请注意，本文编写于 1723 天前，最后修改于 1723 天前，其中某些信息可能已经过时。
目录
1. 对象的创建
import numpy as np # 创建一维数组 a= np.arange(5) print(a) print(a.dtype) print(len(a)) print(a.shape)
[0 1 2 3 4] int64 5 (5,)
# 创建二维数组 b = np.array([np.arange(3),np.arange(3)]) print(b) # 按照列表的长度计算 print(len(b)) #打印对应的行和列 print(b.shape)
[[0 1 2] [0 1 2]] 2 (2, 3)
创建数组时候指定类型
print(np.arange(7,dtype=float)) print(np.arange(7,dtype=int))
[0. 1. 2. 3. 4. 5. 6.] [0 1 2 3 4 5 6]
2. 查询
一维数组的切片和索引(可以参考标准列表)
d = np.arange(10) print(d[4:]) print(d[:-1]) # 逆序打印 print(d[::-1]) print(d[3])
[4 5 6 7 8 9] [0 1 2 3 4 5 6 7 8] [9 8 7 6 5 4 3 2 1 0] 3
多维数据的切片和索引
c = np.array([np.arange(3),np.arange(3,6)]) print(c)
[[0 1 2] [3 4 5]]
获取元素
# 第一个元素为行 第二个元素为列 c[0][1] # c[3][4]
1
c[0]
array([0, 1, 2])
3. 多维数组的形态变换
1. 构建多维数组
b = np.arange(24).reshape(3,8) # 3列 8排 print('In:b \n',b) # 拉平 print(b.ravel()) print(b.flatten())
In:b [[ 0 1 2 3 4 5 6 7] [ 8 9 10 11 12 13 14 15] [16 17 18 19 20 21 22 23]] [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23] [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]
b = np.arange(120).reshape(3,2,5,4) print(b)
[[[[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [ 12 13 14 15] [ 16 17 18 19]] [[ 20 21 22 23] [ 24 25 26 27] [ 28 29 30 31] [ 32 33 34 35] [ 36 37 38 39]]] [[[ 40 41 42 43] [ 44 45 46 47] [ 48 49 50 51] [ 52 53 54 55] [ 56 57 58 59]] [[ 60 61 62 63] [ 64 65 66 67] [ 68 69 70 71] [ 72 73 74 75] [ 76 77 78 79]]] [[[ 80 81 82 83] [ 84 85 86 87] [ 88 89 90 91] [ 92 93 94 95] [ 96 97 98 99]] [[100 101 102 103] [104 105 106 107] [108 109 110 111] [112 113 114 115] [116 117 118 119]]]]
2. 转换为一维数组，拉平
b.ravel()
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, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119])
b.flatten()
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, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119])
3. 转换为二维数组（12x10）
b.shape = (12,10) b
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, 24, 25, 26, 27, 28, 29], [ 30, 31, 32, 33, 34, 35, 36, 37, 38, 39], [ 40, 41, 42, 43, 44, 45, 46, 47, 48, 49], [ 50, 51, 52, 53, 54, 55, 56, 57, 58, 59], [ 60, 61, 62, 63, 64, 65, 66, 67, 68, 69], [ 70, 71, 72, 73, 74, 75, 76, 77, 78, 79], [ 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], [ 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], [100, 101, 102, 103, 104, 105, 106, 107, 108, 109], [110, 111, 112, 113, 114, 115, 116, 117, 118, 119]])
4. 转置
print(b.transpose())
[[ 0 10 20 30 40 50 60 70 80 90 100 110] [ 1 11 21 31 41 51 61 71 81 91 101 111] [ 2 12 22 32 42 52 62 72 82 92 102 112] [ 3 13 23 33 43 53 63 73 83 93 103 113] [ 4 14 24 34 44 54 64 74 84 94 104 114] [ 5 15 25 35 45 55 65 75 85 95 105 115] [ 6 16 26 36 46 56 66 76 86 96 106 116] [ 7 17 27 37 47 57 67 77 87 97 107 117] [ 8 18 28 38 48 58 68 78 88 98 108 118] [ 9 19 29 39 49 59 69 79 89 99 109 119]]
print(b)
[[ 0 1 2 3 4 5 6 7 8 9 10 11] [ 12 13 14 15 16 17 18 19 20 21 22 23] [ 24 25 26 27 28 29 30 31 32 33 34 35] [ 36 37 38 39 40 41 42 43 44 45 46 47] [ 48 49 50 51 52 53 54 55 56 57 58 59] [ 60 61 62 63 64 65 66 67 68 69 70 71] [ 72 73 74 75 76 77 78 79 80 81 82 83] [ 84 85 86 87 88 89 90 91 92 93 94 95] [ 96 97 98 99 100 101 102 103 104 105 106 107] [108 109 110 111 112 113 114 115 116 117 118 119]]
# 数组变形，修改自身吗，同reshape b.resize(12,10) print(b)
[[ 0 1 2 3 4 5 6 7 8 9] [ 10 11 12 13 14 15 16 17 18 19] [ 20 21 22 23 24 25 26 27 28 29] [ 30 31 32 33 34 35 36 37 38 39] [ 40 41 42 43 44 45 46 47 48 49] [ 50 51 52 53 54 55 56 57 58 59] [ 60 61 62 63 64 65 66 67 68 69] [ 70 71 72 73 74 75 76 77 78 79] [ 80 81 82 83 84 85 86 87 88 89] [ 90 91 92 93 94 95 96 97 98 99] [100 101 102 103 104 105 106 107 108 109] [110 111 112 113 114 115 116 117 118 119]]
4. 多维数组的堆叠
1. 横向叠放
a = np.arange(20).reshape(4,5) print(a) b = np.arange(20,40).reshape(4,5) print(b)
[[ 0 1 2 3 4] [ 5 6 7 8 9] [10 11 12 13 14] [15 16 17 18 19]] [[20 21 22 23 24] [25 26 27 28 29] [30 31 32 33 34] [35 36 37 38 39]]
np.hstack((a,b)) #print((a,b))
array([[ 0, 1, 2, 3, 4, 20, 21, 22, 23, 24], [ 5, 6, 7, 8, 9, 25, 26, 27, 28, 29], [10, 11, 12, 13, 14, 30, 31, 32, 33, 34], [15, 16, 17, 18, 19, 35, 36, 37, 38, 39]])
np.concatenate((a,b),axis = 1)
array([[ 0, 1, 2, 3, 4, 20, 21, 22, 23, 24], [ 5, 6, 7, 8, 9, 25, 26, 27, 28, 29], [10, 11, 12, 13, 14, 30, 31, 32, 33, 34], [15, 16, 17, 18, 19, 35, 36, 37, 38, 39]])
2. 竖向叠放
np.vstack((a,b))
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, 24], [25, 26, 27, 28, 29], [30, 31, 32, 33, 34], [35, 36, 37, 38, 39]])
np.concatenate((a,b),axis = 0)
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, 24], [25, 26, 27, 28, 29], [30, 31, 32, 33, 34], [35, 36, 37, 38, 39]])
3. 深度叠加
深度叠加：除此之外，还有一种深度叠加方法，这要用到dstack（）函数和一个元组。这种方法是沿着第三个坐标轴（纵向）的方向来叠加一摞数组。举例来说，可以在一个图像数据的二维数组上叠加另一幅图像的数据。
print("a:",a) print("b:",b) print("v:",np.dstack((a,b)))
a: [[ 0 1 2 3 4] [ 5 6 7 8 9] [10 11 12 13 14] [15 16 17 18 19]] b: [[20 21 22 23 24] [25 26 27 28 29] [30 31 32 33 34] [35 36 37 38 39]] v: [[[ 0 20] [ 1 21] [ 2 22] [ 3 23] [ 4 24]] [[ 5 25] [ 6 26] [ 7 27] [ 8 28] [ 9 29]] [[10 30] [11 31] [12 32] [13 33] [14 34]] [[15 35] [16 36] [17 37] [18 38] [19 39]]]
5. 多维数组的拆分
a = np.arange(20).reshape(4,5) a
array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19]])
1. 横向拆分
np.hsplit(a,5)
[array([[ 0], [ 5], [10], [15]]), array([[ 1], [ 6], [11], [16]]), array([[ 2], [ 7], [12], [17]]), array([[ 3], [ 8], [13], [18]]), array([[ 4], [ 9], [14], [19]])]
np.split(a,5,axis = 1)
[array([[ 0], [ 5], [10], [15]]), array([[ 1], [ 6], [11], [16]]), array([[ 2], [ 7], [12], [17]]), array([[ 3], [ 8], [13], [18]]), array([[ 4], [ 9], [14], [19]])]
2. 纵向拆分
a = np.arange(20).reshape(4,5)
np.vsplit(a,4)
[array([[0, 1, 2, 3, 4]]), array([[5, 6, 7, 8, 9]]), array([[10, 11, 12, 13, 14]]), array([[15, 16, 17, 18, 19]])]
np.split(a,4,axis = 0)
[array([[0, 1, 2, 3, 4]]), array([[5, 6, 7, 8, 9]]), array([[10, 11, 12, 13, 14]]), array([[15, 16, 17, 18, 19]])]
3. 深向拆分
c = np.arange(27).reshape(3,3,3) c
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], [24, 25, 26]]])
np.dsplit(c,3)
[array([[[ 0], [ 3], [ 6]], [[ 9], [12], [15]], [[18], [21], [24]]]), array([[[ 1], [ 4], [ 7]], [[10], [13], [16]], [[19], [22], [25]]]), array([[[ 2], [ 5], [ 8]], [[11], [14], [17]], [[20], [23], [26]]])]
6. 其他属性
b = np.arange(24).reshape(2,12) b
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]])
秩，即轴的数量或维度的数量
print(b.ndim)
2
返回元素个数
print(b.size)
24
返回数组的维度，对于矩阵，n 行 m 列，数组的形状
print(b.shape)
(2, 12)
返回对象中每个元素的大小，以字节为单位 一个元素类型为 float64 的数组 itemsiz 属性值为 8(float64 占用 64 个 bits，每个字节长度为 8，所以 64/8，占用 8 个字节），又如，一个元素类型为 complex32 的数组 item 属性为 4（32/8）。
print(b.itemsize)
8
本文作者:mykernel
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目录