What do Netflix, Facebook, Amazon, Dropbox, and Instagram have in common? Aside from being tech giants, they’re all built with the same core technology: Python. Created almost three decades ago, the technology has now reached its peak popularity among developers and business owners alike.
According to the TIOBE index, Python is the 4th most popular programming language in the world right now. And its usage is continuously growing because it supports cutting-edge technologies such as machine learning or the Internet of Things like no other language.
Businesses use this versatile technology for many purposes, from web development to data science. But it’s mainly the latter that contributes to the growth of Python today – this dynamically typed language offers exceptional support for projects based on technologies such as artificial intelligence (AI) and machine learning. IBM claims that Python is the best language for machine learning.
No wonder that today it’s the first-choice programming language of developers – and businesses that want a stable foundation for their software development.
Read this article to find out what Python is used for, learn more about the benefits it brings, and see some practical examples from 3 industries.
Where Python is used to bring the most benefits
1. Python for web development
Businesses can’t survive on the market these days if they don’t invest in a quality web presence. A functional and well-designed web app today includes elements such as mobile responsiveness, integrated animation, Progressive Web Apps, or machine learning-based chatbots.
Choosing the right tools for building a web application is more important than ever. And that’s why so many companies pick Python as their technology of choice. What is Python programming all about? Efficiency, conciseness, and productivity.
Key advantages of Python for web development
The technology comes with many benefits for web developers and companies looking to create an outstanding web application that wins their customers over:
It offers an extensive collection of libraries
Python’s standard library is pretty amazing, but the language offers a massive ecosystem of tools and libraries provided by its active community. It means that developers don’t have to spend time on building everything from scratch. Instead, they can use pre-built elements for just about anything: from scientific computing and image processing to machine learning and data visualization.
It’s fast to write
The clean and simple syntax help software developers to write Python code faster than when using other popular programming languages – for example, Java. Because of that, it comes in handy to companies that want to experiment with new ideas with quick prototypes or validate their product on the market with a Minimum Viable Product (MVP). This feature is especially important for startups that need to test their product as quickly as possible to gather valuable user feedback and continue optimizing their idea.
It makes unit testing easy-to-use
Thanks to its built-in framework for unit tests, Python helps development teams ship bug-free code faster. Coding with Python is quick, and so is testing.
It boosts the ROI of practically every project
What makes Python coding exceptional is its sheer productivity. Because Python accelerates the development process, it enables teams to write and ship their code faster. As a result, projects are more efficient and bring businesses a higher ROI.
But what makes the programming language stand out on the web development scene is the number and variety of web frameworks it offers. Python’s well-supported frameworks can easily become the starting point for any project, equipping developers with tools that help them get the job done.
The most popular Python web development frameworks
This open-source, high-level web development framework helps developers to focus on writing code without having to reinvent the wheel. Companies use Django to build all kinds of web apps: content management systems (CMS), social networks, and scientific computing platforms.
Django accelerates the development speed and comes with plenty of extra features that handle common web development tasks (like user authentication, RSS feeds, site maps, content administration, and many more). It also helps developers avoid many common security pitfalls like cross-site scripting, cross-site request forgery, and SQL injection. Another key advantage of Django is its scalability – it can quickly and flexibly meet even the heaviest traffic demands.
Copyright (c) 2010 by Armin Ronacher.
This microframework is based on Werkzeug and Jinja 2. Its main purpose is helping developers to build a strong web application base. This lightweight framework is an excellent match for smaller and less demanding projects. It has a built-in development server and debugger and is compatible with the Google App Engine.
2. Python for machine learning
Machine learning (ML) is another key area where Python is used today. Machine learning is all about extracting, processing, cleaning, and understanding data to develop intelligent algorithms.
Developers choose this battle-tested technology for ML projects because it does all the heavy lifting for them. Python offers many packages for loading, processing, and visualizing data, as well as many other machine learning tasks.
Key advantages of Python for machine learning
Clean and simple syntax
Machine learning is a complex area on its own, and approaching it with a programming language that helps engineers to express mathematical ideas within a few lines of code is a massive advantage. Its semantics often correspond to mathematical ideas you can find at the core of ML.
Gentle learning curve
Learning Python is easy – which means that assembling a team of experts for your ML project will be a piece of cake. Its accessibility makes collaborative coding and implementation so much easier.
Just because it’s so simple, it doesn’t mean using Python requires a trade-off on performance. Developers can enjoy a balance of the two, making it a great technology for complex ML projects.
Python is far more scalable than another common ML programming language, R. It’s also faster than Matlab or Stata. The variety of ML applications using the technology indicate that it’s a good match for fast-growing projects.
Python provides developers with a collection of valuable tools that help in building machine learning systems. The frameworks, libraries, and extensions are a great helping hand at implementing ML tasks.
Here are a few libraries that come in handy for ML projects:
- TensorFlow – a general-purpose library that helps to build neural networks with a multi-layer nodes system that allows quick learning on large data sets.
- Keras – a high-level library for deep learning.
- SciPy – a library used for carrying out mathematical operations on data matrices, it contains the main modules for linear algebra, statistics, Fourier transformation, and more.
- Scikit-Learn – flexible and highly-efficient library for machine learning and image processing.
- NumPy – a basic ML library in the SciPy Stack offering functionalities for operations on tables and matrices.
- Pandas – a library from the SciPy Stack, Pandas is used for carrying out operations on data sets (adding/removing columns, filling out missing data, creating DataFrame from basic structures, and more).
- Matplotlib – used for visualizing data sets, the library is great for presentation of results obtained using ML and visualization of input data.
- Scrapy – a framework for web crawling that helps to obtain data required for further processing.
PS. You can learn more about other Python machine learning libraries on our blog.
3. Python for fintech
According to HackerRank, Python is one of the top 3 most popular programming languages among financial institutions and fintech startups.
Its simplicity and robust modeling capabilities are just two of the many features that make it an excellent pick for fintech companies looking to back up their products and services with a reliable technology stack.
Key advantages of Python for fintech
Financial services applications can be very complex, and developers can use the simple syntax of Python to accelerate the development speed. That allows companies to bring new products to market quickly.
Less code = fewer errors
Conciseness also means that developers write less code and, as a result, reduce the potential error rate. That’s critical for heavily-regulated industries like finance that can’t afford to release a buggy product.
It’s a perfect MVP option
Fintechs want to be responsive to the changing customer demands, and that’s why scalability is so important. Using Python with frameworks such as Django, developers can quickly create an MVP to help fintech startups quickly find a product-market fit.
It’s a bridge between two worlds
Matlab and R are popular among data scientists but less widespread among economists – who most often use Python. It enables companies to integrate the work of economists into the software development process easily.
Plenty of libraries and tools
Developers don’t need to build tools for functions such as data analysis or visualization from scratch when building a fintech product. That’s how companies get to save time and money on their development projects. But that’s not everything. Fintech products often require integrations with third parties – specialized libraries make that easier too.
How Python is used by fintechs
It should come as no surprise that payment solutions and online banking platforms such as Venmo or Stripe were built with Python. Its flexibility makes it an excellent technology choice for building ATM software that boosts payment processing.
Data analysis tools
Processing and analyzing large datasets is a piece of cake if your team uses this robust technology. Python is packed with tools and libraries developers can use to carry out statistical calculations, simplify data visualization, or create predictive analytics algorithms.
Stock markets are full of data that requires analysis. The programming language helps developers to create solutions that do that. A Python programme can easily generate results such as predictive analytical insights, best trading strategies, and actionable trading advice.
So, what is Python used for? By now, you should know the answer to this question: practically everything! The flexibility, scalability, and impressive ecosystem make Python into one of the most versatile programming languages on the market.
Want to learn more about Python? Have a look below to get a handy list of resources where our software engineers share their expertise and insights into Python-based projects.
PS. If you’re looking for a team of Python experts, look no further – we specialize in this technology and have been named by Clutch.co as the 4th best Python development company in the world.
- 5 projects that will benefit from Python
- Django, Flask, or Pyramid? How to choose the right Python framework for your project?
- How to know if you’re standing in front of a pro Python developer
Python and machine learning
- Here’s why Python is so popular in Machine Learning
- Developing a recommendation system? Here’s why you should choose Python
- 24 machine learning libraries for data science projects
- 6 best Natural Language Processing (NLP) libraries
- 12 best tutorials for beginners
- 14 resources for intermediate and advanced developers
- How to contribute to open-source projects
- 6 best Django tutorials and books for advanced developers
- What every Pythonista should know about Unicode
- 30 built-in Python modules you should be using
- 6 expert tips for building better Django models
- How to use Elasticsearch with Django
The Ultimate Tutorial for Django REST Framework
- Django REST Framework Tutorial – CRUD
- Django REST Framework Tutorial – Login and Authentiction
- Django REST Framework Tutorial – Custom Fields
- Django REST Framework Tutorial – Pagination
- Django REST Framework Tutorial – Filters and Filtering
- Django REST Framework Tutorial – Functional Endpoints and API Nesting
- Django REST Framework Tutorial – Selective Fields and Related Objects