Numpy 基础运算1

Posted 2020-10-23 zsjblog

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/