA_PASS‘ 

brightdata = BrightData(user=proxy_user, pwd=proxy_pass)

for page in range(1, page_count+1):

    # Retrieve new proxy for each request
    proxy = brightdata.get_proxy()  

    try:
        print(f‘Via Proxy: {proxy}‘)
        response = requests.get(f‘{base_url}page-{page}.html‘, 
            proxies={‘http‘: ‘http://‘ + proxy})
        ...
        # Rest of scraping logic

    except Exception:
        print(‘Proxy Dead, Retrying‘)
        proxy = brightdata.get_proxy() #Get new proxy
        response = requests.get(f‘{base_url}page-{page}.html‘, 
            proxies={‘http‘: ‘http://‘ + proxy})

By routing via proxies and handling errors, the script can now scrape reliably without worrying about blocks!

Handling pagination, parsing, proxies – you‘re well on your way! Next let‘s tackle dynamic JavaScript sites…

Intermediate Scraping: Reddit + Selenium

So far we‘ve covered scraping simple static sites, but many modern sites use dynamic JavaScript to load content.

Common examples are infinite scroll for feeds, or accordion drop-downs for additional text. This dynamically generated HTML can‘t be parsed upfront by Beautiful Soup.

Here headless Selenium browser automation is your friend!

Let‘s see an example scraping everyone‘s favorite site, Reddit:

from selenium import webdriver
from selenium.webdriver.chrome.options import Options

options = Options() 
options.headless = True
driver = webdriver.Chrome(options=options)

driver.get(‘https://www.reddit.com/r/python/‘)

for post in driver.find_elements_by_class_name(‘Post‘):

  print(post.find_element_by_tag_name(‘h3‘).text) # Post Title 
  print(post.find_element_by_css_selector(‘.score‘).text) # Upvotes

  print(‘-------------------‘)

driver.quit()

Here Selenium launches a headless Chrome browser to dynamically render the full Reddit page, including any AJAX-loaded content.

We can then parse with Selenium‘s built-in element finding tools to extract key data points – in this case post titles and vote scores.

The script clicks nothing and no browser GUI pops up, but we still get to leverage dynamic scraping capabilities!

Some key skills you can polish with Reddit:

Scrolling feeds and parsing infinite pages
Working around rate limits by pausing requests
Handling logins and session cookies
Using proxy services to avoid IP blocks
Structuring related data (posts + comments)

If you can scrape Reddit effectively, not a lot of sites will stand in your way!

Expert-Level Scraping: Wikipedia + Scrapy

Once you have selenium browser automation down, let‘s try taking on the category 5 scraping hurricane that is Wikipedia.

Wikipedia contains over 55 million highly structured wiki articles spanning every topic – it‘s a goldmine of data, but a beast to scrape with complex templates and text formatting.

That‘s why I like to leverage Scrapy here, the heavy duty industrial-strength Python scraping framework.

For example, this Scrapy spider configuration could scrape text and infobox templates from Category:Natural_Disasters pages:

import scrapy

class DisastersSpider(CrawlSpider):

    name = ‘disasters‘

    start_urls = [‘https://en.wikipedia.org/wiki/Category:Natural_disasters‘]

    def parse(self, response):

        for link in response.css(‘.mw-category a::attr(href)‘):
            yield response.follow(link, self.parse_disaster)


    def parse_disaster(self, response):

        item = {}

        item[‘title‘] = response.css(‘h1::text‘).get()
        item[‘infobox‘] = response.css(‘.infobox table‘).get()
        item[‘text‘] = response.css(‘.mw-parser-output *::text‘).getall()

        yield item

Key things Scrapy offers out of the box:

Rate limiting/pausing built-in
Powerful CSS & XPath based element extracting
Handling cookies, headers, proxies
Pipeline exporting to file formats
Asynchronousrequest queues & callback handlers

These capabilities let you focus just on data extraction at scale.

With great scale comes great responsibility though! Mind Wikipedia‘s scraping policy by running small jobs and not overloading resources.

If you can master Wikipedia gracefully, not much on the internet can stand in your way!

Recommended Proxy Services

Now that we‘ve covered various sites to scrape, let me share my top proxy recommendations specifically for web scraping based on performance and costs:

Provider	Type	Size	Speed	Plans
Luminati/BrightData	Residential	72M+ IPs	1Gbps+	$500+/mo
Smartproxy	Backconnect Rotating	30M+ IPs	1Gbps	$75+/mo
Soax	Static Residential	195k IPs	100Mbps	$50+/mo

I‘ve used all three proxy services extensively for challenging web scraping and data mining projects.

Luminati (recently rebranded as BrightData) is the world‘s largest proxy provider with over 72 million residential IPs. They offer the highest performance residential proxies starting from $500/month.
Smartproxy has become my go-to choice for general web scraping tasks. Their backconnect rotating proxies provide reliable uptime for only $75/month. Smartproxy also has great customer support!
Soax is my budget pick for basic scraping jobs. Their static residential proxies start at $50/month for 195,000 IPs which is an unbeatable value. Speeds are slower than others though.

I always advise using paid proxies over free ones – they provide better speeds, uptime, and capacity essential for serious scraping workloads. Proxy services also take care of proxy maintenance so you can focus on writing scrapers!

Scraping Best Practices

Throughout this guide, we‘ve explored different tools and sites to help advance your web scraping skills. Here are a few closing best practices to keep in mind:

Review robots.txt: Quickly check if a site permits scrapers before proceeding further.
Limit request volume: Space out requests over long durations and use delays to avoid traffic spikes.
Use proxies: Rotate proxies so you don’t keep hitting sites from the same IP over and over.
Scrape ethically: Don‘t collect private data or brush aside opt-out requests.
Learn JavaScript: Know at least basic JS if you want to scrape modern dynamic sites.

Follow these rules and both your scrapers and target sites will live in harmony!

I hope you found this guide useful for advancing your web scraping prowess – happy practicing! Please feel free to reach out if any questions come up.