- Web Scraping Introduction Example
- Introduction To Web Scraping
- Python Web Scraping
- Web Scraping Introduction Sample
Introduction to Web Scraping Internet is an ocean of information spread accross various websites, where it is categorized, interlinked and mostly freely available for everyone. Web scraping is simply the term for downloading data from various web sources. Note that the data obtained through web scraping is often unstructured, so it requires the use of regular expressions to get it into a usable format.
What is web scraping?
Web scraping, also known as web harvesting and web data extraction, basically refers to collecting data from websites via the Hypertext Transfer Protocol (HTTP) or through web browsers.
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How does web scraping work?
Generally, web scraping involves three steps:
- first, we send a GET request to the server and we will receive a response in a form of web content.
- Next, we parse the HTML code of a web site following a tree structure path.
- Finally, we use the python library to search for the parse tree.
I know what you think -- web scraping looks good on paper but actually more complex in practice. We need coding to get the data we want, which makes it the privilege of who’s master of programming. As an alternative, there are web scraping tools automating web data extraction at fingertips.
A web scraping tool will load the URLs given by the users and render the entire website. As a result, you can extract any web data with simple point-and-click and file in a feasible format into your computer without coding.
For example, you might want to extract posts and comments from Twitter. All you have to do is to paste the URL to the scraper, select desired posts and comments and execute. Therefore, it saves time and efforts from the mundane work of copy-and-paste.
How did web scraping all start?
Though to many people, it sounds like a brand-new concept, the history of the web scraping can be dated back to the time when the World Wide Web was born.
At the very beginning, the Internet was even unsearchable. Before search engines were developed, the Internet was just a collection of File Transfer Protocol (FTP) sites in which users would navigate to find specific shared files. To find and organize distributed data available on the Internet, people created a specific automated program, known as the web crawler/bot today, to fetch all pages on the Internet and then copy all content into databases for indexing.
Then the Internet grows, eventually becoming the home to millions of web pages that contain a wealth of data in multiple forms, including texts, images, videos, and audios. It turns into an open data source.
As the data source became incredibly rich and easily searchable, people started to find it simple to seek the information they want, which often spread across a large number of websites, but the problem occurred when they wanted to get data from the Internet—not every website offered download options, and copying by hand was obviously tedious and inefficient.
Web Scraping Introduction Example
And that’s where web scraping came in. Web scraping is actually powered by web bots/crawlers that function the same way those used in search engines. That is, fetch and copy. The only difference could be the scale. Web scraping focuses on extracting only specific data from certain websites whereas search engines often fetch most of the websites around the Internet.
How is Web scraping done?
1989 The birth of the World Wide Web
Technically, the World Wide Web is different from the Internet. The former refers to the information space, while the latter is the network made up of computers.
Thanks to Tim Berners-Lee, the inventor of WWW, he brought the following 3 things that have long been part of our daily life:
- Uniform Resource Locators (URLs) which we use to go to the website we want;
- embedded hyperlinks that permit us to navigate between the web pages, like the product detail pages on which/where we can find product specifications and lots of other things like “customers who bought this also bought”;
- web pages that contain not only texts but also images, audios, videos and software components.
1990 The first web browser
Introduction To Web Scraping
Also invented by Tim Berners-Lee, it was called WorldWideWeb (no spaces), named after the WWW project. One year after the appearance of the web, people had a way to see it and interact with it.
1991 The first web server and the first http:// web page
The web kept growing at rather mild speed. By 1994, the number of HTTP servers was over 200.
1993-June First web robot - World Wide Web Wanderer
Though functioned the same way web robots today do, it was intended only to measure the size of the web.
1993-December First crawler-based web search engine - JumpStation
As there were not so many websites available on the web, search engines at that time used to rely on their human website administrators to collect and edit the links into a particular format. JumpStation brought a new leap. It is the first WWW search engine that relies on a web robot.
Since then, people started to use these programmatic web crawlers to harvest and organize the Internet. From Infoseek, Altavista, and Excite, to Bing and Google today, the core of a search engine bot remains the same: find a web page, download (fetch) it, scrape all the information presented on the web page, and then add it to the search engine’s database.
As web pages are designed for human users, and not for ease of automated use, even with the development of the web bot, it was still hard for computer engineers and scientists to do web scraping, let alone normal people. So people have been dedicated to making web scraping more available. In 2000, Salesforce and eBay launched their own API, with which programmers were enabled to access and download some of the data available to the public. Since then, many websites offer web APIs for people to access their public database. APIs offer developers a more friendly way to do web scraping, by just gathering data provided by websites.
2004 Python Beautiful soup
Not all websites offer APIs. Even if they do, they don’t provide all the data you want. So programmers were still working on developing an approach that could facilitate web scraping. In 2004, Beautiful Soup was released. It is a library designed for Python.
In computer programming, a library is a collection of script modules, like commonly used algorithms, that allow being used without rewriting, simplifying the programming process. with simple commands, Beautiful Soup makes sense of site structure and helps parse content from within the HTML container. It is considered the most sophisticated and advanced library for web scraping, and also one of the most common and popular approaches today.
Python Web Scraping
2005-2006 Visual web scraping software
In 2006, Stefan Andresen and his Kapow Software (acquired by Kofax in 2013) launched Web Integration Platform version 6.0, something now understood as visual web scraping software, which allows users to simply highlight the content of a web page and structure that data into a usable excel file, or database.
Finally, there’s a way for the massive non-programmers to do web scraping on their own. Since then, web scraping is starting to hit the mainstream. Now for non-programmers, they can easily find more than 80 out-of-box data extraction software that provides visual processes.
How will web scraping be?
We collect data, process data, and turn data into actionable insights. It's proven that business giants like Microsoft and Amazon invest a lot of money on data collection about their consumers so as to target people with personalized ads. whereas, small businesses are muscled out of the marketing competition as they're lack of spare capital to collect data.
Thanks to web scraping tools, any individual, company, and organization are now able to access web data for analysis. When searching “web scraping” on guru.com, you can get 10,088 search results, which means more than 10,000 freelancers are offering web scraping services on the website.
The rising demands in web data by companies across industry prosper the web scraping marketplace, and that brings new jobs and business opportunities.
Meanwhile, like any other emerging industry, web scraping brings legal concerns as well. The legal landscape surrounding the legitimacy of web scraping continues to evolve. Its legal status remains highly context-specific. For now, many of the most interesting legal questions emerging from this trend remain unanswered.
Web Scraping Introduction Sample
One way to get around potential legal consequences of web scraping is to consult professional web scraping service providers. Octoparse stands as the best web scraping company that offers both scraping services and web data extraction tool. Both individual entrepreneurs or big companies will reap the benefits at their advanced scraping technology.
Artículo en español: Web Scraping: Cómo Comenzó y Qué Sucederá en El Futuro
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