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"?

Jul 17, 2018 · I'd like to use Numba to vectorize a function that will evaluate each row of a matrix. This would essentially apply a Numpy ufunc to the matrix, as opposed to looping over the rows. According …

Dec 29, 2023 · How to vectorize operations in pandas data frame? Asked 2 years, 1 month ago Modified 2 years, 1 month ago Viewed 202 times

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 is this …

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 instructions, …

Feb 26, 2018 · The decorator version @vectorize is harder to apply than the function version. Your method f takes two arguments, self and x. vectorize isn't smart enough to assign Dummy() to self, …

Oct 11, 2019 · In other words I'm trying to vectorize the image to a set of lines. I have already looked at the HoughLinesTransform, however this does not cover every part of the image and is more about …

Apr 18, 2022 · The vectorize function is provided primarily for convenience, not for performance. The implementation is essentially a for loop. Remember: the documentation is an API reference guide …

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.

Oct 6, 2018 · If np.vectorize() is in general always faster than df.apply(), then why is np.vectorize() not mentioned more? I only ever see StackOverflow posts related to df.apply(), such as: pandas create …