Using Numpy Vectorize on Functions that Return Vectors Asked 15 years, 4 months ago Modified 2 years, 11 months ago Viewed 125k times

I'd appreciate some help in finding and understanding a pythonic way to optimize the following array manipulations in nested for loops: def _func(a, b, radius): "Return 0 if a>b, otherwise ...

Feb 13, 2021 · Isn't the answer to How to use np.vectorize? usually "Don't. It just pretends to be a vectorized function but is just a loop with a different name"?

Feb 5, 2016 · What is the most efficient way to map a function over a numpy array? I am currently doing: import numpy as np x = np.array ( [1, 2, 3, 4, 5]) # Obtain array of square ...

Sep 5, 2023 · By default vectorize passes scalar arguments to your function, and expects a scalar (read about otypes). signature lets you pass subarrays, with the symbols determining the …

Oct 6, 2018 · np.vectorize, list comprehension + zip and map methods, i.e. the top 3, all have roughly the same performance. This is because they use tuple and bypass some Pandas …

Feb 21, 2013 · Note that np.vectorize isn't really meant as a decorator except for the simplest cases. If you need to specify an explicit otype, use the usual form new_func = …

Nov 17, 2021 · I am using Fast.ai framework for image classification but the previous line runs, I get the following error "cannot call vectorize on size 0 inputs unless otypes is set".

Oct 3, 2013 · Bear in mind that np.vectorize doesn't really give any performance benefit over a plain list comprehension - you'll still end up looping in Python rather than C

Here np.vectorize wraps the frozenset(x) function in code that can take an array or list (keys), and pass each element for evaluation. It a kind of numpy iteration (convenient, but not fast). But to …