What Can I Do With Python? A Detailed Breakdown of Its Applications

A fair question that nearly every beginner in programming has is: "What can you do with Python?"

It's a natural curiosity, shared by those who are just starting out and by those who are already writing some code but wonder whether it's worth the effort. Is Python a language that's in demand? Will it open up new opportunities? Is it the right choice?

Learning to code often feels like traveling without a map. There are many different roads, but no signs showing which one will work best for you. That's why we created this guide --- to help you navigate. This article is a short and practical map. In just a few minutes, you'll get a clear picture of the main fields where Python is used.

You'll see that Python is a skill worth learning and improving --- and you might get a bit of motivation to keep going, too.

What are some useful things you can use Python for?

There's no doubt that Python is one of the most popular programming languages. Today, it's used in far more fields than just software development. Python has found an application in industry research, data science, education, digital marketing, and trendy fields like artificial intelligence, machine learning, and robotics.

Scientists, analysts, educators, and even creatives use Python to solve work problems and streamline everyday tasks.

Here's a list of the main fields where Python is applied:

1. DevOps and system administration
2. Network programming
3. Cybersecurity
4. Internet of Things (IoT)
5. Blockchain and cryptography
6. Finance and trading
7. Media and entertainment
8. Natural language processing (NLP)
9. Bioinformatics
10. Geographic information systems (GIS)
11. Augmented and virtual reality (AR/VR)
12. Desktop application development
13. Image processing and computer vision
14. Robotics
15. Climatology and meteorology
16. Web development
17. Data science
18. Machine learning
19. Automation
20. Software engineering
21. Game development
22. Scientific research
23. Education

Impressive, right? Of course, in some areas Python is essential, while in others it's more of an optional helper. Let's take a closer look at how Python is used in each of these fields.

DevOps and System Administration

In DevOps, Python is the go-to choice for automation, environment configuration, and infrastructure management. It supports the Infrastructure as Code approach and integrates with major DevOps tools like Ansible, Docker, Kubernetes, and CI/CD platforms.

Managing, deploying, and scaling infrastructure in cloud environments is also easier with Python, thanks to libraries built for the most popular cloud platforms: Boto3 for AWS, azure-mgmt for Azure, and SDKs for Google Cloud.

System administrators use Python to streamline user management, analyze logs, monitor service health, and remotely manage servers via SSH.

Python's key benefits in this space include cross-platform compatibility (Python code runs on Windows, macOS, and Linux), clean syntax, and a wealth of open-source libraries. All of this makes it ideal for writing anything from simple scripts to complex automation systems.

Network Programming

Python is commonly used to create programs that transfer data over the internet or local networks. It works equally well for basic and complex tasks --- from connecting devices to exchanging data with web servers.

At the low level, you can use Python to build your own tools or services that directly "talk" to other machines. High-level libraries like requests simplify working with APIs and web requests.

Python also makes it easy to write scripts that automate network tasks, like configuring devices, checking their status, or troubleshooting inevitable issues. That's why network engineers and other IT specialists often know at least the basics of Python.

Cybersecurity

In modern cybersecurity, Python is widely used for penetration testing --- simulated attacks on systems that help find weak points and vulnerabilities hackers might exploit. During such "recon" phases, libraries like requests, BeautifulSoup, and Shodan are essential. Python also integrates with key scanning and vulnerability detection tools like Nmap, OpenVAS, and Metasploit.

Python is also used to build custom exploits, automate log analysis, detect threats, and respond to incidents. For web security, specialists often rely on frameworks like Django (an open-source Python-based web framework), which has built-in protections against SQL injection and CSRF attacks.

Internet of Things (IoT)

The Internet of Things is not just about smart homes and gadgets --- it also includes a wide range of industrial remote monitoring systems and complex sensors. Python has found its place in building IoT solutions for both personal and industrial applications.

What IoT developers themselves highlight as Python's key strengths are its simplicity and flexibility. It's great for rapid prototyping, especially thanks to specialized compilers like MicroPython and CircuitPython that run on microcontrollers such as ESP32 and Raspberry Pi.

When it comes to smart sensors, libraries like NumPy, Pandas, and Matplotlib are incredibly useful. They can be integrated to process and visualize the data these sensors collect.

Python enables the development of IoT systems that can learn, make predictions, and take real-time actions, ranging from smart gadgets to predictive maintenance on industrial equipment.

Blockchain and Cryptography

Python is often used to develop core blockchain logic, process transactions, and interact with smart contracts, especially on platforms like Algorand, which natively support Python.

With libraries such as cryptography and M2Crypto, blockchain and cryptography specialists implement hashing, encryption, and digital signatures. These are all essential security mechanisms in this field.

There's also a useful Python library for web-based blockchain development: web3.py, which allows interaction with smart contracts on Ethereum, one of the leading platforms for building decentralized online services.

Of course, Python's strength in data analysis (in this case, blockchain data) and task automation also plays a role. As with IoT, Python's simplicity and the wide range of libraries and tools make it ideal for rapid prototyping and building decentralized apps.

Finance and Trading

Python is widely used in finance to handle large volumes of data, automate repetitive tasks, and model complex systems with minimal code.

Financial analysts use Python for forecasting, risk analysis, portfolio optimization, and developing algorithmic trading strategies.

If you're looking to explore Python in this field, start by learning libraries like Pandas, NumPy, and SciPy --- they make it easier to work with and visualize financial data. For modeling and testing trading strategies, libraries like TA-Lib, Backtrader, and Zipline are popular choices.

Here's a simple example of how finance professionals can use Python:

import yfinance as yf

data = yf.download("AAPL", start="2024-01-01", end="2024-12-31")

print(data["Close"].mean()) # Average closing price of Apple in 2024

This short script pulls stock data and calculates the average closing price.

Media and Entertainment Industry

Since this industry is massive and diverse, we'll stick to just a few clear examples. Python is in demand for building AI models that generate music and video, edit content, and power recommendation engines. These tasks are often handled using libraries like TensorFlow and PyTorch.

For example, YouTube uses Python in its recommendation system to analyze watch history and user interactions. Python's analytical capabilities stretch even further, helping companies analyze audiences, predict KPIs, and manage huge content libraries.

With tools like MoviePy and OpenCV, you can automate video editing and visual effects. Python also plays a major role in 3D animation for films and games.

Natural Language Processing (NLP)

Python allows you to quickly create tools that understand, analyze, and generate human language, from text classification and sentiment analysis to building chatbots. It's a natural fit for this field thanks to its simplicity and a rich ecosystem of specialized NLP libraries.

If you're learning Python for natural language processing, pay close attention to these 5 libraries:

• NLTK -- great for learning and prototyping. Includes tools for tokenization, stemming, parsing, and more.
• spaCy -- a high-performance production-ready library that supports named entity recognition, part-of-speech tagging, and syntactic analysis.
• TextBlob -- beginner-friendly and intuitive, offering simple APIs for sentiment analysis, translation, and basic NLP tasks.
• Gensim -- specializes in topic modeling and working with word embeddings like Word2Vec.
• Transformers (by Hugging Face) -- a powerful library for using pre-trained models like BERT and GPT in tasks such as summarization and question answering.

Thanks to these and other tools, Python is considered a convenient and powerful language for NLP development.

Bioinformatics

Python is one of the core languages in bioinformatics, a multidisciplinary field where computing is used to analyze large volumes of biological data.

Specifically, Python is applied to analyzing DNA, RNA, and protein sequences. With the Biopython library, you can perform tasks like sequence alignment, mutation detection, and gene expression analysis.

Python is also used in studying protein structures, particularly with visualizers like the PyMOL system. For gene function prediction and drug interaction modeling, Python's machine learning libraries (e.g., scikit-learn) come in handy.

Generally speaking, Python is popular in bioinformatics because it handles large-scale genomic datasets efficiently: something that's essential in genomics, metagenomics, and population genetics.

There are many standard and niche Python libraries to support work in this field. If you're planning to dive into it, start with NumPy, Pandas, Matplotlib, and Seaborn---these will help you process, organize, and visualize biological data.

Geographic Information Systems (GIS)

GIS (Geographic Information Systems) are used to collect, process, analyze, store, manage, and visualize geospatial data. Python has carved out a strong role here due to its flexibility, automation capabilities, and accessibility, even for professionals without a deep technical background.

Python is used to automate routine tasks in ArcGIS (via ArcPy) and QGIS (via PyQGIS). It's particularly effective for buffer analysis, layer merging, and building web maps.

If you're interested in using Python in this area, focus on libraries like GeoPandas and Shapely --- they make it easy to read, process, and analyze spatial data.

Augmented and Virtual Reality (AR/VR)

Python plays an essential role in AR and VR application development, especially during prototyping, scripting, and AI integration. As you already know, Python's simple syntax is great for quick prototyping --- something AR/VR developers also take full advantage of.

Python is used to handle user input, object logic, and backend tasks like data processing, authentication, and more. Among the specialized libraries used in AR and VR, it's worth mentioning OpenCV for real-time object tracking and PyOpenGL for generating 3D graphics for VR.

Additionally, Python acts as a "bridge" to integrate AI into virtual environments, enabling adaptive worlds and intelligent behaviors.

Desktop Application Development

Python has also found its place in building desktop applications, internal tools, and prototypes. It offers several convenient frameworks for GUI development:

• Tkinter --- the standard library for simple applications.
• PyQt --- a powerful framework with a large set of widgets.
• Kivy --- focused on touch-based applications.
• CustomTkinter --- a modern, flexible UI framework built on top of Tkinter.

Python applications can run on any platform --- Windows, macOS, and Linux.

While Python isn't the fastest language for desktop development, it covers most use cases and easily scales thanks to integration with C/C++.

Image Processing and Computer Vision

Python has gained popularity in this field due to a wide range of tools for working with images of varying complexity, from simple scaling or cropping to object detection and image analysis.

To get started with Python in this domain, begin by learning the basic syntax and understanding data structures. Master NumPy for working with arrays and Matplotlib for visualization.

Then move on to OpenCV (for image manipulation and video analysis), scikit-image (for segmentation, transformation, and working with color spaces), SciPy (for filtering and segmentation), and TensorFlow or PyTorch --- for deep learning and pattern recognition.

Robotics

The appeal of using Python in robotics lies in its simplicity and fast development cycle. Python integrates well with frameworks like ROS (Robot Operating System) and helps developers control motors, sensors, and manipulators, as well as handle complex tasks like localization, mapping, and path planning.

Python also enables robots to perceive the world (robot vision), interact with environments, and even coordinate as a group.

Familiar libraries, NumPy, Pandas, and OpenCV, are used to process streaming data from lidars, cameras, and GPS. RoboDK, a platform for programming and simulating robotic systems, supports Python scripts to simulate robot behavior before deployment to physical hardware.

To dive deeper into this topic, check out curated algorithm collections and hands-on Python robotics examples on GitHub.

Climatology and Meteorology

In this domain, Python has gained popularity for its ability to process large volumes of data, perform complex computations, and visualize results.

Researchers use Python to work with formats like netCDF (a file format and library suite for storing and sharing scientific array data) to analyze historical weather records, model climate systems, and build weather maps.

Important Python libraries for climatology and meteorology include:

• MetPy: provides tools for atmospheric sounding, wind shear analysis, and map projections.
• Xarray: supports working with multi-dimensional climate datasets, especially gridded data or model outputs.
• Cartopy: helps generate professional-quality climate plots and spatial maps.

Python also helps tackle complex domain challenges like detecting climate patterns or predicting environmental changes, thanks to integration with machine learning.

Python in Web Development

Python is a popular choice for web development due to its clean syntax, rapid development cycle, and large ecosystem of frameworks. It's beginner-friendly yet powerful enough to build scalable and secure web applications.

So, what should you learn to use Python for the web? First, understand the basics of the language and how the web works --- HTTP, requests, and responses. Then dive into frameworks like Flask or Django. Flask is lightweight and flexible; Django is full-featured and great for larger projects.

You'll also need a foundation in HTML, CSS, and JavaScript. Next, get comfortable with Jinja2 for templating, SQLAlchemy or Django ORM for database work, and tools like Gunicorn and Heroku for deployment. Later, you can explore building APIs using FastAPI or Django REST Framework.

This field is limitless --- web development grows with you. We recommend checking out the Python developer roadmap to complete the picture.

Python in Data Science

Python is essential in data science. It's a language that's both simple and flexible --- something we've already stated. And of course, Python comes with a vast set of open-source tools to support the entire data pipeline, from cleaning to modeling and visualization.

The main libraries to know for using Python in data science are: Pandas for data manipulation, NumPy for numerical computations, Matplotlib and Seaborn for visualization, and scikit-learn for machine learning. Also, take note of Jupyter Notebooks --- a powerful tool for sharing and reproducing experiments.

This is a promising field for growth, so if you're ready to dive deeper, we recommend starting with "Python for Data Analysis" by Wes McKinney.

Python in Machine Learning

Python's popularity in machine learning, as you've likely noticed, has helped the language branch out into many other fields where growth depends on ML implementation. So what makes Python so convenient for machine learning? It has simplicity, ease of use, rapid prototyping capabilities, and an extensive ecosystem of domain-specific libraries.

Planning to master Python for ML? Start by learning Python fundamentals along with some linear algebra. Then move on to libraries you're already familiar with: NumPy, Pandas, and Matplotlib for data preparation and visualization. Learn scikit-learn (for classic ML algorithms) to build models, and dive into TensorFlow or PyTorch for deep learning.

Recommended read: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron.

Python in Automation

It's safe to say that Python is one of the most convenient and popular languages for automation. Not just in software and service development, but in virtually any field that involves repetitive daily tasks. In other words, in almost every field.

Using Python can save hours of manual work by scripting tasks like batch file renaming, form filling, or website data extraction. Automation isn't just about convenience. It reduces the risk of human error and optimizes workflows across IT, marketing, HR, finance, and more.

To start using Python for automation, learn the basics of scripting and key modules like os, shutil, and datetime. Then move on to libraries such as Selenium (web automation), PyAutoGUI (GUI automation), and schedule (task scheduling).

Python in Software Development

While Python is not a new language, it's gaining momentum in software development. According to the TIOBE index, Python holds the #1 spot among programming languages worldwide and is also the most used language on GitHub.

Python is open source --- free to learn and use, even for commercial projects. Its simplicity draws more and more beginners who choose it for professional development. And with its huge community and wealth of learning resources, it's more accessible than ever.

Python developers can build nearly any type of application --- from desktop apps to backend systems, automated solutions, and embedded software. It's cross-platform and integrates easily with other languages (like C/C++). That means you can add Python to any new or existing project with minimal friction.

Python in Game Development

Python may not be the go-to language for game development, but it's a solid choice for getting started, especially if you're exploring game dev as a hobby. For prototyping and 2D games, Python works well.

Beyond the language basics, you'll want to explore libraries like Pygame and Turtle for simple visualizations. Later, look into Panda3D and how to integrate Python scripts into engines like Godot.

What about the downsides? Popular engines like Unity and Unreal only partially support Python, and Python doesn't compete with C++ or C# in terms of speed.

Still, you can build simple games with it --- here's a collection of tutorials to try.

Python in Scientific Research

Python is the primary language of modern science, accessible due to its open-source nature. It is extensively used in research to the point that it deserves a dedicated article. For now, here are a few quick examples to give you the big picture:

• In biology, Python is used for DNA sequence analysis and drug discovery.
• In physics, it models particle behavior and complex systems.
• Economists use it to forecast trends and analyze markets.
• Climatologists rely on Python to track environmental changes using real-time satellite data.

Core libraries in science include NumPy and SciPy for advanced calculations, Matplotlib and Seaborn for data visualization, and Jupyter Notebooks for interactive experiments. Naturally, there are also many domain-specific open-source libraries for specialized tasks.

Python in Education

Python is steadily becoming the #1 programming language taught in schools and universities. No surprise there --- as you know by now, it's very beginner-friendly. Students can focus on logic and problem-solving without being slowed down by complex syntax, allowing them to progress quickly in coding.

Python is also used in many other subjects. You'll often see examples with Python code in data science, robotics, or AI courses. Its clean syntax makes it easy to write concise, readable code for everything from basic scripts to advanced AI systems.

Tools like Thonny, Jupyter Notebooks, and Turtle make it even easier for beginners to experiment and visualize their learning journey.

Quick summary: Python's popularity and benefits across industries

Python's possibilities are nearly limitless. However, it's more widely used in some industries than others. If you're learning Python to boost your career, we recommend focusing on areas like Data Science, Machine Learning, Web Development, Software Development, and Automation. These are the top 5 fields where Python is essential.

To help you digest the massive amount of information you've just gone through, we've put together a quick reference chart summarizing Python's use across industries (ranked by popularity), along with examples of major companies using Python in each field.

Learn Python with CodeGym

The future where coding skills --- or at least the ability to write simple Python scripts --- are essential for numerous professionals, not just developers, is almost here. Even today, Python is used by scientists, educators, content creators, marketers, analysts, and researchers. If you're still wondering whether you should learn it, yes, you should. And the best time is now. The sooner, the better.

Not sure where to begin? Try the Python course by CodeGym. It's perfect for absolute beginners, starting from the basics and guided by mentors. In addition to live lectures, you'll work on fun hands-on projects, solve coding challenges with instant feedback, get practical lessons, and learn to use modern development tools.

Whether you want to simply cover the fundamentals or go all the way to becoming a Python Fullstack Developer, it's all within reach with CodeGym.