Comment by aborsy
2 months ago
My main problem with numpy is that the syntax is verbose. I know from the programming language perspective it may not be considered a drawback (might even be a strength). But in practice, the syntax is a pain compared to matlab or Julia. The code for the latter is easier to read, understand, consistent with math notation.
The syntax of actual array languages can be beautifully concise and expressive. You can express mathematical formulas in ways that makes sense when you read a single line, and once you get used to the notation and some originally non-intuitive quirks (from a programming background perspective), you can write in very few lines what otherwise would take you several lines of rather ugly, less readable code.
In my view, python+numpy is not actually an array language. Numpy as a library adds vectorization operations to python in order to help with speed. This is different. It does not (intend to) bring the advantages that array language syntax has, even if it was a bit more consistent.
Does numpy market itself as an array language or something?
Not that I know of, and I did not claim it does.
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Yeah. Compare
A = [1, 2; 3, 4];
x = [5; 6];
y = A * x;
with this uglier version:
import numpy as np
A = np.array([[1, 2], [3, 4]])
x = np.array([[5], [6]])
y = A @ x
You don't have to wrap the lists in np.array if you use NumPy functions (or if one of the arguments already is a NumPy array, which usually is the case):
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