10 Awesome Examples of Python Applications

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 are 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 to 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). 

Today, you will learn 10 awesome examples of how Python is used – from machine learning and AI algorithms to business analytics.

But first, you should understand that the fundamentals of Python programming and its roots in interpreted programming are essential

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.  

Each year, Python’s popularity reaches an all-time high with a current 31.73% share of popularity amongst modern development languages.

 

 

 

The 9 Traits of Python

You can understand the philosophy of Python through these nine traits of developer-friendly language

1. Code Readability

Python’s inherent syntax structure and seamless library/package imports allow for readability and conciseness.

The result – developers can structure comprehensible code and function blocks with logical organization and storytelling.

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.

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.

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-sourced on Github, Python ensures that an inter-collaborative environment of developers works to release practical up-to-date libraries and packages.

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.

6. Free

Python and its associated standard libraries are free to download and implement.

More specifically, Python leads as a Free/Libre Open-Source Software (FLOSS) which allows users to freely distribute copies of the software, analyze its source code, and modify it accordingly.

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. 

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.

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.

Related read: 12 Examples of Successful React Native Apps

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.

10 Ways Python is Used

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 correspond 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 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 Applications

By taking advantage of Python web frameworks (e.g. 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 interlace 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.

Related read: 7 Companies That Use PHP in 2020 Successfully

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.

Now 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.

Some stand-out examples include computer vision algorithms that can predict gender and diabetic retinopathy detection based on eye fundus photographs.

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 scalably deploy large-structured web and mobile applications:

  • Disqus
  • Instagram
  • Spotify
  • Dropbox
  • The Washington Post
  • Mozilla
  • Pinterest
  • National Geographic
  • The Onion
  • National Aeronautics and Space Administration (NASA)

2. Console-Based Applications

Otherwise known as terminal applications, console-based applications are broadly defined as any Python-associated app that runs inside the terminal.

In the grand scheme of casual Python development, console-based Python deployments have the following advantages:

  • They’re much more fun and fulfilling to write than simple script-based applications.
  • Terminal apps enable you and involved users to play with complex code without ambiguous layers of abstraction between you and the user.
  • You’ll learn about fundamental user interaction issues at a primitive level that doesn’t include wishy-washy UI/UX distractions.

Although console-based applications and command-line interfaces (CLIs) primarily exist to support convenient user interaction during more complex command-line processes, fun and practical use cases include:

3. Data Science Implementations

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 and toxic comment classification or future sales predictions. 

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.

4. Desktop GUI Applications

Admittedly, Python isn’t fully optimized for deploying business-grade desktop GUIapplications, 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 include:

  • 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

5. Software Development

As Python rises to new and unforeseen popularity, product/project development pipelines have gradually adapted to Python’s set of frameworks and best practices.

Although software development with Python covers the technical trails of ML/Deep Learning, scripting, and web/OS-level application development, Python’s most advantageous contribution to software development is its ability to accommodate project ideation and development and advance applications from insecure to robust.

With Python, rising software developers can effectively fulfill the following development-focused goals:

  • Advancing Python applications from conception through full-stack execution and app deployment by integrating Python frameworks and development tools.
  • Contributing to private and open-source Python applications by regulating version-controlled workflows and collaboration with your development team.
  • Understanding interactions between raw code and the client-side facet of your web apps/Python applications
  • Writing secure apps that blockade and deter cybercriminals from exploiting common open patches and vulnerabilities.
  • Adopting best practices in code review and portfolio development with your Python applications

6. Business 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. 

Platforms such as Tryton can be used by business analysts 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 w/ 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)

7. Image Processing 

Python is a useful tool for image manipulation and analysis. With its open-source dependencies, which can oftentimes be installed on your command line, you can extract and manipulate features with a mere 8-10 lines of code.

Whether it’s research, education, or industry-level applications, these following Python libraries provide you with convenient image analysis tools.

8. 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 and Guessing Game.

9. Web Scraping Applications

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:

10. Machine Learning and Artificial Intelligence

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:

ML frameworks such as Tensorflow/Keras/Pytorch can be used to develop and deploy predictive and generative models.

Conclusion

Python is oftentimes 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 programmers 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!

 

 

 

Daniel Fleury

About

Daniel is a machine learning researcher and engineer at Johns Hopkins University

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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. 

Forecasts future revenue spikes and uses global event patterns to anticipate critical stock movements and consumer behavior changes.