Robert Johns | 15 Feb, 2024
Fact checked by Jim Markus

What is Python? Introduction To Python For Beginners

What is Python? If you want the TL-DR, Python is a highly readable and versatile programming language that's used in everything from web development to data science and AI.

But that barely scratches the surface!

That's why I've put together this article about Python, covering its key features, applications, ecosystem, how easy it is to learn, strengths and weaknesses, and the future of Python.

I’ll even show you how to write your first program with a simple Python project.

So, join me as we dive deep to answer the question: what is Python?

What Is Python?

Python is a versatile and widely used programming language that's become a staple in various fields, from web development to data science and artificial intelligence. 

Developed in the 1980s by Guido van Rossum and later released in 1991, Python was designed to be readable and simple, making it an ideal choice for beginners and experts alike.

Python was conceived as a successor to the ABC language, with the aim of addressing its shortcomings and providing a scripting language that emphasizes code readability and developer productivity. 

The name "Python" itself is a tribute to the British comedy group Monty Python, reflecting van Rossum's goal to make programming fun and accessible.

Python's design philosophy, encapsulated by its now-famous "Zen of Python," prioritizes simplicity and elegance. The idea here is that encourages us as programmers to write clear, clean, and logical code for large-scale and small-scale projects alike. 

One of Python's most distinctive features is its use of significant whitespace, which requires proper indentation to define code blocks, making Python code particularly easy to read and understand.

Python’s simplicity does not compromise its power though, as it boasts a comprehensive standard library that supports a wide array of programming tasks. 

From web development with web frameworks like Django and Flask, to scientific computing with Python libraries like NumPy and SciPy, to machine learning with libraries such as TensorFlow and PyTorch.

Python's rise to prominence is also down to the fact that its development is community-driven.

This vibrant and inclusive community formed around Python has contributed to its extensive ecosystem of third-party libraries and frameworks extending its capabilities far beyond its core functionalities.

The adaptability of Python is also clear in its use across different domains. In web development, it provides back-end support, enabling server-side logic and database interactions. 

In data science and machine learning, Python has become the language of choice due to its readability and the powerful data manipulation and analysis libraries available.

Python's reach also extends into scripting and automation, network programming, and even into newer fields like Internet of Things (IoT) devices and embedded systems, showcasing its versatility and adaptability to a wide range of programming needs.

As Python continues to evolve, guided by the Python Enhancement Proposals (PEPs) and an active development community, it remains at the forefront of modern development, consistently adapting to meet the challenges and opportunities of the time.

What Are Python’s Key Features?

Python is celebrated for its simplicity, readability, and broad applicability, making it a favorite among beginners and professionals. 

Here are some of its standout features that contribute to its popularity and wide-ranging use across various domains.

1. Easy to Learn and Read

Python's syntax is designed to be intuitive and mirrors the English language, which significantly lowers the barrier to entry for newcomers. 

The emphasis on readability means that Python code often looks like pseudo-code, making it easy to understand and maintain, even when using complicated Python operators.

2. Dynamic Typing

Python is a dynamically typed language, which means you don't have to declare the type of a variable explicitly when you create it. 

If you’re like me and you started in C programming, this is a fairly wild and freeing innovation!

This works because Python figures out the type of a variable at runtime, which adds flexibility but also demands caution to avoid type-related bugs.

3. Extensive Standard Library

Python comes with a "batteries-included" philosophy.

This means you get a vast standard library with modules and functions for a wide array of tasks, such as file I/O, system calls, sockets, interfaces to various database systems, and even interfaces to graphical user interface toolkits like Tk.

4. Interpreted Language

Because Python is an interpreted language, code is executed line by line, which can make debugging easier and it also eliminates the need for compilation. 

This also enhances Python's portability, and it makes iterative testing and development more straightforward.

5. Object-Oriented and Functional Programming

A common theme with Python is its versatility, which extends to the fact it can supports various programming paradigms, including object-oriented, imperative, functional, and procedural. 

It also has first-class functions, meaning functions can be assigned to variables, passed as arguments, and returned from other functions just like any other object.

6. High-Level Data Structures

Python includes high-level, built-in data structures like lists, dictionaries, sets, and tuples, which allow for the construction of fast and compact code for various complex data manipulation tasks.

7. Extensibility

For tasks requiring speed or the need to execute legacy code, Python allows the inclusion of C or C++ libraries, making it extensible and versatile for performance-critical applications.

8. Embeddable

Python can be embedded within C/C++ programs, adding scripting capabilities for those programs and allowing for rapid prototyping or high-level script-based customization.

9. Wide Range of Libraries and Frameworks

The Python ecosystem is really vast! 

This means you get access to a wide array of libraries and frameworks for web development (think Django and Flask), data analysis (think Pandas and NumPy), machine learning (think TensorFlow and Scikit-Learn), and more.

It’s fair to say that this significantly expands Python's applicability beyond its standard library!

10. Community Support

Python has a large and active community, which means a wealth of tutorials, forums, and third-party resources are available. 

This community support is invaluable for both learning and solving complex programming challenges.

11. Portable and Cross-Platform

Python is designed to be portable, meaning Python programs can run on a wide variety of operating systems without requiring any changes to the code. You’ll just need a Python interpreter on the destination system.

This makes Python a versatile choice for developing software that needs to operate in diverse environments.

The Applications of Python

Python's simplicity, versatility, and robust performance have led to its adoption in numerous fields and applications. 

And Python’s applications range from web and software development to complex scientific computing and machine learning projects. 

Here are some of the key areas where Python's impact is most significant:

1. Web Development

Python is popular for web development thanks to powerful frameworks like Django or Flask that streamline the process of building robust and scalable web applications. 

These come with numerous built-in features for URL routing, database manipulation, and security, enable us as developers to focus on the more unique aspects of our web applications.

2. Data Science, Analysis, and Visualization

Python’s simplicity and powerful data manipulation capabilities with libraries like Pandas and NumPy make it a preferred choice for data science and analysis. 

Furthermore, visualization libraries such as Matplotlib and Seaborn allow for the creation of insightful charts and graphs, making data more accessible and understandable.

3. Machine Learning and Artificial Intelligence

Python is at the forefront of AI and machine learning, thanks to its comprehensive ecosystem of libraries and frameworks, including TensorFlow, Scikit-Learn, and PyTorch. 

These offer pre-built algorithms for tasks like neural networks, natural language processing, and computer vision, enabling the rapid development and deployment of AI-powered applications.

4. Scientific and Numeric Computing

Scientific communities and researchers use Python extensively for complex scientific computing. 

Libraries such as SciPy and NumPy facilitate high-performance operations on multi-dimensional arrays and matrices, which are crucial in various scientific computations.

5. Software Development

Python's readability and straightforward syntax make it an excellent tool for developing both simple scripts and complex, multi-platform applications. 

The language also supports various software development paradigms, including object-oriented, functional, procedural, and more.

6. Education

Python's clear syntax and general readability make it an ideal first language for newcomers to the world of programming. 

It is widely adopted in educational programs and introductory programming courses, helping students grasp fundamental programming concepts without the steep learning curve associated with more complex languages.

7. Automation and Scripting

Python's simplicity makes it ideal for writing scripts to automate mundane tasks, such as data and web scraping, file management, and system administration tasks. 

These scripts can significantly improve efficiency and reduce the time spent on repetitive tasks.

8. Game Development

Python might not be the first choice for high-end game development, it's used in game prototyping and the development of simple games. 

Libraries like Pygame provide modules for graphics, sound, and game control, enabling developers to create games quickly and with less code.

9. Network Programming

Python offers several libraries and tools for developing applications that require network programming, such as socket programming, which is useful for creating client-server applications. 

You can also use Libraries like Twisted to get support for asynchronous network programming, allowing for the development of high-performance network servers.

10. Desktop GUI Applications

Python can be used to develop desktop GUI (Graphical User Interface) applications.

Frameworks like Tkinter, PyQt, and Kivy make it easy to create user-friendly interfaces, making Python a great choice for developing standalone desktop applications.

11. Finance

The financial industry uses Python for a range of activities, including algorithmic trading, financial modeling, and of course, quantitative and qualitative analysis,. 

Libraries like QuantLib and pandas support complex financial calculations and data analysis, making Python indispensable in finance.

12. Internet of Things (IoT)

Python's simplicity and versatility make it suitable for IoT projects, which often require the integration of various types of devices and data streams. 

MicroPython, for example, is a lean and efficient implementation of Python that’s specifically designed for microcontrollers and IoT devices.

The Python Ecosystem

The Python ecosystem is incredibly rich and diverse, with an extensive array of libraries, frameworks, development tools, and community resources. 

This is one of the key drivers behind Python's popularity, making it a versatile tool for developers in web development, data science, artificial intelligence, scientific computing, and more.

Libraries and Frameworks

Python is renowned for its "batteries-included" philosophy, but the ecosystem extends far beyond the standard library with a plethora of third-party Python libraries and frameworks designed for specific tasks:

  • Django and Flask: In web development, Django is known for its "batteries-included" approach, offering a comprehensive set of features for building robust web applications. Flask, on the other hand, is a microframework that provides the essentials to get a web application up and running, with the flexibility to pick and choose additional components as needed.
  • NumPy and Pandas: When it comes data manipulation and analysis, NumPy offers efficient array operations and math functions, while Pandas provides high-level data structures (think DataFrames) and functions to make data analysis fast and easy.
  • Matplotlib and Seaborn: These libraries are staples for data visualization, with Matplotlib providing a wide range of plotting functions and Seaborn offering a high-level interface for drawing attractive statistical graphics.
  • SciPy: Used in scientific and technical computing, SciPy extends NumPy with functions for optimization, linear algebra, calculus, interpolation, FFT, image processing, and more.
  • TensorFlow, PyTorch, and Scikit-Learn: These libraries are at the forefront of machine learning and artificial intelligence, offering comprehensive tools and frameworks for building and training complex models.

Integrated Development Environments (IDEs) and Tools

The Python development experience is enhanced by a variety of Python IDEs and tools designed to facilitate coding, debugging, and project management:

  • PyCharm: Developed by JetBrains, PyCharm is a widely used IDE for Python (and my personal favorite), offering advanced features like AI code assistants, code analysis, a graphical debugger, integrated unit tester, and support for web development.
  • Jupyter Notebooks: Particularly popular in data science, Jupyter Notebooks provide an interactive computing environment that allows you to combine live code, equations, visualizations, and text in a single notebook.
  • Visual Studio Code (VS Code): This editor has gained popularity among Python developers for its lightweight nature, extensive extension marketplace, and built-in support for Python development.

Community and Support

Python's vibrant community is a cornerstone of its success, providing extensive resources for learning and problem-solving:

  • Here at Hackr, we not only find the best online Python courses and resources, but we also provide step-by-step tutorials and educational content for beginners and pros alike.
  • PyPI (Python Package Index): Known as the "Cheese Shop," PyPI hosts thousands of third-party Python libraries and applications, covering a wide range of functionalities and domains.
  • Stack Overflow: A vital resource for developers, offering a vast repository of Python-related questions and answers.
  • GitHub: The platform hosts a significant number of open-source Python projects, allowing developers to collaborate, share code, and contribute to various projects.
  • and Python Software Foundation: These provide official documentation, tutorials, and news, supporting the growth and development of the Python community.

In addition, events like PyCon, EuroPython, and regional meetups offer opportunities for learning, collaboration, and networking within the Python community.

How Easy Is It To Learn Python?

Well, I’d say it depends on your own background in programming and your preferred learning style. 

That said, Python is widely considered as one of the easiest languages for beginners, thanks to its clear syntax, readability, and the extensive support available from its community and resources

Let’s take a deeper dive.

Approachable Syntax

Python's syntax is designed with readability in mind, closely mirroring natural language. 

This reduces the complexity of programming concepts, making it easier for beginners to grasp the fundamentals. 

Python also eschews the use of curly braces for block delimitation and semicolons to terminate statements, relying instead on whitespace indentation. 

This not only enforces readability but also helps beginners understand the flow of a program more intuitively.

Immediate Application

Python's versatile nature allows beginners to apply what they learn in a variety of fields, from web development and data analysis to artificial intelligence and scientific computing. 

This wide applicability provides an immediate sense of achievement as beginners can see the results of their code in real projects, enhancing motivation and the learning experience.

Abundant Resources

The Python community has created an extensive array of learning materials suitable for all levels, ensuring that beginners have access to the tools they need to start their programming journey:

From interactive tutorials and online Python courses to comprehensive documentation and community forums, you have access to a diverse set of resources, including:

  • Online Platforms: Websites like Coursera, Udemy, and offer interactive Python courses that guide beginners through the basics with hands-on exercises.
  • Video Tutorials: Platforms like YouTube have countless tutorials and lecture series on Python, covering everything from basic concepts to advanced topics.
  • Documentation and Books: The official Python documentation provides a comprehensive reference to the language, while Python books offer engaging introductions to Python with practical examples.

Community Support

Python's community is renowned for its welcoming and supportive nature, making it an ideal environment for beginners. 

Numerous Python-related events, such as local meetups, PyCon conferences, and coding bootcamps, offer opportunities for learners to engage with more experienced developers, gain insights, and find mentorship.

Whether you’re using our resources at hackr or looking in online forums like Stack Overflow and Reddit's r/learnpython, there are lots of active spaces where newcomers can ask questions, seek advice, and share their learning experiences.

Advantages & Strengths of Python

Thanks to its simplicity, versatility, and powerful ecosystem for numerous applications, Python stands out in the programming world for its widespread adoption and huge popularity. 

Here are some of the key advantages and strengths that contribute to Python's popularity and widespread use across various fields

Simplicity and Readability

Python's syntax is designed to be clear and readable, closely resembling natural language. 

This simplicity makes Python an excellent language for beginners, reducing the learning curve and enabling new developers to focus on solving problems rather than figuring out complex syntax. 

The emphasis on readability also makes Python code easier to maintain and understand, a significant advantage for long-term projects.


Python's versatility is one of its biggest strengths. It's used across diverse domains, from web and software development to data science, data analysis, machine learning, artificial intelligence, scientific computing, and more. 

This flexibility allows developers to use Python for a wide range of tasks, making it a valuable tool in many professional contexts.

Rich Ecosystem

The Python ecosystem is vast and continually growing, with a wealth of libraries and frameworks that extend its capabilities. 

Libraries like Django and Flask make web development easy, while NumPy, Pandas, and Matplotlib are indispensable in data science. 

In machine learning and AI, libraries such as TensorFlow and PyTorch provide powerful tools for developing and training complex models. 

This rich collection of tools enables developers to build sophisticated applications with less effort.

Strong Community Support

Python benefits from a large, active community that contributes to a wealth of resources, including comprehensive documentation, forums, tutorials, and third-party libraries. 

This community support makes it easier for developers to find solutions to problems, learn new skills, and stay up-to-date with best practices and emerging trends.

Interoperability and Integration

Python plays well with other languages and technologies, offering various integration mechanisms. 

It can call out to C/C++ libraries for performance-critical tasks, interact with other programming languages via wrappers, and integrate with data sources, web services, and other external systems. 

This interoperability makes Python a flexible tool in multi-language projects and complex application ecosystems.

Wide Platform Support

Python is inherently cross-platform, meaning Python programs can run on multiple operating systems, including Windows, macOS, Linux, and more, without requiring changes to the code. 

This wide platform support simplifies development and deployment processes for applications intended to operate in diverse environments.

Productivity and Speed

Python's concise syntax and high-level data structures, combined with its extensive standard library and third-party modules, enable developers to accomplish more with fewer lines of code. 

This efficiency boosts productivity, allowing for rapid development and prototyping, which is particularly beneficial in startups and fast-paced development environments.

Education and Training

Python's clarity and simplicity have made it a favored language for educational purposes. It's often the first programming language taught in universities and coding bootcamps, making it an accessible entry point for new generations of developers.

Continuous Evolution

Python is continuously evolving, with regular updates that introduce new features, enhancements, and security improvements. 

The Python Enhancement Proposal (PEP) process, involving the community in discussions and decision-making, ensures that Python remains relevant, efficient, and aligned with the needs of its diverse user base.

Challenges & Limitations of Python

While Python is undeniably versatile and powerful, it's not without its challenges and limitations. 

I think it’s essential for developers to understand these drawbacks to navigate the complexities of Python development effectively and to apply best practices for optimal results.

Performance Speed

Being an interpreted language, Python executes code line by line, which can lead to slower performance compared to compiled languages like C or Java. 

This can be a limitation that needs serious consideration for performance-critical applications, such as high-frequency trading algorithms in finance or real-time systems.

Global Interpreter Lock (GIL)

Python's Global Interpreter Lock (GIL) is mechanism that prevents multiple threads in your CPU from executing Python bytecode at the same time. 

This is needed because Python's memory management is not thread-safe. 

It also means that the GIL can be a bottleneck, limiting execution to a single thread at a time and undermining the benefits of multi-core processors.

Mobile Development

Python is not well known for for its use in mobile development compared to dominant languages like Java (for Android) or Swift (for iOS). 

That said, there are some frameworks like Kivy and BeeWare that support mobile app development with Python, but the ecosystem is not as mature or widespread as those for more traditional mobile development languages.

Memory Consumption

Python's ease of use and flexibility come with higher memory consumption compared to other languages. 

This can be a drawback for applications where memory efficiency is critical, such as on embedded systems or when processing very large datasets in data science and machine learning projects.

Runtime Errors

Python's dynamic typing system enhances development speed but can also lead to runtime errors that are only caught during execution, as opposed to compile-time errors in statically typed languages. 

This requires thorough testing and can increase the time spent on debugging.

Package Management

While Python's package ecosystem is a strength, managing dependencies and environments can sometimes be challenging, especially for beginners. 

Tools like pip and virtualenv help manage packages and environments, but issues like dependency conflicts or problems arising from system-wide installations versus virtual environments can complicate development and deployment processes.

Database Access

Python's database access layers are generally underdeveloped compared to more mature technologies like JDBC (for Java) or ODBC (for C and C++). 

While Python offers libraries to interface with databases, such as SQLite, MySQL, and PostgreSQL, these can be less efficient and feature-rich compared to counterparts in other languages.

Integration with Other Languages

Python is excellent for rapid development and scripting, but integrating Python code with components written in other languages can be cumbersome. 

While tools like Cython and PyPy can help mitigate performance issues and facilitate integration, they add complexity and may require a deep understanding of both Python and the target language.

Asynchronous Programming

Asynchronous programming in Python, primarily through asyncio, provides a powerful model for writing concurrent code. 

However, it introduces a new syntax and requires a different mindset, which can be a steep learning curve for developers accustomed to synchronous code.

What Does The Future Hold For Python?

Now you know that Python is massively popular and has been for quite some time, what does the future hold?

Let’s tackle this question now.

Continued Growth in Data Science and AI

Python has established itself as a leading language in data science, machine learning, and artificial intelligence, thanks to its simplicity and the powerful libraries like NumPy, Pandas, TensorFlow, and PyTorch. 

Ongoing advancements in these fields are likely to drive further enhancements in Python's ecosystem, making it even more capable and efficient for research, development, and deployment of cutting-edge AI technologies.

Expansion into New Fields

While Python is already versatile, its potential for growth into new areas such as IoT (Internet of Things), embedded systems, and cloud computing is substantial. 

The development of libraries and frameworks that cater to these domains could significantly broaden Python's applicability, making it a more ubiquitous language in the tech landscape.

Performance Improvements

One of Python's criticisms is its execution speed. Initiatives like PyPy, a JIT (Just-In-Time) compilation implementation of Python, and potential enhancements in the CPython interpreter aim to address this issue. 

Future developments in this area could significantly improve Python's performance, making it more competitive with faster languages while retaining its ease of use.

Strengthening of Typing Systems

The introduction of type hints and gradual typing in Python 3.5 and onwards has brought about a shift towards more robust typing systems in Python. 

This trend is likely to continue, with further enhancements that could improve code quality, maintainability, and developer productivity, especially in large and complex projects.

Evolution of Web Development Frameworks

Python's web development frameworks like Django and Flask are set to evolve, incorporating more asynchronous features and enhancing scalability to accommodate the growing demands of modern web applications. 

This evolution will likely include better integration with frontend technologies, making Python an even more compelling choice for full-stack development.

Improved Packaging & Dependency Management

The Python community recognizes the challenges in package and dependency management, and efforts like the introduction of Pipenv and Poetry are steps toward addressing these issues. 

Future improvements in this area could simplify Python development workflows, making it easier to manage projects and environments.

Enhanced Community Initiatives

Python's community is one of its greatest strengths, and the future will likely see an expansion of educational resources, community initiatives, and events like PyCon. 

These efforts will make Python more accessible to newcomers and facilitate the sharing of knowledge and best practices among experienced developers.

Python in Education

Python's role in education is expected to grow, with more institutions adopting it as the first language taught to students due to its readability and simplicity. 

This trend will help cultivate a new generation of developers who are well-versed in Python, further expanding its user base.

Continued Evolution of the Language

Python's development is guided by a series of Python Enhancement Proposals (PEPs), and its evolution is a collaborative process involving the community. 

Future versions of Python will likely introduce new features, optimizations, and enhancements that respond to the needs of its diverse user base, while maintaining the core philosophy that has made Python so successful.

Getting Started With Python

Okay, you know everything there is to know about Python, and you’re just itching to start coding, right? 

Well for starters, I’ll let you know that I’ve created my own Python course if you want to really get stuck in to learning Python like a pro!

But if you’re a total beginner, why not start with the classic "Hello, World!" 

I love starting beginners with this because it’s super simple, a rite of passage for new programmers, and also a great way to get acquainted with the basics of a Python program.

Step 1: Set Up Your Environment

To begin coding in Python, you'll need a Python interpreter installed on your computer. 

Python comes pre-installed on many Unix and Unix-like systems, including macOS. For Windows, you can download the latest version from the official Python website.

Additionally, consider installing a Python IDE like PyCharm, Visual Studio Code, or even a simpler text editor like Sublime Text to write your code.


Step 2: Verify Python Installation

After installing Python, open your command line interface (Terminal on macOS and Linux, Command Prompt or PowerShell on Windows) and type the following command to check if Python is installed correctly:

python --version

If you have multiple versions of Python:

python3 --version

You should see the Python version number if the installation was successful.

Step 3: Create Your Python Script

Open your text editor or IDE and create a new file. Save it with a .py extension, for example,

Step 4: Write Your Python Code

In your new file, type the following line of code:

print("Hello, World!")

This code uses the print() function to output the string "Hello, World!" to the console.

Step 5: Run Your Python Script

Save your file and return to your command line interface. Navigate to the directory where your file is saved using the cd command. 

Then, execute your Python script by typing:

Or, if you're using Python 3 and Python 2 is also installed


You should see "Hello, World!" printed in the console.

You've just written your first piece of Python code. While this is a simple start, it's your entry into the world of programming with Python. 

From here, the possibilities are vast. You can explore more complex topics like variables, control flow, functions, and data structures to build more intricate programs. 

Python's large ecosystem and libraries also open up avenues for web development, data analysis, machine learning, and more. Happy coding!

Be sure to check out more Python projects if you want to get hands-on to learn more.

When you’re ready, consider taking your Python career to the next level by trying to earn a Python certification, as this will really make your resume pop!

Wrapping Up

So there you have it; the next time someone asks, what is Python? You'll be able to tell them it's a versatile programming language that’s also a fundamental tool in everything from web development to cutting-edge scientific research.

You'll also be able to tell them that Python is supported by a vast ecosystem of libraries and frameworks like Django, Flask, Pandas, and TensorFlow. 

We've also covered Python's key features, applications, strengths and weaknesses, and the future of Pyton. I’ve even shown you how to create your first "Hello, World!" program with Python.

Whether you're a budding developer or a seasoned programmer, one thing is for sure: mastering Python can open many doors in your career, so have fun and happy coding!

Want to sharpen up your Python programming skills? Check out:

Angela Yu's 100 Days of Code: The Complete Python Pro Bootcamp on Udemy

By Robert Johns

Technical Editor for | 15+ Years in Python, Java, SQL, C++, C#, JavaScript, Ruby, PHP, .NET, MATLAB, HTML & CSS, and more... 10+ Years in Networking, Cloud, APIs, Linux | 5+ Years in Data Science | 2x PhDs in Structural & Blast Engineering

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