What is the most efficient way to remove negative elements in an array? I have tried numpy.delete and Remove all specific value from array and code of the form x[x != i].
For:
import numpy as np
x = np.array([-2, -1.4, -1.1, 0, 1.2, 2.2, 3.1, 4.4, 8.3, 9.9, 10, 14, 16.2])
I want to end up with an array:
[0, 1.2, 2.2, 3.1, 4.4, 8.3, 9.9, 10, 14, 16.2]
In [2]: x[x >= 0]
Out[2]: array([ 0. , 1.2, 2.2, 3.1, 4.4, 8.3, 9.9, 10. , 14. , 16.2])
If performance is important, you could take advantage of the fact that your np.array is sorted and use numpy.searchsorted
For example:
In [8]: x[np.searchsorted(x, 0) :]
Out[8]: array([ 0. , 1.2, 2.2, 3.1, 4.4, 8.3, 9.9, 10. , 14. , 16.2])
In [9]: %timeit x[np.searchsorted(x, 0) :]
1000000 loops, best of 3: 1.47 us per loop
In [10]: %timeit x[x >= 0]
100000 loops, best of 3: 4.5 us per loop
The difference in performance will increase as the size of the array increases because np.searchsorted does a binary search that is O(log n) vs. O(n) linear search that x >= 0 is doing.
In [11]: x = np.arange(-1000, 1000)
In [12]: %timeit x[np.searchsorted(x, 0) :]
1000000 loops, best of 3: 1.61 us per loop
In [13]: %timeit x[x >= 0]
100000 loops, best of 3: 9.87 us per loop
In numpy:
b = array[array>=0]
Example:
>>> import numpy as np
>>> arr = np.array([-2, -1.4, -1.1, 0, 1.2, 2.2, 3.1, 4.4, 8.3, 9.9, 10, 14, 16.2])
>>> arr = arr[arr>=0]
>>> arr
array([ 0. , 1.2, 2.2, 3.1, 4.4, 8.3, 9.9, 10. , 14. , 16.2])
There's probably a cool way to do this is numpy because numpy is magic to me, but:
x = np.array( [ num for num in x if num >= 0 ] )
