Python: The Language That Powers Modern Tech

Python is the most widely used programming language in the world for data science, machine learning, and scientific computing — and it is increasingly central to how artificial intelligence systems are built, deployed, and used. Before exploring the top libraries that make Python so powerful, it is worth understanding Python itself: where it came from, what makes it different, and why every major AI lab on the planet writes Python. ## What Is Python? Python was created by Guido van Rossum and first released in 1991. Van Rossum named it after Monty Python's Flying Circus, not the snake. His goal was to create a language that was easy to read, easy to write, and productive — one where the structure of the code itself communicated intent rather than hiding it inside braces and semicolons. Python 2 was the dominant version for over a decade until Python 3 was released in 2008 with significant improvements that were not backward-compatible. Python 2 reached its official end of life on January 1, 2020. Today, Python 3 is the only version in active development, with Python 3.13 released in October 2024. The language is open source, maintained by the Python Software Foundation, and free to use for any purpose. ## Why Python Became Dominant Python's rise to dominance is not accidental. Several specific design decisions made it the right tool at the right time. **Readable syntax.** Python uses indentation to define code blocks instead of braces, which forces consistent formatting. Code written by different developers on the same team tends to look more similar in Python than in most other languages. This matters enormously when you are debugging someone else's data pipeline at 11pm. **Interpreted and interactive.** Python runs directly without a separate compile step. Combined with Jupyter Notebooks — the browser-based interactive environment used by virtually every data scientist — this means you can run a single cell of code, see the output, adjust, and run again. That iterative loop is fundamental to how data analysis actually works. **An enormous standard library and ecosystem.** Python ships with a broad standard library covering file I/O, HTTP requests, JSON, regular expressions, and much more. Beyond that, PyPI (the Python Package Index) hosts over 500,000 packages as of 2024, covering virtually every problem domain. **Glue language design.** Python was explicitly designed to integrate with other systems. C and C++ extensions can be called from Python with minimal friction. This is why Python can serve as the high-level orchestration layer while computationally intensive work happens in optimised low-level code — a pattern used extensively in NumPy, PyTorch, and TensorFlow. ## Python for Data Analysis Python became the dominant language for data analysis primarily because of two libraries: **NumPy** and **pandas**. NumPy (Numerical Python) provides the foundational array operations that everything else builds on. When you need to multiply two matrices with millions of elements, NumPy calls optimised BLAS (Basic Linear Algebra Subprograms) routines written in Fortran and C — Python just provides the interface. The result is near-native performance for numerical computation. pandas was created by Wes McKinney in 2008 while he was working at the hedge fund AQR Capital Management. He needed a tool for working with financial time-series data and built it himself. The library introduced the DataFrame — a tabular data structure with labelled rows and columns — that is now ubiquitous in data work. JPMorgan Chase, Deutsche Bank, and virtually every quantitative finance firm use pandas as part of their analytics stack. Jupyter Notebooks became a standalone product in 2014. NASA uses Jupyter Notebooks for data analysis. The Laser Interferometer Gravitational-Wave Observatory (LIGO), which detected gravitational waves for the first time in 2015, published its analysis in a public Jupyter Notebook so the scientific community could reproduce the results. ## Python for Web Development Python's presence in web development is anchored by two frameworks: **Django** and **Flask**, with **FastAPI** becoming increasingly important. Django was created in 2003 by Adrian Holovaty and Simon Willison while building a news website for the Lawrence Journal-World newspaper in Kansas. The framework follows a "batteries included" philosophy — authentication, admin interface, ORM, form handling, and URL routing are all built in. Instagram is the most famous Django deployment. At its peak, Instagram's Django backend was serving over one billion users, making it one of the most scaled Django applications ever built. Pinterest, Disqus, and Mozilla's public-facing websites also run on Django. Flask was created by Armin Ronacher and released in 2010. Where Django is opinionated and comprehensive, Flask is minimal — it provides routing and request handling and gets out of your way. LinkedIn's API layer, Netflix's chaos engineering tools, and Airbnb's internal tooling have all used Flask. FastAPI was released in 2018 and is built on Python's type hint system, generating OpenAPI documentation automatically from your code. It is now one of the fastest-growing Python web frameworks and is widely used for building APIs that serve machine learning models to production. ## Python for Machine Learning Python's dominance in machine learning is almost total. The two primary frameworks — **PyTorch** and **TensorFlow** — are both Python-first. TensorFlow was released by Google Brain in November 2015 as an open-source library for numerical computation. Google Search, Google Translate, Gmail's spam filter, and Google Photos all use models trained with TensorFlow. PyTorch was released by Facebook AI Research (now Meta AI Research) in January 2017. Its dynamic computation graph made it significantly easier to debug and experiment with. PyTorch became the dominant framework in academic research. As of 2024, the majority of papers published at NeurIPS, ICML, and ICLR use PyTorch. scikit-learn, released in 2011, provides clean implementations of classical machine learning algorithms — linear regression, decision trees, random forests, support vector machines, k-means clustering, and many others. It is the standard library for machine learning that does not require deep neural networks, which remains most machine learning in production. ## How OpenAI and Other AI Labs Use Python OpenAI's entire public API is documented and examples are written in Python first. The official `openai` Python package on PyPI has been downloaded over 200 million times. When developers access GPT-4, DALL·E, or Whisper through the API, they overwhelmingly do so through Python. GPT-3, GPT-4, and subsequent OpenAI models were trained using PyTorch. The model weights, training loops, evaluation pipelines, and inference infrastructure are all Python-orchestrated. OpenAI's Codex — the model powering GitHub Copilot — was trained on a dataset that included hundreds of gigabytes of Python code from public GitHub repositories, making Python both the training medium and the primary output target. Anthropic (Claude), Google DeepMind (Gemini), Meta AI (LLaMA), and Mistral AI all use PyTorch as their primary training framework. The HuggingFace Transformers library, which provides pre-trained model weights and inference code for hundreds of open-source models, has over 130,000 stars on GitHub as of 2025. LangChain, which simplifies building applications that chain together language model calls, was Python-first from its creation. The same is true for LlamaIndex, Haystack, and virtually every major LLM application framework. The pattern is consistent: Python provides the interface, the tooling, and the ecosystem. The computationally intensive work happens in GPU-optimised C++ and CUDA code — but developers interact with all of it through Python. ## Python in the Job Market Python's importance is reflected directly in job postings. According to the 2024 Stack Overflow Developer Survey, Python was the most-used language among data scientists and machine learning engineers by a significant margin. Python appears in the majority of data analyst, data engineer, and ML engineer job postings across LinkedIn and Indeed. The TIOBE Index has ranked Python in the top three programming languages consistently since 2018, and it held the #1 position for multiple years running. For anyone building a career in data, AI, or backend development, Python fluency is effectively non-negotiable. ## What Comes Next Python's power is multiplied by its libraries. The language itself is the foundation — but pandas, NumPy, Matplotlib, scikit-learn, and PyTorch are where the practical work happens. Over the next five articles in this series, we will cover the five Python libraries that every developer working in data, machine learning, or AI needs to know: what they do, how they work, and when to use them.