JAX vs. NumPy vs. python-docx vs. python-sql Comparison Chart

​JAX is a Python library designed for high-performance numerical computing and machine learning research. It offers a NumPy-like API, facilitating seamless adoption for those familiar with NumPy. Key features of JAX include automatic differentiation, just-in-time compilation, vectorization, and parallelization, all optimized for execution on CPUs, GPUs, and TPUs. These capabilities enable efficient computation for complex mathematical functions and large-scale machine-learning models. JAX also integrates with various libraries within its ecosystem, such as Flax for neural networks and Optax for optimization tasks. Comprehensive documentation, including tutorials and user guides, is available to assist users in leveraging JAX's full potential. ​

python-docx is a Python library for creating and updating Microsoft Word (.docx) files. Paragraphs are fundamental in Word. They’re used for body text, but also for headings and list items like bullets. You’re free to specify both width and height, but usually, you wouldn’t want to. If you specify only one, python-docx uses it to calculate the properly scaled value of the other. This way the aspect ratio is preserved and your picture doesn’t look stretched. If you don’t know what a Word paragraph style is you should definitely check it out. Basically, it allows you to apply a whole set of formatting options to a paragraph at once. python-docx allows you to create new documents as well as make changes to existing ones. Actually, it only lets you make changes to existing documents; it’s just that if you start with a document that doesn’t have any content, it might feel at first like you’re creating one from scratch.