Python-----numpy函数库基础

1. 安装numpy库：pip install numpy

2. 将numpy函数库中的所有模块引入：

from numpy import *

3. 构造一个4*4的随机数组：

randArray = random.rand(4,4)
print(randArray)

4.mat()函数：将数组转化为矩阵：

randMat = mat(random.rand(4,4))
print(randMat)

5. 求逆矩阵.I操作符

randMat = mat(random.rand(4,4))
print(randMat)
invRandMat = randMat.I
print(invRandMat)

6.执行矩阵乘法：

randMat = mat(random.rand(4,4))
print(randMat)
invRandMat = randMat.I
print(invRandMat)
Multi = randMat*invRandMat
print(Multi)

结果应收是单位矩阵，除了对角线元素为1，其他元素应该为0，实际的输出略有不同，因为矩阵例还留下了许多非常小的元素，这是计算机处理误差产生的结果。

import numpy
vector = numpy.array([5,10,15,20])
matrix = numpy.array([[5,10,15],[20,25,30]])
print(vector)
print(matrix)
print(vector.shape)
print(matrix.shape)

.dtype查看类型

import numpy
numbers = numpy.array([1,2,3,4])
print(numbers)
numbers.dtype

import numpy
numbers = numpy.array([1,2,3,4.0])
print(numbers)
numbers.dtype

import numpy
vector = numpy.array([5,10,15,20])
vector == 10

import numpy
matrix = numpy.array([
        [5,10,15],
        [20,25,30],
        [35,40,45]
                     ])
matrix == 25

import numpy
vector = numpy.array([5,10,15,20])
equal_to_ten = (vector == 10)
print (equal_to_ten)
print(vector[equal_to_ten])

与运算和或运算：

import numpy
vector = numpy.array([5,10,15,20])
equal_to_ten_and_five = (vector == 10) & (vector == 5)
print (equal_to_ten_and_five)

import numpy
vector = numpy.array([5,10,15,20])
equal_to_ten_or_five = (vector == 10) | (vector == 5)
print (equal_to_ten_and_five)

类型转换：

import numpy
vector = numpy.array(["5","10","15","20"])
print(vector.dtype)
print(vector)

vector = vector.astype(float)
print(vector.dtype)
print(vector)

求最小、最大值：

import numpy
vector = numpy.array([5,10,15,20])
min = vector.min()
print(min)
max = vector.max()
print(max)

计算每一行的和值：

import numpy
matrix = numpy.array([
        [5,10,15],
        [20,25,30],
        [35,40,45]
                     ])
matrix.sum(axis=1)

计算每一列的和值：

import numpy
matrix = numpy.array([
        [5,10,15],
        [20,25,30],
        [35,40,45]
                     ])
matrix.sum(axis=0)

import numpy as np #用np代替numpy
print(np.arange(15)) 
a = np.arange(15).reshape(3,5)
print(a)
a.shape
a.size #当前矩阵包含多少个元素
a.ndim  #当前矩阵的维度
a.dtype.name #当前矩阵的类型

import numpy
vector = numpy.array([5,10,15,20])
matrix = numpy.array([[5,10,15],[20,25,30]])
print(vector)
print(matrix)
print(vector.shape)
print(matrix.shape)