虽说python是弱类型的，但在使用时还是需要明确一些基本类型的。在所有的基本类型中最复杂的就是数字类型。本章就一起熟悉中基本类型中的-数值。
一、基础操作 1.1、格式化输出通用格式： 【[<>^]?width[,]?(.digits)?】，1、width和digits为整数，分别表示整体输出的宽度(不足的用空格填充)，digits表示小数点占了几位（默认四舍五入）；2、？可选可不写：3、<>^表示补空格的方向；4、,.分别表示是否使用分隔符；5、f代表格式化的意思，如果档加f则{}当做普通字符串输出。
x = 1234.56789
print(f'8.2f format {x}: {x:8.2f}') //1234.57
print(f'>10.1f format {x}: {x: >10.1f}') //1234.6
print(f'<10.1f format {x}: {x: <10.1f}') //1234.6
print(f'^10.1f format {x}: {x: ^10.1f}') //1234.5
print(f', format {x}: {x: ,}') //1,234.56789,整数部分用,分隔
print(f'0,.1f format {x}: {x: ,.1f}') //1,234.6,整数部分用,分隔，小数部分为.分隔
指数计数
print(f'e format {x} is: {x: e}') //1.234568e+03
print(f'0.2E format {x} is: {x: 0.2E}')//1.23E+03
print(f'x format: {x: 0.1f}') //1234.6
print(f'-x format: {-x: 0.1f}')//取反操作 -1234.6
1.2、进制变换
x = 1234
print(f'binary of {x} is: {bin(x)}') //二进制 0b10011010010
print(f'octal of {x} is: {oct(x)}') //十进制 0o2322
print(f'hex of {x} is: {hex(x)}') //16进制 0x4d2
不显示制表符
print(f'binary not show 0b:{x:b}') //二进制 10011010010
print(f'octal not show 0o:{x:o}') //十进制 0o2322
1.3、函数
print(int('4d2', 16)) //1234
print(int('10011010010', 2)) //1234
print(int('2322', 8)) //1234
1.4、四舍五入
print(round(1.23, 1)) #1.2 #round函数对于5这种中间值会返回离它最近的偶数 print(round(2.5, 0)) #2.0 print(round(1.5, 0)) #2.0 a = 1627731 print(round(a, -2)) #1627700
1.5、无穷
在python中没有特定的对象来表示这样的浮点数，所以需要用float来创建。
print(f"float('inf') = {float('inf')}") #inf
print(f"float('-inf') = {float('-inf')}") #-inf
print(f"float('nan') = {float('nan')}") #nan
import math
print(f"float('inf') type is inf: {math.isinf(float('inf'))}") #true
print(f"float('nan') type is nan: {math.isnan(float('nan'))}") #ture
二、浮点数
建议根据实际情况来，虽然普通的用法有误差但速度比较快，所以不是严格的不要用decimal，它的精度是17位。
a = 2.1
b = 4.2
print(a + b) # 这处是有误差的6.300000000000001
# 浮点数要用这种格式
from decimal import Decimal
a = Decimal('2.1')
b = Decimal('4.2')
print(f'a + b = {a + b}') # a + b = 6.3
from decimal import localcontext
a = Decimal('1.3')
b = Decimal('1.7')
print(f'a / b = {a / b}') # a / b = 0.7647058823529411764705882353
with localcontext() as ctx:
ctx.prec = 3 # 位数
print(f'a / b = {a / b}') # a / b = 0.765
with localcontext() as ctx:
ctx.prec = 50
print(f'a / b = {a / b}') # a / b = 0.76470588235294117647058823529411764705882352941176
# 在做浮点数运算时要注意精度的问题，第一个例子是错误的，第二个是正确的
num_list = [1.23e+18, 1, -1.23e+18]
print(f'sum result is: {sum(num_list)}') # sum result is: 0.0
import math
print(f'math sum result: {math.fsum(num_list)}') # math sum result: 1.0
三、分数
程序中一般很少用到，如果不是必须的还是建议定义成整数或浮点数来运算。
from fractions import Fraction
a = Fraction(5, 4)
b = Fraction(7, 16)
print(f'{a} + {b} = {a + b}')
print(f'{a} * {b} = {a * b}')
c = a * b
print(f'numerator of {c} is: {c.numerator}')
print(f'denominator of {c} is: {c.denominator}')
print(f'float({c}) = {float(c)}')
print(f'{c} limit denominator 8 = {c.limit_denominator(8)}')
x = 3.75
print(f'{x} to fractions is: {Fraction(*x.as_integer_ratio())}')
5/4 + 7/16 = 27/16
5/4 * 7/16 = 35/64
numerator of 35/64 is: 35
denominator of 35/64 is: 64
float(35/64) = 0.546875
35/64 limit denominator 8 = 4/7
3.75 to fractions is: 15/4
四、随机数
import random
values = [1, 2, 3, 4, 5, 6]
"""随机选择1个"""
print(f'random choice from {values} is {random.choice(values)}') #1
"""随机选择2个"""
print(f'random sample 2 from {values} is {random.sample(values, 2)}') #[5, 3]
"""只是打乱元素的顺序"""
random.shuffle(values)
print(f'random shuffle is:{values}') #[3, 5, 4, 6, 2, 1]
"""生成随机数"""
print(f'random.randint(0,10) = {random.randint(0, 10)}') #9
"""生成0~1之间的随机数"""
print(f'random.random() = {random.random()}') #0.6299961967523697
"""获取N位随机数的整数"""
x = random.getrandbits(200)
print(f'random.getrandbits(200) = {x}') #636975414748915127401449454055264672180771111158792457403568
"""random()采用了Twister算法，这是一个确定性算法，可以在计算前用seed()设置种子"""
print(f'random.seed() = {random.seed()}')
print(f'random.seed(123) = {random.seed(123)}')
五、Numpy
这是一个三方的库，是很多科学与工程库的基础。在机器学习中应用广泛。下面只举两个简单的例子。
5.1、数组运算import numpy as np
ax = np.array([1, 2, 3, 4])
ay = np.array([5, 6, 7, 8])
print(f'{ax} * 2 = {ax * 2}') #[2 4 6 8]
print(f'{ax} + 10 = {ax + 10}') #[11 12 13 14]
5.2、矩阵运算
import numpy as np
"""
[[ 1 -2 3]
[ 0 4 5]
[ 7 8 -9]]
"""
m = np.matrix([[1,-2,3],[0,4,5],[7,8,-9]])
print(f'm is:n{m}')
"""
[[ 1 0 7]
[-2 4 8]
[ 3 5 -9]]
"""
print(f'm.T is:n{m.T}')
"""
[[ 0.33043478 -0.02608696 0.09565217]
[-0.15217391 0.13043478 0.02173913]
[ 0.12173913 0.09565217 -0.0173913 ]]
"""
print(f'm.I is:n{m.I}')
v = np.matrix([[2],[3],[4]])
print(f'v is:n{v}')
"""
[[ 8]
[32]
[ 2]]
"""
print(f'm * v is:n{m * v}')
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