python - test for membership in a 2d numpy array -

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i have 2 2d arrays of same size

a = array([[1,2],[3,4],[5,6]]) b = array([[1,2],[3,4],[7,8]])

i want know rows of b in a.

so output should :

array([ true,  true, false], dtype=bool)

without making :

array([any(i == a) in b])

cause , b huge.

there function 1d arrays : in1d

what we'd use np.in1d... except np.in1d works 1-dimensional arrays. our arrays multi-dimensional. however, can view arrays 1-dimensional array of strings:

a = a.ravel().view((np.str, a.itemsize*a.shape[1]))

for example,

in [15]: = np.array([[1, 2], [2, 3], [1, 3]])  in [16]: = a.ravel().view((np.str, a.itemsize*a.shape[1]))  in [17]: a.dtype out[17]: dtype('|s8')  in [18]: a.shape out[18]: (3,)  in [19]: out[19]:  array(['\x01\x00\x00\x00\x02', '\x02\x00\x00\x00\x03',        '\x01\x00\x00\x00\x03'],        dtype='|s8')

this makes each row of a string. matter of hooking np.in1d:

def innd(a, b, assume_unique=false):     = np.asarray(a, order='c')     b = np.asarray(b, order='c')     = a.ravel().view((np.str, a.itemsize * a.shape[1]))     b = b.ravel().view((np.str, b.itemsize * b.shape[1]))     return np.in1d(a, b, assume_unique) 
import numpy np   def innd(a, b, assume_unique=false):     = np.asarray(a, order='c')     b = np.asarray(b, order='c')     = a.ravel().view((np.str, a.itemsize * a.shape[1]))     b = b.ravel().view((np.str, b.itemsize * b.shape[1]))     return np.in1d(a, b, assume_unique)  tests = [     (np.array([[1, 2], [2, 3], [1, 3]]),      np.array([[2, 2], [3, 3], [4, 4]]),      np.array([false, false, false])),     (np.array([[1, 2], [2, 2], [1, 3]]),      np.array([[2, 2], [3, 3], [4, 4]]),      np.array([true, false, false])),     (np.array([[1, 2], [3, 4], [5, 6]]),      np.array([[1, 2], [3, 4], [7, 8]]),      np.array([true, true, false])),     (np.array([[1, 2], [5, 6], [3, 4]]),      np.array([[1, 2], [5, 6], [7, 8]]),      np.array([true, true, false])),     (np.array([[-0.5, 2.5, -2, 100, 2], [5, 6, 7, 8, 9], [3, 4, 5, 6, 7]]),      np.array([[1.0, 2, 3, 4, 5], [5, 6, 7, 8, 9], [-0.5, 2.5, -2, 100, 2]]),      np.array([false, true, true])) ]  a, b, answer in tests:     result = innd(b, a)     try:         assert np.all(answer == result)     except assertionerror:         print('''\ a: {a} b: {b}  answer: {answer} result: {result}'''.format(**locals()))         raise else:     print('success!')

yields

success!

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