Do you work with Python? Are you interested in Big Data and want to work in this area as a Data Analyst? Or as part of your work, you have to process a large amount of data to enrich your decision-making reports? If you answer yes to any of these questions, then web scraping will interest you.
Today, millions of data circulate through the Internet. It is also admitted that 90% of the data generated by humanity has been done over the last 2 years than over all the previous decades. Thus, when you occupy a role of Data Analyst, or Data Engineer, knowing how to collect this data is the first essential step of any decisional analysis. Web scraping is one of the most effective techniques you need to master to capture data that is outside the company’s information system. Let’s illustrate this with a simple example.
Suppose you need to obtain the postal addresses of all ministerial departments in France to support a decision analysis and produce a geographical dashboard. What are you doing ? Well, you can do a Google search and copy the information from the State Open Data site and paste it into your own file. But what if you want to feed data into a machine learning algorithm? In this situation, the copy-paste will not work! And this is where you need to use Web Scraping. Unlike the long and tedious process of obtaining data manually, Web Scraping uses automated methods to obtain data continuously and in a short time.
In this exhaustive tutorial, we will see what this technique consists of. What are the fields of application of web scraping? What are the different web scraping methods? And how to perform web scraping with Python?
What is Web-Scraping?
Web scaping is an automatic method of obtaining large amounts of data from websites. Most of this data is unstructured data in HTML format which is then converted into structured data in a spreadsheet or database so that it can be used in various applications.
There are many ways to web scrape to get data from websites, including using online services, particular APIs, or even building your web scraping code from scratch. In this case, you need to write a script that will automate the whole process, with Python, for example. Moreover, in our Python programming tutorial, we give you all the concepts to understand if you want to use this language in Big Data.
Many big websites like Google, Twitter, Facebook, StackOverflow, etc. have APIs that allow you to access their data in a structured format. But there are other sites that either don’t allow users to access their data in a structured format or are simply not as technologically advanced. In this case, Web Scraping must be used to extract data from the site.
Data mining on the web requires two elements: the crawler and the scraper. The crawler is an artificial intelligence algorithm that crawls the web to find the particular data required by following links on the internet. The scraper, on the other hand, is a specific tool created to extract data from the website. The design of the scraper can vary widely depending on the complexity and scale of the project, so that it can extract data quickly and accurately.
Now that you have an idea of what web scraping is, in the next section of this column we will study the areas of application of web scraping.
What is Web-Scraping used for in a company?
Web scraping is used in many activities, if not in all. This technique is used in multiple fields of application and in different sectors of activity, for example:
Web scraping can be used by businesses to scrape data from competing products and then use it to price their products optimally for maximum revenue.
Companies can use web scraping for their market research. High-quality data obtained in large volumes can be very useful for businesses to analyze consumer trends and understand where the business should be headed in the future.
Machine learning models need raw data to scale and improve in accuracy. Machine learning powers today’s technological marvels, such as driverless cars, spaceflight, image and speech recognition. However, these models need a lot of varied data to improve their accuracy and reliability. Web scraping tools can scrape a wide variety of data, text and images in a relatively short time, to automatically feed these templates.
A good web scraping project ensures that you get the data you are looking for while not disrupting data sources.
Web scraping news sites can provide detailed news reports to a business. This is all the more essential for companies in the journalism sector or which depend on daily news for their operation. After all, news reports can make or break a business in a single day.
If companies want to understand general consumer sentiment towards their products, sentiment analysis is a must. Companies can use web scraping to collect data from social networks such as Facebook and Twitter to find out the general sentiment about their products. This will help them create products that people want and get ahead of their competitors.
Businesses can also use web scraping for email marketing. They can collect email IDs from various sites using web scraping and then send mass marketing and promotional emails to everyone with those email IDs.
There are still many other web scraping applications but we have seen the main ones. In the next section we will talk about the most used tools for web scraping.
Tools used for Web-Scraping
There are a multitude of tools in the form of extensions, frameworks or software on the market to scrape a website. We will discover a few in each of these categories.
Browser extensions for Web-Scraping
There are a lot of website scraper tools that you can install as extensions and add-ons on your browser to help you extract data from websites. Some of them are shown below.
The Webscraper.io browser extension (Chrome and Firefox) presents one of the best web scraping tools that you can use to easily extract data from web pages. It has been installed by over 250,000 users, who have found it incredibly useful.
The Data Miner extension is available only for Google Chrome and Microsoft Edge browser. It can help you extract data from pages and save it to a CSV file or an Excel spreadsheet. Unlike the free extension provided by Webscraper.io, the Data Miner extension is only free for the first 500 scraped pages in a month – after that, you need to subscribe to a paid plan to use it. With this extension, you can scrape any page without thinking about blocks – and your data stays private.
Scraper is a Chrome extension likely designed and maintained by a single developer – it doesn’t even have its own website like the others above. this extension is not as advanced as the rest of the browser extensions discussed above – however, it is completely free. The major problem with this one is that it requires its users to know how to use XPath, because that’s what you’re going to use. For this reason, it is not suitable for beginners.
After extensions, there are many software in the market that you can use to recover all kinds of data online without knowing how to code. We will present below only the 3 best web scraping software.
Octoparse makes web scraping easy for everyone. With Octoparse, you can quickly turn a full website into a structured spreadsheet with just a few clicks. Octoparse does not require any coding skills. Just point and click to get the data you want. Octoparse can extract data from all types of websites, including Ajaxified sites with strict anti-scraping techniques. It uses IP address rotation to hide your IP fingerprints. Apart from its installable software, Octoparse offers a cloud-based solution and you can even get a 14-day free trial.
ParseHub comes in two flavors: a free desktop application and a paid, cloud-based scraping solution that comes with additional features and requires no installation to use. ParseHub desktop app lets you easily scrape any website even without coding skills. In effect, the software provides a point-and-click interface, intended to train the software on the scratch data. It works perfectly for modern websites and allows you to download the scraped data in the most common file formats.
Python Libraries for Web-Scraping
Python is the most popular programming language for coding web scrapers due to its simple syntax, learning curve, and number of available libraries that make developers’ jobs easier. We will present in this section some Python libraries allowing to perform Webscraping.
Scrapy is an open-source python framework designed specifically for web scraping by Scrapinghub co-founders Pablo Hoffman and Shane Evans. You may be wondering “What does this mean?”.
This means that Scrapy is a full-fledged web scraping solution that takes a lot of the work out of building and configuring your spiders off your hands, and most importantly, it transparently handles edge cases that you probably don’t have thought again.
A few minutes after installing the Framework, you can have a fully operational spider for scraping the web. Right off the bat, Scrapy spiders are designed to download HTML, parse and process the data, and save it to CSV, JSON, or XML file formats.
There is also a wide range of built-in extensions and middleware designed to manage cookies and sessions, as well as HTTP features such as compression, authentication, caching, user agents, robots. txt and drill depth restriction.
One of the biggest advantages of using the Scrapy framework is that it’s built on top of Twisted, an asynchronous networking library. This means that Scrapy spiders don’t have to wait to make requests one by one. Instead, they can make multiple HTTP requests in parallel and parse the data as it comes back from the server. This greatly increases the speed and efficiency of a scraper.
The learning curve for Scrapy is a bit steeper than, say, using BeautifulSoup. However, the Scrapy project has excellent documentation and an extremely active ecosystem of developers on GitHub and StackOverflow who are constantly releasing new plugins and helping you fix any issues you encounter.
Requests is an HTTP library that makes it easy to send HTTP requests. It is built on the basis of the urllib library. It is a robust tool that can help you create more reliable web scrapers. It is easy to use and requires fewer lines of code.
Very importantly, it can help you manage cookies and sessions, as well as authentication and automatic connection pooling, among other things. It is free to use and Python developers use it to download pages before using a parser to extract the required data.
BeautifulSoup facilitates the process of parsing data contained in web pages. It sits on top of an HTML or XML parser and gives you Python methods for accessing data. BeautifulSoup has become one of the most prominent web scraping tools in the market due to the ease of parsing it offers.
In fact, most web scraping tutorials use BeautifulSoup to teach beginners how to write web scrapers. When used with Requests to send HTTP requests, web scrapers become easier to develop – much easier than with Scrapy or PySpider.
Now that we’ve covered the theory of webscraping, let’s get down to business with an example case. For a better understanding of this example case you should have a basic knowledge of the python programming language. We have dedicated an entire article to this subject, as we mentioned at the very beginning of this column. And if you want to learn another language widely used in the Big Data world, we offer you this training on Scala programming.
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