{}                           字典
[]                           list
string ""                    str
int/float                 int/float
true/false                True/False
null                         None

import jsondic={'name':'alvin','age':23,'sex':'male'}
print(type(dic))#<class 'dict'>

j=json.dumps(dic)
print(type(j))#<class 'str'>
 f=open('序列化对象','w')
f.write(j)  #-------------------等价于json.dump(dic,f)
f.close()
#-----------------------------反序列化<br>
import json
f=open('序列化对象')
data=json.loads(f.read())#  等价于data=json.load(f)

jsontext = """{"users": [{"name": "agon","age": 68},{"name": "alex","age": 57}]
}"""

# res = json.loads(jsontext)
# print(res)

mydic = {"users": [{"name": "agon","age": 68},{"name": "alex","age": 57}]
}
# with open("b.json","wt",encoding="utf-8") as f:
#     f.write(json.dumps(mydic))# with open("b.json", "wt", encoding="utf-8") as f:
#     json.dump(mydic, f)

import pickle
# 用户注册后得到的数据
name = "wwl"
password = "123"
height = 1.8
hobby = ["吃","喝","赌","飘",{1,2,3}]# with open("userdb.txt","wt",encoding="utf-8") as f:
#     text = "|".join([name,password,str(height)])
#     f.write(text)# pickle支持python中所有的数据类型
user = {"name":name,"password":password,"height":height,"hobby":hobby,"test":3}

# with open("userdb.pkl","ab") as f:
#     userbytes = pickle.dumps(user)
#     f.write(userbytes)

# with open("userdb.pkl","rb") as f:
#     userbytes = f.read()
#     user = pickle.loads(userbytes)
#     print(user)
#     print(type(user))

#dump 直接序列化到文件
# with open("userdb.pkl","ab") as f:
#     pickle.dump(user,f)#load 从文件反序列化
with open("userdb.pkl","rb") as f:user = pickle.load(f)print(user)print(pickle.load(f))print(pickle.load(f))print(pickle.load(f))

import shelvef = shelve.open('shelve_test')  # 打开一个文件names = ["alex", "rain", "test"]
info = {'name':'alex','age':22}f["names"] = names  # 持久化列表
f['info_dic'] = infof.close()

import shelved = shelve.open('shelve_test'，,writeback=True)  # 打开一个文件print(d['names'])
print(d['info_dic'])d['names'].append('wwl')d.close()#del d['test'] #还可以删除

<?xml version="1.0"?>
<data><country name="Liechtenstein"><rank updated="yes">2</rank><year>2008</year><gdppc>141100</gdppc><neighbor name="Austria" direction="E"/><neighbor name="Switzerland" direction="W"/></country><country name="Singapore"><rank updated="yes">5</rank><year>2011</year><gdppc>59900</gdppc><neighbor name="Malaysia" direction="N"/></country><country name="Panama"><rank updated="yes">69</rank><year>2011</year><gdppc>13600</gdppc><neighbor name="Costa Rica" direction="W"/><neighbor name="Colombia" direction="E"/></country>
</data>

    <a><b><c></c></b></a>

把朋友的信息写成xml
<studentinfo><张三><age>20</age><gender>man</gender></张三><李四><age>20</age><gender>man</gender></李四>
</studentinfo>

import xml.etree.ElementTree as ElementTree
# 解析d.xml
tree = ElementTree.parse("d.xml")
print(tree)
# 获取根标签
rootTree = tree.getroot()

# 三种获取标签的方式
# 获取所有人的年龄 iter是用于在全文范围获取标签
# for item in rootTree.iter("age"):
#     # 一个标签三个组成部分
#     print(item.tag) # 标签名称
#     print(item.attrib) # 标签的属性
#     print(item.text) # 文本内容# 第二种 从当前标签的子标签中找到一个名称为age的标签  如果有多个 找到的是第一个
# print(rootTree.find("age").attrib)
# 第三种 从当前标签的子标签中找到所有名称为age的标签
# print(rootTree.findall("age"))

# 获取单个属性
stu = rootTree.find("stu")
print(stu.get("age"))
print(stu.get("name"))# 删除子标签
rootTree.remove(stu)# 添加子标签
# 要先创建一个子标签
newTag = ElementTree.Element("这是新标签",{"一个属性":"值"})
rootTree.append(newTag)# 写入文件
tree.write("f.xml",encoding="utf-8")

import xml.etree.ElementTree as ElementTree
tree = ElementTree.parse("f.xml")rootTree = tree.getroot()
users = []
for item in rootTree.iter("stu"):user = item.attribprint(user)gitem = item.find("girlfriend")user["girlfriend"] = gitem.attribusers.append(user)
print(users)