Web Scraping 101: Basics and Examples
Web scraping is a process by which bots extract data and content from websites. Learn all about web scraping here.
If you manage a company and make regular business decisions, you’ll understand just how important accurate data is for effective decision making. Accurate data can help you identify problems, develop long-lasting solutions, and make future projections.
Today, businesses can access and use a significant amount of data for activities like business intelligence, price comparisons, and sales leads. One effective way to collect this data is through web scraping.
This article will help you understand the concept of web scraping, its uses, and how to implement it. We will also use cases of common web scraping to highlight its need.
What is web scraping?
Web scraping is a method for automatically collecting large amounts of internet data and saving and organizing it locally on a database or file, as demonstrated in the image below.
A web scraping software program or bot crawls the internet and looks for data that fits predefined parameters. When it finds the data, the bot downloads, organizes, and displays it for the user.
You can download the returned data using a scraping bot (either manually or automatically) in JSON, Excel, or CSV formats and save it locally. As a result, crawlers save significant time, which can be invested elsewhere.
You can build data scraping software using popular programming languages like Python, XPath, and JavaScript. The Scrapy and Beautiful Soup Python libraries are specifically built for scraping HTML web pages. Such libraries can simplify your work since they already contain the core functionality and logic for crawling the internet, downloading, and saving data.
How is web scraping used?
People primarily use web scraping for obtaining and organizing data. Businesses, governments, NGOs, and even educational institutions all use web scraping in their decision-making processes. Scraped data is often used in:
- Marketing
- E-commerce
- Real estate
- Web data extraction and analysis
The following sections provide a detailed discussion of web crawling (or scraping) usage.
Lead generation
Market research teams use web scraping for lead generation. In this context, lead generation is the process of searching for prospective customers for your goods and services. Web scrapers can collect customers’ contact information (e.g., social media accounts, email addresses, physical addresses or locations, and phone numbers).
Lead generation helps organizations find new markets. Having a list of potential customers makes marketing teams’ jobs easier since they can determine which market segments to target rather than wasting time on unproductive areas.
Businesses that use web scraping can learn about their customers’ traits and behaviors. Incorporating this specific data into service delivery processes can facilitate better customer satisfaction rates.
Web scraping also boosts lead generation by ensuring the customer relationship management (CRM) system is up-to-date. The CRM system is responsible for keeping track of all customer interactions. An updated CRM system makes it easy to keep in touch with clients. Furthermore, companies can use this platform to understand customer needs and streamline their service delivery.
Real estate
Web scraping allows real estate agencies to access real-time data from different online resources and use it to gain insight into customers’ property-buying habits, competing real estate firms, demographics, and the economic status of certain areas.
Real-estate businesses can use crawlers to look for local listings on the internet. The retrieved data is then organized and stored in a database where it can be put on a website.
Let’s look at other related information collected through web scraping.
- Property prices. Property prices vary according to location, size, and pricing type (mortgage, sale, or rental). Real estate agencies can analyze scrapped data and determine property prices in different areas.
- Customers or home buyers. You can collect information relating to the purchasing habits or patterns of home buyers.
- Public records. This includes average family income, surveys, mortgages, insurance, and loans.
- Competing real estate firms. You can obtain data relating to the properties that rival real estate agencies are selling. Such information can help a real estate firm review and improve its product offerings.
Web data obtained from government records, public insurance firms, real estate agencies’ websites, and property listings can help identify ongoing project developments, areas, and properties that are in high demand, as well as local market expectations. Real estate businesses can use this information to set their prices and make purchasing decisions.
E-commerce
Web scraping helps e-commerce businesses engage in dynamic pricing. This activity involves collecting data from rival e-commerce firms and using it to price products better.
By scraping your competitors’ websites, you can perform a price comparison and analyze their products and marketing strategies. You can then use this data to develop your e-commerce business strategy. Web scraping tools save you significant time, which you can direct to other activities.
Web scraping allows online businesses to engage in lead generation. In e-commerce, lead generation involves attracting individuals who are interested in what the company is selling and converting them into paying customers. You can use scraped data to understand customers’ needs and wants (and incorporate them into your marketing campaign).
Web scraping can also help you find the right keywords for search engine optimization (SEO). In e-commerce, ranking higher in search results than your competitors can increase your visibility to online users.
Scraping social media sites can also help you introduce important or popular people to your target audience. Using these individuals as influencers can help promote your brand and increase web traffic.
Data analysis
Data analysis involves applying logical and statistical methods to collect, condense, organize, and evaluate data. Fields like machine learning and artificial intelligence require significant data to train models and make accurate predictions. Due to the huge amount of data needed for "big data" analytics, machine learning and AI manual collection wouldn't be feasible.
A number of web scraping software options can scan different websites and download specified data to clean and analyze. Data cleaning removes incorrectly formatted, duplicate, incorrect, and corrupted values from a data set. What remains is consistent and useful data.
How to perform a web scrape
You can build a web scraper entirely from scratch that can make HTTP requests to target websites and extract data using selectors like XPath and CSS. By default, the scraping software will parse all the web content as HTML code.
However, building a web scraper is time-consuming and requires significant programming knowledge. You also must perform complex tasks, such as managing proxies, maintaining the software when the target website layout changes, dealing with installed security measures (e.g., Captcha code and antibot algorithms), and executing JavaScript code.
Fortunately, you can download several web scraping software options for free.
In this section, we will perform a simple web scrape using ParseHub. ParseHub is free scraping software that allows you to save data in both CSV and JSON files. Some of ParseHub’s key features include REST API, infinite scroll, IP rotation, regular expressions, and automatic cloud-based storage.
Note that this article covers the installation process on Microsoft Windows. Consult the official documentation if you are on Linux or macOS. However, this article is still beneficial since the procedure for using ParseHub is the same on all platforms.
Step 1. Install ParseHub
You can download ParseHub from the official website. The software installer is available for Mac, Linux, and Windows.
If you are using Windows, click the Windows button to download ParseHub.
ParseHub will download and save to your downloads folder. Navigate to the downloads directory and search for the parsehub-setup.exe file.
Double-click the parsehub-setup.exe file to start the installation. A security dialog will pop up asking whether or not you wish to continue with the installation.
Click Yes to continue with the installation. You should see the following window.
In the above window, click the Next button to proceed.
It will ask you to choose between standard and custom installation packages.
Select the standard package and click the Next button.
The next window will show the location where ParseHub will be installed. No changes are required in this window. Proceed and click the Install button shown below.
The installation process will begin and take some time, depending on your computer’s processing speed.
The following window will display once the installation is complete. Click the Finish button to exit the ParseHub Setup Wizard and proceed to the main dashboard.
Step 2. Create an account or sign in
You must register a user account to access the main dashboard. If you already have a ParseHub account, you can sign in by clicking the sign-in button shown in the window below.
After you sign in to ParseHub, you will be directed to the following dashboard:
Step 3. Create a new project
Before you start scraping data, you must create a new project. Press on the new project button to generate one.
A new project window will pop up asking you to add the website link (i.e., the site where you’d like to collect data).
This tutorial will use a link provided by ParseHub as the target website. As practice, paste “https://parsehub.com/sandbox/showtimes” in the project URL text field.
After adding the website link, click the start project on this URL button to proceed to the next step.
ParseHub will load the website’s content in an adjacent window. In our case, ParseHub has loaded a list of movies, as shown below.
We need to give a name to the items that we will extract from the ParseHub movie website. Click selection1 and rename it movies.
Since we wish to scrape all movie titles from the ParseHub website, let’s select them all.
Click The Shawshank Redemption movie title.
The movie title will be added to the movies list that we created earlier.
In the above image, you will notice that the next movie title (Schindler’s List) is also highlighted in yellow. ParseHub does this to indicate that the element has the same properties as the object we have already selected. This is a powerful feature that saves significant time. To confirm this prediction, proceed and click the Schindler’s List text.
A total of six movie titles will be added to our movies list.
Though ParseHub is good at predicting targeted elements, we still need to confirm that all movie titles were indeed selected.
Scroll through the list of movies pulled from the ParseHub database.
You will notice that certain movie titles were not added to our selection, as shown below.
Click the first unselected item (The Good, the Bad, and the Ugly) to include other titles that were unmarked in our previous selections.
Step 4. Begin scraping data
We are now ready to scrape all movie titles and their links from https://parsehub.com/sandbox/showtimes. You can start this process by clicking the Get Data button (shown below).
You will be directed to the window below, where we can initiate the scraping process. Click the run button to begin.
Once you click the run button, you will be notified that the selected data is being collected.
The following window shows that the site completed the data scrape successfully. You can download the data in JSON or CSV file formats.
Here is how your scraped data looks in CSV format.
You can learn more about scraping complex data from the official ParseHub blog.
Learn more about web scraping by working with an expert
Web scraping is an essential part of modern business research and analytics. Organizations can collect and analyze huge amounts of data within a short time. The data can be used for lead generation, search engine optimization, data analysis, sentiment analysis, and many other functions.
However, you need knowledge and access to the right tools to harness the full power of web scraping. You also need to take time to identify and analyze the websites where you want to scrape data, which can be a challenge in itself.
Avoid the headache that comes with setting up the web scraping environment by finding and hiring an independent web scraping expert on Upwork’s Talent Marketplace.
Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.
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