Python数据序列化技术总结

import pickle

# 要序列化的数据
data = {
    'name': 'Alice',
    'age': 30,
    'is_student': False
}

# 序列化
with open('data.pkl', 'wb') as file:
    pickle.dump(data, file)

# 反序列化
with open('data.pkl', 'rb') as file:
    loaded_data = pickle.load(file)

print(loaded_data)

import json

# 要序列化的数据
data = {
    'name': 'Alice',
    'age': 30,
    'is_student': False
}

# 序列化
with open('data.json', 'w') as file:
    json.dump(data, file)

# 反序列化
with open('data.json', 'r') as file:
    loaded_data = json.load(file)

print(loaded_data)

pip install msgpack

import msgpack

# 要序列化的数据
data = {
    'name': 'Alice',
    'age': 30,
    'is_student': False
}

# 序列化
packed_data = msgpack.packb(data)

# 反序列化
unpacked_data = msgpack.unpackb(packed_data)

print(unpacked_data)

pip install protobuf

syntax = "proto3";

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

protoc --python_out=. person.proto

from person_pb2 import Person

# 创建Person对象
person = Person()
person.name = 'Alice'
person.age = 30
person.is_student = False

# 序列化
serialized_data = person.SerializeToString()

# 反序列化
new_person = Person()
new_person.ParseFromString(serialized_data)

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

import numpy as np

# 要序列化的数据
data = np.array([[1, 2, 3], [4, 5, 6]])

# 序列化
np.save('data.npy', data)

# 反序列化
loaded_data = np.load('data.npy')

print(loaded_data)

import pandas as pd

# 要序列化的数据
data = pd.DataFrame({
    'name': ['Alice', 'Bob'],
    'age': [30, 25],
    'is_student': [False, True]
})

# 序列化
data.to_pickle('data.pkl')

# 反序列化
loaded_data = pd.read_pickle('data.pkl')

print(loaded_data)