Frequently Asked Questions
What is Python?
What is Python 2?
What is Python 3?
Python is currently one of the most loved programming languages according to the Stack Overflow Developer Survey 2022. It also makes for a versatile fintech development tool owing to its vast ecosystem of financial libraries. Plus, it's simple syntax and high-level language tenets make it easy to learn for non-technical people.
Why Is Python Widely Used by the Financial Sector?
The finance sector and fintech in particular operate in a fast, competitive, and heavily-regulated environment. Time-to-market is key and efficiency and ease of use are highly desired in any technological tool.
Python lends itself well to fintechs by scoring high on the above metrics and more. The most significant advantages it brings to the fintech sector are:
The fintech space is marked by hundreds, if not thousands of players vying to crack the next big solution and get funded. There really is no place for laggards or technologically hesitant companies. Python’s flexible and scalable code enables developers to quickly build a minimum viable product (MVP) and test its efficacy on an existing market problem.
Companies no longer need months of work to test the waters with a new idea. Once they’ve validated their MVP, they can easily modify and refine it, thanks to Python’s performant code.
Python was designed with readability in mind. Its syntax resembles plain English and is fairly easy to learn and master which is especially useful for designing complicated financial models. It’s also known for its concise code and is hence perfect for deploying large financial applications.
Its simplicity also minimizes potential errors and can save organizations time and money by avoiding penalties in a heavily regulated industry.
Fintech developers don’t need to design their applications from the ground up. Python makes the job easy with its rich collection of standard and third-party libraries and tools.
Financial software involves analysis, algorithms, models, and other complex mathematical concepts. By providing these solutions built-in using various libraries and third-party integrations, Python surpasses other programming languages.
In terms of popularity, Python sits well above Java and C++ which are also commonly used by the financial sector.
Python provides stellar support to fintech developers, due to its open-source codebase and excellent support from a passionate community. These factors naturally provide fintechs with ample Python talent to choose from.
Economists and other finance professionals don’t need to get their MATLAB models translated into Python code. Python supports end-to-end development of financial models and solutions in a single environment. It’s a versatile tool for prototyping and development for both small and large applications.
4 Applications of Python in Fintech
Fintech applications encompass a wide range of sub-verticals and mathematical disciplines. The beauty of Python is that its multiple libraries give developers access to countless pre-built instances of financial models and solutions.
Here are four major applications of Python in finance.
1. Data Analysis & Visualization
Making sense of large and complex datasets and visualizing them for further predictive analytics is at the core of many fintech solutions. Python libraries are equipped with robust data visualization, statistical analysis, and machine learning capabilities. Pandas, PyBrain, and Scikit are some examples of such libraries.
2. Banking Software
Banking applications, both traditional and modern, make use of Python’s simplicity and scalability. ATM software uses Python. So does Venmo for its payment services. Many other fintech companies like the payment processor Stripe and the trading platform Robinhood utilize Python for their platforms as well.
With new players entering the cryptocurrency space almost every day, the demand for market analysis programs is also increasing. Companies dependent on analyzing cryptocurrency prices and offering predictive insights need these programs on a daily basis.
Data analysis tools like Anaconda and many decentralized platforms on the blockchain utilize Python and its libraries to empower their code.
4. Stock Trading
Python’s impressive data analysis capabilities are a great fit for designing stock trading strategies and offering predictive insights into the markets. Algorithmic trading is no longer limited to large corporations. Anyone can use Python’s libraries to design their own predictive algorithms for stock trading.
What Are the Advantages of Python Over Excel?
Microsoft Excel is one of the most preferred tools for data analysis and visualization. It’s easy to pick up and offers a decent degree of functionality. But Python leaves Excel in the dust when it comes to working with data. Here’s why:
Better Data Import & Processing: Cleaning multiple large datasets in Excel isn’t really what you’d call simple. It’s tedious and time-consuming. Python can easily recognize and clean both structured and unstructured data much faster than Excel.
Plus, performing tasks like merging or recoding data are also much simpler in Python.
Powerful Automation: Excel is many things but it isn’t an automation powerhouse. If you wanted to run the same analysis week after week with different data, you’d have to do it manually in Excel.
But in Python, you can write a simple script that imports the new data every week and performs the analysis automatically, while also outputting the results in your desired format.
Easier Debugging: When something doesn’t go according to plan in Excel, you’re given an error notation and left to fend for yourselves. In Python, however, you'll get an error message explaining what exactly went wrong.
Moreover, if you’ve left comments in your code, you’ll have additional information to help you fix the source of the error.
Open-Source Accessibility: Excel might help you do a lot with your data, but you’re ultimately dependent on Microsoft for feature updates and support. Whereas Python is a free, open-source language which can anyone can update and add functionality to.
Superior Statistics & ML capabilities: Excel isn’t suited to building advanced statistical and machine learning models. Python, with its extensive third-party libraries, has superior tools to easily create advanced data analysis models.
Advanced Data Visualization: Data visualization is essential for effective communication with stakeholders. Although Excel has a robust data visualization interface with its charts and graphs, Python lets you do much more with your data.
You can create more customizable graphs and charts and use techniques that are simply much more flexible as compared to Excel.
Cross-Platform Portability: Python’s scripts are portable across platforms like Windows, MacOS, Linux, and more. This isn’t common with other preferred technologies in the fintech industry, including Excel.
The 10 Most Useful Python Packages for Fintech
Now that you’re aware of Python’s incredible functionality for building fintech applications, it’s time to learn the most useful packages or libraries that will actually help you do it.
NumPy (pronounced num-pi) is one of the most basic yet essential libraries in Python. It brings several mathematical and scientific computing capabilities to the language, which other libraries on this list build on.
It introduces concepts like n-dimensional arrays and matrices into Python and contains basic functions to manipulate such data structures.
SciPy introduces the tools to build sophisticated data models from the basic mathematical structures provided by NumPy. Any statistical model requires algorithms for tasks like clustering, interpolation, transformation, integration, and more.
Hence, SciPy is the library providing developers with the advanced techniques to build predictive data models, a mainstay of many fintech applications.
Pandas is a popular Python library known for its ‘DataFrame’ and ‘Series’ structures that are specially designed for data analysis and model building. Pandas is optimized for multiple kinds of data like tabular, multidimensional, and heterogeneous. You can also create basic visualization plots using this library.
It also provides concise and powerful functions for importing and manipulating data.
While SciPy provides advanced statistical tools and Pandas helps implement them, statsmodels introduces more thorough testing of different statistical models. Diagnostics and results statistics for every model are available and the results are tested against statistical packages to ensure accuracy.
Quandl allows you to access financial datasets from central banks, government websites, multinational companies, and stock markets to help you build the financial model you desire. You simply need an API key to access most of the raw datasets for free, with more in-depth ones available for a fee.
Zipline brings many of the above libraries together to become an algorithmic trading library. Quantopian, a popular platform for building trading strategies, is powered by Zipline.
It imports data from Quandl, and helps you design and implement custom trading algorithms. It also includes backtesting of algorithms and live trading.
Pyfolio enables fintech developers to generate tearsheets containing performance statistics pertaining to the algorithms they designed with Zipline. Stats like annual returns, Sharpe ratios, portfolio turnover, and more are easily available through Pyfolio.
Technical Analysis Library or TA-Lib can be used as an alternative to Zipline and Pyflio. It’s a C++ library with a Python wrapper available. TA-Lib provides common financial tools like momentum indicators, volatility indicators, cycle indicators, pattern recognition, and pure statistical functions.
QuantLib is another alternative to Zipline and Pyflio. It is also written in C++ and then exported to Python. Quantlib is optimized for building tools related to modeling, trading, and risk management.
Its algorithms include features like yield curve models, solvers, Monte Carlo analysis, market conventions, and more.
While Pandas offers some basic visualization tools, it’s not a data visualization library like Matplotlib. It is one of the easiest packages to implement for financial modeling. It has a simple syntax and extensive documentation.
Supercharge Your Python Project With Trio
Python is primed for fintech development but excellent results require excellent developers, If you’re in the market for hiring Python developers for your project, Trio can help make your job easier.
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6 min read
Python frameworks provide the basic infrastructure for developing a robust Python software application. Depending on the software project your team is trying to build, there are better and worse Python frameworks suited for its purpose.
Sources demonstrate that Python's popularity has been steadily increasing in the past decade or so, with approximately 15% of Stack Overflow questions tagged “python” in 2021.
And as such, the corresponding technologies of Python gain similar popularity. It is common to see questions like “What is a Python framework?” or side-by-side Python framework comparisons.
Today, you’ll learn about numerous Python frameworks to use for your software development project. But first, what is a Python framework? Stick around to find out!
What Is a Python Framework?
A Python framework, like any framework, cushions your software development project with a foundation or substructure to build on top of. To be clear, software frameworks render the generic functionality of the program you’re trying to create, so your developers don’t have to start from scratch.
Python frameworks in particular, will automate standard application building steps using the Python programming language. As a result, Python developers will save time on development and your business will benefit overall.
Where Is the Use of Python Most Recommended?
Python is a general-purpose, interpreted, object-oriented programming language. The designers of the language prioritized code readability whilst building Python, making the language highly productive.
Readability being an important part of Python’s founding philosophy, Python’s most salient representations of this tenet include its intuitive use of the English language and easily navigable code blocks.
Python’s prime asset is its significant white space, which developers use to isolate code blocks. This tactic encourages easy-to-read code and gives a more friendly appearance to the program compared to other programming languages that use special characters such as brackets.
Released in 1991 by Guido van Rossum, today Python is known as a “batteries included” language because of its large standard library. Python libraries describe pluggable pieces of code that you can work into your program with a simple function call.
The Python Standard Library is what comes with the language right out of the box. This library is notably extensive, with high-level data structures like dictionaries and lists, turtle graphics, and more.
The Python programming language performs well on both the front-end and back-end of development. Moreover, developers use Python to build a variety of software, from web development to machine learning to data science.
Much of Python’s capacity for a diverse array of programming capabilities involves its simplicity and its standard as well as additional libraries.
Python Framework vs. Python Library
Some people wonder about the difference between Python frameworks vs. Python libraries. Although they both offer developers operational functionalities, their similarities end when it comes to specificity and complexity.
For example, Python libraries are filled with dozens of useful functions for specific purposes, but there is not much complexity. Python libraries perform a simple task and are ultimately a small piece of a large puzzle.
Because frameworks work off a specific comprehension of what your application might look like, even more specificity is permissible. Complexity is the name of the game. Thus, a Python framework will outline the puzzle; the more complex the framework, the more outlining of the internal pieces.
The 3 Main Types of Python Frameworks
Python frameworks come in all shapes and sizes. Factors that may affect which framework you choose include scalability, expertise, and your business-specific goals.
Full Stack Framework
Full-stack frameworks are suitable for both back-end and front-end development. Web development consists of front-end tools for graphic user interface (UI) design and back-end services like databases, security protocols, and business logic.
Microframeworks are by definition lightweight. In some ways, it is the opposite of a full stack framework. Python’s official Flask documentation explains that the “micro” in microframework signifies that the framework’s “core [is] simple, but extensible”.
The components that are fundamental to a full stack framework like a database management system and certain security measures do not come naturally to a microframework.
While this might seem like a bad thing, it actually encourages flexibility for developers who want to leverage control over their software, only adding in the relevant third-party libraries when they’re completely necessary.
Related reading: Ruby vs. Python - Which Programming Language To Choose?
Asynchronous programs are event-driven. Rather than line by line operational handling where one function runs after the other, asynchronous code is non-blocking and doesn’t wait for one event to execute before starting another.
Because of this parallel programming technique, asynchronous frameworks allow for a profusion of high-performance concurrent connections via running on an async-capable server.
10 Awesome Examples of Python Applications
25 Python Frameworks To Use In 2023
In total, there are dozens and dozens of Python frameworks. But since you care about your time, here are 25 Python frameworks that stand out the most.
CubicWeb fits the definition of a full-stack framework. It is also a semantic framework. Semantic technologies rely on ontological and conceptual comprehension to understand data.
Though the typical model-view-controller (MVC) architecture is more commonly in use for developing UIs, CubicWeb uses the concept of cubes. Cubes join together to create an instance with configuration files, a database, and a web server.
AIOHTTP is an asynchronous framework as well as an HTTP client/server for asyncio. Though some of these words may seem foreign, the gist is that AIOHTTP allows your developers to build asynchronous clients and servers for your web application.
Dash is a microframework best suited for building analysis-oriented Python applications. The framework is a frequent choice for businesses that have stakes in machine learning and data science.
Bottle is another microframework. Fast and simple, its original design was specified for building application programming interfaces (APIs).
Like the best of microframeworks, Bottle emphasizes its lightweight build, namely by implementing everything into a single source file and relying on few dependencies. Other important features of Bottle include routing, template, utilities, and a built-in HTTP server.
Bottle works closer to hardware than other frameworks. However, you should opt for a more complex framework if you think your program might span longer than 500 lines of code.
CherryPy calls itself a minimalist framework. With CherryPy, you can create an entire application using the principles of object-orienting programming. As a result, development is faster and easier.
Yet another API-oriented framework is Falcon. Falcon is a minimalist microframework geared towards rapid application development. Developers can take advantage of Falcon to build RESTful APIs, app backends, and microservices without the burden of unnecessary abstractions.
Django is one of Python’s more popular Python frameworks. Being full stack in nature, Django comes equipped with the tools to build large and complex web applications.
Flask is historically in competition with Django. Django and Flask are the top two most popular Python frameworks. That said, they are wildly different.
Unlike Django, Flask is a microframework and does not hold any tools or functions that a third-party library can fulfill as essential to its packaging.
Giotto is a Python framework stressing the separation of concerns that the classic MVC architecture elicits. Because of Giotto’s generic implementation of views, models, and multiple pluggable controllers, the framework is a vessel for simplifying and shortening development.
Growler is both an asynchronous framework and a microframework. The framework uses Python’s asynchronous library, asyncio, along with middleware to process HTTP requests.
Pylons borrows the finest ingredients of Perl, Python, and Ruby to bring to life a full stack framework with a focus on rapid development. Similar to Flask, Pylons is lightweight and flexible.
Pyramid is a leading full stack Python framework. Scalability is its biggest asset as small and full-scale applications can depend on Pyramid for their development.
On its official website, Pyramid calls itself a “Goldilocks Solution: not too small, not too big, just right.”
Sanic shares similarities to Flask, but its asynchronicity makes it even faster than Flask. Benchmark tests show that Sanic can handle tens of thousands of requests per second.
Hug is a microframework for developing APIs. Labeled the fastest framework for Python 3, Hug is paving the way for the technology of the future.
Dubbed the “Super Powered Python Web Framework”, Morepath is an expressive model-driven microframework providing routing to its models.
Like with MVC patterns, models represent data and views represent the UI. In Morepath, an application consists of models which are routed to a URL path. Content is expressed to the web through views.
Initially developed to support a social media company named FriendFeed that has since been acquired by Facebook, Tornado is an asynchronous Python framework that can handle several thousand concurrent users at once with a non-blocking network I/O.
TurboGears has the versatility for use as a full stack framework or a microframework. The framework is data-driven, user-friendly, and extends a templating engine and an object-relational mapper (ORM).
Web2py was originally built as a teaching tool, but as a full stack framework, it is loaded with tons of features to do so much more. For instance, there are no requirements for installation and configuration; web2py is cross-platform; and it comes with its own integrated development environment (IDE).
FastAPI is a bit self-explanatory. It’s a modern Python framework for building APIs with features to speed up the development process. Much of its design owes it credit to Flask.
Grok leverages important programming principles such as ‘don’t repeat yourself’ (DRY) and ‘convention over configuration’ (CoC) to promote an agile development experience.
DRY strives to reduce repetition by standardizing abstractions. Likewise, CoC aims to decrease the grueling decision-making process for developers by starting them off with a conventional foundation to build up from so they can save time on configuration.
BlueBream is a Python framework that is most optimal for building medium and large applications. More than a framework, BlueBream is a server and library too. Some of its best features are its component architecture, transactional object database, and integrated security protocols.
Related reading: Python vs. Java - Side-By-Side Comparison
Quixote is a simple and flexible Python framework for designing web applications. Anyone with a good knowledge base in Python should thrive with Quixote.
Weppy — stylized as the lowercased “weppy” — is a full stack Python framework that centers simplicity. Its elegant syntax encourages developers to focus on the product the’re building rather than struggling to navigate the framework itself.
Zope is an application server and web framework. Its primary use case is for the development of content management systems (CMS).
How To Choose the Best Python Framework To Use
Using a Python framework is a sure-fire way to speed up and optimize development. Python frameworks are not a must, but it would also be unwise to neglect the opportunity to enhance your development process where at all possible.
To figure out which Python framework is best for your software project, start by evaluating the different types of frameworks you can choose from. Three major categories of frameworks stand out: full stack frameworks, microframeworks, and asynchronous frameworks.
Microframeworks are small yet flexible; full stack frameworks are complex yet scalable; and asynchronous frameworks amplify Python’s capacity for concurrency.
Once you pick which class of Python frameworks is a good fit, you can narrow your choices down by focusing on the frameworks themselves. Listed above are 25 different frameworks with different capabilities.
You should choose a Python framework based on scalability, speed, your developers’ expertise in deploying framework-specific skills, and a number of other considerations that may be conducive to your business.
Going through each and every Python framework probably isn’t worth your time. But what you’ve learned here can better help you pinpoint exactly what you need.
What is a Python framework? It’s a tool to empower your next Python project, but only if you use the right one.
There are a diverse bunch of Python frameworks out there for you that will give you and your software development team a head start on your 2022 project. The Python framework world is your oyster.
Having a good understanding of how your next project will work, even if you're not tech-savvy, will be crucial to choosing the optimal tech stack to support your development.
Research is never a bad thing and if you need to consult with some of the more qualified minds in the industry, there’s no shame in it. That’s why Trio is here to help.
Trio can guide you in choosing the best Python framework for your 2022 project as well as build the project itself using the parameters you set.
Contact Trio now to hire the best Senior Python Developers and start your project today!
8 min read
Companies using Python choose the language largely because of its ease of use and high-level capabilities.
Developers can use the Python programming language for a diverse range of applications from building web apps to creating games, and even machine learning.
Because of Python’s versatility and dexterity, companies using Python are often global in scale. To better understand how Python can be useful for your own business, take a look at these 16 global companies using Python:
- Industrial Light and Magic
Throughout this piece, you’ll learn more about Python as a programming language and how it is implemented into the tech stacks of various major companies. Keep reading!
What Is Python?
Python is a high-level general-purpose programming language. Although you can use Python for front-end development, most developers agree that Python works best for back-end development.
Some other important features of Python are that it is dynamically-typed, garbage-collected, and interpreted.
As an interpreted language, this means that Python doesn’t directly translate programming into machine code as compiled languages do. While the disadvantage of this is slower execution at runtime, it does allow for dynamic typing.
Dynamically typed languages perform type checking at runtime. This means that type errors occur only once developers run a program.
But on the plus side, dynamic typing usually elicits more flexibility and more concise coding. And coders do not have to declare a variable expressly.
With garbage collection, Python can automatically manage the memory in a program by dumping any objects that no longer need to be referenced.
These details are just the nooks and crannies of Python’s overall architecture. What drives the global companies using Python to choose this language over others is likely Python’s design philosophy.
Guido van Rossum started designing Python in the late 1980s taking careful note to emphasize code readability in its structure and syntax.
This emphasis naturally results in improved productivity for developers, thanks to the intuitiveness and simplicity of the language.
Python’s use of significant whitespace, rather than the brackets that other languages tend to use to parse blocks of code, stands out as one of the defining features of the language. It’s a utilitarian example of how Python’s design philosophy works with the language in practice.
There is even a document called the Zen of Python summarizing several guidelines behind Python's core philosophy.
What Are the Advantages of Using Python?
There are many advantages to using Python. Below are a few of the main advantages leading companies using Python to choose the language for their tech stack in the first place.
Python is easy in more than one way. It’s easy to read, nearly replicating the English language in composition. Likewise, it is easy to write.
Of course, developers who can understand the code they’re writing do much better than those who don’t.
Global companies using Python can rest assured that their developers are spending less time in Stack Overflow and more time developing.
But besides basic comprehension, code that is easier to write is by definition high-yielding. For example, it takes fewer lines of code to perform a task in Python than using another popular, high-level programming language like Java or C.
In consequence, businesses can speed up development time and decrease their time-to-market (TTM), giving them more leverage to keep up with the competition.
Related reading: Python vs. Java: Side-By-Side Comparison
Like most programming languages, Python is free. It’s also open-source, meaning developers can modify elements of the language to their needs. But more importantly, Python is popular.
All the above characteristics contribute to the fact that the language is in high demand. Python is accessible, both in usage and syntax. And all the companies using Python only further this cyclical chain of demand and implementation.
Popular technologies strive because of their large and dedicated communities which offer tactical solutions to developers in need.
For your own benefit, note that an in-demand language will guarantee that you have a large pool of Python developers to choose from for your next Python project. The larger the pool, the less difficult it will be to find bonafide talent.
Judging from the growing list of global companies using Python, it’s clear that Python is scalable.
In this regard, Python’s utility for a wide array of applications truly shines. First of all, Python supports multiple programming paradigms, including structured, procedural, functional, and object-oriented programming (OOP).
OOP and class structures often serve as the foundation for large-scale software applications.
Python also has sound libraries for machine learning and artificial intelligence, exemplifying just how far-reaching the language is.
Data science is another subject in which Python excels, with financial technology routinely relying on the Python language.
Not to mention, Python is conducive for game development as well, with big names like Sims 4 and Eve Online owing their existence to the infamous Python.
In short, Python can do many things and most of the things Python can do are objectively exceptional.
16 Global Companies Using Python
Now that you have a greater understanding of what Python is and what it can do for your business, you can heed a closer analysis of how other companies are using Python.
Hopefully, Google needs little to no explanation. In fact, it’s fairly safe to bet that you’re using a product of Google, whether that’s your search engine, web browser, or both.
Python is one of Google’s official server-side languages, along with C++, Java, and Go.
Peter Norvig, the director of research at Google, and former director of search quality maintains that, “Python has been an important part of Google since the beginning, and remains so as the system grows and evolves.”
Facebook, the social network that doesn’t get enough credit for coining the ‘poke’, is one of the many companies using Python.
With a net worth of nearly 900 billion US dollars as of May 18, 2021, Facebook has gained mass popularity over the years. Therefore, understanding the technology behind Facebook’s success is essential.
The use of Python on Facebook’s back-end is mostly for production engineering, a specific sector of development at Facebook that combines systems and software engineering.
Essentially, the production engineering team is responsible for making sure that well over two billion people around the world can depend on Facebook to run smoothly. Python plays a pretty big part in this process.
Quora is a mix between a social media network and a question-and-answer platform.
Ironically, a question posted to Quora about the programming languages in use for the website prompted a reply from Adam D’ Angelo, the CEO of Quora himself.
D’ Angelo asserted that using Python was simply less “painful” than writing programs in another language like Java.
At the time, Quora engineers also believed that Python would evolve with the website, or in other words — scale up.
Considering that in 2020, Quora was able to garner 300 unique users per month to visit its platform, this certainly isn’t far from the truth.
Day by day, PayPal is helping people keep their money safe through convenient and secure transactions via the PayPal platform.
PayPal uses Python across its entire infrastructure and one of the most shared articles on Python, “10 Myths of Enterprise Python” is credited to the official PayPal Engineering writers on Medium.
Thus, if you enjoy using PayPal, keep in mind that PayPal is yet another global company using Python.
Though the name Stripe may seem unfamiliar, you might be using Stripe more than you know. Stripe is a fintech startup giving companies the ability to accept online payments.
Major companies such as Amazon and Lyft utilize Stripe as part of their platforms, making Stripe a major company itself; it is also another of the dozens of global companies using Python.
Using Python, Stripe built its application programming interface (API) for cross-compatibility with mobile applications and websites.
Instagram offers an alternative medium for social media where content is largely visual. Most of Instagram’s business logic relies on Python.
In software development, business logic refers to the operations occurring on the back-end that facilitate the exchange of data between the database and user interface (UI).
To put it simply, Python has a central role in how Instagram operates, powering the interactions of over a billion users daily.
Long gone are the days where you have to be worried about your phone, or even better, your MP3 player, running out of space because of all the music you have in storage.
Instead, there’s Spotify, which permits users to stream music for free. Spotify uses Python for its back-end services and for data analysis.
What’s more, Spotify sponsors international Python conferences, namely PyCon and Euro Python. From this, you can infer that Spotify is very eager about its use of Python.
Netflix is a content platform where users can binge-watch TV shows and films of all kinds from a singular web app. Users access Netflix from a smartphone, tablet, smart TV, or even a video game console.
The fact that Netflix is using Python is well-known and well-advertised. Several tech-oriented news sites have spoken enthusiastically about how Netflix uses Python, including Netflix’s very own tech blog.
Python is a facet of many internal structures of Netflix, including but not limited to:
- Open Connect, a content delivery network (CDN)
- Demand Engineering, a component of Netflix’s network availability that works to prevent latencies and improve user experience (UX)
- Experimentation, a platform to perform AB tests and other experiments
- CORE, a team that uses data analysis to alert relevant parties about system issues
- Animation & VFX, a subsection of Netflix for creating animated content and video effects
Uber started as a rideshare service with the aim to make passengers feel more secure in addition to extending convenience at a cheap rate.
Since, Uber has expanded its services to UberEats, a food delivery service.
Reddit is a bit of an underdog in the social networking community. Reserved for those who enjoy the pleasures of cruising internet black holes, Reddit is a network of communities formatted as a plethora of discussion forums and related subforums.
Steve Huffman and Alexis Ohanian, the two surviving co-founders of Reddit — Aaron Swarts being the third — presented at PyCon in 2009, naming Python’s large assortment of libraries as one of the reasons Reddit continues to use Python.
When Dropbox first became a technology of regular use, it was one of the original cloud services to go mainstream. Through Dropbox, users can host files of almost any sort and access them through the internet.
Engineers at Dropbox use Python for many reasons; the language’s cross-platform support, ease of learning, and readability are its biggest strengths.
The best way to describe Pinterest is to call it an online scrapbook. Users on Pinterest can share their interests by way of visual pins that depict their hobbies, design ideas, lifestyle inspirations, and more.
It might be hard to imagine that NASA is another name on the list of global companies using Python, but it is.
NASA, or the National Aeronautics and Space Administration, primarily uses Python in their Workflow Automation System (WAS) for shuttle mission planning and data management.
Python’s ease of use permits NASA to meet project specifications without being set back by unnecessary complexities.
NASA also uses Python for a number of other projects which can be found on their website summarizing NASA’s open-source projects.
Instacarts grants the average person the comfort of home-delivered goods. Grocery delivery was never so accessible.
With Instacart using Python and Ruby on its back-end, the logistics of allocating shoppers to buyers in an orderly and timely manner is not any more difficult than solving 2 + 2. (Okay, that’s an exaggeration, but you get the idea.)
Lyft is Uber’s doppelganger, if you will. But depending on the person, pricing, and location, there are different preferences all around as to whether Lyft or Uber can get the job done — that is, getting people from point A to point B.
In short, Lyft uses a lot of Python. The evidence? Roy Williams, the tech lead manager at Lyft, gets down to the dirt and grit of programming in Python on many of his Medium articles for Lyft Engineering.
16. Industrial Light and Magic
Star Wars fans should be glad to learn more trivia facts behind the franchise’s success. Industrial Light and Magic, for instance, is a visual effects company founded by the notorious George Lucas.
Without a doubt, Industrial Light and Magic, was and is a VIP in the making of the Star Wars films.
Python was a key element in moving towards more CGI-intensive filmmaking. Computer-generated images (CGIs) are the result of a revolutionary digital transformation that all science fiction lovers should be grateful for.
If the success of a major company serves as any justification for how well your business can use Python successfully, then hopefully these 16 global companies using Python served their purpose well.
Python is a developer favorite amongst programming languages because it has the ability to do grand things without overly complicating the code itself.
Figuring out what tools will ultimately further your business’s growth is a big decision. But you will need to have the right developers to use those tools, just the same.
Looking for qualified and experienced Python developers? Look no further. Trio is here to help.
Contact Trio now to hire senior Python developers!
8 min read