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Updated on 2025-04-11

Summary of Python data serialization technology

introduction

In modern software development, data serialization is a key link that allows us to convert complex data structures into storable or transferable formats for sharing and persistence between different systems or programs. Python provides a variety of data serialization technologies, each with its unique performance advantages and applicable scenarios. This article will introduce several powerful Python data serialization techniques in detail and demonstrate how to use them through sample code.

1. pickle: Python's general serialization tool

pickle is a module in the Python standard library for serializing and deserializing Python object structures. It is very flexible and can handle almost all types of Python objects, but is not suitable for cross-language data exchange because its format is Python-specific.

Sample code

import pickle

# The data to be serializeddata = {
    'name': 'Alice',
    'age': 30,
    'is_student': False
}

# Serializationwith open('', 'wb') as file:
    (data, file)

# Deserializationwith open('', 'rb') as file:
    loaded_data = (file)

print(loaded_data)

2. json: lightweight data exchange format

jsonThe module is used to process JSON data, a lightweight data exchange format that is easy to read and write by people, and is also easy to machine parse and generate.jsonFormats are very common in web development and are suitable for cross-language data exchange.

Sample code

import json

# The data to be serializeddata = {
    'name': 'Alice',
    'age': 30,
    'is_student': False
}

# Serializationwith open('', 'w') as file:
    (data, file)

# Deserializationwith open('', 'r') as file:
    loaded_data = (file)

print(loaded_data)

3. msgpack: efficient binary JSON-like format

msgpack(MessagePack) is an efficient binary JSON-like format, which is smaller and faster than JSON, and is suitable for network transmission and storage.msgpackSupports multiple languages ​​and can therefore be used for data exchange across languages.

Install

pip install msgpack

Sample code

import msgpack

# The data to be serializeddata = {
    'name': 'Alice',
    'age': 30,
    'is_student': False
}

# Serializationpacked_data = (data)

# Deserializationunpacked_data = (packed_data)

print(unpacked_data)

4. protobuf: efficient cross-language data exchange format

protobuf (Protocol Buffers) is a language-independent, platform-independent way of serializing structure data developed by Google. It is ideal for network transmission and storage, with the advantages of high efficiency, flexibility and automation. protobuf needs to define the schema file of the data structure and then generate the corresponding code.

Install

pip install protobuf

Define the schema file()

syntax = "proto3";

message Person {
  string name = 1;
  int32 age = 2;
  bool is_student = 3;
}

Generate Python code

protoc --python_out=.

Sample code

from person_pb2 import Person

# Create Person objectperson = Person()
 = 'Alice'
 = 30
person.is_student = False

# Serializationserialized_data = ()

# Deserializationnew_person = Person()
new_person.ParseFromString(serialized_data)

print(new_person.name)
print(new_person.age)
print(new_person.is_student)

5.   and: efficiently process numerical data

For numerical data, especially large arrays,numpyThe library provides very efficient methods for serialization and deserialization.andCan quickly save and load large arrays, with better performance thanpickle。

Sample code

import numpy as np

# The data to be serializeddata = ([[1, 2, 3], [4, 5, 6]])

# Serialization('', data)

# Deserializationloaded_data = ('')

print(loaded_data)

6. pandas.to_pickle and pandas.read_pickle: efficiently process pandas data structures

For pandas data structures such as DataFrame and Series, the pandas library provides to_pickle and read_pickle methods, which are based on pickle but are optimized to handle pandas data structures more efficiently.

Sample code

import pandas as pd

# The data to be serializeddata = ({
    'name': ['Alice', 'Bob'],
    'age': [30, 25],
    'is_student': [False, True]
})

# Serializationdata.to_pickle('')

# Deserializationloaded_data = pd.read_pickle('')

print(loaded_data)

Summarize

When choosing data serialization technology, it needs to be decided based on the specific application scenario and needs. Here are some options:

  • Universality and flexibility: If you need to deal with various complex Python objects,pickleIt's a good choice.
  • Cross-language data exchange: If you need to exchange data between different programming languages,jsonandmsgpackIt's a better choice.
  • High performance and network transmission: For data that needs to be efficiently transmitted and stored,msgpackandprotobufProvides better performance.
  • Numerical data: For large numeric arrays,andProvides efficient serialization and deserialization methods.
  • pandas data structure: For pandas data structures such as DataFrame and Series,pandas.to_pickleandpandas.read_pickleOptimized serialization and deserialization methods are provided.

By rationally selecting and using these data serialization technologies, the performance and maintainability of applications can be significantly improved. Hopefully, the introduction and sample code in this article can help you better apply these technologies in real projects.

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