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"?

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

Aug 11, 2021 · Many CPUs have "vector" or "SIMD" instruction sets which apply the same operation simultaneously to two, four, or more pieces of data. Modern x86 chips have the SSE …

Feb 18, 2025 · 2 numpy.vectorize conveniently converts a scalar function to vectorized functions that can be applied directly to arrays. However, when inputting a single value into the …

Dec 23, 2022 · SIMD, Vectorize. is enabled by -o2, loop unrolling makes the code much larger so it is a separate flag in -o2 and included in -o3. many times -o2 will out-perform -o3.

Oct 4, 2009 · Is it a good idea to vectorize the code? What are good practices in terms of when to do it? What happens underneath?

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 ...

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 …

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 …

Mar 10, 2016 · I have noticed that the gcc flag -ftree-vectorize is very useful for optimizing code. I am trying to understand better how it works, but the doc is fairly concise: Perform vectorization …