Python
Versatile, easy-to-learn programming language that is among the most used worldwide for AI, data science, web development and automation.
Python is one of the fastest-growing programming languages of the last decade. It is the lingua franca for artificial intelligence, data science and machine learning and also a strong choice for web development, automation and scripting. Its simple, readable syntax makes it an ideal first language while its versatility appeals to experienced developers.
What is Python?
Python is an interpreted, object-oriented language with dynamic typing, first released by Guido van Rossum in 1991. Its design emphasises readability and simplicity (The Zen of Python). Python uses indentation instead of braces for structure, leading to consistent, readable code. It has a large standard library (batteries included) and a huge ecosystem on PyPI (Python Package Index) with over 400,000 packages. Python is cross-platform and runs on Windows, macOS, Linux and many embedded systems.
How does Python work?
Python code is executed line by line by an interpreter, enabling fast development cycles. CPython is the reference implementation. For performance-critical code there are alternatives like PyPy (JIT) or Cython (compile to C). Virtual environments (venv, conda) isolate project dependencies. Package managers like pip and Poetry manage external libraries. Frameworks like Django and Flask simplify web development; NumPy, Pandas and scikit-learn dominate data science.
Practical Examples
AI and ML: TensorFlow, PyTorch and Hugging Face Transformers are Python-based. Almost every ML model is developed and trained in Python.
Web backend with Django: Instagram, Pinterest and Mozilla use Django for scalable backends. The framework provides ORM, admin and security out of the box.
Data analysis: A financial services firm uses Pandas and Jupyter to analyse millions of transactions and detect fraud patterns.
Automation: A company automates daily extraction from 20 sources, transformation and upload to the data warehouse with Python scripts.
DevOps: Ansible, a leading automation platform, is written in Python and uses Python modules for infrastructure.
Typical Use Cases
Artificial intelligence: Developing and training ML models with TensorFlow, PyTorch and scikit-learn
Data science: Analysis, visualisation and statistical modelling with Pandas, NumPy and Matplotlib
Web development: Backend with Django, Flask or FastAPI
Automation: Scripting, ETL and system administration
Rapid prototyping: Quick prototypes and PoCs thanks to simple syntax
Advantages and Disadvantages
Advantages
- Easy to learn: Clear, readable syntax lowers the barrier for beginners
- Huge ecosystem: Over 400,000 packages on PyPI for almost any use case
- AI dominance: De facto standard for ML, deep learning and data science
- Versatility: From web backends to automation to research
- Large community: Extensive documentation, tutorials and active forums
Disadvantages
- Performance: As an interpreted language, Python is slower than compiled languages like C++ or Rust
- GIL: True multithreading is limited in CPython, affecting CPU-bound workloads
- Mobile: Python is not a natural fit for native app development
- Type safety: Dynamic typing can make certain bugs hard to find in large codebases
Frequently Asked Questions about Python
Is Python the best language for beginners?
Python or JavaScript – which to learn?
Is Python fast enough for web applications?
Related Terms
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