这篇文章主要介绍了Python中numpy模块常见用法,结合实例形式总结分析了numpy常见的运算操作技巧与注意事项,需要的朋友可以参考下
本文实例总结了Python中numpy模块常见用法。分享给大家供大家参考，具体如下：
import numpy as np
arr = np.array([[1,2,3], [2,3,4]]) print(arr) print(type(arr)) print('number of dim:', arr.ndim) print('shape:', arr.shape) print('size:', arr.size)
[[1 2 3]
[2 3 4]]
number of dim: 2
shape: (2, 3)
size: 6
a32 = np.array([1,23,456], dtype=np.int) print(a32.dtype) a64 = np.array([1,23,456], dtype=np.int64) print(a64.dtype) f64 = np.array([1,23,456], dtype=np.float) print(f64.dtype)
int32
int64
float64
z = np.zeros((3, 4)) print(z) print(z.dtype) print() one = np.ones((3, 4), dtype=int) print(one) print(one.dtype) print() emt = np.empty((3, 4), dtype=int) print(emt) print(emt.dtype) print() ran = np.arange(12).reshape((3,4)) print(ran) print(ran.dtype) print() li = np.linspace(1, 10, 6).reshape(2, 3) print(li) print(li.dtype)
[[0. 0. 0. 0.]
[0. 0. 0. 0.]
[0. 0. 0. 0.]]
float64
[[1 1 1 1]
[1 1 1 1]
[1 1 1 1]]
int32
[[ 0 1072693248 1717986918 1074161254]
[ 1717986918 1074947686 -1717986918 1075419545]
[ 1717986918 1075865190 0 1076101120]]
int32
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
int32
[[ 1. 2.8 4.6]
[ 6.4 8.2 10. ]]
float64
a = np.array([10,20,30,40]) b = np.arange(4) print(a) print(b) print() print(a+b) print(a-b) print(a*b) print() print(a**b) print() print(10*np.sin(a)) print() print(b<3) print()
[10 20 30 40]
[0 1 2 3]
[10 21 32 43]
[10 19 28 37]
[ 0 20 60 120]
[ 1 20 900 64000]
[-5.44021111 9.12945251 -9.88031624 7.4511316 ]
[ True True True False]
a = np.array([[1,2], [3,4]]) b = np.arange(4).reshape(2, 2) print(a) print(b) print() print(a * b) print(np.dot(a, b)) #矩阵乘法，或下面： print(a.dot(b)) print()
[[1 2]
[3 4]]
[[0 1]
[2 3]]
[[ 0 2]
[ 6 12]]
[[ 4 7]
[ 8 15]]
[[ 4 7]
[ 8 15]]
a = np.random.random((2, 4)) print(a) print(np.sum(a)) print(np.min(a)) print(np.max(a)) print() print(np.sum(a, axis=1)) #返回每一行的和。 axis=1代表行 print(np.min(a, axis=0)) #返回每一列的最小值。 axis=0代表列 print(np.mean(a, axis=1)) #返回每一行的平均值
[[0.04456704 0.99481679 0.96599561 0.48590905]
[0.56512852 0.62887714 0.78829115 0.32759434]]
4.8011796551183945
0.04456704487406293
0.9948167913629338
[2.4912885 2.30989116]
[0.04456704 0.62887714 0.78829115 0.32759434]
[0.62282212 0.57747279]
A = np.arange(2, 14).reshape(3, 4) print(A) print(np.argmin(A)) #最小索引 print(np.argmax(A)) #最大索引 print() print(A.mean()) print(np.median(A)) #中位数 print(A.cumsum()) #累加值 print(np.diff(A)) #相邻差值 print()
[[ 2 3 4 5]
[ 6 7 8 9]
[10 11 12 13]]
0
11
7.5
7.5
[ 2 5 9 14 20 27 35 44 54 65 77 90]
[[1 1 1]
[1 1 1]
[1 1 1]]
(array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], dtype=int32), array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], dtype=int32))
A = np.array([[1,0], [0,3]]) print(A) print(A.nonzero()) #分别输出非零元素的行和列值 print(np.sort(A)) #逐行排序后的矩阵 print(np.sort(A, axis=0)) #逐列排序的矩阵 print(np.sort(A).nonzero()) print() B = np.arange(14, 2, -1).reshape(3, 4) print(B) print(B.transpose()) #转置 print((B.T).dot(B)) #转置 print() print(np.clip(B, 5, 9)) #B中将范围限定，大于9的数都为9，小于5的都为5，之间的数不变 print()
[[1 0]
[0 3]]
(array([0, 1], dtype=int32), array([0, 1], dtype=int32))
[[0 1]
[0 3]]
[[0 0]
[1 3]]
(array([0, 1], dtype=in来源gao($daima.com搞@代@#码(网t32), array([1, 1], dtype=int32))
[[14 13 12 11]
[10 9 8 7]
[ 6 5 4 3]]
[[14 10 6]
[13 9 5]
[12 8 4]
[11 7 3]]
[[332 302 272 242]
[302 275 248 221]
[272 248 224 200]
[242 221 200 179]]
[[9 9 9 9]
[9 9 8 7]
[6 5 5 5]]
A = np.arange(3, 7) print(A) print(A[2]) print() B = np.arange(3, 15).reshape(3, 4) print(B) print(B[2]) print(B[2][1]) print(B[2, 1]) print() print(B[2, 2:]) print(B[1:, 2:]) print() for row in B: print(row) print() for col in B.T: print(col) print() print(B.flatten()) for elm in B.flat: print(elm)
[3 4 5 6]
5
[[ 3 4 5 6]
[ 7 8 9 10]
[11 12 13 14]]
[11 12 13 14]
12
12
[13 14]
[[ 9 10]
[13 14]]
[3 4 5 6]
[ 7 8 9 10]
[11 12 13 14]
[ 3 7 11]
[ 4 8 12]
[ 5 9 13]
[ 6 10 14]
[ 3 4 5 6 7 8 9 10 11 12 13 14]
3
4
5
6
7
8
9
10
11
12
13
14
#矩阵合并 A = np.array([1,1,1]) B = np.array([2,2,2]) C = np.vstack((A, B, A, B)) print(C) print(A.shape, (A.T).shape) print(C.shape) print() D = np.hstack((A, B)) print(D) print() print(A[np.newaxis, :]) print(A[:, np.newaxis]) print(np.hstack((A[:, np.newaxis], B[:, np.newaxis]))) print() print(np.stack((A,B), axis=0)) print(np.stack((A,B), axis=1)) #print(np.concatenate((A,B,B,A), axis=0)) #print(np.concatenate((A,B,B,A), axis=1))
[[1 1 1]
[2 2 2]
[1 1 1]
[2 2 2]]
(3,) (3,)
(4, 3)
[1 1 1 2 2 2]
[[1 1 1]]
[[1]
[1]
[1]]
[[1 2]
[1 2]
[1 2]]
[[1 1 1]
[2 2 2]]
[[1 2]
[1 2]
[1 2]]
A = np.arange(12).reshape(3, 4) print(A) print(np.split(A, 2, axis=1)) print(np.split(A, 3, axis=0)) print() print(np.array_split(A, 3, axis=1)) #不等分割 print() print(np.hsplit(A, 2)) print(np.vsplit(A, 1))
[[ 0 1 2 3]
[ 4 5 6 7]
[ 8 9 10 11]]
[array([[0, 1],
[4, 5],
[8, 9]]), array([[ 2, 3],
[ 6, 7],
[10, 11]])]
[array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8, 9, 10, 11]])]
[array([[0, 1],
[4, 5],
[8, 9]]), array([[ 2],
[ 6],
[10]]), array([[ 3],
[ 7],
[11]])]
[array([[0, 1],
[4, 5],
[8, 9]]), array([[ 2, 3],
[ 6, 7],
[10, 11]])]
[array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])]
A = np.arange(4) B = A C = B D = A.copy() print(A, B, C, D) A[0] = 5 print(A, B, C, D) print(id(A), id(B), id(C), id(D)) #id返回指针的值（内存地址） print()
[0 1 2 3] [0 1 2 3] [0 1 2 3] [0 1 2 3]
[5 1 2 3] [5 1 2 3] [5 1 2 3] [0 1 2 3]
172730832 172730832 172730832 172730792
更多关于Python相关内容感兴趣的读者可查看本站专题：《Python数学运算技巧总结》、《Python数据结构与算法教程》、《Python函数使用技巧总结》、《Python字符串操作技巧汇总》、《Python入门与进阶经典教程》及《Python文件与目录操作技巧汇总》
希望本文所述对大家Python程序设计有所帮助。
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