Python学习之路：NumPy初识

Posted 2021-11-08 doubest

import numpy as np;

//一维NumPy数组

myArray = np.array([1,2,3,4]);
print(myArray);

[1 2 3 4]

//打印一维数组的形状

print(myArray.shape);

(4,)

//像普通的Python数组一样，NumPy数组的起始索引编号为0。

print(myArray[0]);
print(myArray[1]);

1
2

//像数组一样直接修改NumPy数组

myArray[0] = 0;
print(myArray[0]);

0

//创建全0或者全1的NumPy数组，默认为浮点数类型

zeroArray = np.zeros((5));
oneArray = np.ones((5));
print(zeroArray);
print(oneArray);
print(type(zeroArray[0]));

[0. 0. 0. 0. 0.]
[1. 1. 1. 1. 1.]
<class 'numpy.float64'>

//创建一个随机值数组

randomArr = np.random.random((5));
print(randomArr);

[0.93867242 0.21884978 0.29078814 0.90407659 0.3323175 ]

//NumPy创建二维数组

dzeroArray = np.zeros((5,4));
doneArray = np.ones((5,4));
print(dzeroArray);
print('----------------');
print(doneArray);

[[0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]
 [0. 0. 0. 0.]]
----------------
[[1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]]

//二维数组依然可以使用一维数组的函数,例如：显示形状、修改元素

testArr = np.ones((5,4));
print(testArr);
print(testArr.shape);
testArr[0,0] = 5
print(testArr);

[[1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]]
(5, 4)
[[5. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]
 [1. 1. 1. 1.]]

//提取多维数组的行/列的方法

testArr = np.array([
    [1,2],
    [3,4]
]);
testArr2 = testArr[:,1]
print(testArr2)

[2 4]

//NumPy矩阵的加减乘除（性质是点运算）

a = np.array([[1.0, 2.0], [3.0, 4.0]]) 
b = np.array([[5.0, 6.0], [7.0, 8.0]]) 
sum = a + b 
difference = a - b 
product = a * b 
quotient = a / b

print('sum = \n',sum);
print('difference = \n',difference);
print('product = \n',product);
print('quotient = \n',quotient);

sum = 
 [[ 6.  8.]
 [10. 12.]]
difference = 
 [[-4. -4.]
 [-4. -4.]]
product = 
 [[ 5. 12.]
 [21. 32.]]
quotient = 
 [[0.2        0.33333333]
 [0.42857143 0.5       ]]

//NumPy矩阵的矩阵乘法

realMul = np.dot(a,b);
print(realMul);
print('-----------')
realMul2 = a.dot(b);
print(a,'\n--------\n',b);
print('-----------');
print(realMul2);

[[19. 22.]
 [43. 50.]]
-----------
[[1. 2.]
 [3. 4.]] 
--------
 [[5. 6.]
 [7. 8.]]
-----------
[[19. 22.]
 [43. 50.]]