How does Pythonic garbage collection with numpy array appends and deletes?

I am trying to adapt the underlying structure of plotting code (matplotlib) that is updated on a timer to go from using Python lists for the plot data to using numpy arrays. I want to be able to lower the time step for the plot as much as possible, and since the data may get up into the thousands of points, I start to lose valuable time fast if I can't. I know that numpy arrays are preferred for this sort of thing, but I am having trouble figuring out when I need to think like a Python programmer and when I need to think like a C++ programmer maximize my efficiency of memory access.

It says in the scipy.org docs for the append() function that it returns a copy of the arrays appended together. Do all these copies get garbage-collected properly? For example:

import numpy as np

a = np.arange(10)
a = np.append(a,10)
print a

This is my reading of what is going on on the C++-level, but if I knew what I was talking about, I wouldn't be asking the question, so please correct me if I'm wrong! =P

First a block of 10 integers gets allocated, and the symbol a points to the beginning of that block. Then a new block of 11 integers is allocated, for a total of 21 ints (84 bytes) being used. Then the a pointer is moved to the start of the 11-int block. My guess is that this would result in the garbage-collection algorithm decrementing the reference count of the 10-int block to zero and de-allocating it. Is this right? If not, how do I ensure I don't create overhead when appending?

I also am not sure how to properly delete a numpy array when I am done using it. I have a reset button on my plots that just flushes out all the data and starts over. When I had lists, this was done using del data[:]. Is there an equivalent function for numpy arrays? Or should I just say data = np.array([]) and count on the garbage collector to do the work for me?