The purpose of np.vectorize is to transform functions which are not numpy-aware (e.g. take floats as input and return floats as output) into functions that can operate on (and return) numpy arrays.

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"?

Sep 14, 2009 · 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 …

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.

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 …

Sep 13, 2017 · As the title, I'd like to know how to define a vectorized function in R. Is it just by using a loop in the function? Is this method efficient? And what's the best practice ?

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 …

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 …

Jan 3, 2009 · The v4 series of the gcc compiler can automatically vectorize loops using the SIMD processor on some modern CPUs, such as the AMD Athlon or Intel Pentium/Core chips. How …