Python is convenient and flexible, yet notably slower than other languages for raw computational speed. The Python ecosystem has compensated with tools that make crunching numbers at scale in Python ...

NumPy is known for being fast, but could it go even faster? Here’s how to use Cython to accelerate array iterations in NumPy. NumPy gives Python users a wickedly fast library for working with data in ...

Since NumPy was introduced to the world 15 years ago, the primary array programming library has grown into the fundamental package for scientific computing with Python. NumPy serves as an efficient ...

Python, being one of the most dynamic landscape in data science, has become a force to be reckoned with, with its uniform set of libraries that are tailored for data manipulation, analysis and ...

It appears that adding or subtracting numpy.ndarrays with scipy.sparse matrices returns a numpy.matrix. Is the inconsistency in returned array type (see code below) a bug or is it intentional? It is ...

As suggested in [1], I need to release the GIL before calling into UseArray so that the Python binary can move on after starting thread_using_array as in numpy_test.py. I did find that without ...