Aug 11, 2021 · Terminology: Note that most modern ahead-of-time compilers are able to auto vectorize very simple loops like this, which can often be enabled via a compile option (on by …

Sep 13, 2019 · Most of the built-in NumPy functions are already vectorized and don't need the np.vectorize decorator at all. In general the numpy.vectorize decorator will produce very slow …

Jul 30, 2022 · Is there some efficient way to "double vectorize" a Numpy function? Consider some function f which is vectorized over its first 3 positional arguments; its implementation consists …

Jul 17, 2018 · import numpy as np a=[1,2] b=[3,4] @np.vectorize def f(x_1,x_2): return x_1+x_2 print(f(a,b)) #-> [4,6] In numba, you would traditionally need to pass in input types to the …

Apr 4, 2018 · numba cuda vectorize and guvectorize are basically expecting to do elementwise operations, with no interdependencies between computed results. prefix sum doesn't really fit …

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 …

May 7, 2018 · Studying Deep Learning with Python, I can't comprehend the following simple batch of code which encodes the integer sequences into a binary matrix. def …

Feb 26, 2018 · Remembering a technique I saw in the memoized decorator, I managed to get the decorator to also work for instance methods by subclassing numpy.vectorize as follows: import …

Jul 29, 2019 · Numpy offers vectorize and frompyfunc with similar functionalies.. As pointed out in this SO-post, vectorize wraps frompyfunc and handles the type of the returned array correctly, …

Dec 20, 2014 · To vectorize this code, you will need to operate on complete dataframe and not on the individual lats and longs. I have made an attempt at this. I need the result df and a new …