pick模块
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
支持几乎所有对象的序列化
对于对象的序列化需要这个对象对应的类在内存中
对于多次dump/load的操作做了良好的处理
dic = {1:(12,3,4),('a','b'):4} import pickle pic_dic = pickle.dumps(dic) print(pic_dic) # bytes类型 new_dic = pickle.loads(pic_dic) print(new_dic) # 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',83) ret = pickle.dumps(alex) 小花 = pickle.loads(ret) print(小花.name) print(小花.age) class Student: def __init__(self,name,age): self.name = name self.age = age alex = Student('alex',83) with open('pickle_demo','wb') as f: pickle.dump(alex,f) with open('pickle_demo','rb') as f: 旺财 = pickle.load(f) print(旺财.name) # 学员选课系统 pickle模块来存储每个学员的对象 # with open('pickle_demo','wb') as f: # pickle.dump({'k1':'v1'}, f) # pickle.dump({'k11':'v1'}, f) # pickle.dump({'k11':'v1'}, f) # pickle.dump({'k12':[1,2,3]}, f) # pickle.dump(['k1','v1','l1'], f) # with open('pickle_demo','rb') as f: # while True: # try: # print(pickle.load(f)) # except EOFError: # break
shelve模块
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) # shelve 如果你写定了一个文件 # 改动的比较少 # 读文件的操作比较多 # 且你大部分的读取都需要基于某个key获得某个value