What is Python Used For? 10+ Coding Uses for the Python Programming Language

Python has emerged as one of the most popular and versatile programming languages in the world. It consistently ranks as a top language in surveys – for instance, Python claimed the #1 spot in both the 2021 Stack Overflow Developer Survey and the 2021 TIOBE Index.

So what makes Python so universally loved by the developer community? A big factor is its remarkable versatility. Python is a true jack-of-all-trades language that can be applied to an incredibly wide range of domains and use cases.

In this article, we‘ll take a deep dive into 10+ of the most popular and exciting applications of Python, from data science to game development to robotics. Whether you‘re a beginner looking for inspiration or an experienced Pythonista, there‘s sure to be something here to pique your interest. Let‘s jump in!

Data Science and Analysis

One of Python‘s biggest claims to fame is its central role in the booming field of data science. Python has become the go-to language for data scientists around the world, thanks to its robust ecosystem of data science libraries and frameworks.

For data analysis and manipulation, the Python data science stack is anchored by NumPy for numerical computing and Pandas for analyzing data in table formats. Wes McKinney, the creator of Pandas, explained the power of the language in a PyCon keynote:

"Python has become the dominant platform for scientific computing and data science largely because of the strength of its general-purpose libraries like NumPy and Pandas that make Python so productive for so many people."

On the data visualization front, Python boasts popular tools like Matplotlib for creating static, animated, and interactive visualizations, and Seaborn for statistical data visualization. Matplotlib has been used to create visualizations for major publications like The Economist and FiveThirtyEight.

For interactive computing and exploratory data analysis, Jupyter Notebook has become an indispensable tool, used by over 6 million people worldwide. Jupyter won the prestigious ACM Software System Award in 2017 for its impact on data science and beyond.

Machine Learning and AI

Closely linked to Python‘s dominance in data science is its starring role in machine learning and artificial intelligence. Python offers an unbeatable combination of cutting-edge ML frameworks and a concise, expressive syntax that appeals to data scientists and researchers.

TensorFlow, Google‘s industry-standard framework for machine learning, offers APIs for both beginners and experts to develop state-of-the-art ML models in Python. Since its initial release in 2015, TensorFlow has been used by major companies like Airbnb, Coca-Cola, DeepMind, Intel, and Twitter.

PyTorch, an open source ML framework led by Facebook, has seen rapid adoption due to its dynamic computation graphs and use of idiomatic "Pythonic" concepts. Yann LeCun, Chief AI Scientist at Facebook, called PyTorch "the most popular deep learning framework by a large margin."

For traditional ML algorithms, Scikit-learn provides a unified interface to train models for tasks like classification, regression, clustering, dimensionality reduction, and model selection. As of 2021, scikit-learn is downloaded over 16 million times per month on the Python Package Index.

Web Development

Another massive use case for Python is building web applications. Python offers developers battled-tested frameworks for getting web apps up and running smoothly and efficiently.

Django is the most popular Python web framework, known for its "batteries included" approach of providing built-in features for common web dev tasks. Django is the framework behind many major websites like Instagram, Spotify, YouTube, The Washington Post, and Bitbucket.

Django‘s key philosophy is Don‘t Repeat Yourself (DRY), which allows developers to reuse code and focus on writing unique aspects of their web app. As explained in the Django docs:

"Django‘s primary goal is to ease the creation of complex, database-driven websites. Django emphasizes reusability and "pluggability" of components, less code, low coupling, rapid development, and the principle of don‘t repeat yourself."

Another popular Python web framework is Flask, a lightweight "microframework" for building web apps with simpler requirements. Flask is used by companies like Netflix, Lyft, Reddit, and Mailgun.

Python web frameworks have been battle-tested in production environments by some of the biggest websites in the world. This proven track record makes them a compelling choice for web developers of all experience levels.

Computer Science Education

Python has rapidly become the language of choice for teaching introductory computer science courses at both the secondary school and university level. Its concise, readable syntax and interactive shell make it an ideal first language for students to learn programming concepts.

The creator of Python himself, Guido van Rossum, explained Python‘s appeal for education in an interview:

"Python is a great first language. You can learn about procedural programming; you can learn about object-oriented programming; you can learn about some high-level data structures. And the nice thing is that you can take those concepts and map them directly onto other popular programming languages."

Python is now the most popular language for introductory CS courses at top-ranked U.S. computer science departments, used by over 70% of the top 10 CS departments and 50% of the top 39 CS departments as of 2020. Even the College Board‘s AP Computer Science class switched from Java to Python in 2020.

Online learning platforms have also embraced Python for their CS curricula. On Coursera, Python is the most popular language, used in over 14% of all courses. Codecademy‘s catalog includes over 50 Python courses on topics ranging from analyze data with Pandas to build chatbots with Python.

Computer Vision and Image Processing

Python has become an essential tool in the rapidly expanding fields of computer vision and image processing, which deal with processing, analyzing, and extracting useful information from digital images.

The most widely used library for computer vision in Python is OpenCV, which provide a wide range of features like facial recognition, object detection, motion tracking, and much more. OpenCV has over 47,000 people in its official user group and has been downloaded over 18 million times as of 2020.

For image processing in Python, key libraries include scikit-image for tasks like segmentation, geometric transformations, color space manipulation, and analysis; Mahotas for traditional image processing functions; and SimpleCV for basic camera access and image processing.

These libraries have been applied to problems ranging from detecting tumors in medical scans to Automating the photo tagging process used by Facebook and Google Photos. As computer vision becomes more ubiquitous, Python will continue to play a central role.

Game Development

Python is also a popular choice for game development, particularly for casual and independent games. The PyGame library provides functionality and a development framework for game devs to create 2D games with Python.

PyGame has been used to create hundreds of games, including some popular titles like Frets on Fire and Metin 2. It‘s also been used as a learning aid – for instance, a version of Valve‘s hit game Portal was recreated using PyGame by a group of students as an educational project.

Another popular game dev library for Python is Pyglet, a cross-platform framework for game development. Pyglet has been used to create games like Minecraft: Pi Edition and Dropsnake.

For 3D rendering, Python can be used with Panda3D, a framework used to create 3D rendering for everything from games to visualizations to training simulators. The ease of use of Python makes it an appealing choice to get started with game programming.

Medicine and Pharmacology

Python is increasingly being applied in the fields of medicine and pharmacology to help develop new treatments and improve patient outcomes. Some key applications include:

• Drug discovery: Python is used to analyze vast amounts of biological data to discover new drug targets and predict potential side effects. The open-source Chemputer platform for automated chemical synthesis uses Python to optimize the drug manufacturing process.

• Clinical decision support: Python-powered machine learning models are being developed to assist doctors in diagnosis and treatment decisions. For example, Stanford researchers created a deep learning model to detect pneumonia from chest X-rays with Python and TensorFlow.

• Genomics analysis: Python is the foundation for popular bioinformatics tools like Biopython and Pymol for tasks like analyzing DNA/protein sequences and 3D molecular modeling. The BioSeq library provides an integrated environment for bioinformatics in Python.

• Medical imaging: Python libraries like SimpleITK are used for processing and analyzing medical images like MRIs and CT scans. Python was used to create InnerEye, a Microsoft Research project aimed at extracting clinically useful insights from 3D medical images.

Biology and Bioinformatics

Adjacent to its medical applications, Python is also widely used in the realms of biology and bioinformatics to make sense of complex biological data. Central tools include:

• BioPython: A set of freely available tools for biological computation written in Python. It‘s used for tasks like working with and manipulating DNA, RNA, and protein sequences. Biopython also includes modules for tasks like machine learning, data visualization, and interfacing with online databases.

• Pymol: An open-source molecular visualization system that uses Python for commands and customization. PyMol supports high-quality 3D rendering of proteins, small molecules, density, surfaces, etc. Pymol has been used in thousands of scientific publications.

• NumPy and SciPy: Not specific to biology but invaluable for numerical and scientific computing. NumPy arrays are used widely for efficiently storing and manipulating biological sequence data. SciPy is used for tasks like clustering, interpolation, and phylogenetics.

The ease of use and extensive ecosystem of scientific libraries in Python make it highly attractive for biologists and researchers in related fields who need to process and make sense of complex data.

Neuroscience and Psychology

Python is also enabling breakthroughs in the study of the human brain and mind. Key Python neuro/psych libraries and projects include:

• PsychoPy: An open-source application written in Python that allows researchers to run a wide range of neuroscience, psychology, and psychophysics experiments. PsychoPy has been used in over 5,500 published studies as of 2021.

• NiPype: A Python framework for processing and analysis of neuroimaging data. NiPype provides an environment that encourages interactive exploration of algorithms from different packages, eases the design of workflows within and between packages, and reduces the learning curve necessary to use different packages.

• Nengo: A Python library for creating and simulating large-scale neural models. Nengo attempts to make modelling the complex system of the human brain more straightforward. Nengo was used to create Spaun, currently the world‘s largest functional brain model.

Python has become a central player in probing the mysteries of the human brain due to its strong ecosystem for scientific computing and data analysis.

Astronomy

Python‘s scientific prowess is also being harnessed to explore the vast reaches of outer space. Important Python libraries for astronomical research and discovery include:

• AstroPy: A community-driven package that contains core functionality and common tools needed for astronomical computing in Python. Astropy is used for tasks like manipulating and analyzing astronomical datasets and computing commonly used quantities like celestial coordinates and orbital mechanics.

• SunPy: A community-developed open source software library for solar physics in Python. SunPy provides data structures and tools for the analysis of solar datasets, including images, spectra, and time series data.

• SpacePy: A Python-based library for spacecraft science data analysis. SpacePy includes modules for magnetospheric, ionospheric, and other space plasma modeling and analysis, as well as coordinate transformations, orbit calculations, and other common tasks in space science.

Python‘s ease of use and expansive collection of astronomy packages have made it a favorite choice of programming language for astronomers and astrophysicists around the world.

Other Areas

Beyond the domains we‘ve covered in-depth, Python has also found significant applications in areas like:

• Robotics: Python can be used to program robots thanks to libraries like PyRobot, a robotics research platform that provides a unified API to access different robots and simulators.

• Autonomous vehicles: Python is used to develop the AI systems and computer vision capabilities essential for self-driving cars. Waymo, Cruise, and Tesla all use Python in their autonomous vehicle development.

• Meteorology: Python is used for weather forecasting and climate modeling. Libraries like the Community Intercomparison Suite allow climate scientists to analyze data and compare models in Python.

• Business: Python is widely used for quantitative and financial analysis on Wall Street and in the broader business world. Libraries like Zipline allow traders to create and test trading strategies with Python.

• GUI development: Python can be used to develop graphical user interfaces for desktop apps using frameworks like PyQt, wxPython, and Kivy.

This is just a small sampling of Python‘s remarkable versatility – in reality, you can probably find a Python use case in just about any field or industry you can imagine.

The Future is Bright for Python

We‘ve taken a whirlwind tour of 10+ exciting applications of Python across a wide swath of fields and industries, but one thing is clear – Python is here to stay and its use cases will only continue to expand.

As the vast amounts of data generated in our modern world keep growing and fields like AI/ML evolve at breakneck speed, Python‘s strengths in scientific computing, ease of use, and extensive ecosystem ensure it will remain an indispensable tool.

At the same time, Python‘s central role in introductory CS education means new generations of programmers are growing up as "native Pythonistas" for whom Python is their first and primary language. This influx of new talent versed in Python will drive further innovation.

No matter your experience level or domain of interest, it‘s never a bad time to learn Python. Its incredibly wide range of applications means that Python skills will serve you well throughout your career journey, from getting a first job to making groundbreaking discoveries. Happy coding!