Numpy slicing python 3

Question

I have 4 arrays. Array X: is 2D array that contain examples (each has 3 features):

X = array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12], [13, 14, 15], [16, 17, 18], [19, 20, 21]])

Array Y contains labels for examples in Array X:

Y = array([11, 44, 77, 22, 77, 22, 22])

Arrays L & R contain subsets of the labels

L = array([11, 44])
R = array([77, 22])

I want to slice both X and Y according to the labels in L and R. So the output should be:

XL = array([[1, 2, 3], [4, 5, 6]])
XR = array([[7, 8, 9], [10, 11, 12], [13, 14, 15], [16, 17, 18], [19, 20, 21]])
YL = array([11, 44])
YR = array([77, 22, 77, 22, 22])

I know I can do something like the following to extract the rows I want when based on value:

Y[Y==i]
X[Y[Y==i], :] 

However, i here is a value, but in my question it is another array (e.g., L and R). I want an efficient solution in python 3 to do that. Any hints?

Answer 1

Using np.isin:

import numpy as np

X = np.asarray([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12], [13, 14, 15], [16, 17, 18], [19, 20, 21]])
Y = np.asarray([11, 44, 77, 22, 77, 22, 22])

L = np.asarray([11, 44])
R = np.asarray([77, 22])

mask_L = np.isin(Y, L)
mask_R = np.isin(Y, R)

print(X[mask_L,:])  # output: array([[1, 2, 3], [4, 5, 6]])

print(X[mask_R,:])  # output: array([[ 7,  8,  9], [10, 11, 12], 13, 14, 15], 16, 17, 18], 19, 20, 21]])

print(Y[mask_L])  # output: array([11, 44])

print(Y[mask_R])  # output: array([77, 22, 77, 22, 22])