Why Every Data Scientist Should Know Beautiful Soup

Data science is a multi-dimensional field, with many career paths and opportunities available to those who pursue employment in the industry. The data science tools that you choose to learn play a large role in the type of opportunities that you will be able to pursue. Your toolkit should be aligned with the type of Data Scientist that you want to be and the industry you plan to work within. For data scientists that learn, or have learned, Python, it is quite possible to utilize this skill set for projects which combine both data science and web development. This combination is especially useful within science and technology-related industries like social media and software engineering where there are several data science tools that support this combination of skills.

Python libraries offer community-generated resources that support a variety of projects and analyses. Through pre-constituted code, functions, and other methods and materials, these libraries simplify the process of programming in Python by saving data scientists time and effort. As a tool that is primarily used for web scraping and image manipulation, Beautiful Soup provides multiple possibilities for data scientists that exist within the intersection of data science and development. The following article focuses on some of the reasons why data scientists should know how to use the Beautiful Soup Python library.

What is Beautiful Soup?

Beautiful Soup is a programming library created by Leonard Richardson in the early 2000s to make sense of data that has been extracted from the web. Many times, when data scientists gather data from websites, the data output can be difficult to understand or work with, therefore the Beautiful Soup library includes functions that make sense of the complicated mixture of data that is returned after the extraction process. Like making a beautiful soup out of a miscellaneous mess of ingredients, this library is especially useful for converting data from one type of code to another. Generally, Beautiful Soup is used to build a web scraper that sits on top of an HTML structure and pulls the information that you need from a website or application.

How is Beautiful Soup Used in Data Science?

The Beautiful Soup Python library can be accessed through the PyPi platform and includes functions and features that include parsing web pages for data, web scraping, creating images and databases, and various machine learning and automation processes. This library is an essential data science tool when it comes to collecting and exploring web-based data.

Parsing and Web Scraping

Including multiple functions for parsing data, the Beautiful Soup Python library is primarily used for extracting data from websites and pages. Parsing, as a Python function well-known within data science, is used to take one type of data and convert it into another type of data for analysis. Within the Beautiful Soup library, you can generate a parser, or use a parser of your choice, which can convert HTML or XML code into Unicode, so that the format is readable within your program. The parser can also be used to create and navigate a parse tree which helps you search and index different objects or parts of the dataset collected from specific websites.

Especially when doing social media research, web scraping is an essential method of data collection that allows you to gather information and data from different types of websites and web-based elements. You can also select content to include in your extraction through a tags function. Beautiful Soup can also be used to clean the HTML or XML data for future projects or analyses. These findings are especially useful for individuals who develop websites, create software applications, or study websites and mobile applications.

Machine Learning and Automation

Through its parsing and web-scraping abilities, the Beautiful Soup library can also be used to automate the process of searching for content and making predictions based on that data collection. After setting a parser into motion, you can essentially create a web crawler that continues to return and collect data from your program of choice. This data collection can then be used to create various machine learning models by transferring the data into a new format such as a data frame or some other database structure, which can be used to make predictions based on the data collection.

This programming library is especially useful for long-term data collection and organization. By automating the process of web scraping data scientists are able to essentially set their tools in motion and wait for the data to come in. For example, automated web scraping can be used to collect a certain category of websites or content over a period of time that can be used to learn more about a topic or type of website or to complete a task. This could be as simple as collecting job listings or even scraping Python libraries to create models that predict which jobs or libraries are gaining popularity, or will become more popular in the future.

Want to Learn More About the Beautiful Soup Library?

Noble Desktop offers multiple Python classes which not only focus on learning this open-source programming language, but also gaining experience with the packages and programs that complement it. The Data Science Certificate and the Data Analytics Certificate both offer hands-on instruction and exercises where you can use Python libraries such as Beautiful Soup. In both of these multi-week programs, you will also be able to develop a portfolio of data science projects to present to potential employers. Taken in conjunction with any of the data science classes and bootcamps, these courses and certificate programs can be used to update your skills or learn new skills to further your studies or career.