1.为数组加上或者乘以一个标量
>>> import numpy as np
>>> a=np.array([1,2,3,4,5])
>>> a
array([1, 2, 3, 4, 5])
>>> a*1
array([1, 2, 3, 4, 5])
>>> a
array([1, 2, 3, 4, 5])
>>> a*2
array([ 2, 4, 6, 8, 10])
>>> a
array([1, 2, 3, 4, 5])
2.两个数组进行计算
1）元素数量相同
>>> a
array([1, 2, 3, 4, 5])
>>> b=np.array([2,3,4,5,6])
>>> b
array([2, 3, 4, 5, 6])
>>> a*b
array([ 2, 6, 12, 20, 30])
>>> a
array([1, 2, 3, 4, 5])
>>> b
array([2, 3, 4, 5, 6])
2）元素数量不同：会报错
>>> b
array([2, 3, 4, 5, 6])
>>> c=np.array([3,4,5,6,7,8,9])
>>> c
array([3, 4, 5, 6, 7, 8, 9])
>>> b*c
Traceback (most recent call last):
File "", line 1, in
ValueError: operands could not be broadcast together with shapes (5,) (7,)
3.可以对一个数组先进行函数运算，该函数运算返回值也是一个数组
>>> a
array([1, 2, 3, 4, 5])
>>> b=np.sin(a)
>>> b
array([ 0.84147098, 0.90929743, 0.14112001, -0.7568025 , -0.95892427])
4.多维数组的运算也是元素级别的
>>> a=np.arange(9).reshape(3,3)
>>> a
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> b=np.zeros((3,3))
>>> b
array([[ 0., 0., 0.],
[ 0., 0., 0.],
[ 0., 0., 0.]])
>>> a*b
array([[ 0., 0., 0.],
[ 0., 0., 0.],
[ 0., 0., 0.]])
5.矩阵积
1）c=np.dot(a,b)求a和b的矩阵积--第一种写法
>>> a=np.arange(9).reshape(3,3)
>>> b=np.random.random(9).reshape(3,3)
>>> a
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> b
array([[ 0.1752667 , 0.61713814, 0.63455636],
[ 0.42635687, 0.9609163 , 0.40790306],
[ 0.01270341, 0.29411413, 0.52187812]])
>>> c=np.dot(a,b)
>>> c
array([[ 0.4517637 , 1.54914457, 1.45165931],
[ 2.29474465, 7.1656503 , 6.14467194],
[ 4.1377256 , 12.78215603, 10.83768457]])
2）c=a.dot(b)
>>> c=a.dot(b)
>>> c
array([[ 0.4517637 , 1.54914457, 1.45165931],
[ 2.29474465, 7.1656503 , 6.14467194],
[ 4.1377256 , 12.78215603, 10.83768457]])
>>> a
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> b
array([[ 0.1752667 , 0.61713814, 0.63455636],
[ 0.42635687, 0.9609163 , 0.40790306],
[ 0.01270341, 0.29411413, 0.52187812]])
6.数组的自运算：不会新生数组，改变原数组
>>> a
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> a+=1
>>> a
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
7.通用函数：ufunx
对数组中的每个元素逐一进行操作，生成一个新数组，如平方根函数sqrt() 对数函数 log() 正弦函数sin()
>>> a
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
>>> b=np.sin(a)
>>> b
array([[ 0.84147098, 0.90929743, 0.14112001],
[-0.7568025 , -0.95892427, -0.2794155 ],
[ 0.6569866 , 0.98935825, 0.41211849]])
>>> c=np.sqrt(a)
>>> c
array([[ 1. , 1.41421356, 1.73205081],
[ 2. , 2.23606798, 2.44948974],
[ 2.64575131, 2.82842712, 3. ]])
>>> d=np.log(a)
>>> d
array([[ 0. , 0.69314718, 1.09861229],
[ 1.38629436, 1.60943791, 1.79175947],
[ 1.94591015, 2.07944154, 2.19722458]])
8.聚合函数
对一数组进行聚合函数的套用，返回一个单一值作为结果
>>> a
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
>>> a.sum()
45
>>> a.max()
9
>>> a.min()
1
>>> a.mean()
5.0
>>> a.std()
2.5819888974716112