Data science has become a big part of today’s world. Many big tech companies have data scientists on their teams to help develop their products and services. Data science allows companies to constantly create innovative products that we consumers will spend money on and is worth millions and even billions. Web scraping refers to the extraction of data from a website.

Web scraping refers to the data that is taken from a website. This information is collected and then exported into a format that is more useful for the user. Be it a spreadsheet or an API. Web scraping can be done in many different ways, such as manual data gathering (simple copy/paste), custom scripts, or web scraping tools, such as ParseHub. But in most cases, web scraping is not a simple task. Websites come in many shapes and forms, as a result, web scrapers vary in functionality and features.

Web scraping 

Although it is possible to perform web scraping manually, it’s not always practical. Using machine learning algorithms to automate the web-scraping process is faster and easier and saves time and resources. Data scientists can compile much more information using a web crawler than a manual process. In addition, automated web scraping allows data scientists to seamlessly extract data from websites and upload it into a categorized system for a more organized database.

Web scraping is a required skill for collecting data from online sources—which include a mix of text, images, and links to other pages—in formats that correspond to different programming languages. Web-based data is less structured than numerical data or plain text and requires a method of translating one type of data format into another. Automated web scraping compiles and transposes unstructured HTML into structured rows-and-columns data making it easier for data scientists to understand and analyze the different data types collected.

Many data scientists create web crawlers and scrapers using Python and data science libraries. Among the different types of web crawlers are those programmed to collect data from specific URLs, on a general topic, or to update previously collected web data. Web crawlers can be developed with many programming languages, but Python’s open-source resources and active user community have ensured that there are multiple Python libraries and tools available to do the job. For example, BeautifulSoup is one of many Python data science libraries for HTML and XML data extraction.

BeautifulSoup – BeautifulSoup is used extract information from the HTML and XML files. It provides a parse tree and the functions to navigate, search or modify this parse tree. Beautiful Soup is a Python library used to pull the data out of HTML and XML files for web scraping purposes. It produces a parse tree from page source code that can be utilized to drag data hierarchically and more legibly. It was first presented by Leonard Richardson, who is still donating to this project, and this project is also supported by Tide lift (a paid subscription tool for open-source supervision). Beautiful soup3 was officially released in May 2006, Latest version released by Beautiful Soup is 4.9.2, and it supports Python 3 and Python 2.4 as well.

Scrapy – Scrapy is a free and open-source web crawling framework written in Python. It is a fast, high-level framework used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing. Scrapy uses spiders to define how a site should be scraped for information. It lets us determine how we want a spider to crawl, what information we want to extract, and how we can extract it.

URLLIB – Urllib is a Python module that can be used for opening URLs. It defines functions and classes to help in URL actions. With Python you can also access and retrieve data from the internet like XML, HTML, JSON, etc.

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Web scraping has become an essential skill for data scientists in today’s world. With the vast amount of information available online, web scraping allows for automated data extraction from websites and the creation of a structured database that is more manageable and easier to analyze. While web scraping can be done manually, using machine learning algorithms and Python libraries such as BeautifulSoup, Scrapy, and urllib, can automate the process and save time and resources. Additionally, data scientists can further their knowledge and skills in web scraping through various free courses available online, such as those available on Coursera.

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