Python appeared in the programming ecosystem more than 30 years ago. However, it has taken the tech world by storm in recent years. Today, it holds first place by a wide margin as the most popular programming language.
But what is Python used for precisely? In this article, we’ll explore 11 of the most common uses of Python. From web development to data science, you'll discover practical ways this powerful language boosts the tech systems in various industries. So, let’s dive in and find out how Python can be your go-to tool for multiple purposes.
What is Python?
Python is a high-level, object-oriented, interpreted programming language. Most programmers would surely advise anyone to choose Python as their first programming language, thanks to its easy-to-learn syntax and smooth codebase maintenance.
Obviously, the simplicity, versatility, and strong community support aren’t unique to modern languages. So what’s more in Python that grants its leadership? Let’s take a look at some hidden benefits:
Dynamic typing and interpreted nature: enables rapid development and testing. The flexibility allows developers to quickly prototype and iterate on their code, making it easier to experiment with new ideas and concepts without worrying about type declarations and compilation.
Rich ecosystem for automation: beyond its well-known applications, Python excels in scripting and automation tasks. Tools like Selenium for web automation, PyAutoGUI for GUI automation, and various other libraries make Python a powerful choice for automating repetitive tasks and boosting productivity across different domains.
Seamless integration with other technologies: Python can be easily integrated with C/C++ for performance-critical applications, used with Java via Jython, or embedded as a scripting language in other languages. This interoperability makes Python irreplaceable in a multi-language development environment.
Why is Python so popular?
First of all, Python has an “increased coverage” of professionals. While some languages are used primarily in backend, frontend, or mobile development, Python is a go-to tool for developers, data scientists, researchers, and others. It's like the Swiss Army knife of programming languages, versatile and handy for almost anything.
Here are five (among many) reasons why Python is so popular:
Readable Code. You can read Python code almost like plain English, and its line breaks help define the code blocks. You can often figure out what the code does just by glancing at it.
Open Source. You can download Python’s source code, tweak it, and use it as you like. It’s like having a cookbook where you can modify recipes to suit your taste.
Portable. Python works on any operating system. You write your code once, and it runs anywhere, just like in other cross-platform languages.
Extendable. You can write Python code using other languages like C++, allowing users to add low-level modules to the Python interpreter for customization and optimization.
Extensive Standard Library. Python comes with a massive library of pre-built modules. There is no need to reinvent the wheel and write code for standard functions — you can use what's already there.
What Are the Examples of Using Python?
Now that you know the reasons behind the popularity of this language, you’re probably curious about what Python programming is used for. Below, we’ll highlight some of the most common and impactful cases from various fields. As we’ve already hinted, you don’t need to be a programmer to reap the benefits of using Python in your daily work!
1. Python for Web Development
Python is a top choice for web development due to its ability to handle complex, multi-protocol applications while keeping the code clean and readable. Many of the most popular apps today are built with Python. One of the main reasons for Python's popularity is its vibrant open-source community, which offers a wealth of reusable code, frameworks, and support.
Assets: Django is a widely used Python framework that was created to help others speed up their web development processes and avoid common issues.
Cases: Spotify’s web backbone, which consists of multiple interconnected services, heavily relies on Python. Reddit also utilizes the Python web framework for platform development.
2. Python for Data Analysis and Visualization
Python has easy-to-understand syntax, which makes it accessible to professionals with little to no programming experience. No wonder it’s the most preferred language for data scientists — in general — and for data analysis and visualization in particular. Data scientists often spend a lot of time collecting, processing, and visualizing data, and Python simplifies these tasks.
Assets: To perform data visualization in Python, you can use different Python data visualization modules such as Matplotlib, Seaborn, and Plotly. The primary data analysis Python libraries used for data analysis are NumPy and Pandas.
Cases: Lyft, Uber’s doppelganger, uses NumPy, Pandas, and PuLP in serving requests. Spotify, again, is also known for relying heavily on Python in data analysis.
3. Python for Machine Learning and AI
Python is a leading language for training machine learning (ML) models. These models leverage algorithms to sift through data, identify patterns, and make predictions or decisions. They continually adapt and improve by learning from new data inputs. To ease the process, data scientists and developers frequently use Python libraries such as NumPy, Pandas, and Matplotlib for tasks like data cleaning, transformation, and visualization.
Developing advanced artificial intelligence (AI) technologies with Python is also a common choice. Its clear and concise syntax enables developers to build consistent and reliable systems.
Assets: Python's library includes frameworks like PyBrain, which provides powerful algorithms for machine learning.
Cases: Notably, OpenAI, a leading AI research lab, uses the Python framework PyTorch as its standard for deep learning and training its AI systems. Google also fully utilizes Python in its artificial intelligence (AI), machine learning (ML), and robotics projects.
4. Python for Scientific Computing
Python is a game-changer in scientific computing. It reduces the effort needed to translate ideas into code and produce scientific data. While lower-level languages might offer faster performance, Python’s ease of use and efficiency often make it the first choice. You can achieve a lot with Python before considering more complex alternatives.
Assets: NumPy is a fundamental package for scientific computing using Python. SciPy, Matplotlib, Pandas, and Scikit-learn are important libraries for scientific computing, too. Lastly, TensorFlow and PyTorch are popular deep-learning frameworks widely used for building and training neural networks.
Cases: If you want to explore scientific computing more, check out this thread on Quora.
5. Python for Automation and Scripting
One of Python’s standout features is its ability to automate repetitive tasks. With Python, you can learn to automate almost anything using built-in modules or leveraging its library of pre-written code. You can also create custom scripts for specific tasks.
For instance, Python can be used for the sending of emails with the “smtplib” module. Or you can copy files with the “shutil” module. Python also excels in test automation, thanks to its powerful testing frameworks.
Assets: tools like Pytest, Behave, and Robot allow developers to write straightforward yet effective tests. Python can be used for ensuring the quality and reliability of code.
Cases: Amazon uses Python for automation for many other reasons. Python is also used for test automation and auto-remediation of issues within the Netflix platform
6. Python for Game Development
Python might be the best choice for beginner game development. If you’re a novice indie game dev, it might be easier to start with Python and actually learn how a game engine works, rather than learning C# or C++ which may be tricky. Python’s simplicity will allow you to focus on game logic and design. With Python, you can create 2D and some 3D games, which is suitable for a start at game development.
Assets Frameworks like Pygame, Panda3D, and Ursina support 2D and 3D game development. At least 10 frameworks, libraries, and game engines support game development in total.
Cases The Sims 4, World of Tanks, EVE Online, and Battlefield 2 are some of the most known games that use Python.
7. Python for Software Development
Python is great for software development because it has a simple and readable syntax, making it easy to use and maintain. Python's strong community support means you have plenty of resources and help available. Plus, Python's cross-platform compatibility ensures your code runs smoothly on different operating systems. If you ask Senior software engineers whether Python is a good choice for a complete software development cycle, you’ll get a definite “yes”.
Assets Studying Flask, FastAPI, or Django is a good way to get into software development with Python.
Cases: Facebook heavily utilizes Python in backend development. Also, most of Uber’s services are powered by Python / Node.js.
8. Python for Finance
Just as Python helps data scientists handle large datasets, it is actively utilized in the financial industry to perform complex calculations swiftly. Stock markets produce massive amounts of data, and Python excels at importing this data and using algorithms to generate trading strategies.
Additionally, Python is top-notch for portfolio optimization, risk management, financial modeling, and visualization. In a modern financial ecosystem, Python is used to analyze cryptocurrencies and detect fraudulent activities. Its efficiency and powerful libraries make it an essential tool for financial analysts who want to manage and interpret substantial amounts of financial data.
Cases: the most famous Python adopter in the financial field is Stripe, a Fintech start-up that enables businesses to accept online payments.
9. Python for Web Scraping
To build a Python web scraper, you need only Python itself (version 3.4 and newer), a code editor (PyCharm or Visual Studio Code), and a set of Python libraries.
The most popular libraries for web scraping are Beautiful Soup and Selenium. However, the whole package for web scraping also includes:
Lxml module: a powerful library for processing XML and HTML documents
Urllib module: a built-in library with functions for working with URLs
PyautoGUI: a cross-platform GUI automation library that allows the control of the mouse and keyboard to automate tasks
Schedule: a simple library that helps to set specified intervals in the execution of Python functions
10. Python for Cybersecurity
Cybersecurity is a broad concept that includes numerous activities, such as identifying vulnerabilities, ensuring web application security, cryptography, task automation, network security, penetration testing, and even reverse engineering. Python is applicable in every aspect of developing a solid foundation for cybersecurity. In fact, this topic deserves a separate piece of content, but let’s not go down the rabbit hole and stick to this high-level-style review.
The web scraping package mentioned earlier may be used for cybersecurity functions. The specific assets include:
Paramiko: a module for SSH protocol implementation
PyCryptodome: a cryptography library
YARA: essential for identifying and classifying malware based on patterns and rules.
Security Monkey: an open-source tool for monitoring and the security of cloud infrastructure
Cases: information security team leverages Python for crucial tasks such as risk classification, identifying vulnerabilities, automating security processes, and implementing auto-remediation.
11. Python for SEO
Indeed, if Python is so great in automation, web scraping, and scripting, it’s easy to put it at service for digital specialists, namely for SEO and marketing purposes. These specialists must simply write or copy/paste premade scripts to automate daily tasks or implement a specific task.
SEO specialists widely use Python. Some of the cases include:
Automation of creating a redirect map for a website
Automation of writing meta descriptions for pages
Automation of analyzing keywords with N-grams
Automation of grouping keywords in clusters
Matching keyword list to a list of preset topics
Creating a page speed checker
Creating an HTTPS checker to ensure the page has an SSL protocol
Creating an image alt checker
Instead Of a Conclusion: Why learn Python?
Learning Python is an excellent idea for many reasons. First, it's easy to understand, so you’ll get into coding quickly and smoothly. Python's simple syntax means you can start writing code faster than many other programming languages.
Second, Python is used in many fields. Whether you want to become a developer, work in data science, or automate tasks in your current job, Python has you covered. Its flexibility makes it a valuable skill in today's job market.
One of the best things about Python is its strong community support, which is excellent for learners who often struggle on their learning journey. There are many tutorials, forums, and resources to help you learn and solve problems. You won’t be learning alone.
If you choose Python to become a developer, you’ll find many job opportunities in the tech industry with high salaries. Python developers are in high demand.
Learning Python can help you automate daily tasks if you're in another profession. This can save you time, make your work more efficient, and even ask for a raise or promotion.
Even if you want to learn coding as a hobby, Python is a great choice. You can quickly start creating cool projects for personal use or even games.
In short, Python is an easy-to-learn, all-rounder language supported by a great community. It’s a wise choice, no matter what your goals are.
Popular Questions About Learning Python
If you feel inspired after reading this article and want to start hands-on with Python coding for any of the mentioned purposes, you might have a few more questions about learning this programming language. We’ve prepared the answers to some common inquiries!
How long Does It Take to Learn Python Language?
Learning Python can take different amounts of time for other people. If you're a newbie, it might take about 2 to 3 months to learn Python programming fundamentals. If you spend a few hours each day, you can get the basics down quickly. Another 4-8 months will help you grasp the advanced programming concepts, tools, and frameworks required for Python development. Study regularly, and try not to take long breaks. It’s easy to roll back in your programming knowledge without constant practice, especially in the early stages of your learning journey.
If you have no background in coding, it's best to learn Python on a course with a well-designed program for beginners. You’ll also require lots of coding practice to consolidate the new knowledge, hands-on projects, and mentor support. This will help you understand concepts better and faster and follow the best learning route.
Of course, there are plenty of accessible sources on Python, which are good enough if you just need the basics. But if you’re serious about diving into Python programming, choose the course that will keep you challenged and motivated. The one that will guarantee you’ll have a structured knowledge and an impressive projects portfolio by the end of your learning.
Start learning Python today with CodeGym, and soon, you can create your programs and projects. With the right course, you'll find it fun and easy to pick up this powerful programming language.
What Are The Best Python Project Ideas for Beginners
Here are some fantastic projects for beginners in Python coding:
To-Do List App: Create a simple app to manage daily tasks.
Weather App: Build an app that fetches and displays weather information.
Calculator: Develop a basic calculator for arithmetic operations.
Quiz Game: Make a fun quiz game with multiple-choice questions.
Currency Converter: Create a tool to convert between different currencies.
Personal Diary: Build an app to store and manage daily journal entries.
Simple Chatbot: Develop a basic chatbot that can have simple conversations.
These projects will help you learn and practice Python programming fundamentals.
What Jobs Are Related to Python?
Quite a lot, honestly. If we’re talking about development/engineering:
Python Fullstack Developer / Software Engineer
Python Backend Developer / Software Engineer
QA Automation Engineer
Game Developers
The “scientific” area of Python specialists is represented by:
The Specialists in Machine Learning and Artificial Intelligence
Data Scientists
Cybersecurity Analyst
Other options that utilize Python but are loosely bound with daily programming:
Data Analysts
Product Managers
Digital Marketing Specialists
These roles showcase the demand for Python in the job market. With the rise of AI technologies, the popularity of Python in the daily work of numerous specialists may increase even more.
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