As the field of data science continues to develop, it has become more important for data science students and professionals to learn how to maximize their knowledge of various programming languages and tools. Especially when working with open-source data science tools, there is an entire ecosystem of programming languages, packages, and libraries that accompany these tools. In particular, Python includes a thriving community of users and developers which contribute to the creation of resources and materials that are helpful to other users.
Some of the main resources that are produced and updated by the community are Python libraries. These libraries can be used to perform specific functions and methods which are unique to certain types of data analysis projects and projections. Consequently, libraries are essential to programming, and Python libraries can be used to develop programs and processes for a variety of tasks. The Selenium library is used for multiple web scraping functions, testing web applications, and machine learning. It is an essential tool for data scientists that are working within science, technology, and product design and development.
What is Selenium?
Selenium is a programming library that is compatible with multiple languages, including Python, C#, Ruby, and JavaScript. Often used for testing web applications, Selenium is popular amongst data scientists, developers, and software engineers alike with an interest in the creation and maintenance of applications. Traditionally, testing applications can be a lengthy process because developers have to manually re-run multiple tests under different conditions to ensure that the application will work well for different users and environments. Working within a web browser, Selenium reduces the time and effort that can be taken up by this tedious process through using different programming tools and automation. There have been multiple upgrades and additions made to the library over the years, with new components making the tool even more useful for data scientists.
Why Data Scientists Use Selenium
Included as part of the PyPi platform, the Selenium Python library is similar to the Beautiful Soup library and is primarily used for data extraction from websites. Once this data is extracted, you can also use Selenium to automate the process of web scraping in order to test various applications and programs.
Extracting Data from Websites and Pages
The main use of Selenium is extracting various data types and elements from websites and applications in order to gain information about a topic or dataset. One method that you can use to extract data from websites with Selenium is creating a headless web browser. Headless web browsers are utilized by writing scripts that can then be controlled through Selenium. This browser function is particularly useful for the constant collection of web-based data because they don’t have a user interface (UI). Without a UI these browsers can run faster and collect large stores of information and data from a web-based environment.
Using the Selenium WebDriver you can not only manipulate a headless browser to collect online information, but you can also use Selenium to find specific elements on a webpage, to capture and collect information about web traffic, and offer features that make it easier to navigate past pop-ups and other blocks to collecting information from a website. This ensures that the data collected from a website is easier to read and organize.
Creating an Element Specific Database
In addition to extracting data from websites, the Selenium Python library also includes functions that allow you to separate out different elements from a website in your data collection. Instead of collecting all of the elements of a webpage, Selenium allows you to just collect portions of a website, such as all of the tables or all of the images. This resource is especially helpful when collecting web-based data on a specific component of websites, such as artwork or graphs.
Once those specific images or elements are collected, there are multiple functions in Python which allow you to create either a folder for holding that dataset or a unique image database. This technique is also useful for data scientists that are working with archives or large datasets online because you can target the data that you want online and then save it to download later without having to manually select each image on a website and download them one by one.
Automation and Agile Development
Another use of the Selenium Python library is for agile automation testing, which is commonly seen within the software engineering and web development industries. Agile development is a method of creating software involving work as part of a team through the software development life cycle. An important component of this lifecycle is the testing of software and products, and there are several components within the Selenium library that are commonly used for the automation of this testing process. Selenium WebDriver and Selenium WebElements offer components that fit well into the principles of agile development by simplifying and streamlining the process of testing.
Using one of Python’s embedded testing modules, you can pair the web element components of Selenium with the Python test method to create an automated model for software development. The processes of automation and agile development are also useful for data scientists because these methods of testing and automating tasks not only save time but ensure that whatever deliverable you are producing is working effectively. The Selenium Python library offers excellent resources for data scientists that are doing research on websites, social media, and product or platform development.