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"?

Dec 29, 2023 · How to vectorize operations in pandas data frame? Asked 2 years, 1 month ago Modified 2 years, 1 month ago Viewed 197 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 …

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 …

Nov 6, 2015 · With the GCC compiler, the -ftree-vectorize option turns on auto-vectorization, and this flag is automatically set when using -O3. To what level does it vectorize?

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 vectorized function, …

Oct 6, 2018 · I am using Pandas dataframes and want to create a new column as a function of existing columns. I have not seen a good discussion of the speed difference between df.apply() and …

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, …

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.

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 …