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!
Answer from Alex Martelli on Stack OverflowI'm wondering if there is a use case where lists are better than numpy arrays besides storing multiple types of data in a relatively small/low dimension list (say i just want a quick-and-dirty list to store some constants i want to call on later?
I'm not well versed enough in how processors/memory work to know if I'm missing something else obvious. I know it won't make a huge performance impact anyways if you don't actually have a lot of math or large lists/arrays to work with, but I've been diving deeper into how numpy/pandas function at a basic level and it got me thinking about this.
Is it ever advantageous to use a standard Python list vs a numpy array when all elements are the same type?
Numpy array vs list
Why is NumPy Much Faster Than Lists?
Differences in terms of performance between list, set, and numpy arrays?
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!
NumPy is not just more efficient; it is also more convenient. You get a lot of vector and matrix operations for free, which sometimes allow one to avoid unnecessary work. And they are also efficiently implemented.
For example, you could read your cube directly from a file into an array:
x = numpy.fromfile(file=open("data"), dtype=float).reshape((100, 100, 100))
Sum along the second dimension:
s = x.sum(axis=1)
Find which cells are above a threshold:
(x > 0.5).nonzero()
Remove every even-indexed slice along the third dimension:
x[:, :, ::2]
Also, many useful libraries work with NumPy arrays. For example, statistical analysis and visualization libraries.
Even if you don't have performance problems, learning NumPy is worth the effort.