Google. YouTube. Instagram. Spotify. Reddit. Aside from being some of the most popular software services in the world, what else do they have in common? That’s right: they all use Python.
Python is everywhere. You may not even realize how widespread it is. The prominence of Guido van Rossum’s creation can be attributed to a number of factors. Most of all, Python is easy to learn, clear to read, and simple to write in. This speeds up development without sacrificing reliability or scalability.
Thanks to the high demand for Python, it’s also very well supported in the community and keeps on growing in popularity.
But what exactly is Python used for? What areas of technology or business does Python benefit the most?Read on if you’re looking to break into any of the following fields and are considering whether to choose Python for your tech stack.
In the current market, a business without a website might as well not exist. Moreover, the trends are pushing for more and more impressive web apps that, among others, include:
Nowadays, more than ever, it’s important to choose the right tools when the time comes to build (or, quite likely, rebuild) your own website or web app.
There are many advantages of Python that help you get results fast within the field of web development:
This helps you ship bug-free code.
In addition to Python’s standard features, one of its major strengths in web development is the variety of web frameworks it offers.
With a large selection of well-supported frameworks, you can find the right starting point for any project. Python gives you the tools to get the job done reliably, no matter whether you’re looking to focus on:
The most widely used Python web framework—at least until recently.
Django’s trademark is its completeness, as it aims to offer all the tools you need to build a web app in a single package.
It’s the perfect choice if your app is fairly standard, since it allows you to skip most of the initial steps and get a working solution faster.
Compared to Django, Flask is much more geared towards microservices, which may be the reason why it’s the new #1 in popularity according to JetBrains.
Contrary to the all-in-one-package philosophy of Django, Flask works more like the glue that allows you to combine libraries with each other.
Flask lends itself well to an iterative approach of adding new features and services “one drop at a time.”
Bottle is another framework that would rather stay out of the way than overwhelm the user with every single thing they might need.
The framework is lightweight and has no external dependencies other than the Python standard library (stdlib).
It works great for prototyping, as a learning tool, or for building and running simple personal web apps.
The maturity of Pyramid stems from the legacy of two previous frameworks: Pylons and repoze.bfg. Now merged into Pyramid, Pylons used to be one of the top Python frameworks.
Pyramid’s prime advantage over Django is that it’s very easy to customize, whereas Django is more “opinionated.” This makes Pyramid a great choice for non-standard projects that could be more complex.
We’ve barely scratched the framework surface here, so to speak. Head over to our article for more exotic examples of Python web frameworks.
Python’s simplicity and wide selection of pre-existing libraries and frameworks make it a great fit for both garden-variety web projects and highly complex web apps.
The Internet of Things can be variously understood, depending on your perspective.
For the sake of this explanation, let’s assume we’re talking about physical objects fitted into an embedded system that connects them to the Internet.
These “things” now have their own IP addresses and can interact with other “things,” remote or local, using the network.
IoT often plays a role in projects involving wireless sensor networks, data analytics, cyber-physical systems, big data, and machine learning. Additionally, IoT projects often involve real-time analytics and processes.
Ideally, your programming language for an IoT project should already be a strong choice for the aforementioned fields, while also being lightweight and scalable.
Python fits these criteria very well. Here’s how.
Have you ever seen an interesting IoT project around the web? If so, there’s a good chance the Raspberry Pi was likely involved. The Raspberry Pi:
Most importantly, it has a Linux distro on board, which means it also uses Python, making coding for the Raspberry Pi simple and straightforward. The Raspberry Pi is an incredibly versatile device you can use to build anything: a media center, a retro gaming machine, a time-lapse camera, a robot controller, an FM radio station, a web server, a motion-capture security system, a Twitter bot, a mini-desktop PC. It’s also one of the most popular tools for teaching programming.
The Raspberry Pi is an incredibly versatile device you can use to build anything:
It’s also one of the most popular tools for teaching programming.
When it comes to Python solutions for IoT, it doesn’t get much smaller than MicroPython: a small microcontroller optimized to run Python on a board that’s only a few square inches in size.
The kit includes a software package, so if you’re just starting out in IoT with Python, you don’t need to look a lot further.
One feature of MicroPython that’s especially enticing is WebREPL (read-evaluate-print loop), which is similar to a command line and accessible through a webpage. With WebREPL, you can use a simple terminal in your browser to run Python code on your IoT device without the need for a serial connection.
To make the deal even sweeter, you don’t need to connect the board to WiFi, since it can create its own network.
Zerynth hails itself as “the middleware for IoT and Industry 4.0.”
It provides developers with a full ecosystem of tools, including an IDE, a toolchain for development, a multithreaded RTOS (real-time operating system), a device manager, and a convenient mobile app to monitor and control Zerynth-powered devices.
Zerynth speeds up IoT development by allowing you to write in Python or a hybrid of C and Python.
You can use Zerynth to program the most popular 32-bit microcontrollers, connect them to Cloud infrastructures, and keep your devices running the latest version of your software with Firmware Over-the-Air updates. It’s also very compact, requiring just 60–80 kB of Flash and 3–5 kB of RAM.
Home Assistant is an open-source Python project for intelligent home automation. You can install it on a PC or a Raspberry Pi.
Home Assistant drives automation; for example, it can control the lights in your house and measure the temperature in each room.
On top of that, Home Assistant is compatible with a variety of drivers and sensors.
To Python’s great advantage, IoT programming is inching closer to desktop programming every day. As the capabilities of “smart things” continue to grow, their similarity to desktop computers is sure to follow.
We’ve already mentioned that a device like the Raspberry Pi could even serve as a miniaturized desktop PC. This trend works in favor of using Python for IoT, because with greater memory and computational power comes greater freedom of choice in terms of picking the right programming language.
Therefore, when developers and project managers are looking to choose a language that brings results fast and makes life easier, they tend to go with Python.
Writing in Python is as quick, easy, and painless in IoT as in other fields.
In the current IoT environment, you choose your programming language just like you would choose it for any other project. Ease of writing matters more than the choice of language, and Python has that in droves.
Machine learning is the latest craze in the software development world. It’s been steadily rising in popularity due to its seemingly limitless possibilities—and rightly so.
The very idea that computers can actively learn instead of operating in strict accordance with codified rules is simply exhilarating. It offers a whole new approach to problem solving.
But why is that? What is Python’s secret?
There are several reasons why Python is a perfect fit for machine learning:
But there is one more argument to be made for Python here, which in the case of machine learning is greater than all the others combined: extensive open-source library support.
Python is famous for rich selection of libraries, especially for data science. It’s the root cause why Python is considered the go-to solution for machine learning.
Here are some of the most popular Python libraries for machine learning.
Scikit-learn is the best known and arguably most popular Python library for machine learning. Built on SciPy and NumPy—and designed to interoperate with them—scikit-learn is open source, accessible to all, and reusable in a number of contexts.
The library features a wide variety of algorithms for: classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. These algorithms include: support-vector machines, random forests, gradient boosting, k-means, and DBSCAN.
Yet despite the barrage of options scikit-learn provides, the data mining and data analysis tools it offers are both simple and efficient.
TensorFlow was originally developed by engineers and researchers at Google to meet their needs for a system that can build and train neural networks to find and decipher correlations and patterns. The process was designed as analogous to the ways humans reason and learn.
The flexible, high-performance architecture of the open-source library makes it easy to deploy numerical computation across multiple platforms, as well as from desktops to server clusters to mobile devices.
TensorFlow is used by companies such as Uber, Dropbox, eBay, Snapchat, or Coca Cola—not to mention Google, of course. This pretty much speaks for itself.
Nilearn is a high-level Python library for easy and fast statistical learning on neuroimaging data. The library leverages scikit-learn for a plethora of advanced machine learning techniques, such as pattern recognition or multivariate statistics. The applications of this include predictive modelling and connectivity analysis, among others.
Constructing domain-specific feature engineering is the highest value nilearn holds for machine-learning experts. This means shaping neuroimaging data into a matrix of features perfect for statistical learning, or the other way around.
Mlpy is a high-performance Python library for predictive modeling, built on top of SciPy, NumPy, and the GNU Scientific Libraries. Multiplatform and open source, mlpy aims to provide solutions for supervised and unsupervised problems, offering an extensive range of cutting-edge methods.
Finding a sensible compromise between efficiency, modularity, reproducibility, maintainability, and usability is the prime goal of mlpy.
Supervised machine learning is one of the most widely-applied uses of AI. In supervised learning, an algorithm learns from a labeled dataset with the output already being known. Two of the main techniques within this category are classification and regression.
Classification is used to categorize data into desired, distinct classes and predict a discrete value. It can serve to assess creditworthiness or help with medical diagnostics.
Regression is used in problems that involve continuous numbers, including demand and financial forecasting, as well as property price estimation. The predicted outcome here is an estimation of a numeric value.
Both classification and regression problems can be solved thanks to a large number of Python libraries, including:
In unsupervised machine learning, the algorithm relies on its own ability to solve problems after it’s been handed an unlabeled dataset without training instructions and a known outcome.
Clustering and matrix factorization are two of the most common unsupervised machine learning methods. Frequently applied in user segmentation and recommender systems, both methods are used to group elements based on the similarity between object properties.
Some of the most popular libraries used in clustering and recommendation system engines are:
Reinforcement learning algorithms learn to modify their behavior to make the right decisions after receiving feedback. They have been tested in self-learning solutions, including video games and traffic light control systems.
Problems posed by reinforcement learning are often highly specific and finding solutions to them may prove quite challenging. These Python libraries can help:
Python is the top choice for machine learning because its myriad of pre-prepared, tried-and-true libraries does most of the heavy lifting during the development process.
The tools are all there, simple and ready to use, complemented by extensive documentation and a vibrant community to go with it.
If you’re looking to get into machine learning in Python yourself, we have a comprehensive tutorial covering all the bases, written by our very own machine learning experts.
And if you’re not new to machine learning and wish to expand your Python skills in ML, maybe you’ll be interested in a more advanced resource: an extensive overview of tree-based ensemble models in Python.
Startups are very special cases. Building them from the ground up is a substantial undertaking, as they require a different approach than an established company. Making the process easier in any way you can is essential.
That’s why when you choose the programming language for your project, you need one that will let you get started quickly and deliver the results you expect efficiently. At the same time, you can’t compromise on high quality.
Python can give you all of that.
There are plenty of reasons why Python is a dream come true for any startup. Just to name a few, it’s because the language is:
We understand that theory is one thing and practice is another.
Here are just a few startups that have rightly bet on Python as their programming language of choice.
21 Buttons is the leading European fashion social network, with a user base of over 6 million bloggers and influencers. The startup provides a platform to share clothing designs and outfits with other like-minded fashion enthusiasts.
21 Buttons built their product using Python and a variety of Python frameworks, like Django or Flask.
Deuce Tennis is a startup connecting tennis players with courts and clubs near them. Their goal is to grow the popularity of the sport and make playing tennis easy, enjoyable, and affordable for everyone.
By leveraging trending technologies such as React Native together with Python, the previously tedious process of booking and managing tennis sessions and courts becomes effortless with Deuce.
Deuce Tennis is available both as a mobile and web application. They have already made a splash on the worldwide tennis scene after they received backing from tennis legend Andy Murray.
TravelPerk made headlines a while back as one of the hottest Barcelonian startups—and for good reason. They offer an all-in-one solution for business travel booking and management.
The startup hopes to change the way businesspeople travel for good. And the best part? It’s totally free. The first and so far only one of its kind to be this bold.
TravelPerk is built in Python and React, putting code quality and user experience over anything else, with the backing of top tech investors like Spark Capital.
Like the previous entries, Zappi also enables individuals to get in contact with others. However, they stand out from the startup crowd, since their focus is putting substitute teachers in contact with schools.
The startup’s mission is to revolutionize the painstaking and stressful process of finding supply staff. Aware of the advantages Python offers, Zappi was adamant about using the language for their software product.
Their platform consists of a mobile solution for teachers, a web application for schools, and an administration platform for the Zappi hub that contains a centralized database of both the substitute teachers and the schools.
The Zappi app is available on Google Play and the App Store.
Startups are both exciting and terrifying endeavors.
They need to launch and grow fast, it takes a while before they turn a profit, and their financial foundations are often shaky.
There’s a lot at stake and the risk of failure is high. That’s why research and choice of programming language are crucial before you set out to launch a startup of your own.
Thankfully, the development speed, high code quality, and efficient scalability of Python make the language seem almost custom-made to help your startup overcome every challenge in its way.
While Python may not be a new technology, the rise in popularity it enjoys among the hedge fund and investment banking industries is a fairly recent development. But the fact that Python is the fastest-growing language in finance should come as no surprise.
If your company is about to venture into the world of fintech, you need a programming language that is high performing, easy to scale, and mature. The tech stack you choose should also have a wealth of ready-made solutions and libraries to fall back on.
This makes Python and fintech a match made in heaven.
Hedge fund and investment banking industries have long decided that Python is an ideal choice for fintech because the language addresses many of their highly specific needs:
Fintech belongs with Python for a number of reasons:
Python code is easy to understand, because it resembles actual English. This allows developers to learn it quickly and to become fairly competent in it within a short time.
Fast time to market
Python is a dynamically typed language, making development in it quicker than in statically typed languages such as Java.
When writing in Python, you need less code, which in turn allows for faster deployment.
Python boasts an extensive array of libraries for a plethora of purposes; a lot of those are excellent for fintech and finance.
Do you need an algorithmic trading library? Try pyalgotrade. Something for scientific and technical computing? There is SciPy. How about quantitative economics? Check out quantecon.py.
Whatever your question, Python has the answer.
Many fintech companies have chosen to use the many benefits Python offers to their advantage.
Here are some examples.
Venmo started out as a fintech startup. These days, it’s more of a social media network. Either way, Venmo is an intuitive tool for people to split bills or pay one another.
Like PayPal, Venmo is an easy way to move money around. Linking a credit card, debit card, or checking account is required to receive or send payments.
Originally owned by Braintree, Venmo was acquired by PayPal in 2013 for $800 million. In Q2 of 2017 alone, Venmo processed a staggering $8 billion in payments.
Backend software developers at Venmo are expected to have experience working in Python and Django.
Newable Business Finance is a platform that can be used to apply for business loans. They offer financial services to clients unwilling or unable to receive loans from traditional banking institutions.
The platform does away with tedious reliance on paperwork and bureaucracy using printed documentation, replacing it with a seamless and efficient online alternative. Business owners get their money quickly and reliably.
Newable Business Finance was built under tremendous time constraints. Within 2 months, the development team delivered the complete product, having started from nothing. Since day one, the platform has been running smoothly and pitfall-free.
How do we know that? We had a hand in building the platform.
Zopa was founded on a bold principle: cut out the middleman and let borrowers and lenders deal directly with each other. The clients’ investments are distributed across multiple loans, meaning not one of them holds more than 1% of a given investment.
The experiment paid off. In 2018, Zopa surpassed £3 billion in lending, the first consumer peer-to-peer lending platform to do so. As regulators put increasing pressure on traditional peer-to-peer lending, Zopa set out to launch its digital challenger bank.
Zopa uses a tech stack that is combination of Python, Java, and C#. Relying on a variety of frameworks, from Django to Pandas, they consider Python to be the key weapon in their arsenal.
Vyze is a leading fintech for manufacturers and retailers. They provide a comprehensive lending service, combining support, technology, and supply to elevate businesses to brand new financing heights.
Vyze offers extraordinary financing to clients of all shapes and sizes. Their solutions are smart, adaptable, and most of all simple in an effort to give you a more satisfying purchasing experience, wherever you shop.
STX Next supported Vyze in building their cutting-edge platform. Some of the solutions Vyze needed to build were nowhere to be found in other fintech products. This meant that the developers had to use every trick in Python’s proverbial book to create the solutions from scratch.
The languages and frameworks you choose for your fintech will to a large extent decide the longevity and ultimate fate of your product. They determine what you can build, who you can hire to build it with, and how long the market validation will take.
This is what you’ll get if you go with Python for your fintech:
We think the choice should be clear.
The applications of Python are numerous.
It’s perfect for web development, the Internet of Things, machine learning, startups, and fintech—among many others.
We’ve discussed at length why Python is such a good fit for all of these purposes.
However, just to give you a quick recap:
This page gives you an overview of what Python is most useful for, but it doesn’t nearly exhaust the subject, so expect more updates to come in the future.
Meanwhile, maybe you’re looking for answers to different questions. Like, say, whether to choose Python or a different programming language for your software project.
If that’s the case, we have another resource that will be perfect for you: a detailed look on how Python compares to other programming languages.
We also have other free resources you may benefit from: