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)?
7 s
NumPy’s arrays are more compact than Python lists — a list of lists as you describe, in Python, would take at least 20 MB or so, while a NumPy 3D array with single-precision floats in the cells would fit in 4 MB. Access in reading and writing items is also faster with NumPy.
Maybe you don’t care that much for just a million cells, but you definitely would for a billion cells — neither approach would fit in a 32-bit architecture, but with 64-bit builds NumPy would get away with 4 GB or so, Python alone would need at least about 12 GB (lots of pointers which double in size) — a much costlier piece of hardware!
The difference is mostly due to “indirectness” — a Python list is an array of pointers to Python objects, at least 4 bytes per pointer plus 16 bytes for even the smallest Python object (4 for type pointer, 4 for reference count, 4 for value — and the memory allocators rounds up to 16). A NumPy array is an array of uniform values — single-precision numbers takes 4 bytes each, double-precision ones, 8 bytes. Less flexible, but you pay substantially for the flexibility of standard Python lists!