numpy 数组维度操作总汇

心已赠人 2022-06-03 00:56 378阅读 0赞

numpy中如何改变数组维度呢？

写在前面

## **所有的重排原则：** ##

### **从原数组最深维度开始依次取元素排到转换后数组最深维度处** ###

### 1、reshape & resize & shape 改变数组维度 ###

*reshape函数：不改变原数组维度，有返回值*  
*resize函数：直接改变原数组维度，无返回值*  
*shape属性：直接改变原数组维度*

>>> import numpy as np
    >>> a=np.arange(12)
    >>> a
    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
    >>> a.reshape(2,6) 
    array([[ 0,  1,  2,  3,  4,  5],
           [ 6,  7,  8,  9, 10, 11]])
    >>> a
    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
    >>> a.reshape(2,3,2)  
    array([[[ 0,  1],  
            [ 2,  3],
            [ 4,  5]],
    
           [[ 6,  7],
            [ 8,  9],
            [10, 11]]])
    >>> a.resize(2,6)
    >>> a
    >>> array([[ 0,  1,  2,  3,  4,  5],
           [ 6,  7,  8,  9, 10, 11]])
    >>> a.shape=(2,6)
    >>> a
    array([[ 0,  1,  2,  3,  4,  5],
           [ 6,  7,  8,  9, 10, 11]])
    >>> a.shape=(2,3,2)
    >>> a
    array([[[ 0,  1],
            [ 2,  3],
            [ 4,  5]],
    
           [[ 6,  7],
            [ 8,  9],
            [10, 11]]])
    >>>

### 2、ravel & flatten 将数组变为一维 ###

两个函数都不会改变原数组维度  
区别在于：ravel、flatt函数都返回一维数组的一个视图（View）  
但是flatten函数还会请求分配内存来保存结果

>>> a
    array([[[ 0,  1],
            [ 2,  3],
            [ 4,  5]],
    
           [[ 6,  7],
            [ 8,  9],
            [10, 11]]])
    >>> a.ravel()
    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
    >>> a
    array([[[ 0,  1],
            [ 2,  3],
            [ 4,  5]],
    
           [[ 6,  7],
            [ 8,  9],
            [10, 11]]])
    >>> a.flatten()
    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
    >>> a
    array([[[ 0,  1],
            [ 2,  3],
            [ 4,  5]],
    
           [[ 6,  7],
            [ 8,  9],
            [10, 11]]])

### 3 transpose & T 矩阵转置 ###

transpose函数与T 属性功能一致：不改变原数组，有转置后的返回值，且一维数组返回值为它本身

>>> a
    array([[ 0,  1],
           [ 2,  3],
           [ 4,  5],
           [ 6,  7],
           [ 8,  9],
           [10, 11]])
    >>> a.T
    array([[ 0,  2,  4,  6,  8, 10],
           [ 1,  3,  5,  7,  9, 11]])
    >>> a
    array([[ 0,  1],
           [ 2,  3],
           [ 4,  5],
           [ 6,  7],
           [ 8,  9],
           [10, 11]])
    >>> a.transpose()
    array([[ 0,  2,  4,  6,  8, 10],
           [ 1,  3,  5,  7,  9, 11]])
    >>> a
    array([[ 0,  1],
           [ 2,  3],
           [ 4,  5],
           [ 6,  7],
           [ 8,  9],
           [10, 11]])

### 4、细说transpose函数 ###

当维度大于等于三维时，transpose函数可以交换维度顺序  
当transpose()参数为空时，默认参数是维度序号的倒序排列  
以三维为例 transpose() 等价于 transpose(2,1,0) 即深度变为行，行变为深度

>>> a
    array([[[ 0,  1],
            [ 2,  3],
            [ 4,  5]],
    
           [[ 6,  7],
            [ 8,  9],
            [10, 11]]])
    #深度变为行，行变为深度
    >>> a.transpose()  
    array([[[ 0,  6],
            [ 2,  8],
            [ 4, 10]],
    
           [[ 1,  7],
            [ 3,  9],
            [ 5, 11]]])
    >>> a.transpose(2，1，0)  
    array([[[ 0,  6],
            [ 2,  8],
            [ 4, 10]],
    
           [[ 1,  7],
            [ 3,  9],
            [ 5, 11]]])
    #列变为行，行变为列
    >>> a.transpose(1,0,2)
    array([[[ 0,  1],
            [ 6,  7]],
    
           [[ 2,  3],
            [ 8,  9]],
    
           [[ 4,  5],
            [10, 11]]])
    #深度变为列，列变为深度
    >>> a.transpose(0,2,1)
    array([[[ 0,  2,  4],
            [ 1,  3,  5]],
    
           [[ 6,  8, 10],
            [ 7,  9, 11]]])

### 5、reshape & np.squeeze 去除值为1的维度轴 ###

numpy.squeeze(a,axis=None)  
a: 数组  
axis: 指定数组a中要删除的值为1的轴

>>> a=np.array([[[1],[2]],[[3],[4]]])
    >>> a
    array([[[1],
            [2]],
    
           [[3],
            [4]]])
    >>> a.shape
    (2, 2, 1)
    >>> a=np.array([[[[1],[2]],[[3],[4]]]])
    >>> a.shape
    (1, 2, 2, 1)
    >>> a
    array([[[[1],
             [2]],
    
            [[3],
             [4]]]])
    >>> a.reshape(2,2).shape
    (2, 2)
    >>> a.reshape(1,2,2).shape
    (1, 2, 2)
    >>> a.reshape(2,2,1).shape
    (2, 2, 1)
    >>> np.squeeze(a)
    array([[1, 2],
           [3, 4]])
    >>> np.squeeze(a).shape
    (2, 2)
    >>> np.squeeze(a,axis=0).shape
    (2, 2, 1)
    >>> np.squeeze(a,axis=3).shape
    (1, 2, 2)

### 6 、reshape & np.newaxis 增加值为1的维度轴 ###

>>> a=np.array([[1,2],[3,4]])
    >>> a
    array([[1, 2],
           [3, 4]])
    >>> a.shape
    (2, 2)
    >>> a.reshape((1,)+a.shape).shape
    (1,2,2)
    >>> a.reshape((1,)+a.shape+(1,))
    (1,2,2,1)
    >>> a[...,np.newaxis].shape
    (2, 2, 1)
    >>> a[np.newaxis,...,np.newaxis].shape
    (1, 2, 2, 1)