Numpy array loss of dimension when masking

Checkout numpy.where

http://docs.scipy.org/doc/numpy/reference/generated/numpy.where.html

To keep the same dimensionality you are going to need a fill value. In the example below I use 0, but you could also use np.nan

np.where(arr>3, arr, 0)

returns

array([[[[ 0, 11],
         [ 0, 22],
         [ 0, 33]],

        [[ 4, 44],
         [ 5, 55],
         [ 6, 66]]],


       [[[ 7, 77],
         [ 8, 88],
         [ 9, 99]],

        [[ 0, 32],
         [ 0, 33],
         [ 0, 34]]]])