Size of data type using NumPy

Question

In NumPy, I can get the size (in bytes) of a particular data type by:

datatype(...).itemsize 

or:

datatype(...).nbytes 

For example:

np.float32(5).itemsize #4 np.float32(5).nbytes   #4 

I have two questions. First, is there a way to get this information without creating an instance of the datatype? Second, what's the difference between itemsize and nbytes?

Answer 1

You need an instance of the dtype to get the itemsize, but you shouldn't need an instance of the ndarray. (As will become clear in a second, nbytes is a property of the array, not the dtype.)

E.g.

print np.dtype(float).itemsize print np.dtype(np.float32).itemsize print np.dtype('|S10').itemsize 

As far as the difference between itemsize and nbytes, nbytes is just x.itemsize * x.size.

E.g.

In [16]: print np.arange(100).itemsize 8  In [17]: print np.arange(100).nbytes 800 

Answer 2

Looking at the NumPy C source file, this is the comment:

size : int     Number of elements in the array. itemsize : int     The memory use of each array element in bytes. nbytes : int     The total number of bytes required to store the array data,     i.e., ``itemsize * size``. 

So in NumPy:

>>> x = np.zeros((3, 5, 2), dtype=np.float64) >>> x.itemsize 8 

So .nbytes is a shortcut for:

>>> np.prod(x.shape)*x.itemsize 240 >>> x.nbytes 240 

So, to get a base size of a NumPy array without creating an instance of it, you can do this (assuming a 3x5x2 array of doubles for example):

>>> np.float64(1).itemsize * np.prod([3,5,2]) 240 

However, important note from the NumPy help file:

|  nbytes |      Total bytes consumed by the elements of the array. | |      Notes |      ----- |      Does not include memory consumed by non-element attributes of the |      array object.