python中的序列化

序列化
- 转向一个字符串数据类型
- 从数据类型转向字符串的过程叫序列化
反序列化
- 从字符串到数据类型的过程叫反序列化
序列
- 字符串
在什么情况下用字符串
- 写文件（数据存储）
- 网络上进行传输
json
- 通用的序列化格式
  - 不仅python用，java等其他语言也用
- 只有很少的一部分数据类型能够通过json转化成字符串
- dumps 序列化方法
- loads 反序列化方法
- 数字字符串列表字典
- 元组的序列化
  - 序列化后成为了列表
  - 反序列化后还是列表
- 集合不能序列化
- dump load
  - 和文件相关

python 复制代码

import json
dic = {"k1": 'v1'}print(type(dic), dic)    # <class 'dict'> {'k1': 'v1'}
str_d = json.dumps(dic)
print(type(str_d), str_d)  # <class 'str'> {"k1": "v1"}


dic_d = json.loads(str_d)
print(type(dic_d), dic_d)  # <class 'dict'> {'k1': 'v1'}

python 复制代码

import json
dic = {1:"a", 2:'b'}
f = open("test", 'w', encoding="utf-8")
json.dump(dic, f)
f.close()

python 复制代码

import json
f = open("test", 'r')
dic = json.load(f)
f.close()
print(type(dic), dic) # <class 'dict'> {'1': 'a', '2': 'b'}

python 复制代码

import json
dic = {1:"中国", 2:'b'}
f = open("test", 'w', encoding="utf-8")
json.dump(dic, f, ensure_ascii=False)
f.close()

# import json
f = open("test", 'r', encoding="utf-8")
dic = json.load(f)
f.close()
print(type(dic), dic) # <class 'dict'> {'1': '中国', '2': 'b'}

python 复制代码

l = [{'k':"123"}, {'k':"dfd"}, {'k':"123df"}]
import json
f = open("file", 'w')
for dic in l:
    str_dic = json.dumps(dic)
    f.write(str_dic+"\n")
f.close()

python 复制代码

f = open("file")
l = []
import json
for line in f:
    dic = json.loads(line.strip())
    l.append(dic)
f.close()
print(l)

pickle
- 所有的python中的数据类型都可以转化成字符串形式
- pickle序列化的内容只有python能理解
- 且部分反序列化依赖代码
- 可以分步dump和load
- 文件读写需要加 'b'

python 复制代码

import pickle
dic = {"k1":"v1", "k2":"v2", "k3":"v3"}
str_dic = pickle.dumps(dic)
print(str_dic) # b'\x80\x03}q\x00(X\x02\x00\x00\x00k1q\x01X\x02\x00\x00\x00v1q\x02X\x02\x00\x00\x00k2q\x03X\x02\x00\x00\x00v2q\x04X\x02\x00\x00\x00k3q\x05X\x02\x00\x00\x00v3q\x06u.'

dic2 = pickle.loads(str_dic)
print(dic2) # {'k1': 'v1', 'k2': 'v2', 'k3': 'v3'}

python 复制代码

import time
import pickle
struct_time = time.localtime(10000000)
print(struct_time) # time.struct_time(tm_year=1970, tm_mon=4, tm_mday=27, tm_hour=1, tm_min=46, tm_sec=40, tm_wday=0, tm_yday=117, tm_isdst=0)
f = open("pickle_file", 'wb')
pickle.dump(struct_time, f)
f.close()

python 复制代码

import pickle
f = open("pickle_file", 'rb')
struct_time2 = pickle.load(f)
print(type(struct_time2)) # <class 'time.struct_time'>
print(struct_time2.tm_year) # 1970

python 复制代码

import time
import pickle
struct_time1 = time.localtime(10000000000)
struct_time2 = time.localtime(20000000000)
print(struct_time1)
print(struct_time2)
f = open("pickle_file", 'wb')
pickle.dump(struct_time1, f)
pickle.dump(struct_time2, f)
f.close()

# import time
import pickle
f = open("pickle_file", 'rb')
struct_time1 = pickle.load(f)
struct_time2 = pickle.load(f)
print(struct_time1.tm_year) # 2286
print(struct_time2.tm_year) # 2603
f.close()

shelve
- 序列化句柄
- 使用句柄直接操作，非常方便
  
  import shelve
  
  f2 = shelve.open("shelve_file", writeback=True)
  
  print(f2["key"])
  
  f2["key"]["new_value"] = "this was not here before"
  
  f2.close()
  
  f2 = shelve.open("shelve_file")
  print(f2["key"]) # {'int': 10, 'float': 9.5, 'str': 'abc', 'new_value': 'this was not here before'}
  f2.close()

python中的序列化

f2 = shelve.open("shelve_file", writeback=True)

print(f2["key"])

f2["key"]["new_value"] = "this was not here before"

f2.close()