#什么叫序列化模块:
# # 将原本的字典,列表等内容转换成一个字符串的过程就叫做序列化
# #序列化的目的:
# #1.以某种存储形式使自定义对象持久化
# #2.将对象从一个地方传递到另外一个地方
# #3.使程序更具维护性.
# # str>>>> 反序列化>>>>数据结构
# # 数据结构>>>>序列化>>>>>>>str
#
# # json
# # json模块提供了四个功能:dumps dump loads load
# import json
# # dumps 和 loads
# dic = {"k1":"v1","k2":"v2","k3":"v3"}
# str_dic = json.dumps(dic) # 序列化:将一个字典转换成一个字符串
# print(type(str_dic),str_dic) # <class 'str'> {"k1": "v1", "k2": "v2", "k3": "v3"}
# # 注意: json转换完的字符串类型的字典中的字符串是由""(双标点)表示
#
# dic2 = json.loads(str_dic) # 反序列化:将一个字符串格式的字典转换成一个字典
# print(type(dic2),dic2) # <class 'dict'> {'k1': 'v1', 'k2': 'v2', 'k3': 'v3'}
# #注意: 用json的loads功能处理的字符串类型的字典中的字符串必须由""表示
#
# list_dic = [1,['a','b','c'],3,{'k1':'v1','k2':'v2'}]
# str_dic = json.dumps(list_dic) # 可以处理嵌套的数据类型
# print(type(str_dic),str_dic) # <class 'str'> [1, ["a", "b", "c"], 3, {"k1": "v1", "k2": "v2"}]
# list_dic2 = json.loads(str_dic)
# print(type(list_dic2),list_dic2) # <class 'list'> [1, ['a', 'b', 'c'], 3, {'k1': 'v1', 'k2': 'v2'}]
#
#
# # dump 和load
#
# f = open('json_file','w')
# dic4 = {'k1':'v1','k2':'v2','k3':'v3'}
# json.dump(dic4,f) # dump方法接收一个文件句柄,直接将字典转换成hson字符串后写入文件
# f.close() # 关闭文件
#
# f = open('json_file')
# dic5 = json.load(f) # load方法接收一个文件句柄,直接将文件中的字符串转换成数据结构返回
# f.close()
# print(type(dic5),dic5) # <class 'list'> [1, ['a', 'b', 'c'], 3, {'k1': 'v1', 'k2': 'v2'}]
#import json
# dumps和dump中ensure_ascii关键字
# f = open('file','w',encoding="utf-8")
# json.dump({'国籍':'中国'},f)
# ret = json.dumps({'国际':'中国'})
# f.write(ret+"\n") # 写入之后里面是字节码
# f.close()
#
# json.dump({'国籍':'中国'},f,ensure_ascii=False)
# ret1 = json.dumps({'国籍':'中国'},ensure_ascii=False)
# f.write(ret1+"\n") # 写入之后显示中文
# f.close()
#json的格式化输出
# import json
# data = {'username':['李华','二愣子'],'sex':'male','age':16}
# json_dic2 = json.dumps(data,sort_keys=True,indent=2,separators=(',',':'),ensure_ascii=False)
# print(json_dic2) # 为了用户方便看,存入文件浪费内存
# 结果:
#{
# "age":16,
# "sex":"male",
# "username":[
# "李华",
# "二愣子"
# ]
# }
#json用法写的一些注意事项
#import json
#json格式的限制1,json格式的key必须是字符串数据类型
#json格式中的字符串只能是""(双引号)
#如果是数字为key,那么dump之后会强行转成字符串数据类型
# dic = {1:2,3:4}
# str_dic = json.dumps(dic)
# print(type(str_dic),str_dic) # <class 'str'> {"1": 2, "3": 4}
# new_dic = json.loads(str_dic)
# print(type(new_dic),new_dic) # <class 'dict'> {'1': 2, '3': 4}
# json是否支持元组,对元组做value的字典会把元组强制转换成列表
# dic = {'abc':(1,2,3)}
# str_dic = json.dumps(dic)
# print(type(str_dic),str_dic) # <class 'str'> {"abc": [1, 2, 3]} 转成列表
# new_dic = json.loads(str_dic)
# print(new_dic) # {'abc': [1, 2, 3]} 转换回去value还是列表
#json是否支持元组做key,不支持
# dic = {(1,2,3):'abc'}
# str_dic = json.dumps(dic) # 报错 键必须是str、int、float、bool或None，而不是tuple
# 对列表的dump
# lst = ["aaa",123,'bbb',4.66]
# with open('json_demo','w') as f:
# json.dump(lst,f)
# with open('json_demo') as f:
# ret = json.load(f)
# print(ret) # ['aaa', 123, 'bbb', 4.66]
#
#能不能多次dump数据到文件里,可以多次dump到文件里但是不能load出来
# dic = {'abc':(1,2,3)}
# lst = ["avc",123,44,34]
# with open('json_demo','w') as f:
# json.dump(lst,f)
# json.dump(dic,f) # 多次dump到文件
# with open('json_demo') as f:
# ret = json.load(f) # 报错
# print(ret)
#
#想dump多个数据进入文件,用dumps
# dic = {'abc':(1,2,3)}
# lst = ['abc',123]
# with open('json_demo','w') as f:
# str_lst = json.dumps(lst)
# str_dic = json.dumps(dic)
# f.write(str_lst+"\n")
# f.write(str_dic+"\n")
#
# with open('json_demo') as f:
# for line in f:
# ret = json.loads(line)
# print(ret) # 全部取出
# 结果:
#['abc', 123]
#{'abc': [1, 2, 3]}
# 中文格式的 ensure_ascii = False
# dic = {'ac':(1,23,3),"国家":"中国"}
# ret = json.dumps(dic,ensure_ascii=False)
# print(ret) # {"ac": [1, 23, 3], "国家": "中国"}
#
# dic_new = json.loads(ret)
# print(dic_new) # {'ac': [1, 23, 3], '国家': '中国'}
# 集合set不能被dump和dumps
# pickle模块的四个功能:dumps,dump(序列化,存),loads(反序列化,读),load
# pickle可以将python中任意的数据类型序列化
#dump的结果是bytes,dump用的f文件句柄需要以'wb'的形式打开,load所用的f是'rb'模式读取
#几乎支持所有数据类的序列化
#对于对象的序列化需要这个对象对象的类在内存中
#对于多次dump\load的操作做了良好的处理
#import pickle
# dic = {1:(1,2,3),('a','b'):4}
# pic_dic = pickle.dumps(dic)
# print(type(pic_dic),pic_dic) # <class 'bytes'> b'\x80\x03}q\x00(K\x01K\ bytse类型
#
# new_dic = pickle.loads(pic_dic)
# print(new_dic) # {1: (1, 2, 3), ('a', 'b'): 4}
#pickle支持几乎所有对象的
# class Student:
# def __init__(self,name,age):
# self.name = name
# self.age = age
# alex = Student("alex",40)
# ret = pickle.dumps(alex)
# print(ret) # 字节
#
# pic_ret = pickle.loads(ret)
# print(pic_ret) # <__main__.Student object at 0x0000000002696EB8>
# print(pic_ret.name) #alex
# print(pic_ret.age) # 40
import pickle
# class Student:
# def __init__(self,name,age):
# self.name = name
# self.age = age
# alex = Student("alex",80)
# with open('pickle_demo','wb') as f:
# pickle.dump(alex,f)
# with open('pickle_demo',"rb") as f:
# ret = pickle.load(f)
# print(ret) # <__main__.Student object at 0x00000000028A6358>
# print(ret.name)
#
# with open('pickle_demo','wb') as f:
# pickle.dump({'k1':'v1'},f)
# pickle.dump({'k2':'v2'},f)
#
# with open('pickle_demo','rb') as f: # 不能用for循环,不知道有多少文件
# while 1:
# try:
# print(pickle.load(f))
# except EOFError: # 异常捕获
# break
#结果
# {'k1': 'v1'}
# {'k2': 'v2'}
#shelve 序列化工具只有一个open方法
import shelve
f = shelve.open('shelve_demo')
f['key'] = {'k1':(1,2,3),'k2':'v2'}
f.close()
f = shelve.open('shelve_demo')
content = f['key']
f.close()
print(content) # {'k1': (1, 2, 3), 'k2': 'v2'}
#shelve 如果写定的一个文件,改动的比较少,读取比较多,且大部分读取都要
#基于某个key获得某个value
