Unnecessary temporary arrays is definitely a major source of inefficiency when working with NumPy, but recent versions of NumPy go to heroic lengths (via Python reference counting) to avoid doing so in many cases: https://github.com/numpy/numpy/blob/v1.18.3/numpy/core/src/m...

So in this case, NumPy would actually only make one temporary copy, effectively translating the loop into the following:

    for j in range(255):
        u = z**2   # create a new squared array
        u += c     # add in-place 
        z = u      # replace the old array