Libraries like CuPy tend to return a 0d-array in scenarios like those above, and even `xp.asarray(0)[()]` remains a 0d-array there. To deal with the reality of the inconsistencies present in NumPy, as ...

As noted above, NumPy arrays behave a lot like other Python objects, for the sake of convenience. For instance, they can be indexed like lists; arr[0] accesses the first element of a NumPy array.

Building Random Arrays. NumPy has a few ways to build random number arrays. These methods are contained in the random library. In particular we will look at random.rand, random.randn, and ...

Efficient initialization of NumPy arrays will significantly improve performance of your scripts: Utilize functions like np.zeros, np.ones, np.empty, and np.arange which are optimized for performance.

The fundamental package for scientific computing with Python. - API: Disallow 0D input arrays in nonzero · numpy/numpy@e723d39 ...

NumPy is known for being fast, but there's always room for improvement. Learn how to use Cython to iterate over NumPy arrays at the speed of C.

When working with numpy arrays, it's easy to overlook the importance of array shape. You might inadvertently change an array's shape, leading to unexpected results or errors.