I would like to perform a slicing on a two dimensional numpy array:
type1_c = type1_c[
(type1_c[:,10]==2) or
(type1_c[:,10]==3) or
(type1_c[:,10]==4) or
(type1_c[:,10]==5) or
(type1_c[:,10]==6)
]
The syntax looks right; however I got the following error message: 'The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()'
I really don't understand what's going wrong. Any idea?
or is unambiguous when it's between two scalars, but what's the right vector generalization? if x == array([0, 0]) and y == array([0,1]), should x or y be (1) False, because not all pairwise terms or-ed together are True, (2) True, because at least one pairwise or result is true, (3) array([0, 1]), because that's the pairwise result of an or, (4) array([0, 0]), because [0,0] or [0,1] would return [0,0] because nonempty lists are truthy, and so should arrays be?
You could use | here, and treat it as a bitwise issue:
>>> import numpy as np
>>> vec = np.arange(10)
>>> vec[(vec == 2) | (vec == 7)]
array([2, 7])
Explicitly use numpys vectorized logical or:
>>> np.logical_or(vec==3, vec==5)
array([False, False, False, True, False, True, False, False, False, False], dtype=bool)
>>> vec[np.logical_or(vec==3, vec==5)]
array([3, 5])
or use in1d, which is far more efficient here:
>>> np.in1d(vec, [2, 7])
array([False, False, True, False, False, False, False, True, False, False], dtype=bool)
>>> vec[np.in1d(vec, [2, 7])]
array([2, 7])
