Reshape,flatten,ravel data in NumPy is flattened, multidimensional arrays become one-dimensional arrays

Reshape,flatten,ravel data in NumPy is flattened, multidimensional arrays become one-dimensional arrays

import numpy as np

Using the Array object

arr1=np.arange(12).reshape(3,4)print(arr1)print(type(arr1))

[[ 0  1  2  3] [ 4  5  6  7] [ 8  9 10 11]]<class ‘numpy.ndarray‘>

• Flatten Flatten

a=arr1.flatten() # 默认参数order=C，按照行进行展平；order=F，按照列进行展平，交叉展平；#A 或K参数用的不多，顾不变多记，到时候找到会用即可a[2]=1000print(arr1)  # arr1 并没有改变,flatten 返回的是copya

[[ 0  1  2  3] [ 4  5  6  7] [ 8  9 10 11]]array([   0,    1, 1000,    3,    4,    5,    6,    7,    8,    9,   10,         11])

• Reshape Transform

arr1=np.arange(12).reshape(3,4)b=arr1.reshape(-1)  #  b=arr1.reshape((-1)) 等同的效果意义 ,  b[2]=1000print(arr1)# 返回的是视图view

[[   0    1 1000    3] [   4    5    6    7] [   8    9   10   11]]

• Ravel transform

arr1=np.arange(12).reshape(3,4)c=arr1.ravel()c

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])

c[2]=10001arr1  # 返回的是视图view

array([[    0,     1, 10001,     3],       [    4,     5,     6,     7],       [    8,     9,    10,    11]])

• Resize

arr1=np.arange(12).reshape(3,4)arr1.resize((4,3))  #  无返回值，即会对原始多维数组直接进行修改，也就是不能赋值arr1

array([[ 0,  1,  2],       [ 3,  4,  5],       [ 6,  7,  8],       [ 9, 10, 11]])

Working with a matrix object

# 使用matrix对象的时候，返回的仍是matrix，得不到想要的结果,不过该matrix仍然可以使用numpy中的一些方法对其操作，比如sum，min,max等等d=np.matrix(np.arange(12).reshape(3,4))d

matrix([[ 0,  1,  2,  3],        [ 4,  5,  6,  7],        [ 8,  9, 10, 11]])

matrix([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11]])

Reshape,flatten,ravel data in NumPy is flattened, multidimensional arrays become one-dimensional arrays