%timeit mymode(np.unique(a, return_counts=True)) Statistics’ mode() It takes the argmax() of the counts, and uses the returned value as index for the values. I created a lambda function that takes the unique values and their respective counts of an array. %timeit int(stats.find_repeats(a)) NumPy’s unique() & argmax() That’s odd, because the values of the array are cast to float and I manually recast them to int. Can we do faster? Sure we can! SciPy also has a find_repeats function, which checks which values of the array occur more than once, it also provided the count and orders it. %timeit stats.mode(a).mode SciPy’s find_repeats() The mean was 112 µs for this array, but it was more than a couple of second on my real data set. What triggered me to go on this quest to find the fastest mode function, was that SciPy seemed to be extremely slow at it.

import numpy as npĪ = np.array() SciPy’s mode() Let’s load the packages I will be using throughout this blog post, and create a simple array with dummy data where the mode is clearly 1.

That goes for many things in computer science: also for this seemingly trivial question. Although all roads lead to Rome, some will take you there faster. Module 'numba_scipy' has no attribute 'xlogy'įailed in nopython mode pipeline (step: nopython frontend)ĭuring: typing of get attribute at example.py (19)ĭuring: typing of get attribute at example.I was curious how many ways there are to calculate the mode of a 1-D numpy array in Python. Module 'numba_scipy' has no attribute 'special' Here are my attempts - they all fail, see errors below: import numpy as npĬomplete error message: /home/yair/.local/lib/python3.8/site-packages/numba/core/dispatcher.py:238: UserWarning: Numba extension module 'numba_scipy' failed to load due to 'ValueError(No function '_pyx_fuse_0pdtr' found in _pyx_capi_ of '_special')'.įailed in nopython mode pipeline (step: nopython rewrites) I cannot get it to work and was hoping for some usage example. Per Andras Deak's suggestion below, I tried using numba-scipy. Of course, I can implement it myself but it would be nice to use a known solution. An answer from 2015 says this could not be done (for some other function) but maybe I thought it changed. However, I believe it should work, becuase it says here that xlogy could be overloaded. Unknown attribute 'xlogy' of type Module() It gives the following error TypingError: Failed in nopython mode pipeline (step: nopython frontend)

Scipy mode