What is Python Used For? Top 5 Use Cases Explained

Python has gained widespread popularity and even surpassed C, C+, Java, and JavaScript over the last few years. Naturally, its phenomenal rise as one of the fastest-growing language begs questions such as: "Why use python?", "what is python language?", "What is the python programming language used for?”, “what are the applications of Python?” etc. 

In this article, as we learn what is python used for, we’ll address these questions and discuss the top five use cases of this dynamic programming language, which serves as a firm foundation for today’s complex applications with its diverse functionality and rich libraries.   

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Why Use Python in Web Development?

Technology giants such as Spotify, Instagram, and Google use the open-source, easy-to-understand Python programming language for developing enterprise-level, robust, and responsive web applications.

Packaged to expedite installation, the ActivePython software, with a comprehensive set of extensions, allows Python developers to start working with state-of-the-art Python frameworks such as Tornado, Django, and Flask within minutes.

Integrating the Python programming language, the ActivePython software package, offered via the ActiveState Platform, also permits users to connect to AWS (Amazon Web Services) API and SDK libraries. This heightens the appeal of the software, making it the preferred choice for web developers around the world.

Now, before we learn what is Python used for, here are the top advantages of using Python in web development.

Python is Easy to Learn

Compared to other programming languages like C++ or Java, the design philosophy of Python - emphasizing general expressions and code readability with significant use of whitespace - makes the programming language easy to understand, even for beginners. Additionally, developers using Python can get their job done by writing much less code.

Python Has a Rich Ecosystem

The extensive Python library offers a wide range of packages and tools, such as SLQALchemy, Pygal, Pandas, and Numpy, which allow developers to access pre-defined code, ensuring fast and smooth application development.

Speedy Prototyping

In comparison to programming languages like C++ or Java, Python reduces the development time to a great extent, making things easier for developers to build prototypes quickly and gain feedback on their projects.

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The Uses of Python Programming Language in Scientific Computing and Data Science

The Defense Advanced Research Projects Agency (DARPA) of the U.S. Department of Defense, which is responsible for promoting technology development for U.S. military use, funded $3 million in 2012 to build a top-notch data processing and data analytics library for Python.

With this high investment, the Python programming language now includes a variety of advanced tools and scientific packages for all facets of data science and scientific computing.

Including APIs, simple syntax, and outstanding features to address statistics, scientific, and mathematical functions, Python, according to seasoned developers and engineers, is best suited for scientific computing and data science.

Python’s data processing and data analytics library include.

Matplotlib

Ideal for generating data visualizations such as bar charts, histograms, scatterplots, and power spectra with minimal coding.

NumPy

With scientific computing and data science at its core, NumPy provides support for large-scale, multi-dimensional matrices and arrays with a range of first-rate mathematical functions.

Pandas

Built on NumPy, Pandas offers operations and data structures for manipulating time series and numerical tables.

SciPy

Working together with the NumPy arrays, SciPy facilitates powerful routines for optimization and numerical integration.

Scikit-learn

A free Machine Learning (ML) library for Python — developed on NumPy, Matplotlib, and SciPy — it features varied classification, clustering and regression algorithms, including SVM (Support Vector Machine).

Uses of Python in Machine Learning

As end-consumers seek enhanced user experience, including improved search functionalities, better personalization, and smarter recommendations, Machine Learning (ML) – an AI (Artificial Intelligence) application – in recent times, is increasingly gaining prominence.

AI and ML applications differ from customary software projects, especially in the overall technology infrastructure, the necessity for deep research, and the skills needed for AI-based projects.

ML and AI applications, therefore, require a high-class programming language, which is steady, yet agile, offering consistency and simplicity.

From project development to implementation and maintenance, the Python programming language, with its excellent frameworks and vast libraries, gives all of that, and more.

What is python coding used for Machine Learning? Well, search results refinement, web spam filtering, intelligent virtual assistants, facial recognition, smart product recommendations, predictive video analytics in surveillance, online fraud detection, and automatic translation services are some machine learning applications that use Python.

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What is Python Used for in Startups?

The startup ecosystem is entirely different from how corporations and big companies operate.

Startups are innovative, young, and collaborative. They are also highly vulnerable, as most startups, in the initial stage, are not quite sure about whether the target audience will accept and appreciate their offerings.

That is why startups require high scalability, quicker development of MVP (Minimum Viable Product), efficacious iterations, the scope of technology integration, and time-monitored development processes.

Taking into account all the above factors, experts opine that the Python programming language is ideal for technology-driven startups. Here are some examples of how startups can benefit from Python software:

Scalability

Startups will grow over time, and eventually, they will seek scalability. Based on Python, the open-source and free Django web framework can help startups develop highly-scalable mobile and web applications, capable of handling huge traffic loads.

Versatile Programming Potential

The exceptionally versatile Python programming language works well on various platforms. With Python, startups can develop applications that everyone can access easily.

Unmatched Security

Python development frameworks, such as Django, incorporate excellent security features. Hiding the source code, Django protects applications from online security threats.

Fast MVP Development

Python allows startups to develop MVPs in a flash, reducing the time-to-market. This gives a leading edge in an intensely competitive business environment.

What is Python Used for in FinTech and the Financial Industry?

Emerging technologies are continually revolutionizing and disrupting the financial industry. Python, with its advanced, flexible frameworks, offers significant opportunities to cope with constant technology shifts.

The high-caliber programming language is excellent for Fin-Techs, when it comes to resolving challenges inherent in today’s financial landscape, in terms of regulation, compliance, analytics, and data volume.

As stated in HackerRank’s survey, an increasing number of financial institutions are now using the Python programming language to build futuristic finance applications. Here are the top factors that drive Python’s use in the financial sector -

Open-Sourced

Developed under OSI-approved open and flexible license, Python is a freely distributable and usable software that offers a broad range of toolsets and libraries for the financial industry.

Cross-Platform

All operating systems, be it the Mac OS, Linux, or Windows, can run the Python programming language.

Convenient

Python incorporates general-purpose data structures that web programmers can use to develop fast data structures runtime.

A dynamically-typed programming language, Python allows for easy deployment with reduced source code footprint.

High Performance

Python offers outstanding code readability, robust integration, simple syntax, a clean design, increased process control, and superb text processing capabilities. All of these improve overall productivity.

The Demand for Python Developers is Dramatically Escalating

Python, as of 22nd April 2020, is the 3rd most popular web programming language worldwide, according to the TIOBE Programming Community index, which monitors the popularity of programming languages.

Technology giants such as Spotify, Google, Lyft, Reddit, Netflix, Instagram, and Bloomberg are already using Python. Recently, Dutch multinational HR-consulting firm Randstad reported that a vast array of businesses listed with them are hiring Python programmers. The heightened interest in Python is making the programming language more and more mainstream.

From coding Bootcamp to online tutorials, and self-study, there are many ways to learn Python and the Python tutorial will help you understand the basic and core components of Python. However, if you are an experienced programmer seeking to upskill, or if you are a beginner looking for a surefire way to land a dream job, enroll in a specialized Python programming course offered by a top educational institution, such as Simplilearn.

With 30 hours of instructor-led training, 38 hours of blended-learning, 8 hours of online self-paced learning, and 20+ assisted practices, Simplilearn’s Python Training Course will equip you with future-ready skills involving data operations, error handling, conditional statements, web scraping, shell scripting, strings, and Django.

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