bottleneck VS NumPy

Fast Array Manipulation in Python: Since Numpy is the de facto standard for storing multi-dimensional data, any performance gain you see using librapid math kernels will need to be realized on data which probably started its life as a numpy array, and needs to be passed to another tool as a numpy array. Hopefully there will be (or already is?) a way to build a librapid array out of a numpy array without copying the data and vice versa. In fact I might suggest that librapid focus on the fast math operations and simply become an accelerator for numpy arrays. For instance, look at CuPy which provides GPU-implemented operations within a numpy-compatible API, and Bottleneck which simply provides fast C-based implementations of some otherwise slow parts of Numpy. Also note that numpy *can* be multi-threaded depending on the operation and some environment variables. Single-threaded to Single-threaded I think you will be hard-pressed to beat Numpy on general math operations, but that doesn't mean there aren't specific "kernels" that are more specialized that can be greatly improved with a C++ back-end.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]