数组运算
import numpy as np
a=np.array([10,20,30,40]) # array([10, 20, 30, 40])
b=np.arange(4) # array([0, 1, 2, 3])数组相加相减
c=a-b # array([10, 19, 28, 37])数组相乘
c=a*b # array([ 0, 20, 60, 120])
数组乘方
c=b**2 # array([0, 1, 4, 9])
Numpy中具有很多的数学函数工具，比如三角函数等
c=10*np.sin(a)
# array([-5.44021111, 9.12945251, -9.88031624, 7.4511316 ])对数组进行逻辑判断
print(b<3)
# array([ True, True, True, False], dtype=bool)矩阵运算
a=np.array([[1,1],
[0,1]])
b=np.arange(4).reshape((2,2))
print(a)
# array([[1, 1],
# [0, 1]])
print(b)
# array([[0, 1],
# [2, 3]])矩阵乘法
#方式一
c_dot = np.dot(a,b)
# array([[2, 4],
# [2, 3]])#方式二
c_dot_2 = a.dot(b)
# array([[2, 4],
# [2, 3]])sum(), min(), max()
import numpy as np
a=np.random.random((2,4))
print(a)
# array([[ 0.94692159, 0.20821798, 0.35339414, 0.2805278 ],
# [ 0.04836775, 0.04023552, 0.44091941, 0.21665268]])np.sum(a) # 4.4043622002745959
np.min(a) # 0.23651223533671784
np.max(a) # 0.90438450240606416如果你需要对行或者列进行查找运算，就需要在上述代码中为 axis 进行赋值。 当axis的值为0的时候，将会以列作为查找单元， 当axis的值为1的时候，将会以行作为查找单元。
print("a =",a)
# a = [[ 0.23651224 0.41900661 0.84869417 0.46456022]
# [ 0.60771087 0.9043845 0.36603285 0.55746074]]
print("sum =",np.sum(a,axis=1))
# sum = [ 1.96877324 2.43558896]
print("min =",np.min(a,axis=0))
# min = [ 0.23651224 0.41900661 0.36603285 0.46456022]
print("max =",np.max(a,axis=1))
# max = [ 0.84869417 0.9043845 ]参考博客:https://morvanzhou.github.io/tutorials/data-manipulation/np-pd/2-3-np-math1/
