Python is now the most popular language on GitHub as AI and ML surge

A new Octoverse report from GitHub shows some remarkable changes in developer trends over the past year. Python has surged past JavaScript as the most-used language on the platform. The move reflects a boom in data science and machine learning applications that have accompanied advances in the field of AI.

Python is also used in a lot of data analysis activities. Given the incredible pace of improvement that generative AI has recently demonstrated, even more developers have started working with Python on advanced projects involving neural networks, deep learning, and other computationally intensive areas. Another factor probably includes the recent emergence of free-to-use AI-powered coding helpers, such as GitHub Copilot.

The adoption of Python has accompanied the development of related technologies. Jupyter Notebooks, interactive coding environments well-suited to data visualization and exploration, have massively increased in popularity on GitHub. At the same time, languages and frameworks oriented toward machine learning workflows, such as R and TensorFlow, are seeing much more use.

There’s a continued increase in first-time contributors to open source projects. 1.4 million new developers globally joined open source with a majority contributing to commercially backed and generative AI projects.

Despite conceding the top overall position, JavaScript maintains growth. The language experienced a 15% year-over-year increase in consumption of code packages via the npm registry.

Developers of emerging markets are leading the broad expansion of GitHub. The United States remains the leading nation on GitHub for making contributions to AI. Still, there is significant growth in progress in places like India, Brazil, Nigeria, and more, especially among students gaining skills.

Of course, these demand-side shifts are being supplemented by supply-side security advances. Users on GitHub integrated secret scanning more than 39 million times last year to prevent leakages of private keys and credentials. Now, AI-powered automation is allowing for quicker fixes when vulnerabilities do crop up.

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