Python 2D NumPy array comprehension -


i new numpy. have 2-d numpy array containing floating point values. wish index of elements greater 70 % of value, t ,in entire matrix.

output = [(1,2),(4,7),(7,1)] meaning arr[1][2], arr[4][7] , arr[7][1] have values greater 70% of t

using 2 loops job done uncomplicated way. pythonic way of getting done (list comprehension etc.) ? please point out duplicates. !

an example:

in [76]: arr=np.arange(20, dtype=float).reshape(4,5)  in [77]: arr out[77]:  array([[  0.,   1.,   2.,   3.,   4.],        [  5.,   6.,   7.,   8.,   9.],        [ 10.,  11.,  12.,  13.,  14.],        [ 15.,  16.,  17.,  18.,  19.]])

a boolean index can select values array

in [79]: arr>15 out[79]:  array([[false, false, false, false, false],        [false, false, false, false, false],        [false, false, false, false, false],        [false,  true,  true,  true,  true]], dtype=bool)  in [80]: arr[arr>15] out[80]: array([ 16.,  17.,  18.,  19.])

indexes condition true, can used select elements

in [81]: i=np.nonzero(arr>15)  in [82]: out[82]: (array([3, 3, 3, 3], dtype=int32), array([1, 2, 3, 4], dtype=int32))  in [83]: arr[i] out[83]: array([ 16.,  17.,  18.,  19.])

or turn index tuple list of pairs

in [84]: list(zip(*i)) out[84]: [(3, 1), (3, 2), (3, 3), (3, 4)]  in [87]: [arr[j] j in zip(*i)] out[87]: [16.0, 17.0, 18.0, 19.0]

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