使用NumPy的数据类型的大小

时间:2022-02-17 16:31:38

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

在NumPy中,我可以通过以下方式获取特定数据类型的大小(以字节为单位):

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?

我有两个问题。首先,有没有办法在不创建数据类型实例的情况下获取此信息?第二,itemsize和nbytes之间的区别是什么?

2 个解决方案

#1


39  

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.)

您需要一个dtype实例来获取itemsize,但您不需要ndarray的实例。 (如果在一秒钟内变得清晰,nbytes是数组的属性,而不是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.

至于itemsize和nbytes之间的区别,nbytes只是x.itemsize * x.size。

E.g.

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

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

#2


13  

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

查看NumPy C源文件,这是评论:

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:

所以在NumPy中:

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

So .nbytes is a shortcut for:

所以.nbytes是一个捷径:

>>> 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):

因此,要获得NumPy数组的基本大小而不创建它的实例,您可以这样做(假设例如3x5x2的双精度数组):

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

However, important note from the NumPy help file:

但是,来自NumPy帮助文件的重要说明:

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

#1


39  

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.)

您需要一个dtype实例来获取itemsize,但您不需要ndarray的实例。 (如果在一秒钟内变得清晰,nbytes是数组的属性,而不是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.

至于itemsize和nbytes之间的区别,nbytes只是x.itemsize * x.size。

E.g.

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

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

#2


13  

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

查看NumPy C源文件,这是评论:

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:

所以在NumPy中:

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

So .nbytes is a shortcut for:

所以.nbytes是一个捷径:

>>> 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):

因此,要获得NumPy数组的基本大小而不创建它的实例,您可以这样做(假设例如3x5x2的双精度数组):

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

However, important note from the NumPy help file:

但是,来自NumPy帮助文件的重要说明:

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