I've had this experience when writing a simple algorithm for run length encoding, a problem which I struggled to vectorize. Compare the numpy algo with pure python:

  def rle_encode_pure_python(sequence):
    """Run length encode array."""
    previous = sequence[0]
    count = 1
    out = []
    for element in sequence[1:]:
        if element == previous:
            count += 1
        else:
            out.append((count, previous))
            previous = element
            count = 1
    out.append((count, previous))
    return out


  def rle_encode_numpy(sequence):
    diffs = np.concatenate(( np.array((True, )), np.diff(sequence)!=0))
    indices = np.concatenate((np.where(diffs)[0],np.array((sequence.size, ))))
    counts = np.diff(indices).astype('uint16')
    values = sequence[diffs].astype('uint8')
    return np.rec.fromarrays((counts, values),names=('count','value'))

The numpy version is faster, but a lot less legible (and makes some assumptions about data types and array length). Luckily the pure python version can be jitted with numba and is faster than numpy.