Introduction to the usage of JSON and pickle under Python (with code)

This article brings you an introduction to the usage of JSON and pickle under Python (with code). It has certain reference value. Friends in need can refer to it. I hope it will be helpful to you.

1: Introduction

(1)JSON (JavaScript Object Notation) is a lightweight (XML heavyweight) data exchange format.
is a rule customized for data exchange, based on a subset of ECMAScript.

(2)JSON is a data format!
String is the representation of JSON. (A string that conforms to JSON format is called a JSON string)

(3) The json module can be used in Python3 to encode and decode JSON data. It contains two functions:
json.dumps() : Encode the data.
json.loads(): Decode the data.

(4)The advantages of JSON are: easy to read, easy to parse, high network transmission efficiency, cross-language data exchange

2: Python encoding to JSON type conversion corresponding table:

    _______________________________________________
    |    python           |         JSON            |
    -------------------------------------------------
    |    dict             |        object           |
    -------------------------------------------------
    |    list,tuple       |         array           |
    -------------------------------------------------
    |    str              |        string           |
    -------------------------------------------------
    |   int,float,Enums   |       number            |
    -------------------------------------------------
    |   True,False,None   |    true,false,null      |
    -------------------------------------------------

Three: If you want to process files instead of strings, you can use
json.dump()
json.load()

Four: Use Pickle serializes and deserializes data

(1) method:
pickle.dump()
pickle.load()
pickle.dumps()
pickle.loads ()

(2) Data type:
All native types supported by python: boolean, integer, floating point number, complex number, string, byte, None.
Lists, tuples, dictionaries and sets composed of any primitive type.
Functions, classes, instances of classes

5: The difference between JSON and pickle

The purpose of JSON serialization and deserialization is to convert Python data types into JSON standard types ,
Or convert JSON type data to python data type to achieve data exchange between different languages!
pickle: If you want to save a piece of data during the running of the program, reuse it or send it to others, you can use this method
to write the data to a file, supporting all data types!

import json
import pickle
# ----------------------------------------------#
# 反序列化
# ----------------------------------------------#
# object
json_str = '{"name":"qiyue", "age":18}'     # JSON字符串
student = json.loads(json_str)    # JSON对象转换为字典
print(student)
print(json_str)
print(type(student))

# object
json_str1 = '[{"name":"qiyue", "age":18, "flag":false}, ' \
            '{"name":"qiyue", "age":18}]'     # JSON字符串
student1 = json.loads(json_str1)    # JSON对象转换为字典
print(type(student1), student1)
print(student1[0])

# ----------------------------------------------#
# 序列化
# ----------------------------------------------#
student2 = [
                {"name": "qiyue", "age": 18, "flag": False},
                {"name": "qiyue", "age": 18}
           ]

json_str1 = json.dumps(student2)    # 转换为字符串后可以利用正则表达式处理字符串
print(type(json_str1), json_str1)

# ----------------------------------------------#
# 处理的是文件
# ----------------------------------------------#
# 将数据写入文件
student3 = [
                {"name": "qiyue", "age": 18, "flag": False},
                {"name": "qiyue", "age": 18}
           ]
with open('data.json', 'w') as f:
    json.dump(student3, f)

# 读取数据
with open('data.json', 'r') as f:
    data = json.load(f)


# dumps(object)将对象序列化
list_a = ["English", "Math", "Chinese"]
list_b = pickle.dumps(list_a)   # 序列化数据
print(list_a)
print(list_b)

# loads(object)将对象原样恢复，并且对象类型也恢复原来的格式
list_c = pickle.loads(list_b)
print(list_c)


# dumps(object,file)将对象序列化后存储到文件中
group1 = ("baidu", "wen", "qingtian")
f1 = open('group.txt', 'wb')
pickle.dump(group1, f1, True)
f1.close()

# load(object, file)将文件中的信息恢复
f2 = open('group.txt', 'rb')
t = pickle.load(f2)
f2.close()
print(t)

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