Axes and dimensions in numpy

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NumPy's main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. In NumPy dimensions are called axes. The number of axes is rank.

For example, the coordinates of a point in 3D space [1, 2, 1] is an array of rank 1, because it has one axis. That axis has a length of 3. In the example pictured below, the array has rank 2 (it is 2-dimensional). The first dimension (axis) has a length of 2, the second dimension has a length of 3.

[[ 1., 0., 0.],
 [ 0., 1., 2.]]

ndarray.ndim

The number of array axes, inpython## In the world of #, the number of axes is called rank

>> X = np.reshape(np.arange(24), (2, 3, 4))
  # 也即 2 行 3 列的 4 个平面（plane）
>> X
array([[[ 0, 1, 2, 3],
    [ 4, 5, 6, 7],
    [ 8, 9, 10, 11]],
    [[12, 13, 14, 15],
    [16, 17, 18, 19],
    [20, 21, 22, 23]]])

shape(x)

shape(x )[0]

x.shape[0]

Let’s look at the composition of each plane separately:

>> X[:, :, 0]
array([[ 0, 4, 8],
    [12, 16, 20]])
>> X[:, :, 1]
array([[ 1, 5, 9],
    [13, 17, 21]])
>> X[:, :, 2]
array([[ 2, 6, 10],
    [14, 18, 22]])
>> X[:, :, 3]
array([[ 3, 7, 11],
    [15, 19, 23]])

reshpae is a method in the array object, used to change the shape of the array.

Two-dimensional array

#!/usr/bin/env python 
# coding=utf-8 
import numpy as np 
 
a=np.array([1, 2, 3, 4, 5, 6, 7, 8]) 
print a 
d=a.reshape((2,4)) 
print d

Three-dimensional array

#!/usr/bin/env python 
# coding=utf-8 
import numpy as np 
 
a=np.array([1, 2, 3, 4, 5, 6, 7, 8]) 
print a 
f=a.reshape((2, 2, 2)) 
print f

##The principle of shape change is that the array elements cannot change, for example, it is written like this Wrong because the array elements have changed.

#!/usr/bin/env python 
# coding=utf-8 
import numpy as np 
 
a=np.array([1, 2, 3, 4, 5, 6, 7, 8]) 
print a 
print a.dtype 
e=a.reshape((2,2)) 
print e

Note: The new array generated by reshape and the original array share the same memory, that is to say, if one is changed elements of the array, the other array will also be changed.

#!/usr/bin/env python 
# coding=utf-8 
import numpy as np 
 
a=np.array([1, 2, 3, 4, 5, 6, 7, 8]) 
print a 
e=a.reshape((2, 4)) 
print e 
a[1]=100 
print a 
print e

a=np.arange(0, 60, 10)
>>>a
array([0,10,20,30,40,50])
>>>a.reshape(-1,1)
array([[0],
[10],
[20],
[30],
[40],
[50]])

If written as a.reshape(1,1), an error will be reported##ValueError:cannot reshape array of size 6 into shape (1,1)

>>> a = np.array([[1,2,3], [4,5,6]])
>>> np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2
array([[1, 2],
    [3, 4],
    [5, 6]])

# 下面是两张2*3大小的照片(不知道有几张照片用-1代替)，如何把所有二维照片给摊平成一维
>>> image = np.array([[[1,2,3], [4,5,6]], [[1,1,1], [1,1,1]]])
>>> image.shape
(2, 2, 3)
>>> image.reshape((-1, 6))
array([[1, 2, 3, 4, 5, 6],
    [1, 1, 1, 1, 1, 1]])

The difference between array and asarray in numpy

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