In this article, we share the top 14 python applications in 2023. Whether you’d like to land a job in data science, web development, IoT, and much more, we’ve covered the essential python applications that you need to know.
As we stride into the second half of 2023, Python remains a top 3 programming language for a range of industries, disciplines, and skill levels. It’s no wonder there are so many diverse python applications.
And with the Bureau of Labor Statistics reporting an average salary of more than $100,000 for programmers, taking the time to consider the various python applications is an excellent way to pursue a career that you’re passionate about with Python.
So if you’re ready, let’s dive into the top 14 python applications in 2023.
Why Is Python So Popular?
Python has firmly secured its place as one of the best programming languages. But why is Python so popular?
Well, we could say that it’s because of the various applications of Python, but that’s obviously why we’re writing this article!
So how about we start with a summary of why Python is the perfect programming language for beginners, learners, and experienced devs alike?
- Versatility: Number 1 has to be the fact that Python can be used for a wide range of applications (read on for more!).
- Simplicity and Readability: Python is known for its straightforward and elegant syntax, which makes it easy to learn, read, and write code. This means lower barriers for beginners and also encourages collaboration.
- Community and Support: The Python community is large and active, with passionate developers that contribute to its growth and provide extensive support through forums, documentation, and online resources.
- Open Source and Free: Being open-source means that developers can access and modify the language freely, promoting innovation and making it accessible to developers worldwide.
- Cross-platform Compatibility: Python can run on any platform that has a Python interpreter installed, including the big three of Windows, macOS, and Linux. This means you don’t need to make modifications to your code to get portability and flexibility in development.
- Interpreted Language: Being interpreted means that Python allows for quick code execution and easy debugging. This is helpful for rapid development and prototyping.
- High Demand in Various Industries: It’s almost a no-brainer to say that Python's popularity has led to it being a sought-after skill in various industries, meaning there are many job opportunities for skilled Python developers.
- Rich Standard Library (Batteries Included): One of the best features of Python is the standard library’s pre-built modules and functions.
- Ecosystem of External Libraries: Next to the standard library, one of the major contributors to Python’s popularity is the impressive range of external libraries (read on for more!).
From data science to web development and game development, there’s a great chance that you’ve chosen Python because of one of the reasons we’ve listed above. That said, let’s dive into the top 14 python applications in 2023.
Top 14 Python Applications In 2023
Whether you’re unsure where to use Python or an experienced dev that wants to branch out, let’s dive into the 14 top Python applications in 2023.
We’ll also cover the popular libraries and tools for each use case, along with real-world examples of where Python has been used for that application. These can be an excellent inspiration for building your own Python projects in that particular field.
1. Data Science, Analytics, & Visualization
When you think about Python, you almost instantly associate it with data science and analytics, thanks to its comprehensive ecosystem of libraries.
So whether you need to perform data manipulation, conduct statistical analyses, create insightful visualizations, use exploratory data analysis, and more, Python is a natural fit. And if you’re new to data science but know Python, considering a data science course is a great way to make the leap.
Let’s take a closer look at the various Python libraries available to you for data science, data analytics, and data visualization.
Libraries and Tools:
- Pandas: Powerful data manipulation and analysis library that provides intuitive data structures and functions for data handling and exploration.
- NumPy: Fundamental library for numerical computations with N-dimensional arrays and mathematical functions for efficient numerical operations and data processing.
- Matplotlib: Versatile visualization library for creating high-quality plots and figures to communicate essential insights.
- Seaborn: Statistical data visualization library built on Matplotlib that provides stylish aesthetics to create visually appealing and informative statistical graphics.
- Jupyter Notebook: Interactive coding environment that supports live code, visualizations, and explanatory text, facilitating exploratory analysis, prototyping, and workflow sharing.
- Hadoop: Distributed computing framework for processing and analyzing large-scale datasets useful for big data analytics and handling massive amounts of data in parallel.
- PySpark: Python API for Apache Spark, a fast and distributed computing system for large-scale data processing and advanced analytics.
- Netflix: Python plays a crucial role in Netflix's data science workflows for tasks like analyzing viewer behavior to improve user experience.
- Airbnb: Python is an integral part of Airbnb's data infrastructure and analytics as it’s used for various data-driven tasks, including demand forecasting and pricing optimization.
- Uber: Python libraries like Pandas are used to make data-driven decisions and improve user experience.
2. Web Development
With a range of modules, libraries, and web application frameworks like Flask and Django, Python programmers can build content management systems, web applications, APIs, data dashboards, real-time apps with WebSocket, and more.
Plus, when you stack these attributes with cross-platform compatibility and interoperability, it’s easy to see why Python web development has grown in popularity.
Libraries and Tools:
- Django: Popular open-source framework following MVC architecture that encourages rapid development through its built-in components and functionalities while prioritizing security.
- Flask: Lightweight and minimalistic, easy-to-use framework providing essential functionalities while remaining extensible and modular; great for small to medium-sized web applications.
- Pyramid: A modular web framework offering simplicity and extensibility, thus allowing devs to choose and use only components they need; great for complex and enterprise-level apps.
- Bottle: A fast and lightweight Python web server gateway interface micro web framework providing developers with various features for creating web applications.
- Instagram: Instagram uses Django to maximize speed and efficiency, allowing it to scale smoothly, especially with rapid iterations and testing.
- Spotify: Spotify uses Python for data analysis and machine learning. It also uses Django for a fast, scalable backend.
- YouTube: Although YouTube was originally built on PHP, it later switched to Django for its backend to enable efficiency and scalability.
3. Artificial Intelligence & Machine Learning
Python is widely regarded as the best programming language for AI and ML for many of the same reasons that it’s so popular for data science and analytics.
Whether you want to train ML models for classification, regression, and clustering or build neural networks for image recognition, natural language processing, and computer vision, Python’s wide array of libraries makes it an ideal fit.
Or if you want to build your own Python chatbot using AI, you can rely on Python’s versatility and range of libraries to get the job done.
Libraries and Tools:
- PyTorch: A flexible and easy-to-use open-source machine learning framework developed by FAIR (Facebook AI Research lab) that supports building deep learning models.
- TensorFlow: An open-source machine learning library by Google for numerical computations, machine learning, and neural network development.
- Keras: A simple and easy-to-learn deep learning and neural network API written in Python on top of TensorFlow that runs efficiently on CPUs and GPUs.
- Scikit-learn: A popular machine learning library for feature engineering, data preprocessing, model evaluation, hyperparameter tuning, and more.
- Spotify: Spotify uses Python for its data analysis and machine learning to improve its music recommendation algorithm.
- Google: Google’s webapp2 framework is written using Python and is used in Google Maps and Google Drive. Google’s TensorFlow is also primarily written in Python.
- Amazon: Uses Python for data analysis, artificial intelligence, and machine learning, especially to recommend suitable items to shoppers.
4. Scientific & Numeric Computing
Another popular Python application is scientific and numeric computing, which can include a broad range of activities like mathematical computations, optimization, signal processing, domain-specific analysis, parallel computing, and more.
With a powerful range of libraries and the strengths we’ve highlighted above, Python continues to be favored by researchers and scientists that need to analyze data and solve complex problems across various scientific fields.
Libraries and Tools:
- IPython: An interactive shell that serves as a powerful interface for the Python language. Its useful syntax additions make it an ideal gateway to high-performance numerical computing.
- Cython: A Python superset that compiles to C, which can help speed up execution times of computations by several orders of magnitude.
- SciPy: One of the leading machine learning libraries for scientific and technical computation that leverages NumPy arrays and is part of the NumPy stack.
- Academic Research: Python is used for scientific and numerical computations and courses that teach Academic Research, particularly in bioinformatics, biology, and mathematics.
5. Blockchain & Smart Contracts
As one of the newest areas of modern development, Python skills can also be useful in the world of blockchain and cryptocurrency.
Whether you want to write smart contracts, build decentralized applications (DApps), perform security audits, develop a private blockchain, or enable blockchain interoperability, Python is up to the task. If this is an intriguing area, it can also be helpful to consider taking a blockchain course.
Libraries and Tools:
- SmartPy: A comprehensive online IDE and framework offering a familiar and intuitive way to work on the development, testing, and deployment of Tezos blockchain smart contracts.
- Web3.py: A Python library used for connecting decentralized apps to the Ethereum blockchain, enabling them to send transactions, read block data, and interact with smart contracts.
- Brownie: A Python-based framework for the development, testing, and debugging of smart contracts targeting the EVM (Ethereum Virtual Machine).
- Quorum: An open-source Python-based blockchain platform offering features tailored to organizations’ needs and used in building enterprise-level blockchain apps.
- Steemit: A blockchain-based social media and blogging platform where users can earn STEEM cryptocurrency for curating and publishing content; built with STEEM-Python library.
6. DevOps & Automation
Owing to its reputation for automation and scripting, Python is also well suited to modern DevOps practices, whether that’s automating deployments, infrastructure management, building continuous integration and continuous delivery (CI/CD) pipelines, or monitoring and logging.
Of course, DevOps skills extend beyond Python, but it’s a natural area to branch out into if you already know Python and you enjoy the infrastructure aspect of development.
Libraries and Tools:
- Fabric: An open-source Python library for executing shell commands remotely via SSH and used in app deployment or system administration tasks.
- Ansible: A simple open-source platform in Python that allows software automation and handles configuration management, app deployment, task execution, and more.
- Selenium: An open-source tool used for automating web browsers that provides a single interface for writing testing scripts in various programming languages, including Python.
- Requests: A popular third-party library simplifies making HTTP requests (like GET, PUT, POST, and DELETE) to web servers and handling the responses.
- PyTest: A Python testing framework originating from the PyPy project that’s used to write different types of software tests like unit tests, end-to-end tests, functional tests, etc.
- AutoPy: A simple cross-platform graphics user interface (GUI) automation library for Python that features functions for controlling the mouse and keyboard, displaying alerts, and more.
- Virtualenv: The most common Python virtual environment tool for creating isolated environments; easy to install and use, even for beginners.
- Netflix: Netflix is a company and application using Python throughout its content life cycle, including operating its Open Connect content delivery network.
7. IoT & Embedded Systems
Another area of growth for Python developers is IoT, and there’s also a natural synergy with DevOps and data analytics, demonstrating the versatility of Python.
From collecting and processing IoT device data and controlling IoT devices via MQTT to building real-time data streaming apps, Python is ideal. And while C++ is used for embedded systems projects with Arduino, Python is the language of choice for the hugely popular Raspberry Pi.
You can even use your data analytics and predictive modeling skills or implement edge computing solutions with IoT platforms like AWS IoT or Google Cloud IoT.
Libraries and Tools:
- CircuitPython: A programming language designed to make learning to program and experimentation simpler on low-cost microcontroller boards like the Raspberry Pi.
- MicroPython: An open-source version of Python that is optimized to run on microcontrollers and embedded systems like the Pyboard.
- PySerial: A library providing support for RS-232 serial connections over various devices, which makes serial communication in Python easier.
- Bluepy: A module in Python that allows communication with devices, specifically Bluetooth Low-Energy devices.
- Zerynth: A software implementation of Python for the purpose of programming microcontrollers, specifically 32-bit microcontroller platforms.
- Raspberry Pi: A low-cost single-board computer that’s about the size of a credit card that can plug into a TV, monitor, or other compatible screen and uses standard keyboards and mice.
- Micropython Pyboard: Compact yet powerful electronics development boards that run MicroPython. They can connect to computers (Windows, Linux, or Mac) using micro USB.
8. Software Development
When it comes to software development, Python is an excellent choice as a support language, which means it’s helpful for various aspects of the development process.
But how does this work? Well, Python is used for scripting, task automation, and managing repetitive tasks. It’s also ideal for various types of software testing and QA, continuous integration and deployment (CI/CD) pipelines, and it can be used to customize version control workflows.
Libraries and Tools:
- Jenkins: An open-source server for automation that enables developers worldwide to build, test, and deploy software projects and do so reliably.
- GitPython: A Python library used for reading from and writing to, or generally interacting with Git repositories.
- Unittest: A unit testing framework inspired by JUnit that supports sharing of test setup/shutdown code, test automation, test aggregation into collections, and more.
- TestProject: A free platform for web, API, and mobile testing and test automation.
9. Game Development
When you think of gaming languages, you may not immediately think of Python! But its simplicity makes it easy for anyone to get into game development and create Python games, even if they aren’t pros.
Python is also used in developing tools for simplifying specific game development-related actions like dialog tree creation, game logic, or level design.
Although we should point out that Python games are generally simple, and the language is most widely used for game prototyping rather than AAA game development with engines like Unreal and Unity.
Libraries and Tools:
- PyGame: A free and open-source cross-platform library used for developing multimedia apps such as video games. It provides features that help simplify game development.
- Ren’Py: A free and open-source engine for visual novels that uses Python. It enables the creation of life simulation games and visual novels.
- Godot Python: A project created to provide the Godot game engine with Python language scripting support.
- Battlefield 2: A first-person strategy shooter game using Python specifically for its server controls and game logic.
- Mount and Blade: A single-player historical-type game using Python scripting for its module system.
- EVE Online: A space-based massively multiplayer online roleplaying game written using Stackless Python.
10. Desktop Apps
While most people probably think about C++ when it comes to developing desktop apps, Python is actually quite adept in this area.
Whether you want to create graphical user interfaces (GUIs), build cross-platform apps, design data-driven desktop apps, develop multimedia apps, or build text editors and productivity tools, Python has the tools and libraries to help you create an app.
In fact, JetBrains even built one of the most popular Python IDEs with Python. Yep, PyCharm was made with Python.
Libraries and Tools:
- Tkinter: A Python library that acts as the standard interface for the Tk graphics user interface (GUI) toolkit.
- PyQT: Allows the creation of desktop, web, and mobile apps using Python and is enabled by various Qt libraries.
- Kivy: An open-source Python framework and library for developing multi-platform GUI applications.
- wxPython: A cross-platform GUI toolkit for the Python language and a popular alternative to Tkinter. Allows for easier development of apps with robust, functional GUIs.
- The Dropbox Desktop client: Dropbox’s desktop client is written in Python, though it also utilizes some Rust, TypeScript, and Go.
- PyCharm: An IDE (integrated development environment) for Python that’s written in Python.
11. Business & Enterprise
This is a relatively broad area that can vary by industry, but in general, Python is ideal for building and maintaining bespoke applications and codebases for domain-specific purposes.
For example, Python's image processing and computer vision can be ideal for enhancing inventory management via image-based search, developing image-based quality control and automation, and facial recognition apps for security and authentication systems.
Similarly, Python's OCR capabilities are ideal for financial firms that want to digitize and extract document data. Healthcare can also leverage Python's image recognition for medical imaging analysis and diagnostics. The list can go on and on!
Like other python applications in our list, there is some crossover here, so many of the libraries and tools we’ve listed above can be used here.
Libraries and Tools:
- Celery: A Python library for task queue implementation; handles asynchronous tasks and background processes in conjunction with web frameworks like Django and Flask.
- PySpark: A Python library providing an interface for Apache Spark and supports all of its features. Highly valuable for large-scale data processing.
- Dash: A framework used in building interactive web applications and data visualization interfaces in Python; useful in enterprise applications to help present data to laypeople.
- Zoho CRM: Online sales CRM software for marketing, sales, and support. Provides a Python SDK for developers to create client apps, integrations, and extensions.
- Zapier: Online automation tool allowing integrations with various apps and services; supports Python usage for creating “Zaps” or custom actions and integrations.
12. Web Scraping
In lots of ways, you can view web scraping as a sub-discipline of data science. That said, there’s a lot you can do with Python when it comes to learning to web scrape that extends beyond data science!
For example, with one of the various Python web scraping libraries or modules, you can extract data from websites, automate data collection, parse HTML or XML, interact with web forms, handle cookies and sessions, and simulate user interactions.
You can even integrate it as part of a data aggregation workflow, making it valuable for various purposes like research, data analysis, and business intelligence.
Libraries and Tools:
- Beautiful Soup: An extremely popular Python library for web scraping and HTML/XML document parsing.
- Scrapy: An open-source Python web scraping and crawling framework that crawls websites and extracts data.
- Selenium: A Python library and tool used for web browser automation that is also used for web scraping.
13. Image Processing & Graphic Design
Working with images is an interesting and lesser-known area for Python, as you can apply manipulations and enhancements, apply filters and transformations, perform image segmentation and object detection, and create custom visual effects and animations.
Python can also integrate with existing image processing and graphic design software, with tools like GIMP, Photoshop, and Blender providing Python APIs and allowing developers to create plug-ins and add-ons to enhance software functionality.
Libraries and Tools:
- OpenCV: A Python library for performing computer vision tasks and image processing.
- Python Image Library: A free and open-source library for use with Python that adds support for opening, editing, and saving images in various image file formats.
- Scikit-image: A Python package built on NumPy specifically for image processing; features a collection of image processing algorithms providing image processing functionalities.
- Pyglet: A cross-platform multimedia library supporting UI event handling, windowing, and more. Used for developing visually rich applications, including video games.
- GIMP: A free and open-source raster graphics editor like Adobe Photoshop. It offers an API that allows the creation of add-ons and plug-ins with Python (with the Python-fu plugin).
- Blender: A free and open-source 3D graphics software for 3D animation, motion graphics, visual effects, and more. Also offers a Python API for plug-in and add-on development.
14. Education & Training
With a strong reputation as the best programming language for beginners, it’s not surprising to see so many Python courses and educational programs available.
And when you factor in the 13 other python applications in our list, it’s logical to expect educational and training institutions to favor Python for teaching newcomers how to program.
In fact, more and more colleges and universities are offering Python training for a wide array of disciplines outside of computer science, including social sciences, engineering, and natural sciences. So regardless of your major, picking up a Python book might make sense!
Even beyond the teaching aspect. Python is helpful for automating administrative tasks, streamlining workflows for educators, presenting research findings, and analyzing educational data.
- Python for Everybody Specialization: With nearly 3 million previous and current students and taught by the University of Michigan, this Python course is ideal for learning the foundational knowledge you need to understand and use Python.
- [edX] Professional Certificate in Introduction to Programming: In this 4-section course from the Georgia Institute of Technology, students learn everything about Python, from basic to advanced concepts.
- Python with Dr. Johns: Our own Python course takes an academic approach to truly teach you the fundamentals and helps you to think like a Python pro for less than the cost of a college credit.
There you go, the top 14 python applications in 2023. Whether you’re looking for a job in web development, machine learning, DevOps, and more, we’ve covered the essential python applications you need to know.
And with the future continuing to look promising for Python, now is the time to do your research and investigate the top python applications for this ever-evolving programming language.
New to Python and ready to learn this versatile language? Check Out:
Frequently Asked Questions
1. What Famous Applications Use Python?
Many famous applications use Python in some form or another, including Dropbox, Reddit, Spotify, YouTube, Uber, Pinterest, Facebook, and Instagram. Some of these use web application frameworks like Django, while others use Python behind the scenes for machine learning and recommendation engines.
2. Where Is Python Used In Real Life?
Python can be used in a vast array of industries and sectors in real life, as shown by the 14 different python applications that we’ve covered above. And while it’s most often associated with data science, Python is also used in web development, software development, IoT, Blockchain, scientific and numeric applications, and more.
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