Selecting rows from numpy ndarray

I want to select only certain rows from a numpy array based on the value in the second column. For example, this test array has integers from 1 to 10 in second column.

>>> test = numpy.array([numpy.arange(100), numpy.random.randint(1, 11, 100)]).transpose()
 >>> test[:10, :]
 array([[ 0,  6],
        [ 1,  7],
        [ 2, 10],
        [ 3,  4],
        [ 4,  1],
        [ 5, 10],
        [ 6,  6],
        [ 7,  4],
        [ 8,  6],
        [ 9,  7]])
 

If I wanted only rows where the second value is 4, it is easy.

>>> test[test[:, 1] == 4]
 array([[ 3,  4],
        [ 7,  4],
        [16,  4],
        ...
        [81,  4],
        [83,  4],
        [88,  4]])
 

But how do I achieve the same result when there is more than one wanted value. The wanted list can be of arbitrary length. For example, I may want all rows where the second column is either 2, 4 or 6.

>>> wanted = [2, 4, 6]
 

The only way I have come up with is to use list comprehension and then convert this back into an array and seems too convoluted, although it works.

>>> test[numpy.array([test[x, 1] in wanted for x in range(len(test))])]
 array([[ 0,  6],
        [ 3,  4],
        [ 6,  6],
        ...
        [90,  2],
        [91,  6],
        [92,  2]])
 

Is there a better way to do this in numpy itself that I am missing?

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