These examples highlight its ability to handle both straightforward and complex tasks securely: Basic Tasks: Execute simple Python code snippets, such as creating arrays, ...

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NumPy arrays are central to Python data science. Unlike lists, which can store different data types, NumPy arrays are homogenous and can therefore be stored more efficiently in memory.

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Replacing the GIL with other mechanisms to handle referencing counting, garbage collection and memory management tasks will slow down single-threaded CPU-bound programs. So Python still has the GIL, ...

Some thoughts: To ensure you can rely more on pa.array(..) to actually return an Array, we could concat chunked arrays in the example above, and then users could use pa.asarray(..) to get either Array ...

JS2Py supports a great deal of cross-interoperation between Python and JavaScript. You can import existing Node.js modules in your Python code, by way of a js2py.require method.