The ndarray property reflects the information of the array itself, and the internal information of the array can be accessed or set by accessing the property.
The ndarray property includes memory layout attributes, data type attributes, and other attributes.
The memory layout properties of the ndarray are as follows:
Description:
ndarray.flags obtains the memory information of the ndarray object, including the following attributes:
Example
>>import numpy as npDescription:ndarray.ndim gets the number of dimensions of the numpy array, or the number of axes, called rank.>arr=np.array([1,2,3])
>arr
array([1, 2, 3])
# arr.flags to get the array memory information.
>arr.flags
c_contiguous : true
f_contiguous : true
owndata : true
writeable : true
aligned : true
writebackifcopy : false
Access memory information in the form of a dictionary.
>arr.flags['c_contiguous']
true accesses memory information through a period + lowercase property name.
>arr.flags.c_contiguous
true
In numpy, each linear array is called an dimensions or an axis.
Each element of a one-dimensional array is a single number or string with a number of axes of 1 and an outermost one.
Each element of a two-dimensional array is a one-dimensional array, with 2 axes and 2 outermost layers.
Each element of a three-dimensional array is a two-dimensional array. The number of axes is 3, and the most predicate is 3.
Example
>>import numpy as npDescription:ndarray.shape returns tuples that represent the size of each axis of the array.A one-dimensional array where each element is a single number or a single string.
>ar1=np.array([1,2,3])
A two-dimensional array where each element is a single number.
>ar2=np.array([[1,2,3]])
Three-dimensional arrays, where each element is a two-dimensional array.
>ar3=np.array([[1,2,3]]]
>ar1
array([1, 2, 3])
>ar2
array([[1, 2, 3]])
>ar3
array([[1, 2, 3]]]
The number of axes of a one-dimensional array is 1, and the outermost layer is 1.
>ar1.ndim
The number of axes of the two-dimensional array is 2, and the outermost layer is 2.
>ar2.ndim
The number of axes of the three-dimensional array is 3, and the outermost layer is 3.
>ar3.ndim
The size of the axis represents the number of elements of the same dimension.
Example
>>import numpy as npDescription:ndarray.size returns the total number of elements in the array.>ar1=np.array([1,2,3])
>ar2=np.array([[1,2,3],[5,6,7]])
>ar3=np.array([[1,2,3],[5,6,7]]]
>ar1.shape
>ar2.shape
shape returns a tuple of the size of each axis.
The size of the axis represents the number of elements of the same dimension.
The axis counts the size from the outside to the inside.
The outermost three-dimensional array has one two-dimensional array [[1,2,3],[5,6,7]] with a number of elements
The sub-outer, two-dimensional array has two one-dimensional arrays [1,2,3], [5,6,7], and the number of elements is 2
The innermost layer, a one-dimensional array has 3 elements.
>ar3.shape
Example
>>import numpy as npDescription:ndarray.ItemSize returns the byte length (size) of an element.>ar1=np.array([1,2,3])
>ar2=np.array([[1,2,3],[5,6,7]])
>ar3=np.array([[1,2],[3,5]],6,7],[8,9]]]
# ndarray.size returns the total number of elements in the array.
>ar1.size
>ar2.size
>ar3.size
Example
>>import numpy as npDescription:ndarray.nbytes returns the total byte length (size) of the array element.>ar3=np.array([[1,2],[3,5]],6,7],[8,9]]]
itemsize returns the byte length of the element.
>ar3.itemsize
dtype returns the element type, int32 is 4 bytes.
>ar3.dtype
dtype('int32')
Use the nbytes itemsize to obtain the total number of elements.
Example
>>import numpy as np>ar3=np.array([[1,2],[3,5]],6,7],[8,9]]]
nbytes returns the total byte length of the array element.
>ar3.nbytes
>ar3.itemsize
Use the nbytes itemsize to obtain the total number of elements.
>ar3.nbytes/ar3.itemsize
Same as size.
>ar3.size
