Answer by Tacio Medeiros for A fast way to find the largest N elements in an...
I had this problem and, since this question is 5 years old, I had to redo all benchmarks and change the syntax of bottleneck (there is no partsort anymore, it's partition now).I used the same arguments...
View ArticleAnswer by hennyk for A fast way to find the largest N elements in an numpy array
You can also use numpy's percentile function. In my case it was slightly faster then bottleneck.partsort():import timeitimport bottleneck as bnN,M,K = 10,1000000,100start = timeit.default_timer()for k...
View ArticleAnswer by Akavall for A fast way to find the largest N elements in an numpy...
numpy 1.8 implements partition and argpartition that perform partial sort ( in O(n) time as opposed to full sort that is O(n) * log(n)).import numpy as nptest = np.array([9,1,3,4,8,7,2,5,6,0])temp =...
View ArticleAnswer by kwgoodman for A fast way to find the largest N elements in an numpy...
Each negative sign in the proposed bottleneck solution-bottleneck.partsort(-a, 10)[:10]makes a copy of the data. We can remove the copies by doingbottleneck.partsort(a, a.size-10)[-10:]Also the...
View ArticleAnswer by NPE for A fast way to find the largest N elements in an numpy array
The bottleneck module has a fast partial sort method that works directly with Numpy arrays: bottleneck.partition().Note that bottleneck.partition() returns the actual values sorted, if you want the...
View ArticleAnswer by Mike Graham for A fast way to find the largest N elements in an...
If storing the array as a list of numbers isn't problematic, you can use import heapqheapq.nlargest(N, a)to get the N largest members.
View ArticleAnswer by Akavall for A fast way to find the largest N elements in an numpy...
Perhaps heapq.nlargestimport numpy as npimport heapqx = np.array([1,-5,4,6,-3,3])z = heapq.nlargest(3,x)Result:>>> z[6, 4, 3]If you want to find the indices of the n largest elements using...
View ArticleA fast way to find the largest N elements in an numpy array
I know I can do it like the following:import numpy as npN=10a=np.arange(1,100,1)np.argsort()[-N:]However, it is very slow since it did a full sort.I wonder whether numpy provide some methods the do it...
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