In an ever-evolving 21st-century tech bubble overwhelmed with cutting-edge research scientists, full-stack developers, data scientists, and emerging entrepreneurs, finding the right programming language to drive innovation and maintain long-term scalability is almost like finding a needle in a haystack.
The search to find a high-level programming language while keeping end-to-end functionality, efficient runtime, scalability, and versatility in mind has been a challenge for almost every business starting their next big projects.
However, hitting that sweet spot of efficiency, functionality, and versatility has gotten a lot easier in the previous decade(s).
This is probably because of the emergence of Python – the modern holy grail of data science, machine learning, web scraping, and all of the above.
As a high-level and general-purpose programming language, Python has become a popular option for web app development, general software development, and data science/machine learning (ML).
Python has grown an incredible amount, now ranking as one of the most popular programming languages in the world. It’s incredibly important where security matters, from AI to IoT, and even in industries like fintech.
Whether you’re a new developer wondering if it’s worthwhile to learn Python or a startup founder researching tech stacks for your project, understanding where and how Python is used can give you an edge over less-informed competitors.
This article will give an extensive overview of how Python is used, featuring twelve examples of Python applications:
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Web Development
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Data Science and Analytics
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Machine Learning and Artificial Intelligence
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Automation and Scripting
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Game Development
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Desktop GUI Applications
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Web Scraping and Data Extraction
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Cybersecurity and Network Tools
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IoT and Embedded Systems
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Blockchain and Fintech
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Business and Enterprise Applications
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Cloud and API Integrations
We’ll also go into practical examples by taking a deeper look at how real companies like Instagram, Dropbox, Spotify, and Reddit use Python to power portions of their applications.
But first, you should understand that the fundamentals of Python programming and its roots in interpreted programming are essential.
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What is Python?
Originally developed by Guido van Rossum in 1991, Python was released as an interpreted, high-level, general-purpose programming language with code readability and modularity in mind. The idea was something simple and powerful that anyone could use, regardless of their project’s complexity.
Each year, Python’s popularity reaches an all-time high, with 51% of developers stating that they used the language.
It’s intuitive, has a vast number of libraries, and thanks to how widely it’s used, the community is very active, and there are plenty of resources available.
Why Use Python
You can understand the philosophy of Python through these eleven traits of the developer-friendly language.
1. Code Readability
Python’s inherent syntax structure and seamless library/package imports allow for readability and conciseness. It flows like English normally would, making it very intuitive, as already mentioned.
This is one of the many reasons why we at Trio like to recommend Python as one of the first programming languages to learn if you are just getting started.
The result: developers can structure comprehensible code and function blocks with logical organization and storytelling. It is also very easy for beginners and experienced engineers to collaborate.
2. Cross-platform Versatility
Since Python uses internal C libraries and package installations, it can be used on various operating systems such as Mac, Windows, Linux, and Unix. In other words, you can develop large blocks of code once and deploy almost everywhere.
This can be a major advantage in today’s society, where you need to cater to a variety of devices and ecosystems.
This cross-platform capability enhances software application development and development team collaboration.
3. Easy to Learn
Engineered as a high-level and expressive programming language, Python has a shallow learning curve with built-in functions and capabilities to call external third-party dependencies and packages.
So long are the days of developing mammoth-sized algorithms in C.
With just under ten functional code blocks, beginner programmers can begin deploying baseline web applications and regression algorithms.
What does this mean practically? Less time learning and struggling with the code, more time actually creating new technology or fixing problems.
4. Open-Source and Community-Oriented
Since its early founding in 1991, Python has oriented itself with open-source community standards and long-term development frameworks.
With several community repositories open-source on GitHub, Python ensures that an inter-collaborative environment of developers works to release practical, up-to-date libraries and packages.
In layman’s terms, this means that you’ll get all the support you need when reaching out, and the language adapts as new technologies emerge. If you decide to use Python, we would say you can rest easy, knowing the language and support aren’t going anywhere anytime soon.
5. Large Standard Library
As highlighted earlier, Python is full of built-in first and third-party dependencies that enable application development and practical, high-level programming beyond manually implemented scripts made from scratch.
Python takes advantage of convenient code and functions that programmers can integrate into their scripting and app development processes.
The result is oftentimes over a thousand lines of conventional code broken down into under 10 functional lines of Python.
But surely this is limited? Not really. At least, not permanently, since the language continues to evolve as we have already discussed.
You’ll probably be able to find something for everything in the standard library, from data manipulation (Pandas) to HTTP requests (Requests), and much more.
6. Free
Python and its associated standard libraries are free to download and implement.
More specifically, Python is a Free/Libre Open-Source Software (FLOSS) that allows users to freely distribute copies of the software, analyze its source code, and modify it accordingly.
This means to licensing costs or anything else. If you’re a startup or scaleup on a tight budget, this is particularly appealing.
7. Support for Exception Handling
Python supports exception handling, enabling you to write less error-prone code.
Exceptions are defined as events that can occur during program execution and disrupt the normal flow of a program.
Using exception handling also helps developers with debugging, as they can test various scenarios that may or may not trigger exceptions.
All of this means you get better, more maintainable code out there faster than if you were using another language.
8. Packaged with Advanced Features
Python supports list comprehensions and generator expressions.
This allows developers to use lists and generators efficiently with respect to memory and time consumption during program execution.
It also includes powerful concepts like decorators, lambda functions, and context managers, which help make Python even more efficient.
9. Automatic Memory Management
By effectively utilizing automatic memory management methods, Python clears and frees excess memory during and after execution.
For developers, this means less time clearing extraneous, residual program memory and more time reserved for time and memory-sensitive programming.
This becomes particularly important if you are preparing to scale your application in the future, or if you are already dealing with very large datasets.
Related reading: 12 Examples of Successful Companies Using React Native in 2021
10. Strong Integration Capabilities
If you decide to use Python, you won’t be locked into your decision for all parts of your product. Python can integrate well into Java, C, and C++ applications. It’s also great alongside REST APIs, databases, or even cloud services.
You can use the best, most powerful languages in part of development, making Python one of the best choices if you are building very complicated systems, or if you’re creating enterprise solutions.
11. Data and AI Focus
Python’s extensive AI and machine learning libraries have made it the first choice for a lot of developers working in the industry.
The result has been a bit of a snowball, where frameworks encourage developers to use Python for data science, machine learning, and deep learning, these developers then contribute to fill gaps and create more frameworks, and Python becomes even more attractive for these categories.
We’ll cover more of this below!
What is Python Used For?
Imagine working tirelessly at a mega-corporate financial analytics firm, manually sifting through thousands upon thousands of quantitative revenue datasets.
That probably sounds gruesome, right?
But it gets worse.
After you’ve manually preprocessed data and deleted any remaining dataset anomalies and unnecessary financial variables, you have to perform predictive analytics and create a precise regression model that:
1) forecasts future revenue spikes and
2) uses global event patterns to anticipate critical stock movements and consumer behavior changes.
It is very difficult to create a predictive modeling architecture that both maintains accuracy on new and incoming variable data and efficiently forecasts revenue and stock movements in real-time.
Fortunately, by manipulating external Python machine learning frameworks such as Tensorflow/Keras/Pytorch and data visualization/linear algebra libraries such as Scipy and Matplotlib, you can automate the data preprocessing stage and engineer predictive ML models with out-of-the-box packages.
12 Real-World Examples of Python Applications
Hard-numbered data analytics is only one of many modern use cases where Python is helpful.
By strategically meshing Python package functions and libraries, developers and data scientists can create ML-driven web apps with frameworks such as Django or Flask, or perform image processing scripts with Numpy.
In industry sectors such as business analytics, ML/Deep Learning models can be deployed to monitor changing customer behavior and automatically correlate those trends to product changes, advertisement strategies, etc.
On the other end of the aisle, emerging healthcare IT and biomedical engineering departments are actively leveraging deep learning/AI and Python frameworks to forecast the onset of terminal diseases and neurological/respiratory illnesses.
Python-driven applications span ‘coast to coast’ in terms of their ability to generate meaningful insights and impacts, sometimes in only a couple of dozen lines of code.
Whether you’re manipulating data visualizations with data science techniques or extracting key insights with ML, Python could revolutionize how you extract meaning from information and develop new applications.
1. Web Development
By taking advantage of Python web frameworks (e.g., FastAPI, Flask, and Django), Python can be manipulated in the backend to build effective server-side web applications.
Although web frameworks are not required to construct a sustainable backbone for a web application, it’s rare that existing open-source libraries wouldn’t be implemented in conjunction with server-side Python development to speed up the app deployment process.
However, you should note that Python is not directly compiled and interpreted in the web browser.
Although projects such as PyJS can compile Python to JavaScript, Python developers typically interleave Python and JavaScript to create both server and client-side applications.
In other words, Python is solely executed on the server-side while JavaScript is downloaded to the client-side of the web browser and executed accordingly.
Although a mountainous number of Python web frameworks and micro-frameworks exist out there, Django, Flask, and Pyramid take the cake.
Let’s briefly explore Django and Flask below with consideration for their distinct complexity levels and learning curves.
Django
Established as an open-source Python framework, Django is notorious for building complex data-driven web application instances.
The framework is packaged with a variety of templates, libraries, and application programming interfaces (APIs) that allow for scalable web development projects.
It is the most popular large-scale framework, integrated onto leading websites, including Instagram, Pinterest, Bitbucket, The Washington Times, and Nextdoor.
Its “batteries-included” approach makes it especially powerful for enterprise platforms.
Related reading: 7 Global Websites That Use PHP in 2021
Flask
Although Flask is better characterized as a micro-framework for Python-driven web applications, it still leads as an up-and-coming leader in concise, yet complex, data-driven web app programming.
As a micro-framework, it possesses built-in development servers and support for convenient unit testing.
Additionally, it is prominent as a Unicode-based Python micro-framework that supports RESTFUL request dispatching.
Flask also has extensive documentation for programmers to get a head start, while its lightweight design makes it ideal for everything from smaller projects to quick MVPs, and even AI model demos.
Now that you understand how each of these frameworks differs, you can better understand the following example applications of these frameworks in action.
Since Flask has risen to prominence as a scalable, yet compact and efficient, mini-framework, an array of machine learning applications have been deployed. A good example is models that have been used for image recognition, like those trained to pick up diabetic retinopathy from eye scans or infant gender from sonograms.
Now see what Django – the more complex of the two frameworks – can do for you.
The modern websites below have used Django, along with Sentry, to deploy large-structured web and mobile applications:
- Disqus
- Spotify
- Dropbox
- The Washington Post
- Mozilla
- National Geographic
- The Onion
- National Aeronautics and Space Administration (NASA)
2. Data Science and Analytics
Data science might be the most salient Python implementation of them all.
By using already-integrated code dependencies, such as Pandas, NumPy, or Matplotlib, amateur and expert-level Python programmers can seamlessly visualize and manually extract actionable insights from massive public and private datasets, both off and on the cloud.
At a high level, developers can even synthesize data science manipulations with ML models to extract, visualize, and predict features from unstructured data (e.g., imaging data, time-series stock market data, or house price predictions).
What’s more, open-source Github repositories are filled to the brim with business-level Python applications such as NLP-driven sentiment analysis, sales forecasting tools, and customer churn models.
Additionally, data science communities such as Kaggle provide that extra bit of inspiration for you to get started on novel data science solutions to big corporate problems.
Python’s accessibility and toolset allow even small businesses to run powerful data pipelines and dashboards with minimal overhead.
3. Machine Learning and AI
In nearly every corporate and research-focused department, massive machine learning/deep learning models and architectures are beginning to be used to solve big data problems.
Whether it’s an ML algorithm that can generate new cancer drugs or a model that detects dangerous highway activity for an autonomous car, ML is bound to revolutionize every industry sector.
In short, machine learning can be divided into visual and non-visual tasks that are supervised, semi-supervised, or unsupervised. Examples of business and research-level ML Python applications include:
- Classifying toxic and insincere questions on Quora with NLP
- Retrieving Google landmarks images with computer vision
- Generating images and transferring image styles
- Creating real-time fraud detection systems in fintech
- Forecasting product demand in retail and logistics
ML frameworks such as Tensorflow/Keras/Pytorch can be used to develop and deploy predictive and generative models.
4. Automation and Scripting
Redundant tasks can be easily automated with Python. It can be used by DevOps engineers, system admins, and even marketers to free up their time and let them focus on more creative tasks.
Selenium, PyAutoGUI, and Schedule are just a couple of examples of libraries that let developers automate things like:
- Organizing and renaming files
- Extracting data from large spreadsheets and APIs
- Generating reports
- Filling out forms and testing pipelines.
This might not seem like much if you are dealing with small datasets, but in large enterprises, this can save days of manual effort. Think of how marketers for a large firm can analyze all the user engagement across a web page in simple graphs instead of needing to look at each visit manually.
5. Game Development
In addition to research and business applications, Python has a critical role in backend game development with logic and mods.
Perhaps the most memorable Python applications in game development include the following:
- Civilization IV
- Battlefield 2
- Sims 4
- Toontown Online
- World of Tanks
- EVE Online
- Frets on Fire
Additionally, by using open-source game development libraries such as Pygame, programmers can build simple games such as a Dungeon Game, a Guessing Game, or an educational tool.
Python is particularly popular for indie developers thanks to how it lets them prototype gameplay mechanics, UI, and AI behaviors very quickly before shifting to production engines like Unity or Unreal.
6. Desktop GUI Applications
Admittedly, Python isn’t fully optimized for deploying business-grade desktop GUI applications, at least not when you compare it to C and Java libraries.
However, developers can get started with official Python documentation and implement toolkits/frameworks such as PyQT, Tkinter, Kivy, WxPython, and PyGUI to create lightweight user interfaces with full software functionality in mind.
For example, this GitHub repository boasts 15 distinct PyQt-driven projects that use Python-driven UI and backend components to create responsive and functional Python applications.
A preview of these applications includes:
- Untabbed Web Browser
- Tabbed Web Browser
- Minesweeper
- Notepad
- Calculator
- Word Processor
- Webcam/Snapshot
- Media Player
- Post-it Note
- Paint Software
- Unzip
- Translator
- Weather
- Currency converter GUI
- And Solitaire
7. Web Scraping and Data Extraction
By using prebuilt Python libraries such as Beautiful Soup, developers with minimal hands-on Python experience can extract specific, purposeful data from virtually any webpage.
At a higher level, Python web scraping libraries can be combined with NLP models to effectively scrape the surface web for textual and visual information associated with specific words, intentions, or emotions.
Examples include:
- Extracting the table from Nepia.com’s shipping impact page
- Scraping dates and times when governmental safety orders were imposed
- Scraping information about trending GitHub repositories, such as name, stars, links, etc.
- Aggregating job listings, product reviews, or real estate data
- Tracking competitors’ metadata or SEO elements
All of this makes web scraping a powerful asset for data-driven niches like marketing, finance, and recruitment. It can even play a big role in something like journalism.
8. Cybersecurity and Network Tools
Security is becoming more important as technology deals with sensitive information. Python libraries generally allow for security to be considered when closing, but libraries like Scapy, Impacket, and Socket can also be used to create quick scripts that help with:
- Penetration testing
- Exploiting overall development
- Dealing with packets
- Analyzing malware
- Testing software using brute-force attacks
- Automating security
Python plays a big role in being able to hack into your own software to figure out where you might be vulnerable (ethical hacking), and then presenting that information in the form of a security audit that you can use to address issues.
9. IoT and Embedded Systems
The IoT and embedded systems are becoming more normalized, and thanks to frameworks like MicroPython and CircuitPython, you can use the language to code quickly and efficiently.
This lets Python play a role in coding controllers or single-board computers (like the good old Raspberry Pi). But what does this mean in practice? It means you can use Python to write the code for:
- Home automation
- Smart agriculture, like irrigation controllers
- Wearables
- Industrial sensors
Since Python prototyping can be done so quickly, you can experiment with the latest technology available and carefully monitor the results.
10. Blockchain and Fintech
When we hear Python, we think of blockchain and fintech. A big reason why Python is synonymous with these industries is its reliability, although the sheer number of numerical libraries out there also plays a role.
Common apps include things like smart contracts, trading bots, investment tools, risk analysis, and even fraud detection.
Of course, automation also plays a role in these apps and allows for:
- Real-time reporting
- Automated compliance checks
- Regulatory reporting tools
Python is so reliable that JP Morgan Chase, Citigroup, and Robinhood all use Python. If you come across a crypto wallet or blockchain explorer, chances are that Python is the main driving force behind them.
11. Business and Enterprise Applications
With modern business applications covering the spectrum of e-commerce, ERP, and much more, having a scalable and open-source language is crucial.
Python allows businesses to develop scalable and fully functional applications right out of the box.
Business analysts can use platforms such as Tryton to develop and deploy Python applications.
The umbrella of business development in Python can encompass many tools, including:
- Web development (e.g. Instagram)
- Data science solutions (e.g., Spotify with their Discovery Playlists)
- ML frameworks (e.g. Uber’s driver-rider algorithms)
- Natural Language Processing (NLP) (e.g., Google Home Assistant, Amazon Alexa, and chatbots for customer service).
- Video game development (e.g., Sims 4)
- Entertainment (e.g., Light and Magic studio)
- Blockchain-enabled contracts (e.g., Pyethereum)
- Fintech platforms (e.g., Citigroup and JP Morgan Chase)
12. Cloud and API Integrations
Python is perfect for cloud-native ecosystems. Its integration abilities make it perfect fr working with RESTful APIs and cloud platforms like AWS or Google Cloud.
There’s also a myriad of libraries available that target cloud and API integrations, and play a role in:
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Automating cloud infrastructure provisioning (Pulumi or the Terraform Python SDK)
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Deploying serverless functions
Practically, this means you can combine automation with cloud platforms. Automating cloud reports is just one example. Integrating third-party services and even deploying scalable microservices further make Python a critical part of cloud development.
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Finding Python Developers
If everyone and their mother can learn Python, how can you find a reliable Python developer who has all the coding skills to build the software you need?
Look for Python developers with experience in similar projects. Make sure you spend time going through their portfolio and even following up on their references.
Also, pay attention to how they test their code and collaborate with others.
If you aren’t sure what you should be looking for, this can be rather daunting. Instead of making costly mistakes, you can reach out to a staff augmentation or outsourcing firm like Trio.
We keep only the top 1% of developers on our team and fit them to you based on the unique requirements of your project, rather than trying to go for a one-size-fits-all solution. Our attention to detail has paid off, with a 97% placement success rate.
Conclusion
Python is often underestimated as a rather simple programming language that wasn’t engineered with versatility in mind.
However, Python has emerged amongst the stars as one of the most popular programming languages.
Whether you’re building web applications with convenient Python frameworks or using data science and machine learning to optimize your business outcomes and strategies, Python produces actionable benefits at both a low and high level.
Trio has senior-level Python developers who are familiar with advanced libraries and frameworks to get the backend of your project up and running.
If you’re starting a Python application project, consider hiring Python developers at Trio!
