Serialization and Deserialization of Python Objects: Part 1

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request.

Serialization and deserialization are the most boring things in the world in a sense. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time.

This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format, or protocol you choose may determine how quickly the program runs, security, freedom of maintenance status, and the degree of interoperability with other systems.

There are so many options because different situations require different solutions. The "one-size-fits-all" approach doesn't work. In this two-part tutorial, I will:

• Overview of the advantages and disadvantages of the most successful serialization and deserialization schemes
• Show how to use them
• Provides guidelines for choosing between specific use cases

Running example

In the following section, we will serialize and deserialize the same Python object graph using different serializers. To avoid duplication, let's define these object graphs here.

Simple object diagram

A simple object graph is a dictionary containing a list of integers, strings, floating point numbers, boolean and datetime objects, as well as a user-defined class instance with dump, load, and dump() methods that can be serialized to an open file (file-like object).

11. The

    load() method deserializes from an open file-like object.

12. TypeError: as follows: ``` Traceback (most recent call last):
    File "serialize.py", line 49, in
    print(json.dumps(complex)
    File "/usr/lib/python3.8/json/init.py", line 231, in dumps
    return _default_encoder.encode(obj)
    File "/usr/lib/python3.8/json/encoder.py", line 199, in encode
    chunks = self.iterencode(o, _one_shot=True)
    File "/usr/lib/python3.8/json/encoder.py", line 257, in iterencode
    return _iterencode(o, 0)
    File "/usr/lib/python3.8/json/encoder.py", line 179, in default
    raise TypeError(f'Object of type {o.class.name} '
    TypeError: Object of type A is not JSON serializable
    

    <code>
    哇！这看起来一点也不好。发生了什么？错误消息是 JSONEncoder 类使用的 default() 方法在 JSON 编码器遇到无法序列化的对象时调用的。
    
    自定义编码器的任务是将其转换为 JSON 编码器能够编码的 Python 对象图。在本例中，我们有两个需要特殊编码的对象：A 类。以下编码器可以完成这项工作。每个特殊对象都转换为“\_\_A\_\_”和 pprint 函数的 load() 和 object_hook 参数，允许您提供自定义函数来将字典转换为对象。
    </code>

    def decode_object(o):
    if 'A' in o:
    a = A()
    a.dict.update(o['A'])
    return a
    
    elif 'datetime' in o:
    return datetime.strptime(o['datetime'], '%Y-%m-%dT%H:%M:%S')
    
    return o
    

    <code>
    让我们使用 object_hook 参数进行解码。
    </code>

    deserialized = json.loads(serialized, object_hook=decode_object)
    print(deserialized)
    # prints: {'a': <main.a at="" object="">, 'when': datetime.datetime(2016, 3, 7, 0, 0)}
    
    deserialized == complex
    # evaluates to False
    main.a>

    <code>
    
    结论
    ----------
    
    在本教程的第一部分中，您学习了 Python 对象序列化和反序列化的通用概念，并探讨了使用 Pickle 和 JSON 序列化 Python 对象的来龙去脉。
    
    在第二部分中，您将学习 YAML、性能和安全问题，以及对其他序列化方案的快速回顾。
    
    *这篇文章已更新，并包含 Esther Vaati 的贡献。Esther 是 Envato Tuts+ 的软件开发人员和撰稿人。*</code>

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