Introduction to the usage of JSON and pickle under Python (with code)-Python Tutorial-php.cn

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

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Release： 2018-10-10 16:41:15

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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      |
    -------------------------------------------------

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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 = &#39;{"name":"qiyue", "age":18}&#39;     # 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 = &#39;[{"name":"qiyue", "age":18, "flag":false}, &#39; \
            &#39;{"name":"qiyue", "age":18}]&#39;     # 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(&#39;data.json&#39;, &#39;w&#39;) as f:
    json.dump(student3, f)

# 读取数据
with open(&#39;data.json&#39;, &#39;r&#39;) 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(&#39;group.txt&#39;, &#39;wb&#39;)
pickle.dump(group1, f1, True)
f1.close()

# load(object, file)将文件中的信息恢复
f2 = open(&#39;group.txt&#39;, &#39;rb&#39;)
t = pickle.load(f2)
f2.close()
print(t)

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