Python AI – the language of artificial intelligence.
Python is one of the most popular programming languages, particularly valued for its simplicity and readability. It was created by Guido van Rossum in 1991 and has since become a key tool in many areas of technology.
Thirty years ago, when programming was the domain of a select few and creating even basic applications required months of work and an in-depth knowledge of complex languages. Today, thanks to Python, a child can learn the basics of programming in an hour, while a scientist can build an image recognition system using the latest artificial intelligence algorithms over a weekend. This democratization of technology is due not only to the language itself, but to the entire ecosystem that has developed around it.
The development of artificial intelligence makes Python even more important—for both technologically advanced users and beginners. The simplicity of this language and its capabilities make Python the foundation of language models and other algorithms. For example, the core of ChatGPT was written in Python.
On the other hand, vibecoding—where AI writes simple programs—makes learning Python and applying it in practice much more accessible and significantly more effective. Do you need, for example, a simple program to organize the files in a folder? Within anywhere from a dozen minutes to a few hours, with the help of AI, anyone can write a simple application that performs this function.
Table of contents
Python at a Glance
Python Basics
Python is characterized by a highly accessible syntax that resembles natural English. It uses indentation to organize code instead of curly braces, making it exceptionally readable. The language supports various programming paradigms: object-oriented, functional, and procedural programming. It is interpreted, which means that code can be run directly without the need for compilation.
The basic elements of Python include dynamically typed variables, data structures such as lists, dictionaries, and tuples, as well as powerful mechanisms such as list comprehensions and generators. The language has an extensive standard library and an active community that creates thousands of additional packages available through PyPI.
Python and Artificial Intelligence
In the field of AI, Python has dominated the landscape thanks to several key factors. Libraries such as NumPy and Pandas provide efficient numerical data processing and analysis of large datasets. Scikit-learn offers ready-to-use implementations of machine learning algorithms, from classification to clustering.
For deep learning, Python provides frameworks such as TensorFlow, PyTorch, and Keras, which enable the construction and training of complex neural networks. OpenCV is used for image processing and computer vision, while NLTK and spaCy are used in natural language processing.
Python also makes experimenting with AI models easier through Jupyter Notebooks, which enable interactive coding and visualization of results. The simplicity of the language allows researchers to focus on algorithms and business logic rather than complex syntax.
Python in Automation
In automation, Python shines thanks to its versatility. It can automate system tasks using modules such as os, subprocess, and shutil. The requests library enables the automation of interactions with APIs and web services, while Beautiful Soup and Scrapy are used for web scraping.
Python is excellent for office automation—it can manipulate Excel files with openpyxl, generate PDF documents with reportlab, or automate email sending with smtplib. Selenium enables web browser automation, which is invaluable for testing web applications.
For server and infrastructure automation, Python offers libraries for managing servers via SSH (Paramiko), monitoring systems, and orchestrating tasks with tools such as Celery. It can also be used to create chatbots, automate social media, or manage business processes.
A key advantage of Python in automation is its ability to connect different systems and technologies into one cohesive workflow, from databases to the cloud, and from local scripts to distributed data processing systems.
Why Is Python Important?
In today’s world of technology, where the pace of development is breathtaking and the demand for efficient software solutions continues to grow, one programming language stands out in particular—Python. Since its creation in 1991 by Dutch programmer Guido van Rossum, Python has undergone an incredible transformation from a simple scripting language into one of the most powerful and widely used tools in the modern programmer’s arsenal.
Python’s philosophy, expressed in the famous “Zen of Python,” emphasizes code readability, simplicity, and elegance. These values are not merely theoretical ideals—they translate into practical benefits that have made Python the language of choice in such diverse fields as artificial intelligence, process automation, web application development, data analysis, and even astronomy and bioinformatics.
Python’s popularity is no coincidence. According to the TIOBE index, which measures the popularity of programming languages, Python consistently ranks among the top languages, often competing for first place with giants such as Java and C. This position is the result of a unique combination of ease of learning, tremendous power, and versatility of applications.
History and Evolution of Python
The history of Python began at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, where Guido van Rossum was working on a successor to the ABC language. The name “Python” does not come, as one might think, from the snake, but from the British comedy group “Monty Python’s Flying Circus,” which indicated from the very beginning the language’s informal and friendly character.
The first version of Python, 0.9.0, was released in February 1991 and already included many elements that we consider fundamental today: classes with inheritance, exception handling, functions, and basic data types. Python 1.0 appeared in January 1994, introducing functional programming tools such as lambda, map, filter, and reduce.
A breakthrough moment was Python 2.0 in 2000, which introduced list comprehensions, garbage collection, and Unicode support. However, the greatest revolution was Python 3.0 in 2008—a version that deliberately broke backward compatibility in the name of greater consistency and language purity. Although the transition from Python 2 to Python 3 took years, the developer community ultimately accepted these changes, recognizing their long-term benefits.
Modern Python 3.x is a mature, stable language that continues to evolve. New versions are released annually, each adding further improvements in performance, code readability, and functionality. Python 3.12, one of the latest versions, introduces significant performance enhancements and new language features, demonstrating that the language is not slowing its pace of development.
Fundamentals of the Python Language
Syntax and Philosophy
Python stands out among other programming languages with its unique syntax, which places great emphasis on readability. Its most distinctive feature is the use of indentation to define block structures instead of curly braces or keywords such as “end”. This seemingly simple choice has profound consequences—it compels programmers to write clear, well-formatted code.
Python’s philosophy is codified in “PEP 20 – The Zen of Python”, which can be displayed by typing
import this
in the interpreter. The key principles are: “Beautiful is better than ugly”, “Explicit is better than implicit”, “Simple is better than complex”. These rules are not merely poetic slogans—they have a real impact on the design of the language and its library ecosystem.
Data Types and Structures
Python offers a rich set of built-in data types. Basic types such as int, float, str, and bool are dynamically typed, which means that we do not need to declare variable types. The language automatically recognizes and converts types as needed, greatly simplifying the code.
Python’s data structures are particularly powerful. Lists provide dynamic arrays capable of storing different types of objects. Dictionaries (dicts) offer efficient key-value mapping, forming the foundation for many advanced data structures. Tuples provide immutable sequences, ideal for data that should not change. Sets implement mathematical set operations with automatic duplicate removal.
One of Python’s most elegant features is list comprehensions—a way to create lists concisely and clearly. Instead of writing several lines of code with a loop, you can create a list in a single line:
\[x\*\*2 for x in range(10) if x % 2 == 0\]
creates a list of squares of even numbers from 0 to 8.
Object-Oriented Programming and Functional Programming
Python is a multiparadigm language, supporting both object-oriented and functional programming. Its class system is intuitive and powerful, supporting inheritance, polymorphism, and encapsulation. Python also introduced the concept of “magic methods” (special methods), which allow you to define how objects behave with built-in operators and functions.
On the other hand, Python offers powerful functional programming tools. Functions are first-class objects—they can be assigned to variables, passed as arguments, and returned from other functions. Decorators allow functions to be modified without changing their code, while generators enable efficient processing of large datasets without loading them entirely into memory.
Python Ecosystem and Libraries
PyPI and Package Management
Python Package Index (PyPI) is a treasure trove of over 400,000 packages, making Python so versatile. The pip system (Pip Installs Packages) enables easy installation and management of external libraries with a single command:
pip install nazwa\_pakietu
.
Modern management of Python environments has evolved toward tools such as pipenv, poetry, and conda, which address issues related to version conflicts and project isolation. Virtual environments enable the creation of isolated environments for different projects, preventing conflicts between library versions.
Standard Libraries
Python’s standard library is impressively extensive. The module
os
offers an interface to the operating system,
datetime
for manipulating dates and times,
re
to regular expressions,
json
for working with the JSON format,
urllib
for handling URLs and internet protocols.
The modules for network and web programming are particularly important:
socket
for low-level network communication,
http.server
for simple HTTP servers,
email
for handling electronic mail. These tools make Python an ideal choice for automating administrative tasks and creating system tools.
Python in Artificial Intelligence
Revolution in Machine Learning
Python became the lingua franca of artificial intelligence not by accident. The combination of simple syntax and powerful libraries has created an ideal environment for AI researchers and practitioners. Scientists can focus on algorithms and experiments instead of struggling with complicated language syntax.
NumPy forms the foundation of Python’s scientific ecosystem, offering efficient operations on multidimensional arrays. Pandas extends these capabilities with high-level data structures and data analysis tools, making data manipulation and exploration intuitive and efficient.
Scikit-learn democratizes machine learning by offering a unified interface to hundreds of ML algorithms. From simple linear regression to complex ensemble algorithms, everything is accessible with just a few lines of code. The library also includes tools for data preprocessing, model validation, and result evaluation.
Deep Learning and Neural Networks
In the field of deep learning, Python has achieved dominance thanks to frameworks such as TensorFlow and PyTorch. TensorFlow, created by Google, offers a complete platform for building and deploying machine learning models at various scales—from mobile applications to distributed cloud systems.
PyTorch, developed by Facebook (Meta), gained popularity in academia thanks to its dynamic approach to defining computational graphs. This enables more intuitive debugging and experimentation with network architectures.
Keras, initially an independent library and now part of TensorFlow, provides a high-level interface for building neural networks. It enables rapid prototyping and experimentation with various architectures without delving into low-level implementation details.
Computer Vision and NLP
In the field of computer vision, OpenCV-Python offers powerful tools for image and video processing. From basic operations such as filtering and geometric transformations to advanced techniques such as object detection and facial recognition.
In natural language processing, libraries such as NLTK, spaCy, and Hugging Face Transformers are revolutionizing the way we work with text. NLTK offers an academic approach with a wealth of linguistic algorithms, spaCy focuses on production performance, and Transformers provides access to the latest language models, such as BERT, GPT, and their variants.
Jupyter Notebooks and Interactive Analysis
Jupyter Notebooks have revolutionized the way we explore data and prototype AI models. Combining code, visualizations, and documentation in a single document enables interactive analysis and facilitates collaboration between teams. Google Colab extends these capabilities by providing free access to GPUs and TPUs, democratizing access to the computing resources needed to train deep learning models.
Thanks to AI, anyone can start using Python. All you need to do is devote 3 hours to learning it once.
AI can significantly streamline working with Python, especially when automating everyday tasks. Here are a few practical examples:
Office Work Automation
AI can write scripts that save hours of work. For example, a program for automatically organizing files into folders—AI will create code that sorts documents by type, date, or name. Or a script for batch-renaming files, merging PDF documents, or extracting data from Excel spreadsheets.
Data Analysis and Processing
Imagine you have hundreds of CSV files containing sales data. AI can write a program that automatically loads all the files, performs analyses (averages, trends, charts), and generates reports in HTML or PDF format. Without having to open each file manually.
Web Scraping and Monitoring
AI can create bots that monitor product prices in online stores, check product availability, or collect information from social media. The program can automatically send email notifications when the price falls below a specified threshold.
Communication Automation
AI will write scripts for sending personalized emails in bulk, automatically replying to messages, or managing your calendar. It can create a chatbot that answers customers’ basic questions.
Examples of Specific Programs
- Script for automatically backing up important folders
- Program for converting images between different formats
- Bot for automatically publishing posts on social media
- Tool for analyzing server logs and detecting problems
- Application for tracking habits and generating statistics
The greatest advantage is that you don’t have to be a programmer—simply describe to the AI what you want to achieve, and you’ll receive ready-to-use Python code. You can later modify it, adapt it to your needs, or combine it with other scripts. AI acts like a personal programmer who never gets tired and is always available to help.
How to Start Using Python?
General principles of good translation for language models; in this case, the examples below were created by Grok:
Study mode (also available in the free version of ChatGPT) is also quite helpful:
And ideally, it is best to start creating programs with AI early on. ChatGPT can generate not only the text itself, but also an entire ZIP folder with multiple files right away.
To check whether the code works, you can use online interpreters (including those built into ChatGPT if you use Canvas, Claude, or Gemini).
Best of all, install Python on your computer.
Python in Automation
System Automation
Python excels at automating system tasks thanks to its ability to easily integrate with operating systems. Module
subprocess
allows system commands to be executed and communication with external programs.
pathlib
offers an object-oriented approach to manipulating file paths, replacing the older module
os.path
more intuitive interface.
Library
psutil
expands system monitoring capabilities by providing information about processes, memory, disks, and the network. This enables the creation of advanced monitoring and diagnostic tools that can automatically respond to changes in the system.
Web Scraping and Internet Automation
Python has become the standard in the field of web scraping. The library
requests
simplifies making HTTP requests and handling sessions and authorization. Beautiful Soup offers intuitive HTML and XML parsing, allowing data to be easily extracted from web pages.
Scrapy is a framework for professional web scraping, offering asynchronous crawling, JavaScript support through integration with Splash, rate limiting management, and automatic retry mechanisms. For more advanced scenarios, Selenium enables web browser automation, allowing interaction with dynamic pages and SPA applications.
Office Automation
Python is revolutionizing office workflow automation. Openpyxl and xlsxwriter enable reading, writing, and manipulating Excel files without the need to install Microsoft Office. Reports can be generated automatically, data updated, and complex spreadsheets created.
ReportLab enables programmatic generation of PDF documents, from simple reports to complex documents with charts, tables, and images. Python-docx allows for manipulating Word documents, automating the creation of documentation and reports.
Communication Automation
Python offers powerful tools for automating communication. The built-in module
smtplib
enables sending emails, and
imaplib
allows for the automatic processing of incoming messages. Libraries such as
yagmail
simplify these processes by offering a more intuitive interface.
Integration with communication platforms such as Slack, Discord, or Microsoft Teams is possible through dedicated libraries or webhooks APIs. This enables the creation of chatbots, automated notifications, and the integration of business systems with communication platforms.
Performance and Optimization
Limitations and Solutions
Python is often criticized for its relatively low performance compared to compiled languages such as C++ or Rust. The Global Interpreter Lock (GIL) limits true multithreading, which can be a problem in CPU-intensive applications. However, these limitations can be effectively overcome.
NumPy and other scientific libraries implement critical operations in C, achieving performance comparable to low-level languages. Cython enables the compilation of Python code to C, offering significant speedups with minimal code changes.
Optimization Tools
Profiling is a key element of optimization. Built-in module
cProfile
identifies bottlenecks in the code, while tools such as
line\_profiler
offer detailed line-by-line performance analysis.
Just-in-time compilers such as PyPy and Numba can dramatically improve the performance of certain types of code. PyPy offers a drop-in replacement for the standard CPython interpreter, often achieving several-fold speedups. Numba specializes in compiling numerical functions, making it ideal for scientific computing.
Parallelization and Distributed Processing
Although the GIL limits multithreading, Python offers other parallelization mechanisms. The module
multiprocessing
enables true parallel processing by creating separate processes.
concurrent.futures
offers a high-level interface for asynchronous task execution.
For distributed processing, libraries such as Dask extend the familiar NumPy and Pandas APIs to computing clusters, enabling analyses to scale to terabytes of data. Ray offers an even more flexible approach to distributed computing, making it ideal for training machine learning models across multiple machines.
The Future of Python
Python 3.12 and Beyond
The latest versions of Python continue the trend of improving performance and adding new language features. Python 3.12 introduces significant improvements to error messages, making them more informative and helpful for developers. The new interpreter also offers better support for debugging and profiling.
Python’s development roadmap includes further performance improvements, better support for asynchronous programming, and the possible removal of the GIL in future versions. The “nogil” project experiments with an alternative interpreter implementation without the Global Interpreter Lock.
Emerging Technologies
Python is actively adapting to new technological trends. In the area of quantum computing, libraries such as Qiskit enable the programming of quantum computers. Blockchain and cryptocurrencies have their representation in the Python ecosystem through libraries such as web3.py.
The Internet of Things (IoT) is another growth area for Python. MicroPython and CircuitPython adapt the language for microcontrollers, enabling IoT devices to be programmed in the familiar Python language.
Community and Education
Python’s strength lies not only in its technology but also in its community. The Python Software Foundation (PSF) actively promotes diversity and inclusion within the developer community. Initiatives such as PyLadies support women in programming, while numerous local groups organize meetups and conferences around the world.
In education, Python has become the standard first programming language at many universities. Its simplicity and readability make it ideal for learning the basics of programming, while its power enables the implementation of advanced projects at higher levels of education.
Challenges and Criticism
Static Typing
One of the most frequently raised issues is the lack of static typing in Python. Although dynamic typing increases flexibility and accelerates development, it can also lead to runtime errors that are difficult to detect during development.
Python addressed these concerns by introducing type hints in Python 3.5. MyPy and other static type analysis tools allow optional type checking, offering a compromise between flexibility and type safety.
Ecosystem Fragmentation
The wealth of Python libraries can paradoxically become a problem. There are often several competing libraries for the same task, which can be confusing for new users. However, the Python community is developing standards and best practices that help navigate the ecosystem.
Summary
Python has come a long way from a simple scripting language to one of the most important tools in modern technology. Its impact on artificial intelligence, automation, and software development is undeniable. Its philosophy of simplicity and readability, combined with a powerful ecosystem of libraries, makes Python not only a technical tool but also a means of democratizing programming.
In an era where automation and artificial intelligence are becoming crucial to most industries, Python offers an accessible path to participating in this technological revolution. Whether you are a beginner programmer learning the basics, an experienced developer building complex systems, or a researcher exploring the frontiers of artificial intelligence, Python provides the tools and community needed to achieve your goals.
The future of Python looks bright. With the language’s continued development, a growing ecosystem of libraries, and an active, inclusive community, Python will remain a key language in the technological landscape for many years to come. In a world increasingly driven by data and automation, Python is not just an option—it is a necessity.