Python

Python is a high‑level, general‑purpose programming language known for its simple, readable syntax and extensive ecosystem of scientific and machine‑learning libraries. In 2026, it remains the dominant language for data science, machine learning, AI and research. Python’s strength comes from its vast library ecosystem — including NumPy, pandas, scikit‑learn, TensorFlow, and PyTorch — and its seamless use within notebooks for exploratory analysis and rapid prototyping.

The team at Inflecto tend to use other languages for core development but Python is often our first choice once we are dealing with data science, machine learning, advanced algorithms or AI.

Benefits of Python

• Unmatched ecosystem for AI/ML: Python continues to be the #1 language for machine learning experimentation and model development, supported by frameworks like PyTorch, TensorFlow, and scikit‑learn.
• Powerful data handling: Libraries such as pandas, NumPy, and matplotlib make data loading, cleaning, manipulation, and visualisation extremely efficient.
• Ideal for research and prototyping: Python’s concise syntax and notebook‑based workflows (e.g., Jupyter, Google Colab) allow rapid experimentation and iterative model refinement.
• Strong position in the job market: Python appears in roughly half of AI/ML job listings and is considered essential in most data science curricula.
• Rich ecosystem for modern AI: Python integrates seamlessly with tools for LLMs, vector search, RAG pipelines, and LangChain‑based workflows.
• Cross‑platform & beginner‑friendly: Python’s simple syntax lowers the barrier to entry and runs consistently across Windows, macOS, and Linux.

Typical Use Cases

• Data science and analytics: Loading datasets, performing exploratory data analysis, building statistical models, and generating visual reports.
• Machine learning & deep learning: Training models for prediction, classification, NLP, computer vision, and AI‑assisted workflows.
• LLM/GenAI development: Fine‑tuning models, running inference pipelines, building RAG systems, and integrating models into experiments.
• Automation & scripting: Writing efficient scripts to clean data, run scheduled analysis, or connect system workflows.
• Scientific computing: Numerical simulations, mathematical modeling, and research‑focused computing tasks.
• Prototyping before production: Teams often prototype models in Python, then implement production‑ready services in other languages.