What are the advantages of __NumPy__ over regular Python lists?

I have approximately 100 financial markets series, and I am going to create a cube array of 100x100x100 = 1 million cells. I will be regressing (3-variable) each x with each y and z, to fill the array with standard errors.

I have heard that for "large matrices" I should use NumPy as opposed to Python lists, for performance and scalability reasons. Thing is, I know Python lists and they seem to work for me.

What will the benefits be if I move to NumPy?

What if I had 1000 series (that is, 1 billion floating point cells in the cube)?