Python3基础

目录

一、入门

1.1 编辑器

1.2 扩展工具

1.3 Python是怎么运行的

二、基本类型

2.1 字符串

2.1.1 字符串长度 len(str)

2.1.2 获取指定位置的字符

2.1.3 切片

2.1.4 字符串拼接

2.1.5 字符串大小写转换

2.1.6 strip() lstrip() rstrip()

2.1.7 str.count(str1)

2.1.8 find()  index() 查找子字符串

2.1.9 replace(old, new) 字符串替换

2.1.10 split()  字符串分割

2.1.11 字符串遍历

2.2 number 

2.2.1 数据类型介绍和算术运算符

2.2.2 数字的相关方法

2.3 bool类型

2.4 字符串格式化

2.4.1 方式一

2.4.2 方式二

三、控制流

3.1 if 语句

3.3 循环

3.2.1 for循环

3.2.2 while循环

3.3 无限循环

四、方法functions

4.1 方法声明和调用

4.2 含参数的方法

4.3 方法的类型

4.3.1 执行指定功能

4.3.2 返回计算结果

4.4 方法的参数

4.4.1 位置参数

4.4.2 关键字参数

4.4.3 缺省参数

4.4.3 不定长参数

4.5 方法内变量的生效范围

4.6 匿名函数

4.6.1 函数作为参数传递

4.6.2 lambda匿名函数

五、数据结构

5.1 list 列表

5.1.1 定义列表

5.1.2 获取列表指定下标内容

5.1.3 列表的切片

5.1.3 把列表内元素赋值给多个变量

5.1.4 列表内循环

5.1.5 插入/删除列表元素

1、append(value) 在列表尾部插入数据

2、insert(i, value)在指定下标插入数据

3、pop() 删除列表末尾的数据

 4、pop(i) 删除指定下标数据

5、remove(value) 删除指定数据

6、del 删除指定下标数据

7、clear() 清空列表

5.1.6 index() 查找指定数据在列表中的下标

5.1.7 count(value) 获取指定数据在列表中出现的次数

5.1.8 sort() 列表数据排序

1、sort() 升序

2、sort(reverse=Ture) 降序

3、sorted(list)升序

4、sorted(list, reverse=Ture)降序

5、 lambda方法

5.1.9 列表遍历

1、while循环遍历

 2、for 循环遍历

5.1.10 map()映射列表数据 

5.1.11 filter() 过滤列表数据

5.1.12 zip()组合多个列表

5.1.13 扩展

1、堆栈

2、队列

5.2 tuple 元组

5.2.1 元组的定义

5.2.2 元组合并

5.3 集合

5.3.1 集合定义

5.3.2 add(value) 添加元素

5.3.3 删除元素

5.3.4 len(set) 获取集合长度

5.3.5 clear() 清空集合

5.3.6 获取多个集合的并集

5.3.7 获取多个集合的交集

5.3.8 获取集合的差集

5.3.9 获取多个集合的对称性差异

5.3.10 集合内部数据的无序性

5.3.11 集合遍历

5.4 dictionary 字典

5.4.1 定义字典

5.4.2 添加元素

5.4.3 修改元素

5.4.4 获取指定key的value

5.4.5  删除指定元素

5.4.6 清空字典

5.4.7 获取字典全部key

5.4.8 获取字典的元素

5.4.9 字典应用于循环

5.5 生成器对象

5.6 解包操作

5.6.1 多个列表合并

5.6.2 多个字典合并

5.7 字符串

5.8 练习

六、文件操作

6.1 文件读取

6.1.1 open() 打开文件

6.1.2 read()读取文件内容

6.1.3 readlines()读取文件 

6.1.4 readline()读取文件 

6.1.5 使用for循环读取文件 

6.1.6 关闭文件连接 

6.1.7 使用with open打开文件 

6.2 文件写入

正则匹配

1. 匹配单个字符

2. 匹配多个字符

3. 匹配开头和结尾

4. 匹配分组

5. 匹配转义字符

七、异常exceptions

7.1 捕获异常

7.2 捕获多个异常

7.3 异常的else和finally语句

7.3.1 在try模块连接资源在finally模块关闭资源

7.3.2 使用with语句自动释放资源

7.4 异常具有传递性

7.5 抛出异常

八、类classes

8.1 类定义

8.2 构造函数

8.3 类和实例属性

8.3.1 实例属性

8.3.2 类属性

8.4 类和实例方法

8.4.1 实例方法

8.4.2 类方法

8.5 magic methods魔术方法

8.5.1 __str__

8.5.2 比较对象大小的魔法方法

8.5.3 对象进行算术运算的魔法方法

8.5.4  自定义的magic method

8.6 python的私有成员

8.7 类属性property

8.7.1 使用property()方法创建类属性

8.7.2 装饰器@property 创建类属性

8.8 继承inheritance

8.8.1 单继承和多层继承

1. 单继承

2.  多层继承

3. isinstance(instance, class)

4. issubclass(sub, base)

5.  重写父类方法/属性

子类重写父类同名方法

子类调用父类同名方法

8.8.2 多继承

1、 多继承--查看子类继承父类的继承顺序

2、多继承--调用父类同名方法

默认调用情况

子类调用父类同名方法

8.8.3 私有和继承

 8.8.4 多态

8.8.5 抽象基类

8.8.6 类型注解

1、变量的类型注解

2、函数和方法的类型注解

3、Union联合类型 注解

8.8.7 属性类

九、模块和包

9.1 模块

9.1.1 模块的导入和使用

 9.1.2 自定义模块

 9.2 包

9.2.1 自定义包

9.2.2 第三方包

9.3 练习

9.3.1 疫情确诊人数走势折线图

1、json数据格式

2、pyecharts模块介绍

3、pyecharts快速入门

4、数据处理

5、创建折线图

9.3.2 疫情地图绘制

1、基础地图使用

2、全国疫情地图

3、省级疫情地图

9.3.3 柱状图绘制

1、基础柱状图绘制

2、基础时间线柱状图

3、GDP动态柱状图绘制

十、python的标准库

10.1 pathlib

10.1.1 创建目录对象

10.1.2 目录对象的相关操作

10.1.3 目录对象的使用

1、path.iterdir()

2、path.glob("匹配模式")

3、path.rglob("匹配模式")

---

一、入门

1.1 编辑器

编辑器：vscode

1.2 扩展工具

语法检查工具pylint

 点击install即可

 确认下当前使用的Python版本是不是3以上，之后点击install即可

如果代码有错误，代码行会飘红，点击View—— Problems(shift+ctrl+M)打开报错的详细信息框

命令策略面板Command Palette(shift+ctrl+P) ，输入linter

点击select linter

pylint 是最受欢迎和vscode中默认的语法检查工具

代码格式化工具pep8

：PEP 8 – Style Guide for Python Code | peps.python.org

命令策略面板Command Palette(shift+ctrl+P)，打开面板输入format

 

最受欢迎和默认的代码格式化工具是autopep8，点击Yes即可安装pep8工具

再次打开命令策略面板Command Palette(shift+ctrl+P)，输入format点击format document命令就可以对当前代码文件进行格式化。

可以对格式化时机进行设置，不能每次都要执行format document命令，设置操作如下：

这样当你保存文件时会自动完成格式化校验和修正。

代码运行扩展工具：code runner

 点击install

以后需要运行代码时不需要输入Python xx.py，ctrl+alt+N（ios是Ctrl+option+N）即可

1.3 Python是怎么运行的

二、基本类型

number：int  和 float

boolean：True    False

string："Python Programming"

字符串中有特殊字符，如单引号 双引号 反斜杠等需要输出时，使用转义字符\即可

course = "Python \''Programming"

course = "Python \'Programming"

course = "Python \\Programming"

变量命名按照驼峰

2.1 字符串

2.1.1 字符串长度 len(str)

len()为Python内置函数，并非字符串特有

course = "Python Programming"

str_len = len(course)

2.1.2 获取指定位置的字符

获取数据指定下标指的方法是通用的，对列表  字典  

course = "Python Programming"

# 获取下标0对应的字符

print(course[0])

# 获取字符串的最后一个字符
print(course[-1])
print(course[17])

2.1.3 切片

注意：切片可以得到一个全新的字符串，它有单独的存储地址，原来的父字符串不会有任何改变

course = "Python Programming"
# 获取course字符串从下标0开始到下标3，但不包含下标3对应字符的一段字符串------即Pyt
print(course[0:3])
print(course[:3])
# 注意切片不会影响course字符串本身

# 获取course字符串从下标3开始一直到末尾的一段字符串
# -----即hon Programming
print(course[3:])

# 获取course字符串从下标0开始，到下标8为止（不包括下标8对应字符），没3个取1个
# 下标0~下标8对应Python Pr，不包含8，则为Python P
# 取下标0 对应的P
# 向后数3个yth，3个取1个，取h
# 再向后数3个on ,3个取1个，取空格
# 再向后数只有1个字符P，所以不能继续
# 所以最后得到的是Ph 
print(course[0:8:3])


# 复制一个和course字符串完全一样的子字符串，但对新字符串的改动不能影响到course字符串本身
new_str = course[:]
print(new_str)
# 注意不能使用赋值 new_str = course，因为course和new_str这样的变量名其实只是一个指针，指向的内存地址，该地址内存储的内容是字符串内容，使用赋值会导致 new_str 和 course 指向的是同一个内存地址

2.1.4 字符串拼接

方法一：sum_str = str1 + " " + str2

方法二：sum_str = f"{str1} {str2}"

# 字符串拼接
first = "John"
last = "Smith"
full_name = f"{first} {last}"
print(full_name)

2.1.5 字符串大小写转换

str.upper()  ——字符串str全部变为大写

str.lower() ——字符串str全部变为小写

str.title() —— 字符串内单词的首字母都变为大写

原始字符串str在经过对应方法处理后得到的是一个新的字符串，存储在新的地址

原始字符串str没有任何改变

course = "python programming"
print("新的字符串是：", course.title())
print("原始字符串course是：", course)

2.1.6 strip() lstrip() rstrip()

不传参数就是去除空格和换行符

传参数就是去除指定字符串

str.strip() —— 去除字符串左右两侧多余的空格

str.lstrip() —— 去除字符串左侧多余的空格

str.rstrip() —— 去除字符串右侧多余的空格

slogen = "12I like learning21"
new_str = slogen.strip("12")
print(new_str)  # I like learning
# 把“12”看成2个字符串“1”  和  “2”
# 所以slogen左右的12 21 都会被去除

course = "      python programming      "
print(course.strip())

2.1.7 str.count(str1)

计算子串str1在字符串中出现的次数

2.1.8 find()  index() 查找子字符串

str.find(str1) —— 在str字符串中查找str1，如果找到了，就返回第一次匹配时对应的下标

如果没找到就返回-1

str.index(str1)—— 在str字符串中查找str1，如果找到了，就返回第一次匹配时对应的下标

如果没找到就返回ValueError

course = "python programming"
print(course.find("p"))
# 返回0
print(course.find("H"))
# 返回-1
print(course.find("pro"))
# 返回7


course = "python programming"
value = course.index("pro")
print(value)  # 7
value2 = course.index("p")
print(value2)   # 0
value3 = course.index("P")
# print(value3)    # ValueError: substring not found

2.1.9 replace(old, new) 字符串替换

str.replace(oldStr, newStr) —— 用newStr 替换字符串str中的oldStr

course = "python programming"
print(course.replace("p", "hhhh"))
# 返回hhhhython hhhhrogramming

2.1.10 split()  字符串分割

slogen = "I like learning"
arr = slogen.split(" ")
print(arr)  # ['I', 'like', 'learning']

str1 in str 判断str1是否是str的子字符串，是就返回True，不是就返回False

str1 not in str

course = "python programming"
print("pro" in course)

2.1.11 字符串遍历

2.2 number 

2.2.1 数据类型介绍和算术运算符

整数   小数  复数a+bi

算数运算符：加+    减-     乘*    除尽/     除后取整//      除后取余%      取幂**

+=     -=      *=     /=     //=      %=      **=

print(10 + 3)
# 13
print(10 - 3)
# 7
print(10 * 3)
# 30
print(10 / 3)
# 3.3333333333333335
print(10 // 3)
# 3
print(10 % 3)
# 1
print(10 ** 3)
# 1000

x = 10
# x += 3
# x -= 3
# x *= 3
# x /= 3
# x //= 3
# x %= 3
x **= 3
print(x)

2.2.2 数字的相关方法

可以参考：math — Mathematical functions — Python 3.11.4 documentationPython3.9标准库math中的函数汇总介绍(53个函数和5个常数)_python的math库中的函数有哪些_昊虹AI笔记的博客-CSDN博客

round(num) —— 四舍五入取整

print(round(2.1))
# 2
print(round(2.6))
# 3

abs(num) —— 取num的绝对值

print(abs(-7.5))
# 7.5
print(abs(0))
# 0

导入数学模块 import math

math.ceil(num) —— 向上舍入到最接近的整数

import math

print(math.ceil(2.2))
# 3

input()获取用户输入内容以字符串形式存储，所以需要注意数据处理时进行类型转换。

x = input("x: ")

print(type(x))

y = int(x) ** 2

# 表准字符串输出print(f"")

print(f"x: {x}, y: {y}")

类型转换的方法有：

int(x)

float(x)

bool(x)

str(x)

2.3 bool类型

print(bool(0))

#  返回false

print(bool(''"))

# 返回false

2.4 字符串格式化

2.4.1 方式一

在字符串内（即引号内）使用%s   %d    %f  进行占位，后面使用按照占位顺序传入变量，并放在%()的括号内

 

 

2.4.2 方式二

快速字符串格式化：

f"ababbajajaik{变量名}"

这种方式不关心变量类型，同样也不会做变量精度的控制。

 

 

三、控制流

3.1 if 语句

if 条件1:

        balabala

elif 条件2:

        balabal

.

.

.

elif 条件n:

        balabala

else:

        如果以上条件判断都不满足，执行这里的逻辑

逻辑运算符and   or    not 可以在条件表达式中使用

 random.random(): 返回随机生成的一个浮点数，范围在[0,1)之间

random.uniform(a, b): 返回随机生成的一个浮点数，范围在[a, b)之间

random.randint(a,b)：生成指定范围内的整数 

random.randint(0,100) 就是随机生成一个0~100的数，包括0和100

np.random.randint(0,10)  和上面作用一样， 左闭右开，包括0 不包括10

3.3 循环

3.2.1 for循环

for number in range(3):

# range(3)取值范围[0, 3) 即0  1  2

# range(1, 4)取值范围[1, 4) 即1  2  3

# range(1, 10, 2)  取值[1, 10) 且时隔1个数字取一个  即1   3   5   7   9

        print("hello", number)

# 99乘法表
for row in range(1, 10):
    for col in range(1, row+1):
        print(f"{col} * {row} = {col * row}\t",end='')
        # 在print方法里使用end=''实现不换行
    # 一行结束时需要换行
    print()

输出内容但是不换行：print(f"要输出的内容", end='')

3.2.2 while循环

while 条件表达式:

        balabala

row = 1
while row < 10:
    col = 1
    while col <= row:
        print(f"{col} * {row} = {col * row}\t", end='')
        col += 1
    print()
    row += 1

3.3 无限循环

就是for或者while后面的条件永远是Ture，这个时候在循环体内需要设置其他判断，当满足这个判断时用break结束循环。

四、方法functions

4.1 方法声明和调用

def test():

        balkabala

test()

4.2 含参数的方法

def test(参数1, 参数2, ...参数n):

        balabal

hh 

test(参数值1, 参数值2, ...参数值n)

4.3 方法的类型

4.3.1 执行指定功能

def greet(name):

        print(f"hi {name}")

#执行完任务后，可能会把结果输出在终端或者什么也不输出

greet("mosh")

# 函数没有设置返回值时，python默认会返回None

 函数说明文档：

4.3.2 返回计算结果

def get_greeting(name):

        reurn f"hi {name}"

# 方法执行完后会返回结果

message = get_greeting("mosh")

方法可以有多个返回值，并且可以使用多个变量接收函数的返回值

 

4.4 方法的参数

4.4.1 位置参数

4.4.2 关键字参数

 

4.4.3 缺省参数

def test(teacher, course = "python"):

    return f"{teacher} 负责 {course}"

test("mosh")

4.4.3 不定长参数

# 定义可变参数

def test(*numbers):
        print(numbers)

        # 元组可以用于循环

        sum = 0

        for number in numbers:

                sum += number

        return sum

# 调用方法传递多个数据时，这些数据存储在  元组中，

# 元组中的元素是不允许修改和删除的，只能增加，如果非要删除只能把整个元组都删了

test(1, 2, 3, 4)

# 得到的是元组：(1, 2, 3, 4)

def save_user(**user):

    print(user)

    print(user["name"])

# 带2个*号的参数会存储在字典中  {key1 : value1, key2 : value2 }，可以通过key访问字典内部的值

save_user(id = 1, name = "john", age = 22)

# 得到的是字典： {'id': 1, 'name': 'john', 'age': 22}

4.5 方法内变量的生效范围

定义在方法内部的变量是局部变量，生命周期仅限于方法内。

全局变量定义在方法外，在整个python文件都是生效的，除非必要尽量不要定义全局变量。

局部变量可以和全局变量同名，但最好不要这样做。

还有不要在方法内修改全局变量，容易把业务逻辑改坏。

message = "b"

def greet(name):

    # global message 在方法内部使用全局变量，后面可以用message = "hello"随意修改全局变量的值，但是最好别修改

    message = "a" # 可以定义局部变量名和全局变量同名   但是最好不要这样做

    print(message)

greet("mosh") # 里面使用的是局部变量 打印"a"

print(message) # 打印的是全局变量 "b"

4.6 匿名函数

4.6.1 函数作为参数传递

4.6.2 lambda匿名函数

五、数据结构

5.1 list 列表

5.1.1 定义列表

# 定义列表
letters = ["a", "b", "c"]
# 列表内嵌套列表
matrix = [[0, 1], [2, 3]]
# 初始化一个含有100个0元素的列表
zeros = [0] * 100
print(zeros)
# 列表组合
combined = letters + matrix
print(combined)
# ['a', 'b', 'c', [0, 1], [2, 3]]
# 使用list()方法创建列表
numbers = list(range(3, 20, 2))
print(numbers)
# [3, 5, 7, 9, 11, 13, 15, 17, 19]
greet = list("hello mosh")
print(greet)
#  ['h', 'e', 'l', 'l', 'o', ' ', 'm', 'o', 's', 'h']
print(len(greet))
#  10

5.1.2 获取列表指定下标内容

letters = ["a", "b", "c","d"]
# 获取列表指定下标的内容
print(letters[1]) 
#  b
print(letters[-1])
#  d
# 修改列表指定下标的值
letters[0] = "A"
print(letters)
# ['A', 'b', 'c', 'd']

5.1.3 列表的切片

letters = ["a", "b", "c","d"]
#  获取列表的一段数据作为新列表的内容

# new_letters = letters[0:3]
new_letters = letters[:3]
print(new_letters)
#  ['A', 'b', 'c']

# new_letters = letters[0:]
# new_letters = letters[:4]
new_letters = letters[:]
print(new_letters)
#  ['A', 'b', 'c', 'd']


new_letters = letters[::2]
print(new_letters)
#  ['A', 'c']


print(letters)
#  ['A', 'b', 'c', 'd']

numbers = list(range(20))
# 取偶数
new_numbers = numbers[::2]
print(new_numbers)
#  [0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

#  列表逆序
new_numbers = numbers[::-1]
print(new_numbers)
#  [19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

print(numbers)
#  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]

5.1.3 把列表内元素赋值给多个变量

需要注意的是变量的个数和列表内元素个数要一致，或者使用可变数量的变量接收剩余列表元素

numbers = [1, 2, 3]
first, second, third = numbers
print(f"first = {first} second = {second} third = {third}")
# first = 1 second = 2 third = 3

numbers = [1, 2, 3, 4, 5, 6]
#  使用可变数量的变量third接收列表剩余元素
first, second, *third = numbers
#  赋值的时候third前面只能有一个星号
print(f"first = {first} second = {second} third = {third}")
#  first = 1 second = 2 third = [3, 4, 5, 6]
#  注意：接收列表剩余多个元素的third也是一个列表

numbers = [1, 2, 3, 4, 5, 6]
# 如果只需要用到列表的第一个和最后一个元素
first, *others, last = numbers
print(f"first = {first} others = {others} last = {last}")
# first = 1 others = [2, 3, 4, 5] last = 6

5.1.4 列表内循环

# 列表用于循环体
letters = ["a", "b", "c"]
for letter in letters:
    print(letter)
# a
# b
# c

# 获取列表元素的同时打印元素的下标
for index, letter in enumerate(letters):
    print(f"index = {index} letter = {letter}")
# index = 0 letter = a
# index = 1 letter = b
# index = 2 letter = c

5.1.5 插入/删除列表元素

1、append(value) 在列表尾部插入数据

list.append(value)

letters = ["a", "b", "c"]
letters.append("d")
print(letters)
# ['a', 'b', 'c', 'd']

2、insert(i, value)在指定下标插入数据

list.insert(i, value)

letters.insert(1, "f")
print(letters)
# ['a', 'f', 'b', 'c', 'd']

3、pop() 删除列表末尾的数据

list.pop()     pop()方法会把移除的数据元素返回

letters = ['a', 'f', 'b', 'c', 'd']
c = letters.pop()
print(c)   # d
print(letters)  # ['a', 'f', 'b', 'c']

 4、pop(i) 删除指定下标数据

list.pop(i)

letters = ['a', 'f', 'b', 'c', 'd']
c = letters.pop(1)
print(c)   #  f
print(letters)  # ['a', 'b', 'c', 'd']

5、remove(value) 删除指定数据

list.remove(value) 只会把列表中第一个匹配的数据删除，如果列表中有重复数据，剩余的数据会保留

letters =["a", "b", "c", "d", "b"]
letters.remove("b")
print(letters)
# ['a', 'c', 'd', 'b']

6、del 删除指定下标数据

del list[i]  删除指定下标的元素

letters =["a", "b", "c", "d", "b"]
del letters[1]
print(letters)
# ['a', 'c', 'd', 'b']

del list[n:m]  删除下标n到下标m-1对应的元素值    n < m

letters =["a", "b", "c", "d", "b"]
# 删除列表内连续几个数据
del letters[0:2]
# 删除的是下标0  1对应的数据，不包括下标2的数据
print(letters)
# ['c', 'd', 'b']

7、clear() 清空列表

list.clear() 

letters =["a", "b", "c", "d", "b"]
letters.clear()
print(letters)
# []

5.1.6 index() 查找指定数据在列表中的下标

index("目标值") 只会返回从左匹配的第一个匹配成功的数据下标

如果查找的数据在列表中没有则会报ValueError

letters =["a", "b", "c", "d", "b"]
print(letters.index("b"))
# 1
print(letters.index("f"))
# ValueError: 'f' is not in list

为了避免因为查找额数据在列表中不存在导致的报错，可以加上if判断

letters =["a", "b", "c", "d", "b"]
if "b" in letters:
    print(letters.index("b"))
    # 1
if "f" in letters:
    print(letters.index("f"))

5.1.7 count(value) 获取指定数据在列表中出现的次数

count(value) 统计指定元素值在列表中出现的次数

如果给定的元素值在列表中不存在，则返回0

letters =["a", "b", "c", "d", "b"]
print(letters.count("b"))
#  2
print(letters.count("f"))
#  0

5.1.8 sort() 列表数据排序

1、sort() 升序

list.sort() 列表升序排列，不会另存排序结果是直接改变原列表的排序方式

numbers = [3, 51, 2, 8, 6]
numbers.sort()
print(numbers)
# [2, 3, 6, 8, 51]

2、sort(reverse=Ture) 降序

list.sort(reverse=Ture)  列表降序排列，不会另存排序结果是直接改变原列表的排序方式

numbers = [3, 51, 2, 8, 6]
numbers.sort(reverse=True)
print(numbers)
# [51, 8, 6, 3, 2]

3、sorted(list)升序

new_list = sorted(list) 新排序后另存为新的列表，原列表不改变

numbers = [3, 51, 2, 8, 6]
new_numbers = sorted(numbers)
print(new_numbers)
# [2, 3, 6, 8, 51]
print(numbers)
# [3, 51, 2, 8, 6]

4、sorted(list, reverse=Ture)降序

new_list = sorted(list, reverse=Ture) 新排序后另存为新的列表，原列表不改变

numbers = [3, 51, 2, 8, 6]
new_numbers = sorted(numbers, reverse=True)
print(new_numbers)
# [51, 8, 6, 3, 2]
print(numbers)
# [3, 51, 2, 8, 6]

5、 lambda方法

items = [
    ("prodct1", 10),
    ("prodct2", 9),
    ("prodct3", 12)
]

items.sort()
print(items)
# [('prodct1', 10), ('prodct2', 9), ('prodct3', 12)]
# 列表内元素是元组，无法排序

# 自定义排序方法
def sort_item(item):
    return item[1]
    # 返回商品的价格


items.sort(key=sort_item)
# 按照商品的价格进行升序
print(items)
# [('prodct2', 9), ('prodct1', 10), ('prodct3', 12)]

key=lambda parameters:expression 

# items.sort(key=lambda parameters:expression)
items.sort(key=lambda item:item[1])so

items = [
    ("prodct1", 10),
    ("prodct2", 9),
    ("prodct3", 12)
]


# 可以使用lambda表达式  替换上面定义的sort_item方法，可读性会更好
# items.sort(key=lambda parameters:expression)
items.sort(key=lambda item:item[1])
print(items)
# [('prodct2', 9), ('prodct1', 10), ('prodct3', 12)]

5.1.9 列表遍历

1、while循环遍历

 2、for 循环遍历

 

5.1.10 map()映射列表数据 

prices = list(map(lambda item: item[1], items))
# map的语义表达式为：item[1] for item in items

items = [
    ("prodct1", 10),
    ("prodct2", 9),
    ("prodct3", 12)
]
prices = []
for item in items:
    prices.append(item[1])

print(prices)
# [10, 9, 12]


# 使用map方法简单实现上述功能
prices = list(map(lambda item: item[1], items))
# map的语义表达式为：item[1] for item in items
# prices = [item[1] for item in items]
print(prices)

5.1.11 filter() 过滤列表数据

filtered = list(filter(lambda item: item[1] >= 10, items))
# filter方法的语义表达式为：item for item in items if item[1]>=10

items = [
    ("prodct1", 10),
    ("prodct2", 9),
    ("prodct3", 12)
]

filtered = list(filter(lambda item: item[1] >= 10, items))
# filter方法的语义表达式为：item for item in items if item[1]>=10
# filtered = [item for item in items if item[1]>=10]
print(filtered)
# [('prodct1', 10), ('prodct3', 12)]

5.1.12 zip()组合多个列表

list1 = [1, 2, 3, 4]
list2 = [10, 20, 30]
combine = list(zip("abcd", list1, list2))
print(combine)
# 组合后的元素式元组形式，元素个数和长度最短的那个一致
# [('a', 1, 10), ('b', 2, 20), ('c', 3, 30)]

5.1.13 扩展

1、堆栈

套娃式网站点击跳转，点击返回按钮回到上一个网站，直到回到第一个页面不能继续返回

堆栈：后进先出，python中的堆栈实现逻辑

browsing_session = []
browsing_session.append(1)
browsing_session.append(2)
browsing_session.append(3)
print(browsing_session)
while browsing_session:
    last = browsing_session.pop()
    if not browsing_session:
        print("已经回到初始页面了")
    else:
        now = browsing_session[-1]
        print(f"从{last}回到{now}")

2、队列

队列：先进先出，类似商店排队

前面的移出后  后面的需要移动一个位置，使用deque里的popleft()方法可以便捷操作

from collections import deque
queue = deque([])
queue.append(1)
queue.append(2)
queue.append(3)
print(queue)
# deque([1, 2, 3])
while queue:
    last = queue.popleft()
    print(last)
    if not queue:
        print("队列空了")

5.2 tuple 元组

 

5.2.1 元组的定义

# 标准元组定义方法
point = (1, 2)
print(type(point))
# 

# 下面这种也可以，但是最好别这么写
point = 1, 2
print(type(point))
# 

# 定义一个空元组
point = ()

# 定义一个至于一个元素的元组，注意逗号不能少
point = （1，）
# 或者
point = 1,

 使用tuple()进行数据类型转换：

point = tuple("hello")
print(point)
# ('h', 'e', 'l', 'l', 'o')

point = tuple([1, 2])
print(point)
# (1, 2)

5.2.2 元组合并

point = (1, 2) + (3, 4)
print(point)
# (1, 2, 3, 4)

point = (1, 2) * 3
print(point)
# (1, 2, 1, 2, 1, 2)

元组不允许修改内部元素。

# 获取元组内指定下标的元素
point = (1, 2, 3)
print(point[1])

# 元组的切片操作
point = tuple(range(20))
new_point = point[1:10:2]
print(new_point)
# (1, 3, 5, 7, 9)
print(point)
# (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)

# 把元组内元素赋值给变量
point = tuple(range(10))
first, *other, last = point
print(f"first = {first} other = {other} last {last}")
# first = 0 other = [1, 2, 3, 4, 5, 6, 7, 8] last 9

 元组的一个应用场景：两值互换

# 实现2值互换
x = 10
y = 11
x, y = y, x
print(f"x = {x} y = {y}")

补充：

python中array()也可以用来定义一个列表，但是在使用这个函数时需要指定列表内的元素的数据类型，即这个列表内以后只能存储这种类型的数据，插入或者修改为其他数据类型时就会报错，所以一般情况不会使用array()来定义列表，除非有特定的需求。

array — Efficient arrays of numeric values — Python 3.11.4 documentation

from array import array

numbers = array("i", [1, 2, 3])
numbers.insert(2, "b")
# 会报错
# Traceback (most recent call last):
#   File "test.py", line 353, in 
#     numbers.insert(2, "b")
# TypeError: an integer is required (got type str)

5.3 集合

集合：内部元素没有重复，集合内部数据是无序的所以不能通过下标来获取元素值，支持修改。

numbers = [1, 2, 3, 3, 4]
uniques = set(numbers)
print(uniques)
# {1, 2, 3, 4}

5.3.1 集合定义

my_set = set() # 定义一个空集合 

set1 = {1, 2}

5.3.2 add(value) 添加元素

set.add(value)

set1 = {1, 2, 3, 4, "hello"}
set1.add(9)
print(set1)  # {1, 2, 3, 4, 9, 'hello'}

5.3.3 删除元素

set.remove(value)    没有返回值

set.pop(value)     返回值是被删除的那个元素

set1 = {1, 2, 3, 4, "hello"}
# 使用remove 删除集合中指定元素
temp = set1.remove(2)  
print(temp)  # None
print(set1)  # {1, 3, 4, 'hello'}

# 使用pop 删除集合中随机一个元素
temp1 = set1.pop()
print(temp1)  # 1 
print(set1)   # {3, 4, 'hello'}

5.3.4 len(set) 获取集合长度

set1 = {1, 2, 3, 4, "hello"}
l = len(set1)  
print(l)  # 5

5.3.5 clear() 清空集合

set.lear()

set1 = {1, 2, 3, 4, "hello"}
set1.clear()
print(set1)  # set()

5.3.6 获取多个集合的并集

原集合内容不变，生成一个新的集合

set1 = {1, 2, 3, 4}
set2 = {1, 3, 5, 7}
set3 = set1 | set2
print(set3)  # {1, 2, 3, 4, 5, 7}
set4 = set1.union(set2)
print(set4)  # {1, 2, 3, 4, 5, 7}

print(set1)  # {1, 2, 3, 4}
print(set2)  # {1, 3, 5, 7}

5.3.7 获取多个集合的交集

first = {1, 2, 3, 4}
second = {1, 3, 5, 7}
third = first & second
print(third)
# {1, 3}

5.3.8 获取集合的差集

语法：

集合1有  但是集合2没有的元素

集合1 - 集合2

或者 集合1.difference(集合2)

set1 = {1, 2, 3}
set2 = {1, 5, 6}
set3 = set1.difference(set2)
print(set3)  # {2, 3}
set4 = set1 - set2
print(set4)   # {2, 3}
print(set1)   # {1, 2, 3}
print(set2)   # {1, 5, 6}

5.3.9 获取多个集合的对称性差异

first = {1, 2, 3, 4}
second = {1, 3, 5, 7}
third = first ^ second
print(third)
# {2, 4, 5, 7}
# 获取first有  但是second没有的元素，和second有但是first没有的元素
# 也就是交集以外的元素

5.3.10 集合内部数据的无序性

集合内部元素不支持使用索引进行数据获取

first = {1, 2, 3, 4}

print(first[0])

# 会报错  TypeError: 'set' object is not subscriptable

5.3.11 集合遍历

集合是无序的，不支持索引，所以不能用while遍历集合

可以用for遍历集合

5.4 dictionary 字典

5.4.1 定义字典

字典的key和value可以是任意数据类型（key不能是字典）

# 定义一个空字典

my_dict = {}

dict1 = dict()

# 定义字典
point = {"x": 1, "y": 2}
# 使用dict()方法定义字典
point = dict(x=1, y=2)

5.4.2 添加元素

stu_score = {}
stu_score["01"] = "xiaoming"
stu_score["02"] = "lili"
print(stu_score)  # {'01': 'xiaoming', '02': 'lili'}

5.4.3 修改元素

stu_score = {'01': 'xiaoming', '02': 'lili'}
stu_score["02"] = "xiaoliang"
print(stu_score)  # {'01': 'xiaoming', '02': 'xiaoliang'}

5.4.4 获取指定key的value

stu_score = {}
stu_score["01"] = "xiaoming"
stu_score["02"] = "lili"
print(stu_score)  # {'01': 'xiaoming', '02': 'lili'}

# 获取字典内某个key对应的value，为了避免因为key不存在，需要先进行判断
if key in stu_score :
    print(stu_score [key])

point = {"x":1, "y":2}

# 如果有另外一种取值逻辑：如果key为"a"存在，则获取对应的value，
# 如果不存在key为"a"，则获取某个指定的默认值（比如0）
print(point.get("a", 0))

5.4.5  删除指定元素

删除字典元素的方式：

dict.pop(key)   

del dict[key]     

stu_score = {'01': 'xiaoming', '02': 'xiaoliang', '03': 'xiaohong'}


temp = stu_score.pop("01")
print(temp)  # xiaoming
print(stu_score)  # {'02': 'xiaoliang', '03': 'xiaohong'}


del stu_score['02']
print(stu_score)  # {'03': 'xiaohong'}

5.4.6 清空字典

dict.clear()

stu_score = {'01': 'xiaoming', '02': 'xiaoliang', '03': 'xiaohong'}

stu_score.clear()
print(stu_score)  # {}

5.4.7 获取字典全部key

dict.keys()

stu_score = {'01': 'xiaoming', '02': 'xiaoliang', '03': 'xiaohong'}
keys = stu_score.keys()
print(type(keys))  # 
print(keys)  # dict_keys(['01', '02', '03'])

5.4.8 获取字典的元素

dict.items()

stu_score = {'01': 'xiaoming', '02': 'xiaoliang', '03': 'xiaohong'}
element = stu_score.items()
print(type(element))  # 
print(element)  # dict_items([('01', 'xiaoming'), ('02', 'xiaoliang'), ('03', 'xiaohong')])

5.4.9 字典应用于循环

# 字典用于循环
for key in point:
    print(key, point[key])
# x 1
# y 2


for key, value in point.items():
    print(key, value)
# x 1
# y 2


for x in point.items():
    print(x)
# ('x', 1)
# ('y', 2)

5类数据容器对比：

 

5.5 生成器对象

values = {}
for x in range(5):
    values[x] = x * 2
print(f"dict: {values}")
#  dict: {0: 0, 1: 2, 2: 4, 3: 6, 4: 8}

# 上面的语句等价于
values = {x: x * 2 for x in range(5)}
print(f"dict: {values}")
# dict: {0: 0, 1: 2, 2: 4, 3: 6, 4: 8}

values = [x * 2 for x in range(5)]
print(f"list: {values}")
# list: [0, 2, 4, 6, 8]

values = (x * 2 for x in range(5))
print(f"tuple: {values}")
# tuple:  at 0x01656808>
# 得到的是一个可迭代的对象————》 生成器对象

生成器对象的作用？？

from sys import getsizeof

values = [x * 2 for x in range(100)]
print("list: ", getsizeof(values))
# list:  452

values = (x * 2 for x in range(100))
print("gen: ", getsizeof(values))
# gen:  56

values = [x * 2 for x in range(1000000)]
print("list: ", getsizeof(values))
# 看下内存占用情况
# list:  4348728

values = (x * 2 for x in range(1000000))
print("gen: ", getsizeof(values))
# 看下内存占用情况
# gen:  56

# 可以看出使用迭代器，随着处理的数据量急剧增大，每次迭代时占用的内存没有变化

所以在处理大数据集或者可能是一个无限的数据流的情况时，使用生成器表达式得到一个生成器，把每次处理的数据量存入内存，会比把全部数据存入内存，数据处理速度快很多。

但是注意在迭代过程中，无法获取处理数据的总数，因为内存存储的是部分数据：

values = (x * 2 for x in range(1000000))
print("gen: ", getsizeof(values))
# 看下内存占用情况
# gen:  56
print(len(values))
# 报错： TypeError: object of type 'generator' has no len()

5.6 解包操作

5.6.1 多个列表合并

numbers = [1, 2, 3]
print(numbers)
# 打印的是一个列表  [1, 2, 3]
print(*numbers)
# 打印的是列表内的数据  1 2 3

values = list(range(5))
print(values)
# [0, 1, 2, 3, 4]

# 多个数据类型转换为列表，再直接合并
values = [*range(5), *"hello"]
print(values)
# [0, 1, 2, 3, 4, 'h', 'e', 'l', 'l', 'o']


first = [1, 2]
second = [3, 4]
values = [*first, *"world", *second, *"hello"]
print(values)
# [1, 2, 'w', 'o', 'r', 'l', 'd', 3, 4, 'h', 'e', 'l', 'l', 'o']

5.6.2 多个字典合并

first = {"x": 1}
second = {"x": 10, "y": 20}
combined = {**first, **second, "z": 30}
print(combined)
# {'x': 10, 'y': 20, 'z': 30}
# 多个字典合并时，如果出现key值相同的情况，会取最后那个对应的value

5.7 字符串

上面第二部分已经介绍过

5.8 练习

获取文本中出现次数最多的字符

str = "fddhbfegrkthwbcndvgpoirnjbugrnmbqplfekvjmfi jfcdrgvfcydsnvj b "
# 去除文本内部空格
str.strip()
list = list(str)
char_num = {}
for char in list:
    if char in char_num:
        char_num[char] += 1
    else:
        char_num[char] = 1

count = 0
value = ""
for char, num in char_num.items():
    if num > count:
        count = num
        value = char
print(f"字符{value}出现次数最多, 出现了{count}次")

str = "fddhbfegrkthwbcndvgpoirnjbugrnmbqplfekvjmfi jfcdrgvfcydsnvj b "
# 去除文本内部空格
str.strip()
list = list(str)
char_num = {}
for char in list:
    if char in char_num:
        char_num[char] += 1
    else:
        char_num[char] = 1

# print(char_num)
print(type(char_num.items()))
# 
print(type(sorted(char_num.items())))
# 
char_num_sorted = sorted(char_num.items(), key=lambda kv: kv[1], reverse=True)
print(char_num_sorted[0])

六、文件操作

6.1 文件读取

6.1.1 open() 打开文件

文件夹之间用斜线隔开。在 Windows 中使用倒斜杠\来分隔，在 Mac OS 和 Linux 中使 用正斜杠/作为路径分隔符
文件路径可以是相对路径也可以是绝对路径

print("==================================")
# 文件夹之间用斜线隔开。在 Windows 中使用倒斜杠\来分隔，在 Mac OS 和 Linux 中使 用正斜杠/作为路径分隔符
# 文件路径可以是相对路径也可以是绝对路径
f = open("ecommerce\shopping\sales.py", "r", encoding="UTF-8")
print(type(f))  # 

6.1.2 read()读取文件内容

 

f = open("../../test.txt", "r", encoding="UTF-8")
print(type(f))  # 
content = f.read(10)
print(type(content))  # 
print(content)  # gfdskjhfrk

6.1.3 readlines()读取文件 

 

可以看到使用f.read(10)读了10个字节，之后再使用f.readlines()读取剩余全部内容，并按行存入列表中。读文件时是有一个类似指针的东西，标记当前读取的进度，只要不关闭文件或者重新把文件指针放在文件开头，下次读取时就会从上次读取结束的位置开始读数据。 

f = open("../../test.txt", "r", encoding="UTF-8")
print(type(f))  # 

content = f.read(10)
print(type(content))  # 
print(content)  # gfdskjhfrk

content1 =f.readlines()
print(type(content1))  # 
print(content1)  # ['lgjnjlbdfifghrughbnt\n', 'sgdfbifjrn vfgdb ujhuegyrg\n', 'hfcidufgv dvheuigfrrgfbh\n']

6.1.4 readline()读取文件 

 

f = open("../../test.txt", "r", encoding="UTF-8")
print(type(f))  # 
content = f.readline()
print(type(content))  # 
print(content)  # gfdskjhfrklgjnjlbdfifghrughbnt

content2 = f.readline()  # sgdfbifjrn vfgdb ujhuegyrg

6.1.5 使用for循环读取文件 

f = open("../../test.txt", "r", encoding="UTF-8")
print(type(f))  # 
for line in f:
    print(line)
"""
gfdskjhfrklgjnjlbdfifghrughbnt

sgdfbifjrn vfgdb ujhuegyrg

hfcidufgv dvheuigfrrgfbh
"""

6.1.6 关闭文件连接 

不关闭文件连接，文件就会被一直占用，也就不能对文件进行其他操作。

6.1.7 使用with open打开文件 

with open("../../test.txt", "r", encoding="utf-8") as f:
    print(f.readlines())
    
# ['gfdskjhfrklgjnjlbdfifghrughbnt\n', 'sgdfbifjrn vfgdb ujhuegyrg\n', 'hfcidufgv dvheuigfrrgfbh\n']

6.2 文件写入

 

如果不使用flush()把缓冲区的内容刷入磁盘，只要再文件操作完毕后使用close()也是可以保存内容到磁盘的，因为close()内置了flush()功能 

 

 

 

 练习：

账单文件bill.txt  内容如下：

 需求：读取文件，把文件备份到bill.txt.bak，同时把标记为测试的数据丢弃

正则匹配

Python正则表达式是一种强大的文本处理工具（也就是处理字符串 ），可以用来匹配、搜索、替换文本中的特定模式。下面是Python正则表达式的用法详解：

1. 匹配单个字符

 .      匹配任意一个字符，除了换行符
 []     匹配括号中任意一个字符
 [^]   匹配不在括号中的任意一个字符
\d     匹配任意一个数字
\D    匹配任意一个非数字字符
\w    匹配任意一个字母、数字或下划线
\W   匹配任意一个非字母、数字或下划线字符
\s     匹配任意一个空白字符
\S    匹配任意一个非空白字符：

import re

# 匹配任意一个字符
pattern = r'.'
text = 'hello world'
result = re.findall(pattern, text)
print(result)  # ['h', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']

# 匹配数字
pattern = r'\d'
text = 'abc123def456'
result = re.findall(pattern, text)
print(result)  # ['1', '2', '3', '4', '5', '6']

# 匹配非数字字符
pattern = r'\D'
text = 'abc123def456'
result = re.findall(pattern, text)
print(result)  # ['a', 'b', 'c', 'd', 'e', 'f']

# 匹配字母、数字或下划线
pattern = r'\w'
text = 'hello_world123'
result = re.findall(pattern, text)
print(result)  # ['h', 'e', 'l', 'l', 'o', '_', 'w', 'o', 'r', 'l', 'd', '1', '2', '3']

# 匹配非字母、数字或下划线字符
pattern = r'\W'
text = 'hello_world123'
result = re.findall(pattern, text)
print(result)  # []

# 匹配空白字符
pattern = r'\s'
text = 'hello world'
result = re.findall(pattern, text)
print(result)  # [' ']

# 匹配非空白字符
pattern = r'\S'
text = 'hello world'
result = re.findall(pattern, text)
print(result)  # ['h', 'e', 'l', 'l', 'o', 'w', 'o', 'r', 'l', 'd']

2. 匹配多个字符

*      匹配前面的字符0次或多次

+     匹配前面的字符1次或多次
?     匹配前面的字符0次或1次
{n}    匹配前面的字符恰好n次
{n,}   匹配前面的字符至少n次
{n,m}   匹配前面的字符至少n次，但不超过m次

例如：

import re

# 匹配前面的字符0次或多次
pattern = r'ab*'
text = 'a ab abb abbb abbbb'
result = re.findall(pattern, text)
print(result)  # ['a', 'ab', 'abb', 'abbb', 'abbb']

# 匹配前面的字符1次或多次
pattern = r'ab+'
text = 'a ab abb abbb abbbb'
result = re.findall(pattern, text)
print(result)  # ['ab', 'abb', 'abbb', 'abbb']

# 匹配前面的字符0次或1次
pattern = r'ab?'
text = 'a ab abb abbb abbbb'
result = re.findall(pattern, text)
print(result)  # ['a', 'ab', 'ab', 'ab', 'ab']

# 匹配前面的字符恰好n次
pattern = r'ab{2}'
text = 'a ab abb abbb abbbb'
result = re.findall(pattern, text)
print(result)  # ['abb']

# 匹配前面的字符至少n次
pattern = r'ab{2,}'
text = 'a ab abb abbb abbbb'
result = re.findall(pattern, text)
print(result)  # ['abb', 'abbb', 'abbb']

# 匹配前面的字符至少n次，但不超过m次
pattern = r'ab{2,3}'
text = 'a ab abb abbb abbbb'
result = re.findall(pattern, text)
print(result)  # ['abb', 'abbb', 'abbb']

 

3. 匹配开头和结尾

 ^    匹配字符串的开头
 $   匹配字符串的结尾

例如：

import re

# 匹配字符串的开头
pattern = r'^hello'
text = 'hello world'
result = re.findall(pattern, text)
print(result)  # ['hello']

# 匹配字符串的结尾
pattern = r'world$'
text = 'hello world'
result = re.findall(pattern, text)
print(result)  # ['world']

 

4. 匹配分组

 ()   将括号中的内容作为一个分组
 |    匹配多个模式中的任意一个 

例如：

import re

# 将括号中的内容作为一个分组
pattern = r'(hello) (world)'
text = 'hello world'
result = re.findall(pattern, text)
print(result)  # [('hello', 'world')]

# 匹配多个模式中的任意一个
pattern = r'hello|world'
text = 'hello world'
result = re.findall(pattern, text)
print(result)  # ['hello', 'world']

 

5. 匹配转义字符

 \   转义字符

例如：

import re

# 匹配转义字符
pattern = r'\$'
text = 'The price is $10.'
result = re.findall(pattern, text)
print(result)  # ['$']

 

七、异常exceptions

7.1 捕获异常

捕获全部异常：

方式一：

try:

        可能发生错误的代码

except:

        如果发生异常就执行这里的代码

方式二：

try:

        可能发生错误的代码

except Exception as er:

        如果发生错误执行这里的代码

age = int(input("age: "))
# ValueError: invalid literal for int() with base 10: 'e'
# 数据异常情况需要提前捕获并设计处理逻辑，避免程序崩溃


try:
    age = int(input("age: "))
except ValueError as er:
    # 如果try模块内语句有相应的ValueError报错，就会执行except模块里的逻辑
    # 一般需要把报错信息和类型打印出来，所以先给异常取一个别名er
    print("输入的年龄不正确，请重新输入")
    print(er)
    # invalid literal for int() with base 10: 'a'
    print(type(er))
    # 
else:
    # 如果try模块里没有代码报错就会执行else里面的语句
    print("没有代码报错")

7.2 捕获多个异常

try:
    age = int(input("age: "))
    xfactor = 10 / age
except ValueError as er:
    print("输入的年龄不正确，请重新输入")
    print(er)
    print(type(er))
except ZeroDivisionError as zero:
    print("输入的年龄不正确，请重新输入")
    print(zero)
    print(type(zero))
else:
    # 如果try模块里没有代码报错就会执行else里面的语句
    print("没有代码报错")

如果我们希望可以一次捕获多个异常，并且抛出具有概括性的提示文案

try:
    age = int(input("age: "))
    xfactor = 10 / age
except (ValueError, ZeroDivisionError) as er:
    print("输入的年龄不正确，请重新输入")
else:
    # 如果try模块里没有代码报错就会执行else里面的语句
    print("没有代码报错")

7.3 异常的else和finally语句

 在代码中会用到外部资源，如文件、网络连接、数据库连接等，使用完要记得释放资源，可以把资源释放的语句放在finally语句里面。

7.3.1 在try模块连接资源在finally模块关闭资源

try:
    file = open("test.py")
    age = int(input("age: "))
    xfactor = 10 / age
except (ValueError, ZeroDivisionError):
    # try模块内有except中指定的异常时就会执行except模块，捕获异常
    print("输入的年龄不正确，请重新输入")
else:
    # try模块里没有代码报错就会执行else里面的语句
    print("没有代码报错")

finally:
    # 在finally模块内关闭所有资源的连接，释放资源
    # 不管try模块是否有异常，finally模块总会执行
    file.close()

7.3.2 使用with语句自动释放资源

with语句后面可以打开多个资源，并且在代码执行完后会自动释放这些资源。

try:
    # 使用with语句可以打开多个资源
    with open("test.py") as file, open("app.py") as target:
        print("test.py opened")
        print("app.py opened")
    age = int(input("age: "))
    xfactor = 10 / age
except (ValueError, ZeroDivisionError):
    # try模块内有except中指定的异常时就会执行except模块，捕获异常并抛出
    print("输入的年龄不正确，请重新输入")
else:
    # try模块里没有代码报错就会执行else里面的语句
    print("没有代码报错")

7.4 异常具有传递性

7.5 抛出异常

可以在编写自己的函数时抛出异常，但是不推荐这么做，代价比较大。

raise 异常类型("这里给出一个异常提示信息")

如：

raise ValueError("error 细节描述")

def calculate_xfactor(age):
    if age <= 0:
        raise ValueError("这里是你定制的异常提示信息")
    return 10 / age

try:
    x = int(input("年龄："))
    calculate_xfactor(x)
except ValueError as error:
    print(error)

python的内置异常有哪些？？Built-in Exceptions — Python 3.11.4 documentation

from timeit import timeit
# timeit 可以用来计算执行时间

code1 = """
def calculate_xfactor(age):
    if age <= 0:
        raise ValueError("这里是你定制的异常提示信息")
    return 10 / age

try:
    calculate_xfactor(-1)
except ValueError as error:
    print(error)
"""


code2 = """
def calculate_xfactor(age):
    if age <= 0:
        return None
    return 10 / age


xfactor = calculate_xfactor(-1)
if xfactor == None:
    pass
"""


print("code1= ",timeit(code1, number = 10000))
# 计算code1代码执行10000次使用的时间
# code1=  2.6951908
print("code2= ", timeit(code2, number = 10000))
# code2=  0.0021061000000002217

可以看到在自己函数中抛出&捕获异常，处理时间差距很大，而且不抛异常代码也更简洁。

八、类classes

8.1 类定义

类命名使用驼峰，每个单词首字母大写

每个方法都至少有一个参数——》 self

# 定义类Point
class Point:
    # 定义一个类方法
    def draw(self):
        print("draw")

# 类的实例化
point = Point()
print(type(point))
# 
print(isinstance(point, Point))
# True
# isinstance()方法判断判断point是不是Point的实例

8.2 构造函数

创建实例时执行构造函数

定义类的方法，每个方法至少包含一个参数self，self是对当前类的实例对象的引用
使用self可以读取当前实例对象的属性，调用这个实例对象的方法

成员方法中使用成员属性（类属性、实例属性）必须通过self

 

# 定义类Point
class Point:
    # 定义类的方法，每个方法至少包含一个参数self，self是对当前类的实例对象的引用
    # 使用self可以读取当前实例对象的属性，调用这个实例对象的方法
    
    # 定义构造函数 init前后各有2个下划线
    # 构造函数在实例化时执行
    # 一个点需要x y坐标来唯一确定
    def __init__(self, x, y):
        self.x = x
        self.y = y
        #  x  y是实例属性

    # 定义一个类方法
    def draw(self):
        print("draw")

# 类的实例化
point = Point(1, 2)
print(type(point))
# 
print(isinstance(point, Point))
# True
# isinstance()方法判断判断point是不是Point的实例

# 调用类属性
print(point.x)
# 1
# 类实例化后，调用实例对象的方法时，不用传递实例对象point.draw(point) 
# 没必要传参，python解释器会替我们把实例化的对象传递进去
# 调用实例对象的方法
point.draw()
# draw

8.3 类和实例属性

8.3.1 实例属性

在类定义中可以创建实例属性，在类实例化后，实例对象也可以定义属于自己的新属性。

每个实例对象的实例属性值可以不同

class Point:
    def __init__(self, x, y):
        # 定义实例属性x  y 
        self.x = x
        self.y = y

    def draw(self):
        print("draw")

point1 = Point(5, 6)
# 类实例化后，还可以定义实例特有的属性z
point1.z = 7

point2 = Point(3, 7)

8.3.2 类属性

类属性是在类级别定义的属性，所以在所有实例中都是相同的。

可以通过实例对象访问类属性，也可以通过类访问类属性。

class Point:
    # 定义类 属性，类的实例共有这个属性
    default_color = "red"

    def __init__(self, x, y):
        self.x = x
        self.y = y

    def draw(self):
        print("draw")

point1 = Point(5, 6)
point2 = Point(3, 7)
# 通过实例对象访问类属性
print(point1.default_color)
# red
print(point2.default_color)
# red
# 通过类访问类属性
print(Point.default_color)
# red

修改类属性的值： 

1）通过类修改类属性，那么之后对类属性的访问，不管是实例对象访问还是类访问，该属性值都是最新的

class Point:
    default_color = "red"

    def __init__(self, x, y):
        self.x = x
        self.y = y

    def draw(self):
        print("draw")

# 在访问类属性之前对类属性进行修改，之后访问类属性获得的就是最新的值
# 通过类名.类属性 = 新值  重新赋值
Point.default_color = "yellow"

point1 = Point(5, 6)
point2 = Point(3, 7)

# 通过实例对象访问类属性
print(point1.default_color)
# yellow
print(point2.default_color)
# yellow
# 通过类访问类属性
print(Point.default_color)
# yellow

2）通过实例对象修改类属性的值，那么之后类属性访问获得的值，只有这个实例对象获得到最新的值，其他实例对象和类都是获得之前的旧值

class Point:
    default_color = "red"

    def __init__(self, x, y):
        self.x = x
        self.y = y

    def draw(self):
        print("draw")


point1 = Point(5, 6)
point2 = Point(3, 7)

# 通过实例对象修改类属性的值，那么之后类属性访问时，只有这个实例对象可以拿到最新的类属性值，其他的实例对象和类访问到的是旧的类属性值
point1.default_color = "green"

# 通过实例对象访问类属性
print(point1.default_color)
# green
print(point2.default_color)
# red
# 通过类访问类属性
print(Point.default_color)
# red

一般情况我们使用实例属性比较多，类属性只有在所有实例对象都共有某个属性值时才会定义，并且一般情况不会修改类属性的值。

8.4 类和实例方法

8.4.1 实例方法

class Point:
    default_color = "red"
    # 定义实例方法
# 实例方法__init__在实例创建的时候调用
    def __init__(self, x, y):
        self.x = x
        self.y = y
# 实例方法draw
    def draw(self):
        print("draw")

# 调用实例方法
point1 = Point(3, 8)
point2 = Point(5, 3)
point1.draw()
point2.draw()

8.4.2 类方法

为什么要定义类方法呢？？

在创建实例对象时需要调用__init__方法初始化一些参数，如果某些特殊实例对象  在初始化时需要传递一些特殊的参数，但是又不是所有实例对象都需要传递这些特殊参数。这样的话在创建这些特殊实例对象时就需要重复传递这些参数，为了使代码简化，可以定义 工厂方法 来产出这些参数，这样你在创建特殊实例对象时只需要调用这些工厂方法就行了。

类方法的第一个参数设置为cls，也就是class的简写，这个方法是供类调用的
在类方法定义前面需要加一个类装饰器 @classmethod

举个例子：

class Point:
    # 定义实例方法
    # 实例方法的第一个参数设置为self，指代的是实例对象本身，也就是这方法是供实例对象调用的
    def __init__(self, x, y):
        self.x = x
        self.y = y
    
    # 定义类方法
    # 类方法的第一个参数设置为cls，也就是class的简写，这个方法是工类调用的
    # 在类方法定义前面需要加一个类装饰器 @classmethod
    @classmethod
    def zero(cls):
        return cls(0, 0)


    def draw(self):
        print("draw")

# 有一些特殊需求，需要创建多个零点实例对象
# 可以使用普通的类实例化方法创建这些实例对象
point1 = Point(0, 0)
point2 = Point(0, 0)
point3 = Point(0, 0)
# 但是调用工厂方法更简单  代码也更好看
point4 = Point.zero()
point4.draw()

8.5 magic methods魔术方法

方法名前后有2个下划线的方法，由python解释器自动调用

参考：A Guide to Python's Magic Methods « rafekettler.com

下面介绍几个类里面的magic method

8.5.1 __str__

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

   
    def draw(self):
        print(f"draw ({self.x}, {self.y})")
    
point = Point(3, 4)
print(point)
# <__main__.Point object at 0x018AF430>  是一个对象地址
# 但是我们想要打印的是点对象的坐标(3, 4)

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

    # 定义__str__方法就可以得到点对象字符串
    def __str__(self):
        return f"({self.x}, {self.y})"

    def draw(self):
        print(f"draw ({self.x}, {self.y})")
    
point = Point(3, 4)
print(point)
# (3, 4)

8.5.2 比较对象大小的魔法方法

比较的是2个对象的内存地址，2个对象的地址不一样  所以是False

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y


    def draw(self):
        print(f"draw ({self.x}, {self.y})")
    
point = Point(3, 4)
point1 = Point(3, 4)
print(point == point1)
# False
# 比较的是2个对象的内存地址，2个对象的地址不一样  所以是False

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __eq__(self, other):
        return self.x == other.x and self.y == other.y
    
point1 = Point(1, 2)
point2 = Point(1, 2)
# 这个时候比较 point1 == point2 就可以了
print(point1 == point2)
# True
print(point1 > point2)
# TypeError: '>' not supported between instances of 'Point' and 'Point'

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y
    
    def __gt__(self, other):
        return self.x > other.x and self.y > other.y
    
point1 = Point(1, 2)
point = Point(10, 20)

print(point > point1)
# True
print(point < point1)
# False
# 不用再去定义__lt__方法就可以实现> < 的比较

8.5.3 对象进行算术运算的魔法方法

# 统计标签出现的次数
class TagCloud:
    def __init__(self):
        # 定义一个字典存储标签和对应次数
        self.tags = {}
    
    # 添加标签
    def add(self, tag):
        # self.tags.get(tag, 0)获取字典内key为tag的value，如果没有对应的key则添加，并给默认value为0
        # tag名称不区分大小写，统一转换为小写
        self.tags[tag.lower()] = self.tags.get(tag.lower(), 0) + 1


cloud = TagCloud()
cloud.add("Python")
cloud.add("sql")
cloud.add("python")
print(cloud.tags)
# {'python': 2, 'sql': 1}

8.5.4  自定义的magic method

# 统计标签出现的次数
class TagCloud:
    def __init__(self):
        # 定义一个字典存储标签和对应次数
        self.tags = {}
    
    # 添加标签
    def add(self, tag):
        # self.tags.get(tag, 0)获取字典内key为tag的value，如果没有对应的key则添加，并给默认value为0
        self.tags[tag.lower()] = self.tags.get(tag.lower(), 0) + 1
    
    # 获取字典对象中指定key的value
    def __getitem__(self, tag):
        return self.tags.get(tag.lower(), 0)
    

cloud = TagCloud()
cloud.add("Python")
cloud.add("sql")
cloud.add("python")
print(cloud.tags)
# {'python': 2, 'sql': 1}

# 获取标签为Java出现的次数
print(cloud["java"])
#  返回0
print(cloud["python"])
# 返回2

# 统计标签出现的次数
class TagCloud:
    def __init__(self):
        # 定义一个字典存储标签和对应次数
        self.tags = {}
    
   # 添加标签
    def add(self, tag):
        # self.tags.get(tag, 0)获取字典内key为tag的value，如果没有对应的key则添加，并给默认value为0
        self.tags[tag.lower()] = self.tags.get(tag.lower(), 0) + 1
    
    
    # 插入键值对  或者修改指定key的value
    def __setitem__(self, tag, count):
        self.tags[tag.lower()] = count

cloud = TagCloud()
cloud.add("Python")
cloud.add("sql")
cloud.add("python")
print(cloud.tags)
# {'python': 2, 'sql': 1}

cloud.tags["java"] = 5
print(cloud.tags)
# {'python': 2, 'sql': 1, 'java': 5}

# 统计标签出现的次数
class TagCloud:
    def __init__(self):
        # 定义一个字典存储标签和对应次数
        self.tags = {}
    
    # 添加标签
    def add(self, tag):
        # self.tags.get(tag, 0)获取字典内key为tag的value，如果没有对应的key则添加，并给默认value为0
        self.tags[tag.lower()] = self.tags.get(tag.lower(), 0) + 1
    
    # 获取字典对象中指定key的value
    def __getitem__(self, tag):
        return self.tags.get(tag.lower(), 0)
    
    # 插入键值对  或者修改指定key的value
    def __setitem__(self, tag, count):
        self.tags[tag.lower()] = count

    # 获取字典长度
    def __len__(self):
        return len(self.tags)
    
    def __iter__(self):
        # 使用内置方法iter获取一个迭代器对象，迭代器对象是遍历容器的对象，一次获取一个元素
        return iter(self.tags)

cloud = TagCloud()
cloud.add("Python")
cloud.add("sql")
cloud.add("python")
print(cloud.tags)
# {'python': 2, 'sql': 1}


print(len(cloud.tags))
#  3
for tag, count in cloud.tags.items():
    print(f"tag= {tag}, count= {count}")
# tag= python, count= 2
# tag= sql, count= 1
# tag= java, count= 5

for tag, count in iter(cloud.tags.items()):
    print(f"tag= {tag}, count= {count}")
# tag= python, count= 2
# tag= sql, count= 1
# tag= java, count= 5

8.6 python的私有成员

在属性名前面加上2个下划线__   （快速修改：选中属性名 点击F2修改后回车即可全局修改）

实例对象不能直接访问私有属性，会报错：

实例对象.__tags  就会报错AttributeError: 'TagCloud' object has no attribute '__tags'

可以通过实例对象.__dict__  获取一个字典，字典内部是这个类包含的全部属性，所以还是可以访问到这个私有属性的。

# 统计标签出现的次数
class TagCloud:
    def __init__(self):
        # 定义一个字典存储标签和对应次数
        self.__tags = {}
    
    # 添加标签
    def add(self, tag):
        # self.tags.get(tag, 0)获取字典内key为tag的value，如果没有对应的key则添加，并给默认value为0
        self.__tags[tag.lower()] = self.__tags.get(tag.lower(), 0) + 1
    
    # 获取字典对象中指定key的value
    def __getitem__(self, tag):
        return self.__tags.get(tag.lower(), 0)
    
    # 插入键值对  或者修改指定key的value
    def __setitem__(self, tag, count):
        self.__tags[tag.lower()] = count


cloud = TagCloud()
cloud.add("Python")
cloud.add("sql")
cloud.add("python")
# print(cloud.__tags) 

# 可以看到从外部可以访问实例对象的属性tags，我们需要设置属性为私有，避免从外部访问
# 怎么把属性设置为私有呢？？  在属性名前面加上2个下划线__  选中属性名 点击F2修改后回车即可全部修改
# 再次执行代码 只要访问到了私有属性：cloud.__tags  就会报错AttributeError: 'TagCloud' object has no attribute '__tags'
# 但是把属性设置私有并不能完全避免从外部访问它，还是由其他方法能够访问到私有属性
# cloud.__dict__  可以获取到一个字典，字典内部是这个类包含的全部属性
print(cloud.__dict__)  # {'_TagCloud__tags': {'python': 2, 'sql': 1}}

print(cloud._TagCloud__tags)  # {'python': 2, 'sql': 1}

print(cloud._TagCloud__tags["python"])  # 2

# 使用上面的方法还是可以访问到类的私有属性
# 所以虽然python能够设置私有属性， 但是它并没有真正意义的私有

8.7 类属性property

8.7.1 使用property()方法创建类属性

class Product:
    def __init__(self, price):
        self.price = price 

product = Product(-50)
# 显然上面的代码没有考虑属性的取值

代码优化方案一：

class Product:
    def __init__(self, price):
        # self.price =price 
        # self.__price = price
        self.set_price(price)

    def get_price(self):
        return self.__price
    
    def set_price(self, value):
        if value < 0:
            raise ValueError("价格不允许为负数")
        self.__price = value

product = Product(-50)

可以抛出异常

Traceback (most recent call last):
  File "test.py", line 799, in
    product = Product(-50)
  File "test.py", line 789, in __init__
    self.set_price(price)
  File "test.py", line 796, in set_price
    raise ValueError("价格不允许为负数")
  File "test.py", line 799, in
    product = Product(-50)
  File "test.py", line 789, in __init__
    self.set_price(price)
  File "test.py", line 796, in set_price
    raise ValueError("价格不允许为负数")
ValueError: 价格不允许为负数

处理方案二：

 类属性————property

 实例对象属性————attribute

# 内置类属性函数  property(fget, fset, fdel, doc)  返回一个类属性对象，这个类属性对象会获取 实例对象的属性值

# 参数fget是获取 实例对象属性attribute 的方法名

# 参数fset是设置 实例对象属性 的方法名

# 参数fdel是删除 实例对象属性 的方法名

# 参数doc是说明文档

注意这里传参只是方法名，是fget  不是fget()，而且并没有调用方法

fget、 fset、fdel这些方法还是需要自己编写的，名称可以自定义

class Product:
    def __init__(self, price):
        self.set_price(price)

    def get_price(self):
        return self.__price
    
    def set_price(self, value):
        if value < 0:
            raise ValueError("价格不允许为负数")
        self.__price = value

    
    price = property(get_price, set_price)
    # 此处定义的类属性price，实例对象也可以访问

# 传入负数也会抛出异常
# product = Product(-50)
# Traceback (most recent call last):
#   File "test.py", line 826, in 
#     product = Product(-50)
#   File "test.py", line 813, in __init__
#     self.set_price(price)
#   File "test.py", line 820, in set_price
#     raise ValueError("价格不允许为负数")
# ValueError: 价格不允许为负数

# 传入正确数值
product = Product(50)
# product.price = -10  也会触发抛出异常，和上面的实例化传参抛出的异常一样
print(product.price)

虽然使用类属性返回了需要的价格，但是我们发现实例对象依然可以调用实例方法set_price()和 get_price()   污染了对象接口，可以把get_price和set_price 改为__get_price 和__set__price变为私有方法

或者在方法定义前添加@classmethod把实例方法 变为 类方法

8.7.2 装饰器@property 创建类属性

使用@property装饰器定义类属性，类属性名就是下面的方法名

class Product:
    def __init__(self, value):
        self.__jiage = value

    # 使用@property装饰器定义类属性，类属性名就是下面的方法名
    @property
    def price(self):
        return self.__jiage
    # python解释器看到@property和方法名price 会自动创建 类属性price
    
    # 类属性有2个方法：setter和getter
    # 定义装饰器
    @price.setter
    def price(self, value):
        if value < 0:
            raise ValueError("价格不能是负数")
        self.__jiage = value

# product = Product(-19) # 会抛出异常
product = Product(10)
print(product.price)  # 10
# 修改类属性的值
product.price = 20
print(product.price)  # 20

如果没有定义类属性的setter方法，则在实例化后就不能进行类属性值的修改：

# 类属性有2个方法：setter和getter

# 定义装饰器

@price.setter

def price(self, value):

        if value < 0:

                raise ValueError("价格不能是负数")

        self.__jiage = value

8.8 继承inheritance

8.8.1 单继承和多层继承

1. 单继承

单继承：子类只继承一个父类

class Animal:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def eat(self):
        print("eat")

# 哺乳类动物Mammal  继承  动物Animal的方法和属性
class Mammal(Animal):
    def walk(self):
        print("walk")

m = Mammal("sheep", 3)
m.eat()
m.walk()
print(m.name)

2.  多层继承

多层继承：继承关系为多层传递，如生活中的爷爷、父亲、儿子

3. isinstance(instance, class)

作用：判断instance 是否是class的实例对象，返回的是True  或者False

class Animal:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def eat(self):
        print("eat")

# 哺乳类动物Mammal  继承  动物Animal的方法和属性
class Mammal(Animal):
    def walk(self):
        print("walk")

m = Mammal("sheep", 3)
print(isinstance(m, Mammal))  # True
print(isinstance(m, Animal))  # True

所有的类都是从object类继承而来的，object是所有类的父类

print(isinstance(m, object))  # True

4. issubclass(sub, base)

作用：判断sub类是不是base类的子类，返回的是True  或者False

print(issubclass(Mammal, Animal))  # True
print(issubclass(Mammal, object))  # True

5.  重写父类方法/属性

在子类中重新定义同名佛如属性或方法即可。

子类重写父类同名方法

• 父类的方法不能满足子类的需要，可以对父类的方法重写，重写父类方法的目的是为了给他扩展功能
• 在子类中定义了一个和父类同名的方法(参数也一样)，即为对父类的方法重写
• 子类调用同名方法，默认只会调用子类的

 

 使用第一种方式别忘了方法里面的self

子类调用父类同名方法

• 父类名.同名方法(self, 形参1, ……)
• super(子类名, self).同名方法(形参1, ……)
• super().同名方法(形参1, ……)  ————推荐使用这种方式

8.8.2 多继承

所谓多继承，即子类有多个父类，并且具有它们的特征

class 子类名(父类1, 父类2, ……)：
    pass

1、 多继承--查看子类继承父类的继承顺序

查看子类的继承顺序：子类名.__mro__

# 定义2个类，它们没有继承关系，是平级的
class SmallDog(object):
    def eat(self):
        print('吃小东西')
        
# 再定义一个类
class BigDog(object):
    def drink(self):
        print('大口喝水')

# 定义一个子类，多继承于上面2个父类
class SuperDog(SmallDog, BigDog):
    pass

# 查看类的继承顺序
print(SuperDog.__mro__)

运行结果

(, , , 

2、多继承--调用父类同名方法

默认调用情况

如果继承过来的2个父类的方法同名，默认调用先继承的那个父类的同名方法

子类调用父类同名方法

• 父类名.同名方法(self, 形参1, ……)：调用指定的父类的同名方法
• super(类名x, self).同名方法(形参1, ……)：调用继承顺序中类名x的下一个类的同名方法
• super().同名方法(形参1, ……)：调用先继承的那个父类的同名方法

# 定义2个类，它们没有继承关系，是平级的
class SmallDog(object):
    def eat(self):
        print('吃小东西')

# 再定义一个类
class BigDog(object):
    def eat(self):
        print('啃大骨头')

# 定义一个子类，多继承于上面2个父类
class SuperDog(SmallDog, BigDog):
    def eat(self):
        print('吃蟠桃')
        print('='*20)

        # 子类调用父类同名方法：
        # 1. 父类名.同名方法(self, 形参1, ……)
        SmallDog.eat(self)  # 调用SmallDog的eat()
        print('=' * 20)

        # 2. super(类名, self).同名方法(形参1, ……)：调用继承顺序中类名的下一个类的同名方法
        # 继承顺序中，SmallDog的下一个类是BigDog，所以调用BigDog的eat()
        super(SmallDog, self).eat()
        print('=' * 20)

        # 3. super().同名方法(形参1, ……) ：调用先继承父类的同名方法
        # 所以调用的是SmallDog的eat()方法 
        super().eat()

# 定义子类对象，调用方法
sd = SuperDog()
sd.eat()

运行结果：

吃蟠桃
====================
吃小东西
====================
啃大骨头
====================
吃小东西

8.8.3 私有和继承

父类中的 私有方法和私有属性 子类 不能继承

如果父类的公有方法中有调用父类的私有方法和私有属性，那么子类可以通过调用父类公有方法 间接的访问父类的私有方法、私有属性。

# 定义一个父类， Animal
class Animal(object):
    # 添加一个type属性
    def __init__(self):
        self.__type = '动物'  # 私有

    def __leave(self):  # 私有
        print('休产假3个月')

    # 通过公有方法，间接访问私有元素
    def use_private(self):
        print(self.__type)
        self.__leave()

# 定义一个子类
class Dog(Animal):
    def test(self):
        # print(self.__type) # err，私有不能直接继承使用
        # self.__leave() # err，私有不能直接继承使用
        pass

# 创建子类对象
dog1 = Dog()
dog1.use_private()

 8.8.4 多态

多态：多种形态，调用同一个函数，传入不同参数就会有不同表现

实现多态的步骤:

• 实现继承关系
• 子类重写父类方法
• 通过对象调用该方法

 实例化子类：

dog=Dog()

cat = Cat()

方法 make_noise()定义时形参animal的类型是父类类型Animal

并在方法内调用父类子类都有的方法

但在真正调用方法时传入的实参是子类的实例对象

make_noise(dog)

make_noise(cat)

"""
1. 多态：多种形态，调用同一个函数，不同表现

2. 实现多态的步骤:
  1. 实现继承关系
  2. 子类重写父类方法
  3. 通过对象调用该方法

"""

# 定义一个父类， Animal
class Animal(object):
    def eat(self):
        print('吃东西')

# 定义一个子类Dog，继承于Animal
class Dog(Animal):
    def eat(self):
        """重写父类方法"""
        print('啃骨头')

# 定义一个子类Cat，继承于Animal
class Cat(Animal):
    def eat(self):
        """重写父类方法"""
        print('吃小鱼')

# 定义一个函数，用于测试多态
def func(temp: Animal):
    temp.eat()

# 创建子类对象
d = Dog()
c = Cat()

# 调用同一个函数，不同表现
# 传递d参数，调用Dog的eat()
# 传递c参数，调用Cat的eat()
func(d)  # 啃骨头
func(c)  # 吃小鱼

8.8.5 抽象基类

如果上面多态例子中父类不需要实例化，就可以直接把父类定义为抽象基类，把子类重写的方法定义成抽象实例方法（abstractmethod），不同子类根据自己的需求去实现这个方法即可。

抽象基类有两个特点：

　　　　1.规定继承类必须实现抽象基类指定的方法

　　　　2.抽象基类无法实例化

在Python的abc模块中，ABC和ABCMeta是两个关键类，用于创建抽象基类（Abstract Base Class）。

ABC（Abstract Base Class）：

1. ABC是一个可继承的基类，用于定义抽象基类。
2. 当一个类继承自ABC时，可以通过使用@abstractmethod装饰器来定义抽象方法。
3. 抽象方法是一种声明，用于指示子类必须实现这些方法。
4. 子类必须实现抽象基类中定义的所有抽象方法，否则在实例化时会引发TypeError异常。
5. ABC类本身并不强制要求实现任何方法或属性，它主要用于定义抽象方法和作为抽象基类的标识。

ABCMeta（Abstract Base Class MetaClass）：

1. ABCMeta是一个元类（metaclass），用于定义抽象基类的元信息。
2. 通过将ABCMeta作为元类，可以在类定义阶段对类进行检查和修饰。
3. ABCMeta通过在类定义中使用metaclass参数或在类的基类列表中包含ABCMeta来指定。
4. ABCMeta元类提供了一些功能，例如检查子类是否实现了抽象方法、注册具体实现类等。

总结：

- ABC是一个可继承的基类，用于定义抽象基类，并通过装饰器@abstractmethod定义抽象方法。

- ABCMeta是一个元类，用于定义抽象基类的元信息，并提供了一些功能来检查和修饰类定义。

在实际使用中，通常通过继承ABC类来创建抽象基类，并在类定义中指定ABCMeta作为元类来实现抽象基类的功能。

from abc import ABC, abstractmethod, abstractproperty, abstractclassmethod, abstractstaticmethod
# 定义抽象基类需要导入abc包
class Human(ABC):
    @abstractmethod  # 规定子类必须有名为introduce的实例方法
    def introduce(self):
        pass
    
    @abstractproperty  # 规定子类必须有名为country的装饰器方法
    def country(self):
        pass
    
    @abstractclassmethod  # 规定子类必须有名为gender的类方法
    def gender(cls):
        pass
    @abstractstaticmethod  # 规定子类必须有名为hello的静态方法
    def hello():
        pass

class Person(Human):
    __country = "China"
    def __init__(self, name, age):
        self.name = name
        self.age = age

    # 定义名为为introduce的实例方法
    def introduce(self):
        return "I'm {}, {}.".format(self.name, self.age)
    
    # 定义名为country的装饰器方法
    @property
    def country(self):
        return Person.__country
    
    # 定义名为gender的类方法
    @classmethod
    def gender(cls):
        return "female"
    
    # 定义名为hello的静态方法
    @staticmethod
    def hello():
        print("What the hell?")

person = Person("John", 22)
print(person.introduce())
print(person.country)
print(Person.gender())
person.hello()

# 输出内容如下：
# I'm John, 22.
# China
# female
# What the hell?

8.8.6 类型注解

1、变量的类型注解

可以在变量定义语句中进行类型注解，也可以把类型注解放在注释中

 

 

 开发工具只会提示可能有错误  

 

2、函数和方法的类型注解

3、Union联合类型 注解

 

8.8.7 属性类

指的是这个类中只有属性定义，没有方法，如下：

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

对于这种类型的类，可以使用namedtuple()定义

namedtuple("类名", ["属性名1", "属性名2", ...])

Point = namedtuple("Test", ["x", "y","z"])
# 定义一个名称为Test的类。类只有属性：x   y   z

# 实例化  对应属性名代码会比较清晰
p1 = Point(x=1, y=2, z=3)
p2 = Point(x=1, y=2, z=4)

from collections import namedtuple
Point = namedtuple("Test", ["x", "y","z"])
# 定义一个名称为Test的类。类只有属性：x   y   z


# 实例化  对应属性名代码会比较清晰
p1 = Point(x=1, y=2, z=3)
p2 = Point(x=1, y=2, z=4)


# 实例化后实例对象的属性值就不能修改了，不然会报错
p1.x = 10  # AttributeError: can't set attribute


print(p1 == p2) # False
print(p1 < p2) # True
# 比较的是值不是地址
print(p1) # test(x=1, y=2, z=3)
print(type(p1))  # 
print(p1.x)  # 1

# 使用id()获取对象的内存地址
print(id(p1))  # 31257648
print(id(p2))  # 35713976

补充：

1、python内置数据类型也可以使用继承

# python内置数据类型也可以使用继承
# 类Text继承了str
class Text(str):
    def duplicate(self):
        # 字符串拼接的方法可以继承
        return self + self

str1 = Text()
print(str1)  # 空字符串
str2 = Text("hello") 
print(str2) # 传参后就是hello
print(str2.duplicate())  # hellohello


# 类TrackableList继承了list
class TrackableList(list):
    def append(self, item):
        print("在列表最后追加元素item")
        super().append(item)

list1 = TrackableList()
print(list1) # 空列表 []
list2 = TrackableList([1, 2])
list2.append("hello")
print(list2)  # [1, 2, 'hello']

2、对象的比较 判断的是对象的内存地址是否一致

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y


p1 = Point(1, 2)
p2 = Point(1, 2)
print(p1 == p2) # False
# 使用id()获取对象的内存地址
print(id(p1))  # 31257648
print(id(p2))  # 35713976

如果想改变对象比较的默认逻辑，可以自行实现对应的magic method

 A Guide to Python's Magic Methods « rafekettler.com

九、模块和包

9.1 模块

模块：modules

一个文件就是一个模块

9.1.1 模块的导入和使用

 

 

 9.1.2 自定义模块

 9.2 包

包：package

包就是文件夹，一个文件夹内可以有多个文件，一个包中可以有多个模块

9.2.1 自定义包

 

 

9.2.2 第三方包

 

 

9.3 练习

9.3.1 疫情确诊人数走势折线图

需要用到的第三方包 pyecharts

1、json数据格式

 

import json

data = [{"name": "小明", "age": 11}, {"name": "小红", "age": 13}, {"name": "小亮", "age": 15}]
json_str = json.dumps(data, ensure_ascii=False)
# 加上ensure_ascii=False  可以让给中文正常展示，不被转换
print(type(json_str))  # 
print(json_str)
# [{"name": "小明", "age": 11}, {"name": "小红", "age": 13}, {"name": "小亮", "age": 15}]

newdata = json.loads(json_str)
print(type(newdata))  # 
print(newdata)
# [{'name': '小明', 'age': 11}, {'name': '小红', 'age': 13}, {'name': '小亮', 'age': 15}]

2、pyecharts模块介绍

pyecharts 官网：

画廊：

3、pyecharts快速入门

# 基础折线图

# 导包 导入Line功能 构建折线图对象
from pyecharts.charts import Line


# 得到折线图对象
line = Line()
# 添加x轴数据
line.add_xaxis(["中国", "美国", "英国"])

# 添加y轴数据
line.add_yaxis("GDP", [30, 20, 10])

# 生成图表
line.render()

运行代码在同层目录会得到一个render.html，使用浏览器 打开就会得到如下折线图

全局配置选项:

set_global_opts方法：标题配置项TitleOpts、 图例配置项LegendOpts、工具箱配置项ToolboxOpts、视觉映射配置项VisualMapOpts、提示框配置项ToltipOpts、区域缩放配置项DatazoomOpts

 

# 基础折线图

# 导包 导入Line功能 构建折线图对象
from pyecharts.charts import Line


# 得到折线图对象
from pyecharts.options import TitleOpts, LegendOpts, ToolboxOpts, VisualMapOpts, TooltipOpts

line = Line()

# 设置全局配置项
# 使用set_global_opts方法：
# 标题配置项TitleOpts、
# 图例配置项LegendOpts、
# 工具箱配置项ToolboxOpts、
# 视觉映射配置项VisualMapOpts、
# 提示框配置项TooltipOpts、
# 区域缩放配置项DatazoomOpts
line.set_global_opts(
    title_opts=TitleOpts(title="GDP展示", pos_left="center", pos_bottom="1%"),
    legend_opts=LegendOpts(is_show=True),
    toolbox_opts=ToolboxOpts(is_show=True),
    visualmap_opts=VisualMapOpts(is_show=True),
    tooltip_opts=TooltipOpts(is_show=True),
)

# 添加x轴数据
line.add_xaxis(["中国", "美国", "英国"])

# 添加y轴数据
line.add_yaxis("GDP", [30, 20, 10])

# 生成图表
line.render()

4、数据处理

JSON在线 | JSON解析格式化—SO JSON在线工具

美国.txt

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日本.txt

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印度.txt

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5、创建折线图

import json
from pyecharts.charts import Line
from pyecharts.options import TitleOpts, LabelOpts

# 处理数据
f_us = open("..\折线图数据\美国.txt", "r", encoding="utf-8")
us_data =f_us.read()

f_jp = open("..\折线图数据\日本.txt", "r", encoding="utf-8")
jp_data =f_jp.read()

f_in = open("..\折线图数据\印度.txt", "r", encoding="utf-8")
in_data =f_in.read()

# 去掉无用的开头
us_data = us_data.replace("jsonp_1629344292311_69436(", "")
jp_data = jp_data.replace("jsonp_1629350871167_29498(", "")
in_data = in_data.replace("jsonp_1629350745930_63180(", "")

# 去掉无用的尾部 );
us_data = us_data[:-2]
jp_data = jp_data[:-2]
in_data = in_data[:-2]

# 把json类型转换为 python数据类型
us_data = json.loads(us_data)
jp_data = json.loads(jp_data)
in_data = json.loads(in_data)


us_trend_data = us_data["data"][0]["trend"]
jp_trend_data = jp_data["data"][0]["trend"]
in_trend_data = in_data["data"][0]["trend"]


# 获取日期数据，作为x轴数据
us_x_data = us_trend_data["updateDate"]
jp_x_data = jp_trend_data["updateDate"]
in_x_data = in_trend_data["updateDate"]


us_x_data = us_x_data[:314]
jp_x_data = jp_x_data[:314]
in_x_data = in_x_data[:314]


# 获取 确诊数据 ，作为y轴数据
us_y_data = us_trend_data["list"][0]["data"]
jp_y_data = jp_trend_data["list"][0]["data"]
in_y_data = in_trend_data["list"][0]["data"]

us_y_data = us_y_data[:314]
jp_y_data = jp_y_data[:314]
in_y_data = in_y_data[:314]


# 生成折线图
line = Line()

# 添加x轴数据，x轴数据是一样的，美国  日本 印度 选一个就行
line.add_xaxis(us_x_data)

# 添加y轴数据
# 给y轴数据设置series_name  当鼠标移到对应折线上就会显示，容易区分折线是哪个国家的
# 为y轴元素设置属性  label_opts=LabelOpts(is_show=False)  这样在折线图上就不会展示数字了，因为折线之间的数字会互相覆盖，显得很乱
line.add_yaxis("美国确诊人数", us_y_data, label_opts=LabelOpts(is_show=False))
line.add_yaxis("日本确诊人数", jp_y_data, label_opts=LabelOpts(is_show=False))
line.add_yaxis("印度确诊人数", in_y_data, label_opts=LabelOpts(is_show=False))

# 设置全局配置项
line.set_global_opts(
    title_opts=TitleOpts(title="2020年美日印肺炎确诊人数对比折线图", pos_left="center", pos_bottom="1%")
)

# 生成render 图表
line.render()

# 关闭文件数据连接
f_us.close()
f_jp.close()
f_in.close()

给y轴数据设置series_name  当鼠标移到对应折线上就会显示，容易区分折线是哪个国家的
为y轴元素设置属性  label_opts=LabelOpts(is_show=False)  这样在折线图上就不会展示数字了，因为折线之间的数字会互相覆盖，显得很乱

9.3.2 疫情地图绘制

1、基础地图使用

# 基础地图绘制

from pyecharts.charts import Map
from pyecharts.options import VisualMapOpts, TitleOpts, TooltipOpts, LabelOpts

# 准备地图对象
map = Map()

# 准备数据
data = [
    ("北京市", 99),
    ("上海市", 199),
    ("湖南省", 299),
    ("台湾省", 199),
    ("广东省", 599)
]

# 添加数据
map.add("测试地图", data, "china")
# 设置全局选项
map.set_global_opts(
    # title_opts=TitleOpts(title="测试地图", pos_left="center", pos_bottom="1%"),
    visualmap_opts=VisualMapOpts(
        is_show=True,
        is_piecewise=True,
        pieces=[
            {"min": 1, "max": 9, "label": "1-9人", "color": "#CCFFFF"},
            {"min": 10, "max": 99, "label": "10-99人", "color": "#FFFF99"},
            {"min": 100, "max": 499, "label": "100-499人", "color": "#FF9966"},
            {"min": 500, "max": 999, "label": "500-999人", "color": "#FF6666"},
            {"min": 1000, "max": 9999, "label": "1000-9999人", "color": "#FF3333"},
            {"min": 10000, "label": "10000以上", "color": "#990033"},
        ]
    )
)

# 绘制地图
map.render()

 色值设置：网页设计常用色彩搭配表 | 网页配色表

2、全国疫情地图

# 绘制全国疫情地图

import json
from pyecharts.charts import Map
from pyecharts.options import VisualMapOpts, TitleOpts

# 1-数据获取
# 打开数据文件
f = open("..\地图数据\疫情.txt", "r", encoding="utf-8")
# 读取文件内容
f_data = f.read()
# 关闭文件连接
f.close()

# 2-数据处理
# 把json数据转换为python的数据类型
data = json.loads(f_data)
#获取全部省份的疫情数据
data = data["areaTree"][0]["children"]
# print(data)

# 定义一个新列表接收处理好的省份和对应省份确诊人数
final_data = []
for element in data:
    name = element["name"]
    if name in ['上海', '天津', '北京', '重庆']:
            name = name + "市"
    elif name in ["香港", "澳门"]:
        name = name + "特别行政区"
    elif name in ["西藏", "内蒙古"]:
        name = name + "自治区"
    elif name == "宁夏":
        name = name + "回族自治区"
    elif name == "广西":
        name = name + "壮族自治区"
    elif name == "新疆":
        name = name + "维吾尔自治区"
    else:
        name = name + "省"
    confirm = element["total"]["confirm"]
    final_data.append((name, confirm))

# print(final_data)


# 3-地图绘制
# 创建地图对象
map = Map()

# 数据添加
map.add("总确诊人数", final_data, "china")

# 全局配置项设置
map.set_global_opts(
    title_opts=TitleOpts(title="全国各省确诊人数汇总", pos_left="center", pos_bottom="1%"),
    visualmap_opts=VisualMapOpts(
        is_show=True,
        is_piecewise=True,
        pieces=[
            {"min": 1, "max": 9, "label": "1-9人", "color": "#CCFFFF"},
            {"min": 10, "max": 99, "label": "10-99人", "color": "#FFFF99"},
            {"min": 100, "max": 499, "label": "100-499人", "color": "#FF9966"},
            {"min": 500, "max": 999, "label": "500-999人", "color": "#FF6666"},
            {"min": 1000, "max": 9999, "label": "1000-9999人", "color": "#FF3333"},
            {"min": 10000, "label": "10000以上", "color": "#990033"},
        ]
    )
)

# 绘制地图
map.render("全国疫情地图.html")

3、省级疫情地图

# 河南省疫情地图

import json
from pyecharts.charts import Map
from pyecharts.options import VisualMapOpts, TitleOpts

# 获取数据
f = open("..\地图数据\疫情.txt", "r", encoding="utf-8")
f_data = f.read()
f.close()

# 处理数据
# 把json转换为python数据类型
f_data = json.loads(f_data)

data = f_data["areaTree"][0]["children"][3]["children"]
# print(data)
# 创建一个列表接收河南省下的城市和对应确诊人数
final_data = []

for element in data:
    city = element["name"]
    if city == "境外输入":
        continue
    city = city + "市"
    confirm = element["total"]["confirm"]
    final_data.append((city, confirm))

# print(final_data)



# 绘制地图
# 创建地图对象
map = Map()
# 添加数据
map.add("各市确诊人数", final_data, "河南")

# 全局配置项设置
map.set_global_opts(
    title_opts=TitleOpts(title="河南省疫情地图", pos_left="center", pos_bottom="1%"),
    visualmap_opts=VisualMapOpts(
        is_show=True,
        is_piecewise=True,
        pieces=[
            {"min": 1, "max": 9, "label": "1-9人", "color": "#CCFFFF"},
            {"min": 10, "max": 99, "label": "10-99人", "color": "#FFFF99"},
            {"min": 100, "max": 499, "label": "100-499人", "color": "#FF9966"},
            {"min": 500, "max": 999, "label": "500-999人", "color": "#FF6666"},
            {"min": 1000, "max": 9999, "label": "1000-9999人", "color": "#FF3333"},
            {"min": 10000, "label": "10000以上", "color": "#990033"},
        ]
    )
)

# 绘制地图
map.render("河南省疫情地图.html")

9.3.3 柱状图绘制

1、基础柱状图绘制

 给y轴数据设置series_name  当鼠标移到对应折线上就会显示，容易区分折线是哪个国家的
为y轴元素设置属性  label_opts=LabelOpts(is_show=False)  这样在折线图上就不会展示数字了，因为折线之间的数字会互相覆盖，显得很乱

# 创建柱状图对象
bar = Bar()

# 数据准备
# 添加x轴数据
bar.add_xaxis(["中国", "美国", "英国"])
# 添加y轴数据
bar.add_yaxis("GDP",[30, 20, 10])

# x轴  y轴反转
bar.reversal_axis()

# 绘图
bar.render("基础柱状图.html")

x轴  y轴反转后： 

from pyecharts.charts import Bar

# 创建柱状图对象
from pyecharts.options import LabelOpts

bar = Bar()

# 数据准备
# 添加x轴数据
bar.add_xaxis(["中国", "美国", "英国"])
# 添加y轴数据
# 给y轴数据添加 label_opts=LabelOpts(position="right")  可以让数据放在对应数据柱的右侧
bar.add_yaxis("GDP",[30, 20, 10], label_opts=LabelOpts(position="right"))

# x轴  y轴反转
bar.reversal_axis()

# 绘图
bar.render("基础柱状图.html")

 给y轴数据添加属性label_opts=LabelOpts(position="right) 可以让数据放在对应数据柱的右侧

2、基础时间线柱状图

 这样随着x轴时间线的移动，y轴的柱状图就会切换

时间线柱状图的 颜色设置：

 

#  导入包
from pyecharts.charts import Bar, Timeline
from pyecharts.globals import ThemeType
from pyecharts.options import *

# 创建柱状图对象
bar1 = Bar()
bar1.add_xaxis(["中国", "美国", "英国"])
bar1.add_yaxis("GDP", [30, 20, 10], label_opts=LabelOpts(position="right"))
bar1.reversal_axis()

bar2 = Bar()
bar2.add_xaxis(["中国", "美国", "英国"])
bar2.add_yaxis("GDP", [50, 40, 30], label_opts=LabelOpts(position="right"))
bar2.reversal_axis()

bar3 = Bar()
bar3.add_xaxis(["中国", "美国", "英国"])
bar3.add_yaxis("GDP", [55, 45, 35], label_opts=LabelOpts(position="right"))
bar3.reversal_axis()

# 创建时间线对象  并设置时间线柱状图的主题颜色类型
timeline = Timeline({"theme": ThemeType.LIGHT})

# 给时间线对象 添加bar柱状图
timeline.add(bar1, "2021年GDP")
timeline.add(bar2, "2022年GDP")
timeline.add(bar3, "2023年GDP")

# 设置时间线自动播放
timeline.add_schema(
    play_interval=1000,   # 自动播放的时间间隔，单位毫秒
    is_timeline_show=True,   # 是否在自动播放时显示时间线
    is_auto_play=True,     # 是否自动播放
    is_loop_play=True     # 是否循环自动播放
)



# 绘制 时间线柱状图
timeline.render("基础时间线柱状图.html")

3、GDP动态柱状图绘制

 

 需求：

import json
from pyecharts.charts import Bar, Timeline
from pyecharts.globals import ThemeType
from pyecharts.options import *

# 获取数据
f = open("..\\动态柱状图数据\\1960-2019全球GDP数据.csv", "r", encoding="gb2312")
f_data = f.readlines()  # 得到一个列表
f.close()

# 处理数据
# 创建一个字典存储每年全球各国的GDP
# {
#   1960:[
#           [”美国”, ”155亿“],
#           [“中国“, ”146亿“],
#           ....
#       ],
#   1961:[
#           [”美国”, ”155亿“],
#           [“中国“, ”146亿“],
#           ....
#       ]
#   ...
# }
dict_data = {}
# 删除第一行无用数据
f_data.pop(0)

for element in f_data:
    # 去除每个字符串前后的空格和换行符
    new_element= element.strip()
    # 把字符串按照逗号切割到列表内
    element_list = new_element.split(",")
    year = (element_list[0])   # 年份
    country = element_list[1]     # 国家
    gdp_num = float(element_list[2])     # gdp数据  有的gdp使用了科学计数法需要使用float()进行转换为正常的数字
    item = [country, gdp_num]
    try:
        # 如果字典中已经存在某个年份的key  直接追加列表数据
        dict_data[year].append(item)
    except:
        # 如果字典中没有这个年份的key  则新增一个空列表  之后在追加列表元素
        dict_data[year] = []
        dict_data[year].append(item)


# 创建一个时间线对象  并添加颜色主题类型
timeline = Timeline({"theme": ThemeType.LIGHT})

# 按年份排序
years_list = sorted(dict_data.keys())
print(years_list)

for year in years_list:
    # 获取指定年份key对应的value   按照gdp降序排列
    # sort()是直接修改原列表的排序，不会另存新列表
    dict_data[year].sort(key=lambda item: item[1], reverse=True)
    # 获取前8名gdp排名数据
    year_datas = dict_data[year][:8]

    # 获取x  y轴数据list
    x_list = []
    y_list = []
    for item in year_datas:
        country = item[0]
        gdp = item[1]
        x_list.append(country)   # x轴添加数据
        y_list.append(gdp / 100000000)   # y轴添加数据

    # 创建柱状图对象，每一年都有一个柱状图
    bar = Bar()
    x_list.reverse()
    y_list.reverse()
    # 添加柱状图的x  y轴数据
    bar.add_xaxis(x_list)
    # 给y轴数据设置series_name  当鼠标移到对应折线上就会显示，容易区分折线是哪个国家的
    # 给y轴数据添加 label_opts=LabelOpts(position="right")  可以让数据放在对应数据柱的右侧
    # 为y轴元素设置属性  label_opts = LabelOpts(is_show=False)  这样在折线图上就不会展示数字了，因为折线之间的数字会互相覆盖，显得很乱
    bar.add_yaxis(f"{year}年GDP(亿)", y_list, label_opts=LabelOpts(position="right"))
    # x  y 轴反转
    bar.reversal_axis()

    # 设置每一年图表的标题
    bar.set_global_opts(
        title_opts=TitleOpts(title=f"{year}全球GDP前8排名")
    )

    # 给时间线对象添加柱状图
    timeline.add(bar, year)



# 设置时间线自动播放
timeline.add_schema(
    play_interval=1000,   # 自动播放的时间间隔，单位毫秒
    is_auto_play=True,    # 是否自动播放
    is_loop_play=True,    # 是否循环自动播放
    is_timeline_show=True   # 是否在自动播放时显示时间线
)

# 绘制时间线柱状图
timeline.render("1960-2019全球GDP前8排名.html")

十、python的标准库

10.1 pathlib

10.1.1 创建目录对象

pathlib — Object-oriented filesystem paths — Python 3.11.4 documentation

from pathlib import Path


path = Path(r"D:\个人学习\技能专项学习\Mosh老师的python课程\笔记\test.py")  # windows系统

path = Path("/usr/local/bin") # linux 或者mac系统

# 获取当前文件夹的路径对象
path = Path() 

# 把路径对象组合在一起
path = Path() /Path("ecommerce")
path = Path() / "ecommerce" / "__init__.py"

# 获取当前用户的主目录
path = Path.home()

path = Path("ecommerce/__init__.py")

10.1.2 目录对象的相关操作

path = Path("ecommerce/__init__.py")

#  查看文件或目录是否存在
print(path.exists())  # True

# 是文件吗
print(path.is_file())  # True
# 是目录吗
print(path.is_dir()) # False

# 获取文件名（包括文件扩展名）
print(path.name) # __init__.py
from pathlib import Path

# 获取文件名（不包括文件扩展名）
print(path.stem)  # __init__

# 获取文件扩展名
print(path.suffix)  # .py

# 获取父级目录
print(path.parent) # ecommerce

# 基于现有传递创建新的路径对象，仅修改文件名和文件扩展名，但是这个文件并不是真实存在
path = path.with_name("file.txt")
print(path) # ecommerce\file.txt
# 获取绝对路径
print(path.absolute()) # D:\个人学习\技能专项学习\Mosh老师的python课程\笔记\ecommerce\file.txt

# 基于现有传递创建新的路径对象，仅修改文件扩展名, 这个文件也不是真实存在的
path = path.with_suffix(".java")
print(path.absolute()) # D:\个人学习\技能专项学习\Mosh老师的python课程\笔记\ecommerce\file.java

10.1.3 目录对象的使用

from pathlib import Path

path = Path("ecommerce1")
print(path.exists()) # False

path.mkdir()
print(path.exists()) # True
print(path) # ecommerce1

# 需要注意的是：如果进行path.rename("ecommerce2")后没有把结果重新赋值给path
# 则path还是ecommerce1
# 但是目录对象的名字已经替换为ecommerce2了
# 这个时候print(path.exists()) 会得到False 目录对象ecommerce1已经不存在了
# 进行path.rmdir()就会报错  因为目录对象ecommerce1已经不存在了，现在存在的是ecommerce2
# 所以改名后需要重新赋值给path
path = path.rename("ecommerce2")

print(path.exists())  # True
print(path)  # ecommerce2
path.rmdir()
print(path.exists())  # False

from pathlib import Path

path = Path("ecommerce\__init__.py")

# 判断目录对象是否发存在
print(path.exists())

# 如果文件不存在就创建 path.touch()
# 如果目录对象是文件夹就用path.mkdir()
path.touch()
print(path.exists())

# 删除文件  path.unlink()
# 如果目录对象是文件夹就用path.rmdir() 但前提是这个文件夹是空的
path.unlink()
print(path.exists())

from pathlib import Path

path = Path("ecommerce\__init__.py")

# path.stat()用于在给定的路径上执行一个系统 stat 的调用，获取堆栈结果
print(path.stat())
# os.stat_result(st_mode=33206, st_ino=1125899906873216, st_dev=2282291619, st_nlink=1, st_uid=0, st_gid=0, st_size=0, st_atime=1687363247, st_mtime=1687363247, st_ctime=1687363247)

stat 结构的成员列表：

• st_mode− inode保护模式。
• st_ino- inode 节点编号。
• st_dev- inode驻留设备。
• st_nlink- 硬链接的数量。
• st_uid− 所有者的用户 ID。
• st_gid− 所有者的组 ID。
• st_size- 文件的大小，以字节为单位。
• st_atime− 最近访问的时间。
• st_mtime− 最近一次内容修改的时间。
• st_ctime− 最近元数据更改的时间。

 

目录对象可以作为一个迭代器，分别处理

1、path.iterdir()

使用path.iterdir()遍历此目录中的文件，只涉及第一层子目录。

from pathlib import Path

path = Path("ecommerce")

# 使用path.iterdir()遍历此目录中的文件，只涉及第一层子目录。
# 注意只在ecommerce子目录迭代，没有继续往customer和shopping目录里面递归
print(path.iterdir())
# 

for p in path.iterdir():
    print(p)
# ecommerce\customer
# ecommerce\shopping
# ecommerce\__init__.py
# ecommerce\__pycache__



paths = [p for p in path.iterdir()]
print(paths)
# [WindowsPath('ecommerce/customer'), WindowsPath('ecommerce/shopping'), WindowsPath('ecommerce/__init__.py'), WindowsPath('ecommerce/__pycache__')]

paths = [p for p in path.iterdir() if p.is_dir()]
print(paths)
# [WindowsPath('ecommerce/customer'), WindowsPath('ecommerce/shopping'), WindowsPath('ecommerce/__pycache__')]

2、path.glob("匹配模式")

可以使用path.glob("匹配模式")在path子树上迭代，并生成与给定的相对模式匹配的所有现有文件（任何类型的文件，包括目录）

# path.glob("*.py")这个匹配模式只在在path的第一层子目录迭代，并生成与给定的相对模式匹配的所有现有文件（任何类型的文件，包括目录）
# 注意只在ecommerce子目录迭代，没有继续往customer和shopping目录里面递归
print(path.glob("*.py"))
# 

for p in path.glob("*.py"):
    print(p)
# ecommerce\__init__.py


py_files = [p for p in path.glob("*.py")]
print(py_files)
# [ WindowsPath('ecommerce/__init__.py')]

# path.glob("**/*.py")  这个匹配模式就可以走到下一层目录了，进入customer和shopping目录里面进行匹配了
py_files = [p for p in path.glob("**/*.py")]
print(py_files)
# [WindowsPath('ecommerce/__init__.py'), WindowsPath('ecommerce/customer/contact.py'), WindowsPath('ecommerce/customer/__init__.py'), WindowsPath('ecommerce/shopping/sales.py'), WindowsPath('ecommerce/shopping/__init__.py')]

3、path.rglob("匹配模式")

path.rglob("*匹配模式") 递归地生成与给定的相对模式匹配的所有现有文件（任何类型的文件，包括目录），目录有几层就会深入几层去匹配。

# 使用path.rglob("*.py") 递归地生成与给定的相对模式匹配的所有现有文件（任何类型的文件，包括目录），该子树中的任何位置
# ecommerce目录有几层就会深入几层去匹配.py文件
print(path.rglob("*.py"))
# 

for p in path.rglob("*.py"):
    print(p)
# ecommerce\__init__.py
# ecommerce\customer\contact.py
# ecommerce\customer\__init__.py
# ecommerce\shopping\sales.py
# ecommerce\shopping\__init__.py

py_files = [p for p in path.rglob("*.py")]
print(py_files)
# [WindowsPath('ecommerce/__init__.py'), WindowsPath('ecommerce/customer/contact.py'), WindowsPath('ecommerce/customer/__init__.py'), WindowsPath('ecommerce/shopping/sales.py'), WindowsPath('ecommerce/shopping/__init__.py')]