Part 2 of our Web Scraping for Beginners Series. Learn how to build a web scraper for extracting data from Reddit Top Links using Python 3 and Beautifulsoup. We also talk about inspecting the web…
Web scraping is used to extract or “scrape” data from any web page on the Internet.
Copying a list of contacts from a web directory is an example of “web scraping”. But copying and pasting details from a web page into an Excel spreadsheet works for only a small amount of data and it requires a significant amount of time. Web scraping takes the pain out of this experience by automating the whole process.
Web scraping is performed using a “web scraper” or a “bot” or a “web spider” or “web crawler” (words used interchangeably). A web-scraper is a program that goes to web pages, downloads the contents, extracts data out of the contents, and then saves the data to a file or a database.
As more and more of our business activities and our lives are being spent online there are infinite uses for web scrapers. Web scraping is used in almost all fields such as price monitoring, real estate, SEO monitoring, and location intelligence.
What we talk about in this guide to web scraping will help you with your own web scraping projects.
Uses of Web Scraping
People use web scrapers to automate all sorts of scenarios. Web scrapers have a variety of uses in the enterprise. We have listed a few below:
- Price Monitoring – Product data is impacting eCommerce monitoring, product development, and investing. Extracting product data such as pricing, inventory levels, reviews and more from eCommere websites can help you create a better product strategy.
- Marketing and Lead Generation – As a business, to reach out to customers and generate sales, you need qualified leads. That is getting details of companies, addresses, contacts, and other necessary information. Publicly information like this is valuable. Web scraping can enhance the productivity of your research methods and save you time.
- Location Intelligence – The transformation of geospatial data into strategic insights can solve a variety of business challenges. By interpreting rich data sets visually you can conceptualize the factors that affect businesses in various locations and optimize your business process, promotion, and valuation of assets.
- News and Social Media – Social media and news tells your viewers how they engage with, share, and perceive your content. When you collect this information through web scraping you can optimize your social content, update your SEO, monitor other competitor brands, and identify influential customers.
- Real Estate – The real estate industry has myriad opportunities. Including web scraped data into your business can help you identify real estate opportunities, find emerging markets analyze your assets.
How to build a web scraper
There are many ways to build a web scraper. Writing code from scratch is fine for smaller data scraping needs. But once it goes beyond a few different types of web pages and you need to scrape on a large scale it’s better to use custom services.
Many companies build their own web scraping departments but other companies use Web Scraping services. The benefits of outsourcing web scraping requirements are that you can concentrate on your projects. Companies that provide web scraping services like ScrapeHero can help you save time. ScapeHero has the technology and scalability to handle web scraping tasks that are complex and massive in scale – think millions of pages an hour scale.
Point and click web scraping tools have a visual interface, where you can annotate the data you need, and it automatically builds a web scraper with those instructions. Web Scraping tools (free or paid) and self-service applications can be a good choice if the data requirement is small, and the source websites aren’t complicated.
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How does a web scraper work?
A web scraper is a software program or script that is used to download the contents (usually text-based and formatted as HTML) of multiple web pages and then extract data from it.
Web scrapers are more complicated than this simplistic representation. They have multiple modules that perform different functions.
What are the components of a web scraper
Web scraping is like any other Extract-Transform-Load (ETL) Process. Web Scrapers crawl websites, extracts data from it, transform it into a usable structured format, and loads it into a file or database for subsequent use.
A typical web scraper has the following components:
First, we start at the data source and decide which data fields we need to extract. For that, we have web crawers, that crawl the website and visits the links that we want to extract data from. (e.g the crawler will start at https://scrapehero.com and crawl the site by following links on the home page.)
The goal of a web crawler is to learn what is on the web page, so that the information when it is needed, can be retrieved. The web crawling can be based on what it finds or it can search the whole web (just like Google search engine does).
2. Parse and Extract
Extracting data is the process of taking the raw scraped data that is in HTML format and extracting and parsing the meaningful data elements. In some cases extracting data may be simple such as getting the product details from a web page or it can get more difficult such as retrieving the right information from complex documents.
You can use data extractors and parsers to extract the information you need. There are different kinds of parsing techniques: Regular Expression, HTML Parsing, DOM Parsing (using a headless browser), or Automatic Extraction using AI.
Now the data extracted needs to be formatted into a human-readable form. These can be in simple data formats such as CSV, JSON, XML, etc. You can store the data depending on the specification of your data project.
The data extracted using a parser won’t always be in the format that is suitable for immediate use. Most of the extracted datasets need some form of “cleaning” or “transformation.” Regular expressions, string manipulation, and search methods are used to perform this cleaning and transformation.
4. Store and Serialize Data
After the data has been scraped, extracted, and formatted you can finally store and export the data. Once you get the cleaned data, it needs to be serialized according to the data models that you require. Choosing an export method largely depends on how large your data files are and what data exports are preferred within your company.
This is the final module that will output data in a standard format that can be stored in Databases using ETL tools (Check out our guide on ETL Tools), JSON/CSV files, or data delivery methods such as Amazon S3, Azure Storage, and Dropbox.
Web Crawling vs. Web Scraping
People often use Web Scraping and Web Crawling interchangeably. Although the underlying concept is to extract data from the web, they are different.
Web Crawling mostly refers to downloading and storing the contents of a large number of websites, by following links in web pages. A web crawler is a standalone bot, that scans the internet, searching, and indexing for content. In general, a ‘crawler’ means the ability to navigate pages on its own. Crawlers are the backbones of search engines like Google, Bing, Yahoo, etc.
A Web scraper is built specifically to handle the structure of a particular website. The scraper then uses this site-specific structure to extract individual data elements from the website. Unlike a web crawler, a web scraper extracts specific information such as pricing data, stock market data, business leads, etc.
Is web scraping legal?
Although web scraping is a powerful technique in collecting large data sets, it is controversial and may raise legal questions related to copyright and terms of service. Most times a web scraper is free to copy a piece of data from a web page without any copyright infringement. This is because it is difficult to prove copyright over such data since only a specific arrangement or a particular selection of the data is legally protected.
Legality is totally dependent on the legal jurisdiction (i.e. Laws are country and locality specific). Publicly available information gathering or scraping is not illegal, if it were illegal, Google would not exist as a company because they scrape data from every website in the world.
Terms of Service
Although most web applications and companies include some form of TOS agreement, it lies within a gray area. For instance, the owner of a web scraper that violates the TOS may argue that he or she never saw or officially agreed to the TOS
Some forms of web scraping can be illegal such scraping non-public data or disclosed data. Non-public data is something that isn’t reachable or open to the public. An example of unethical web scraping is the stealing of intellectual property.
Ethical Web Scraping
If a web scraper sends data acquiring requests too frequently, the website will block you. The scraper may be refused entry and may be liable for damages because the owner of the web application has a property interest. An ethical web scraping tool or professional web scraping services will avoid this issue by maintaining a reasonable requesting frequency. We talk in other guides about how you can make your scraper more “polite” so that it doesn’t get you into trouble.
Let’s do something hands-on before we get into web pages structures and XPaths. We will make a very simple scraper to scrape Reddit’s top pages and extract the title and URLs of the links shared.
Check out part 2 and 3 of this post in the link here – A beginners guide to Web Scraping: Part 2 – Build a web scraper for Reddit using Python and BeautifulSoup
Web Scraping Tutorial for Beginners – Part 3 – Navigating and Extracting Data – Navigating and Scraping Data from Reddit
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