Data collection and evaluation help you improve business processes and understand your customers. Both startups and established brands leverage data about their customer’s needs, habits, and buying preferences. Moreover, data is necessary to keep an eye on your competitor’s business strategy and pricing models.
You can easily collect data from websites, but there is no option to download the data automatically. You have two ways to accomplish this task:
First, manually copy the required data by visiting every URL and pasting the copied data on your local files. This is a time-taking and hectic job.
The second is by using a web scraping program. Scraping automates the process of copy-pasting and saves tons of time.
Hence, the second method of web scraping is one of the best ways to collect data. In this article, we will understand the process of web scraping and learn to use a powerful programming language like Python to scrape the web.
Let’s begin. 👇
What is web scraping?
Web scraping is a data extraction technique that uses a scraping program and a proxy server to copy large volumes of data from different URLs and store them as a local file in your computer or as cloud storage online.
You can use scraping software to extract product details from eCommerce sites, or contact details like name, address, email ids, or phone numbers from business directories. The scraping software will help you to extract data according to your business needs.
What is Python?
Python is a versatile and most-loved programming language that is commonly used for web scraping. The syntax is simple and easy to learn. Also, the cost of program maintenance is lower. It has several modules and packages that let you do more with less code.
Why is Python suitable for web scraping?
Python is a high-level and interpreted programming language best for web scraping because it handles all the web crawling related processes smoothly. Two of the most widely used frameworks that are essential for web scraping are Scrapy and Beautiful Soup. These frameworks offer debugging tools and features for searching and modifying a parse tree for efficient large scale web scraping.
The core concepts of Python are easy to understand. You can create scraping programs even if you don’t have enough coding experience because coding is easy, and there is no need for heavy coding. The presence of useful libraries makes it easier to write programs with less code.
Why use a proxy for web scraping?
You should never run your scraping program without a proxy because doing so might result in an IP ban. Nowadays, websites employ mechanisms such as anti-scraping bots that stop programs from accessing the website’s contents. Hence, when you use your scraping program to access your target website’s contents, it will detect and block your IP.
Python proxy scraper is the best way to scrape the web without worrying about the IP ban. A proxy server acts as an intermediary between your computer and the target website. You can work with a pool of proxies to display a different IP address and location for every request made to the target website. A proxy mimics regular browsing activity, and the anti-scraping tools can’t detect that you are using a bot to access the website. Moreover, it will hide your IP address so that you can carry out all your scraping work anonymously.
Web scraping with Python
Python and Beautiful Soup library is one of the most powerful combinations for scraping on a large scale. The best part is, you get a lot of online help and how-to videos to master the basics and advanced concepts of using Python for web scraping
Here is a basic overview of how you can perform web scraping with Python:
1- Find the URL that you want to scrape:
The first step is finding the URL or a set of URLs that you wish to scrape. You can identify and prepare a list of URLs depending on your scraping needs. As a beginner, you should start with just one website and then proceed with other sites only when you can run your scraping program smoothly.
2- Inspect the Page:
The second step is web crawling, where your scraper inspects the HTML of the page to identify the data points you wish to copy. Crawling is a necessary component of web scraping. While inspection, you should find the elements of the page that has the information that you need.
3- Find the data you want to extract:
The next step is identifying the exact elements on a web page that need to be copied. You should store all the data points that have the HTML. For example, you should choose the elements of a webpage that contains the data to copy and then look at the respective HTML codes of the elements to locate the data points.
4- Write the code:
Now, you have the URLs to scrape and all the data points that contain the exact data. The next step is writing the code that can automatically extract the content in the data points. You should also specify in your program the format in which you wish to store the scraped data. There are several formats available with Python-like CSV, SQL, JSON, XML, and others.
5- Run the code and extract the data:
After you have written the code, the test runs it with just one URL to see if it’s working correctly. Once you can copy the required data from a webpage, you can run your entire program with several URLs to scrape the whole data. Before you run your program, you should use a Python proxy scraper because it will help you send a high volume of requests to the target website with the risk of your IP getting banned.
6- Store the data in the required format:
Once the program runs successfully, it will automatically store the data in your desired format in your local computer or the cloud.
Please note: Before you start running your web scraping program, make sure to read the robots.txt file of the target websites. Robots.txt file contains instructions that allow or disallow scraping. Please follow and respect the instructions in the file before you choose to scrape any website.
Scraping is essential for marketers, analysts, and data scientists because it lets you make better business decisions. Python is the most preferred language to scrape complete websites. Beautiful Soup and Scrapy are two of the most useful libraries that make the work of scraping easier for you. You only need to configure your Python proxy and run your scraping program to start copying the data. Start leveraging the power of scraping to extract meaningful data for your business and improve your profits.
Source: Efrat Vulfsons is a data-driven writer and freelance publicist, parallel to her soprano opera singing career. Efrat holds a B.F.A from the Jerusalem Music Academy in Opera Performance.