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前言

numpy数组的升维和降维

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一、数组的升维

1. np.atleast_2d(array) 转为二维数组

a = np.array([1,2,3,4,5])
>array([1, 2, 3, 4, 5])
#将数组升为二维数组
a = np.atleast_2d(a)
>array([[1, 2, 3, 4, 5]])
#通过转置来改变二维数组的形状
a = a.T
>array([[1],
       [2],
       [3],
       [4],
       [5]])

2. np.atleast_3d(array) 转为三维数组

a = np.array([1,2,3,4,5])
>array([1, 2, 3, 4, 5])
#将数组升为三维数组
a = np.atleast_2d(a)
>array([[[1],
        [2],
        [3],
        [4],
        [5]]])

3. array[:,np.newaxis] 升维一次 n行一列

a = np.array([1,2,3,4,5])
a[:,np.newaxis]
>array([[1],
       [2],
       [3],
       [4],
       [5]])

4. array[np.newaxis,:] 升维一次 一行n列

a = np.array([1,2,3,4,5])
a[np.newaxis:,]
>array([[1, 2, 3, 4, 5]])

5. array.reshape(-1,1) 变成n行一列

a = np.array([1,2,3,4,5])
a.reshape(-1,1)
>array([[1],
       [2],
       [3],
       [4],
       [5]])

6. array.reshape(1,-1) 变成一行n列

a = np.array([1,2,3,4,5])
a.reshape(1,-1)
>array([[1, 2, 3, 4, 5]])

7. np.expand_dims(a, axis)

axis=0

a = np.array([1,2,3,4,5])
np.expand_dims(a, axis=0)
>array([[1, 2, 3, 4, 5]])

axis=1

a = np.array([1,2,3,4,5])
np.expand_dims(a, axis=1)
>array([[1],
       [2],
       [3],
       [4],
       [5]])

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二、数组的降维

1. array.ravel()

a = np.array([[1,2,3,4,5]])
a.ravel()
>array([1, 2, 3, 4, 5])

2. np.squeeze(array)

a = np.array([[1,2,3,4,5]])
np.squeeze(a)
>array([1, 2, 3, 4, 5])

3. array.reshape(-1)

a = np.array([[1,2,3,4,5]])
a.reshape(-1)
>array([1, 2, 3, 4, 5])

4.array.flatten（）：返回源数据的副本

a = np.array([[1,2,3,4,5]])
a.flatten()
>array([1, 2, 3, 4, 5])

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注意

矩阵可以通过转置（array.T或array.transpose() ）来生成想要的m行n列或n行m列

import numpy as np
a = np.array([1,2,3,4,5])
>array([[1, 2, 3, 4, 5, 6]])

a.shape
>(1,6)

a = a.T
>array([[1],
       [2],
       [3],
       [4],
       [5],
       [6]])

a.shape
>(6, 1)