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Loading Web Pages with 'request' The requests module allows you to send HTTP requests using. Web Scraping with C#. With the passage of time, the process of extracting data is increasing. The data in different websites can be accessed through their web API or web services. If some websites don’t provide or allow access to their data then Web scraping is used which is used to accessed data. Dec 23, 2020 In this Python Selenium switch tabs tutorial, we will be focusing on opening and switching between the tabs using Python & Selenium. We have dedicated articles for on-page testing operations like handling alerts, drag and drop objects using a mouse, or taking full-page screenshots.
Introduction to web scraping
Web scraping is one of the tools at a developer’s disposal when looking to gather data from the internet. While consuming data via an API has become commonplace, most of the websites online don’t have an API for delivering data to consumers. In order to access the data they’re looking for, web scrapers and crawlers read a website’s pages and feeds, analyzing the site’s structure and markup language for clues. Generally speaking, information collected from scraping is fed into other programs for validation, cleaning, and input into a datastore or its fed onto other processes such as natural language processing (NLP) toolchains or machine learning (ML) models. There are a few Python packages we could use to illustrate with, but we’ll focus on Scrapy for these examples. Scrapy makes it very easy for us to quickly prototype and develop web scrapers with Python.
Scrapy vs. Selenium and Beautiful Soup
If you’re interested in getting into Python’s other packages for web scraping, we’ve laid it out here:
Scrapy concepts
Before we start looking at specific examples and use cases, let’s brush up a bit on Scrapy and how it works.
Spiders: Scrapy uses Spiders to define how a site (or a bunch of sites) should be scraped for information. Scrapy lets us determine how we want the spider to crawl, what information we want to extract, and how we can extract it. Specifically, Spiders are Python classes where we’ll put all of our custom logic and behavior.
Selectors:Selectors are Scrapy’s mechanisms for finding data within the website’s pages. They’re called selectors because they provide an interface for “selecting” certain parts of the HTML page, and these selectors can be in either CSS or XPath expressions.
Items:Items are the data that is extracted from selectors in a common data model. Since our goal is a structured result from unstructured inputs, Scrapy provides an Item class which we can use to define how our scraped data should be structured and what fields it should have.
Reddit-less front page
Suppose we love the images posted to Reddit, but don’t want any of the comments or self posts. We can use Scrapy to make a Reddit Spider that will fetch all the photos from the front page and put them on our own HTML page which we can then browse instead of Reddit.
To start, we’ll create a RedditSpider which we can use traverse the front page and handle custom behavior.
Above, we’ve defined a RedditSpider, inheriting Scrapy’s Spider. We’ve named it reddit and have populated the class’ start_urls attribute with a URL to Reddit from which we’ll extract the images.
At this point, we’ll need to begin defining our parsing logic. We need to figure out an expression that the RedditSpider can use to determine whether it’s found an image. If we look at Reddit’s robots.txt file, we can see that our spider can’t crawl any comment pages without being in violation of the robots.txt file, so we’ll need to grab our image URLs without following through to the comment pages.
By looking at Reddit, we can see that external links are included on the homepage directly next to the post’s title. We’ll update RedditSpider to include a parser to grab this URL. Reddit includes the external URL as a link on the page, so we should be able to just loop through the links on the page and find URLs that are for images.
In a parse method on our RedditSpider class, I’ve started to define how we’ll be parsing our response for results. To start, we grab all of the href attributes from the page’s links using a basic XPath selector. Now that we’re enumerating the page’s links, we can start to analyze the links for images.
To actually access the text information from the link’s href attribute, we use Scrapy’s .get() function which will return the link destination as a string. Next, we check to see if the URL contains an image file extension. We use Python’s any() built-in function for this. This isn’t all-encompassing for all image file extensions, but it’s a start. From here we can push our images into a local HTML file for viewing.
To start, we begin collecting the HTML file contents as a string which will be written to a file called frontpage.html at the end of the process. You’ll notice that instead of pulling the image location from the ‘//a/@href/‘, we’ve updated our links selector to use the image’s src attribute: ‘//img/@src’. This will give us more consistent results, and select only images.
As our RedditSpider’s parser finds images it builds a link with a preview image and dumps the string to our html variable. Once we’ve collected all of the images and generated the HTML, we open the local HTML file (or create it) and overwrite it with our new HTML content before closing the file again with page.close(). If we run scrapy runspider reddit.py, we can see that this file is built properly and contains images from Reddit’s front page.
But, it looks like it contains all of the images from Reddit’s front page – not just user-posted content. Let’s update our parse command a bit to blacklist certain domains from our results.
If we look at frontpage.html, we can see that most of Reddit’s assets come from redditstatic.com and redditmedia.com. We’ll just filter those results out and retain everything else. With these updates, our RedditSpider class now looks like the below:
We’re simply adding our domain whitelist to an exclusionary any()expression. These statements could be tweaked to read from a separate configuration file, local database, or cache – if need be.
Extracting Amazon price data
If you’re running an ecommerce website, intelligence is key. With Scrapy we can easily automate the process of collecting information about our competitors, our market, or our listings.
For this task, we’ll extract pricing data from search listings on Amazon and use the results to provide some basic insights. If we visit Amazon’s search results page and inspect it, we notice that Amazon stores the price in a series of divs, most notably using a class called .a-offscreen. We can formulate a CSS selector that extracts the price off the page:
Web Scraping Ecommerce Websites Python Interview
With this CSS selector in mind, let’s build our AmazonSpider.
A few things to note about our AmazonSpider class: convert_money(): This helper simply converts strings formatted like ‘$45.67’ and casts them to a Python Decimal type which can be used for computations and avoids issues with locale by not including a ‘$’ anywhere in the regular expression. getall(): The .getall() function is a Scrapy function that works similar to the .get() function we used before, but this returns all the extracted values as a list which we can work with. Running the command scrapy runspider amazon.py in the project folder will dump output resembling the following:
It’s easy to imagine building a dashboard that allows you to store scraped values in a datastore and visualize data as you see fit.
Considerations at scale
As you build more web crawlers and you continue to follow more advanced scraping workflows you’ll likely notice a few things:
- Sites change, now more than ever.
- Getting consistent results across thousands of pages is tricky.
- Performance considerations can be crucial.
Sites change, now more than ever
On occasion, AliExpress for example, will return a login page rather than search listings. Sometimes Amazon will decide to raise a Captcha, or Twitter will return an error. While these errors can sometimes simply be flickers, others will require a complete re-architecture of your web scrapers. Nowadays, modern front-end frameworks are oftentimes pre-compiled for the browser which can mangle class names and ID strings, sometimes a designer or developer will change an HTML class name during a redesign. It’s important that our Scrapy crawlers are resilient, but keep in mind that changes will occur over time.
Getting consistent results across thousands of pages is tricky
Slight variations of user-inputted text can really add up. Think of all of the different spellings and capitalizations you may encounter in just usernames. Pre-processing text, normalizing text, and standardizing text before performing an action or storing the value is best practice before most NLP or ML software processes for best results.
Performance considerations can be crucial
You’ll want to make sure you’re operating at least moderately efficiently before attempting to process 10,000 websites from your laptop one night. As your dataset grows it becomes more and more costly to manipulate it in terms of memory or processing power. In a similar regard, you may want to extract the text from one news article at a time, rather than downloading all 10,000 articles at once. As we’ve seen in this tutorial, performing advanced scraping operations is actually quite easy using Scrapy’s framework. Some advanced next steps might include loading selectors from a database and scraping using very generic Spider classes, or by using proxies or modified user-agents to see if the HTML changes based on location or device type. Scraping in the real world becomes complicated because of all the edge cases, Scrapy provides an easy way to build this logic in Python.
This post is a part of Kite’s new series on Python. You can check out the code from this and other posts on our GitHub repository.
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Web scraping, web crawling, html scraping, and any other form of web data extraction can be complicated. Between obtaining the correct page source, to parsing the source correctly, rendering javascript, and obtaining data in a usable form, there’s a lot of work to be done. Different users have very different needs, and there are tools out there for all of them, people who want to build web scrapers without coding, developers who want to build web crawlers to crawl large sites, and everything in between. Here is our list of the 10 best web scraping tools on the market right now, from open source projects to hosted SAAS solutions to desktop software, there is sure to be something for everyone looking to make use of web data!
1. Scraper API
Website: https://www.scraperapi.com/
Who is this for: Scraper API is a tool for developers building web scrapers, it handles proxies, browsers, and CAPTCHAs so developers can get the raw HTML from any website with a simple API call.
Why you should use it: Scraper API is a tool for developers building web scrapers, it handles proxies, browsers, and CAPTCHAs so developers can get the raw HTML from any website with a simple API call. It doesn’t burden you with managing your own proxies, it manages its own internal pool of over a hundreds of thousands of proxies from a dozen different proxy providers, and has smart routing logic that routes requests through different subnets and automatically throttles requests in order to avoid IP bans and CAPTCHAs. It’s the ultimate web scraping service for developers, with special pools of proxies for ecommerce price scraping, search engine scraping, social media scraping, sneaker scraping, ticket scraping and more! If you need to scrape millions of pages a month, you can use this form to ask for a volume discount.
2. ScrapeSimple
Website: https://www.scrapesimple.com
Who is this for: ScrapeSimple is the perfect service for people who want a custom scraper built for them. Web scraping is made as simple as filling out a form with instructions for what kind of data you want.
Why you should use it: ScrapeSimple lives up to its name with a fully managed service that builds and maintains custom web scrapers for customers. Just tell them what information you need from which sites, and they will design a custom web scraper to deliver the information to you periodically (could be daily, weekly, monthly, or whatever) in CSV format directly to your inbox. This service is perfect for businesses that just want a html scraper without needing to write any code themselves. Response times are quick and the service is incredibly friendly and helpful, making this service perfect for people who just want the full data extraction process taken care of for them.
3. Octoparse
Website: https://www.octoparse.com/
Who is this for: Octoparse is a fantastic tool for people who want to extract data from websites without having to code, while still having control over the full process with their easy to use user interface.
Why you should use it: Octoparse is the perfect tool for people who want to scrape websites without learning to code. It features a point and click screen scraper, allowing users to scrape behind login forms, fill in forms, input search terms, scroll through infinite scroll, render javascript, and more. It also includes a site parser and a hosted solution for users who want to run their scrapers in the cloud. Best of all, it comes with a generous free tier allowing users to build up to 10 crawlers for free. For enterprise level customers, they also offer fully customized crawlers and managed solutions where they take care of running everything for you and just deliver the data to you directly.
4. ParseHub
Website: https://www.parsehub.com/
Who is this for: Parsehub is an incredibly powerful tool for building web scrapers without coding. It is used by analysts, journalists, data scientists, and everyone in between.
Why you should use it: Parsehub is dead simple to use, you can build web scrapers simply by clicking on the data that you want. It then exports the data in JSON or Excel format. It has many handy features such as automatic IP rotation, allowing scraping behind login walls, going through dropdowns and tabs, getting data from tables and maps, and much much more. In addition, it has a generous free tier, allowing users to scrape up to 200 pages of data in just 40 minutes! Parsehub is also nice in that it provies desktop clients for Windows, Mac OS, and Linux, so you can use them from your computer no matter what system you’re running.
5. Scrapy
Website: https://scrapy.org
Who is this for: Scrapy is a web scraping library for Python developers looking to build scalable web crawlers. It’s a full on web crawling framework that handles all of the plumbing (queueing requests, proxy middleware, etc.) that makes building web crawlers difficult.
Why you should use it: As an open source tool, Scrapy is completely free. It is battle tested, and has been one of the most popular Python libraries for years, and it’s probably the best python web scraping tool for new applications. It is well documented and there are many tutorials on how to get started. In addition, deploying the crawlers is very simple and reliable, the processes can run themselves once they are set up. As a fully featured web scraping framework, there are many middleware modules available to integrate various tools and handle various use cases (handling cookies, user agents, etc.).
6. Diffbot
Website: https://www.diffbot.com
Who is this for: Enterprises who who have specific data crawling and screen scraping needs, particularly those who scrape websites that often change their HTML structure.
Why you should use it: Diffbot is different from most page scraping tools out there in that it uses computer vision (instead of html parsing) to identify relevant information on a page. This means that even if the HTML structure of a page changes, your web scrapers will not break as long as the page looks the same visually. This is an incredible feature for long running mission critical web scraping jobs. While they may be a bit pricy (the cheapest plan is $299/month), they do a great job offering a premium service that may make it worth it for large customers.
7. Cheerio
Website: https://cheerio.js.org
Who is this for: NodeJS developers who want a straightforward way to parse HTML. Those familiar with jQuery will immediately appreciate the best javascript web scraping syntax available.
Why you should use it: Cheerio offers an API similar to jQuery, so developers familiar with jQuery will immediately feel at home using Cheerio to parse HTML. It is blazing fast, and offers many helpful methods to extract text, html, classes, ids, and more. It is by far the most popular HTML parsing library written in NodeJS, and is probably the best NodeJS web scraping tool or javascript web scraping tool for new projects.
8. BeautifulSoup
Website: https://www.crummy.com/software/BeautifulSoup/
Who is this for: Python developers who just want an easy interface to parse HTML, and don’t necessarily need the power and complexity that comes with Scrapy.
Why you should use it: Like Cheerio for NodeJS developers, Beautiful Soup is by far the most popular HTML parser for Python developers. It’s been around for over a decade now and is extremely well documented, with many web parsing tutorials teaching developers to use it to scrape various websites in both Python 2 and Python 3. If you are looking for a Python HTML parsing library, this is the one you want.
9. Puppeteer
Website: https://github.com/GoogleChrome/puppeteer
Who is this for: Puppeteer is a headless Chrome API for NodeJS developers who want very granular control over their scraping activity.
Why you should use it: As an open source tool, Puppeteer is completely free. It is well supported and actively being developed and backed by the Google Chrome team itself. It is quickly replacing Selenium and PhantomJS as the default headless browser automation tool. It has a well thought out API, and automatically installs a compatible Chromium binary as part of its setup process, meaning you don’t have to keep track of browser versions yourself. While it’s much more than just a web crawling library, it’s often used to scrape website data from sites that require javascript to display information, it handles scripts, stylesheets, and fonts just like a real browser. Note that while it is a great solution for sites that require javascript to display data, it is very CPU and memory intensive, so using it for sites where a full blown browser is not necessary is probably not a great idea. Most times a simple GET request should do the trick!
10. Mozenda
Website: https://www.mozenda.com/
Who is this for: Enterprises looking for a cloud based self serve webpage scraping platform need look no further. With over 7 billion pages scraped, Mozenda has experience in serving enterprise customers from all around the world.
Why you should use it: Mozenda allows enterprise customers to run web scrapers on their robust cloud platform. They set themselves apart with the customer service (providing both phone and email support to all paying customers). Its platform is highly scalable and will allow for on premise hosting as well. Like Diffbot, they are a bit pricy, and their lowest plans start at $250/month.
Honorable Mention 1. Kimura
Website: https://github.com/vifreefly/kimuraframework
Who is this for: Kimura is an open source web scraping framework written in Ruby, it makes it incredibly easy to get a Ruby web scraper up and running.
Why you should use it: Kimura is quickly becoming known as the best Ruby web scraping library, as it’s designed to work with headless Chrome/Firefox, PhantomJS, and normal GET requests all out of the box. It’s syntax is similar to Scrapy and developers writing Ruby web scrapers will love all of the nice configuration options to do things like set a delay, rotate user agents, and set default headers.
E-commerce Companies
Honorable Mention 2. Goutte
Website: https://github.com/FriendsOfPHP/Goutte
Web Scraping Ecommerce Websites Python Github
Who is this for: Goutte is an open source web crawling framework written in PHP, it makes it super easy extract data from the HTML/XML responses using PHP.
Web Scraping Using Python
Why you should use it: Goutte is a very straight forward, no frills framework that is considered by many to be the best PHP web scraping library, as it’s designed for simplicity, handling the vast majority of HTML/XML use cases without too much additional cruft. It also seamlessly integrates with the excellent Guzzle requests library, which allows you to customize the framework for more advanced use cases.
Python Web Scraping
The open web is by far the greatest global repository for human knowledge, there is almost no information that you can’t find through extracting web data. Because web scraping is done by many people of various levels of technical ability and know how, there are many tools available that service everyone from people who don’t want to write any code to seasoned developers just looking for the best open source solution in their language of choice.
Python Web Scraping Sample
Hopefully, this list of tools has been helpful in letting you take advantage of this information for your own projects and businesses. If you have any web scraping jobs you would like to discuss with us, please contact us here. Happy scraping!