Beginners' Guide to Web Scraping: What You Need to Know to Get Started

Beginners’ Guide to Web Scraping: What You Need to Know to Get Started

Post Summary.

Reading time: ~6 minutes

  1. What is Web Scraping?
  2. What is Web Scraping Used for?
  3. Different Programming Languages for Web Scraping
  4. What Is the Best Programming Language for Web Scraping?
  5. Web Scraping Steps with BeautifulSoup
  6. Conclusion

Nowadays, an increasing number of firms post data online. This information comprises facts about the product, the customer, the price, and the provider.

Companies in the telemarketing sector, for example, scrape this data from websites to conduct competition intelligence and strategic positioning analysis.

To make this process easier and more efficient, web scraping helps to extract all info from any website by using bots. If you’re new to this, you’ve landed at the right place.

We’ll share the main tips on how to web scrape for beginners.

What is Web Scraping?

Web scraping is a technique for automating the extraction of vast volumes of data from websites. You may know that the website’s data is unstructured. Web scraping enables the collection of this unstructured material and its subsequent storage in a structured format.

There are several methods available for scraping web pages, including internet services, APIs, and custom software.

But what’s the basic process of scraping a website?

Here are the essential steps you must take to extract data with Python web scraping:

  • Locate the URL you want to scrape.
  • Inspect the web page
  • Find the data you’re looking for and write the code to extract it
  • Extract the data by running the code
  • Make that the data is stored in the correct format.

What is Web Scraping Used for?

Web scraping is a technique for extracting large volumes of data from websites. But web scraping can do more than gather data. People use the extracted data for multiple purposes, including the following:

Price Comparison: Web scraping can gather data from online shopping websites and compare product pricing.

Research and Development: Web scraping extracts important statistical data from websites. It is then processed and utilized for surveys or R&D. Also, it helps with data mining ventures.

Job listings: Information about job vacancies and interviews are gathered from several websites and then consolidated in one location for easy access by the user. Many job portals use scraping to include job listings on their website.

Email address collection: Email marketing is a popular form of marketing that businesses use for promotional or service purposes. Web scraping can quickly gather email addresses to use for marketing.

Social Media Scraping: Web scraping is used to gather data from social media websites such as Facebook, Twitter, or Instagram to determine what is trending.

Different Programming Languages for Web Scraping

You can scrape data from the Internet using pre-built tools or learn a programming language and do web scraping projects manually. There are also options to get a custom web scraper that’ll provide you with a more customized solution.

Python

Python is a popular web scraping language. You can easily manage many data crawling or web scraping operations without learning complex coding.

C and C++

Both C and C++ give an excellent user experience and are excellent web scraping development languages. These languages are ideal for developing simple data scrapers but not for web crawlers.

PHP

PHP is one of the greatest programming languages for online scraping. It is used to create strong web scrapers and extensions.

What Is the Best Programming Language for Web Scraping?

What Is The Best Programming Language For Web Scraping

Python is a high-level, interpreted programming language for general-purpose programming that enables rapid data scraping from the Internet.

Why is Python Good for Web Scraping?

Python is the best programming language for web scraping. It comes equipped with a dynamic type system and intelligent memory management to make your job easier.

The following is a list of Python features that make it better for web scraping.

  • Ease of Use: Python is easy to learn and use. No semicolons “;” or curly brackets are required anywhere. This makes it easier to use and less cluttered.
  • Small codes: Web scraping is a time-saving technique. Is it worth putting in extra time coding? Writing a few lines of code can do a lot of work in Python. As a result, you’ll save time.
  • Large Library: Numpy, Matlplotlib, Pandas, and a slew of other libraries in Python offer methods and functions for a wide range of needs. As a result, online scraping and data manipulation are no problem.
  • Dynamic: Python is a dynamically typed language, which means that you don’t need to declare data types for variables. You’ll save time and get more done with this method.
  • Easy syntax: Reading Python code is like reading a statement in English, making learning the language’s syntax easy. Moreover, the indentation used in Python enables the user to distinguish between distinct scopes/blocks in the code, making it easy to read and understand.

Also, Python makes it easy to track social media and scrape information.

Web Scraping Steps with BeautifulSoup

BeautifulSoup is an excellent, simple-to-use application for scraping static HTML web pages. This implies that when a page is viewed, the whole page is loaded instantly and does not alter dynamically as you scroll over the page and interact with the site.

The next section will describe how to use Selenium to handle dynamically loaded pages.

Setting Up BeautifulSoup

BeautifulSoup is a widely used Python parsing module. So, it is somewhat of a prerequisite for web scraping. Downloading the library (via the command line/terminal) is necessary before we begin scraping the data.

  • After downloading, setup BeautifulSoup with the following codes:

pip install beautifulsoup4

  • Then import it along with other necessary libraries:

from bs4, import BeautifulSoup

Also, from urllib.request import urlopen

from urllib.parse import urljoin

Understanding the URL Structure of a Site

The site’s structure we’re scraping and where we might uncover valuable content is critical before we begin extracting data.

Suppose we’re trying to get the player rankings for tennis. For example, we’ll pick tennis.com. After visiting this site, we will see a tab named: “player ranking,” and when you click on it, the URL should be:

“https://www.tennis.com/players-rankings/.”

Then we pick the top one, Djokovic, and click on it. It will show us the latest tournament scores. And the new query parameter will appear.

“https://www.tennis.com/players-rankings/novak-djokovic-sr-competitor-14882/activity/” will be the URL, and “novak-djokovic-sr-competitor-14882/activity/” will be the parameter.

To fully automate the information retrieval procedure, we must first identify the URL structure. We need to visit every player’s link to gather all the latest tournament scores.

Understanding the Page

For the most part, websites follow a similar structure throughout their pages. Since the page’s HTML structure is consistent regardless of when it was created, we can use it to gather information about the page.

Scraping tournament score URLs for a certain player from these sites is our primary objective for this scraper.

Once you understand the website URL and the HTML page, the very next thing is to inspect it.

Inspecting the Page

The inspect element function in most current web browsers is a fantastic resource. Most popular browsers like Chrome, Edge, and Firefox have built-in options to inspect a page.

You can start inspecting an element by right-clicking on a website that brings up a window with the HTML code for that element. This is very important since we’ll use BeautifulSoup to choose components based on their HTML tags in the following phase.

Hovering your mouse pointer over various HTML tags in the inspect window reveals the contents of that element. To investigate a link, right-click on it and choose “Inspect Link,” to directly see the specific elements of that content.

Making the Links Ready for Extraction

Next, we’ll have to extract the links.

BeautifulSoup is here to help us acquire the score lists we need. When the urlopen object is decoded, the HTML is converted into a string representation using the utf-8 character encoding.

A BeautifulSoup HTML parser parses the HTML string and returns an object called BeautifulSoup(HTML, ‘html.parser’). The term “object soup” is often used to describe this mixture. If data is in that format, it is possible to install and download other parsers, such as an XML parser.

When a tag and optional attributes are provided, soup.find all() can get all components that fulfill those criteria (returned in a list). When just one item matches the specified description, you may use soup.find() instead of searching for it, which will return a string.

urllib.parse.urljoin() provides us with an absolute URL which we can visit. In the absence of this, the URLs will not be correctly displayed.

Writing the Code for Extraction

Let’s name our file tourney-s, so the code for that will be:

gedit tourney-s.py

Then we need to configure webdriver for browser, and the code will be:

webdriver.Chrome(“/usr/lib/chromium-browser/chromedriver”)

We have already installed BeautifulSoup and imported the library. Thanks to Python, you don’t have to write any more codes for a callback.

Extracting the Link

So, we’re all set for data extraction from that website. All you need to do is write the following simple code:

Python tourney-s.py

Storing the Data

Once you extract (scrape) the data you need to store it, CSV is by far the easiest and most used format for storing scraped data.

So to store it we need to type the following code:

df = pd.DataFrame({Player Name’:first name, second name, ‘Score’:scores,’Nationality’:nationality})

df.to_csv(‘scores.csv’, index=False, encoding=’utf-8′)

Then, you have to run all the codes together (except the setup code for BeautifulSoup), and you’ll get an excel sheet in .csv format containing tournament data. We scraped a player’s first name, second name, scores, and nationality for this example.

Conclusion

Web scraping is a big thing, and there are a lot of things we didn’t include in this article. But hopefully, you’ll have a basic idea of the steps to start web scraping.

We have mentioned there are other ways to scrape websites; Python has several other methods of scraping data. Once you grasp the basics, move on to the next level. Don’t rush. Try to understand the steps and you can scrape with ease.

Thank you for reading!

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