本文目录：
一、pickle模块
二、shelve模块
三、json模块
四、json练习
五、xml模块
六、xml练习
七、configparser模块
一、pickle模块
pickle是一个用来序列化的模块
序列化是什么？
#指的是将内存中的数据结构转换为一种中间格式 并存储到硬盘上
反序列化是什么？
#将硬盘上存储的中间格式数据再还原为内存中的数据结构
为什么要序列化？
就是为了将数据持久存储
之前学过的文件也能持久化存储 但是操作起来非常麻烦
总结：pickle模块主要功能
dump
load
dumps 序列化
loads 序列化
不带s是帮你封装了read和writh
import pickle #用户注册得到的数据 name = "高根" password = "123" height = "1.5" hobby = ["吃","喝","飘"] with open("userdb.txt","wt",encoding="utf-8") as f: text="|".join([name,password,str(height)]) f.write(text) #序列化的过程 user={"name":name,"password":password,"height":height,"hobby":hobby} #支持python中所有格式 with open("userdb.pkl","wb",)as f: userbytes=pickle.dumps(user) f.write(userbytes) #反序列化过程 with open("userdb.pkl","rb")as f: userbytes=f.read() user=pickle.loads(userbytes) print(user) print(type(user)) #dump使用 直接序列化到文件 with open("userdb.pkl","wb")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) #打包成函数（序列化） def myload(filename): with open(filename,"rb") as f: return pickle.load(f)
二、shelve模块
shelve模块也用于序列化
它与pickle不同之处在于 不需要关心文件模式什么的 直接把他当成一个字典来看待
它可用于进行修改
#内存->内置->sys.path import shelve """""" user={"name":"高根"} s = shelve.open("userdb.shv") s["user"]=user s.close() s=shelve.open("userdb.shv",writeback=True) print(s["user"]) s["user"]["age"] = 20 s.close()
三、json模块
"""
pickle和shelve 序列化后的到的数据只能python才能解析
通常企业开发不可能做一个当即程序 都需要联网进行计算机间交互
我么必须要保证这个数据 能够跨平台使用
JSON是什么？ Java script object notation 就是对象表示法
var obj = {“name”：“egon”}
对于我们开发而言，json就是一种通用的数据格式 任何语言都能解析
js中的数据类型 python数据类型的对应关系
{} 字典
[] list
string“” str
int/float int/float
true/false True/False
null None
json的语法规范
最外层通常是一个字典或列表
{} or []
只要你想写个json格式的数据最外层直接写{}
字符串必须是双引号，可以在面套任意多的层次
json模块的核心功能
dump
dumps
load
loads
不带s 封装了write和read功能
"""
import json # 反序列化 with open("a.json","rt",encoding="utf-8")as f: res=json.loads(f.read()) print(type(res)) with open("a.json",encoding="utf-8") as f: print(json.load(f)) # 直接解析字符串的json为python对象 jsontext = """{ "user": [{ "name": "egon", "age": "18" }, { "name": "egon", "age": "18" } ] }""" res=json.loads(jsontext) print(res) mydic={ "user": [{ "name": "egon", "age": "18" }, { "name": "egon", "age": "18" } ] } 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) dic = {"a":"理查德姑妈","b":"杰克船长","c":"神秘客人"} with open("c.json","wt",encoding="utf-8") as f: f.write(json.dumps(dic)) with open("c.json","rt",encoding="utf-8")as f: d = json.loads(f.read()) print(d)
四、json练习
#将下面内容存储到（news.json）文件
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习题：把news.json中的新闻数据提取出来打印控制台
with open("news.json","rt",encoding="utf-8") as f: res = json.load(f) print(type(res)) for dic in res["result"]["data"]: for key in dic: print("%s:%s"%(key,dic[key]))
五、xml模块
"""
XML 可扩展的标记语言
<> </>
也是一种通用的数据格式，也是一种跨平台
学习的重点还是语法格式
一、任何的起始标签都必须有⼀一个结束标签。
<> </>
二、可以采用另一种简化语法，可以在一个标签中同时表示起始和结束标
签。这种语法是在⼤于符号之前紧跟一个斜线（/），XML
解析器会将其翻译成<百度百科词条></百度百科词条>。
例例如<百度百科词条/>。
三、标签必须按合适的顺序进⾏行行嵌套，所以结束标签必须按镜像顺序匹配
起始标签。这好⽐比是将起始和结束标签看作是数学中的左右括号：在没有关闭所有
的内部括号之前，是不不能关闭外⾯面的括号的。
四、所有的特性都必须有值。
五、所有的特性都必须在值的周围加上双引号。
一个标签的组成部分
<tagname 属性名称=“属性值”>文本内容
<></>
单标签写法
</tagename> 属性名称=“属性值”/>
镜像关闭顺序实例：
<a>
<b>
<c>
</c>
</b>
</a>
案例：
<studentinfo>
<张三>
<age>20</age>
<gender>boy</gender>
</张三>
<李四>
<age>21</age>
<gender>gril</gender>
</李四>
</studentinfo>
总结：
xml也是一种中间格式 也属于序列化之一
与json相比较，同样的数据 json会比xml更小， 效率更高
xml 需要根据文档结构 手动解析 而json直接对象
"""
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() #三种获取数据的方式 #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重找到一个名称为age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 #要创建一个子标签出来 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") #ElementTree.pasre
六、xml模块的练习
#f.xml文件
<studentinfo> <stu name="张三" age="20"> <girlfriend name="张三女朋友" age="19"></girlfriend> </stu> <stu name="李四" age="21"> <girlfriend name="李四女朋友" age="19"></girlfriend> </stu> <这是新标签 一个属性="值" /></studentinfo>
#执行文件
import xml.etree.ElementTree as ElementTree tree = ElementTree.parse("f.xml") rootTree = tree.getroot() users=[] for item in rootTree.iter("stu"): user = item.attrib print(user) gitem = item.find("girlfriend") user["girlfriend"] = gitem.attrid users.append(user) print(users)
七、configparser模块
"""
config parser
用于解析配置文件的模块
何为配置文件
包含配置程序信息的文件就称为配置文件
什么样的数据应作为配置信息
需要改 但是不经常改的信息 例如数据文件的路径 DB_PATH
config parser
用于解析配置文件的模块
何为配置文件
包含配置程序信息的文件就称为配置文件
什么样的数据应作为配置信息
需要改 但是不经常改的信息 例如数据文件的路径 DB_PATH
配置文件中 只有两种内容
一种是section 分区
一种是option 选项 就是一个key=value形式
一种是section 分区
一种是option 选项 就是一个key=value形式
我们用的最多的就是get功能 用来从配置文件获取一个配置选项
练习:
做一个登录 首先查看配置文件 是否有包含 用户名和密码 如果有直接登录 如果没有就进行输入用户名密码登录
登录成功后 询问是否要保存密码 如果是 写入配置文件
做一个登录 首先查看配置文件 是否有包含 用户名和密码 如果有直接登录 如果没有就进行输入用户名密码登录
登录成功后 询问是否要保存密码 如果是 写入配置文件
"""
import configparser # 创建一个解析器 config = configparser.ConfigParser() # 读取并解析test.cfg config.read("test.cfg",encoding="utf-8") # 获取需要的信息 # 获取所有分区 print(config.sections()) # 获取所有选项 print(config.options("user")) # 获取某个选项的值 print(config.get("path","DB_PATH")) print(type(config.get("user","age"))) # # # get返回的都是字符串类型 如果需要转换类型 直接使用get+对应的类型(bool int float) print(type(config.getint("user","age"))) print(type(config.get("user","age"))) # 是否由某个选项 config.has_option() # 是否由某个分区 config.has_section()