A Peek at Python's Prowess in AI/ML Application Development

Python is perhaps the most popular language for AI and ML application development.

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Its rich libraries and tooling streamline everything from rapid prototyping to production deployment, while builtin data structures and libraries make preprocessing, exploration, and manipulation of datasets straightforward and efficient.

Python streamlines rapid AI/ML application development, automates machine learning workflows, and supports innovation and integration with cloud platforms and IoT for AI/ML applications. For developers, Python combines productivity, community support, and production-grade capabilities, making it the natural choice for building, training, and deploying intelligent systems.

Python continues to evolve, yet many developers still rely on the techniques they first learned. Those legacy approaches are not inherently wrong, but can hide newer, more efficient patterns, libraries, and idioms that improve performance, readability, and maintainability. Regularly updating skills, adopting modern libraries, idiomatic constructs, and contemporary tooling reduces bugs, speeds development, and smooths the path from prototype to production.

Python adoption in the industry

Python powers over half of global data science projects and holds a commanding lead in industry adoption. The TIOBE Programming Community Index (December 2025) shows Python ranked #1 with a 23.64% share, ahead of C (10.11%) and C++ (8.95%), making it the most popular programming language for AI and ML. Over 51% of all data science projects worldwide are built with Python. More than 1.25 million open positions globally require Python skills, with senior ML developers earning up to $200,000 in the US.

Python powered 41% of all AI-generated code in 2025, with 256 billion lines written by AI assistants like GitHub Copilot. The Python IDE market was valued at US$ 559 million in 2025 and is expected to grow at 8.1% CAGR through 2033.