x = np.array([_ for _ in range(1000)]) This works, but its performance is hidebound by the time it takes for Python to create a list, and for NumPy to convert that list into an array.

In general, np.linspace(a,b,n+1) creates n + 1 points, a 0, a 1, …, a n, starting at a and ending at b, each spaced out by Δ x = b − a n, where a k = a 0 + k Δ x. Building Random Arrays NumPy has a ...

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 ...