199. Data Serialization

Here are 10 Python code snippets covering various data serialization methods, including JSON, XML, and binary formats for storage or communication:

---

1. JSON Serialization

Serializing Python objects into JSON format using the json module.

Copy

import json

data = {"name": "Alice", "age": 30, "city": "New York"}

# Convert Python object to JSON string
json_data = json.dumps(data)
print(json_data)

# Save to a file
with open('data.json', 'w') as f:
    json.dump(data, f)

---

2. JSON Deserialization

Deserializing JSON data back into Python objects.

Copy

import json

# JSON string
json_data = '{"name": "Alice", "age": 30, "city": "New York"}'

# Convert JSON string to Python object
data = json.loads(json_data)
print(data)

# Read from a file
with open('data.json', 'r') as f:
    data_from_file = json.load(f)
    print(data_from_file)

---

3. XML Serialization with xml.etree.ElementTree

Converting Python data structures to XML format.

Copy

import xml.etree.ElementTree as ET

data = {'name': 'Alice', 'age': 30, 'city': 'New York'}

# Create the root element
root = ET.Element("person")

# Adding child elements
for key, value in data.items():
    child = ET.SubElement(root, key)
    child.text = str(value)

# Convert to string
tree = ET.ElementTree(root)
tree.write("data.xml")

---

4. XML Deserialization with xml.etree.ElementTree

Parsing an XML file into Python objects.

Copy

import xml.etree.ElementTree as ET

tree = ET.parse('data.xml')
root = tree.getroot()

data = {}
for child in root:
    data[child.tag] = child.text

print(data)

---

5. Pickling: Binary Serialization with pickle

Using pickle to serialize Python objects into binary format.

Copy

import pickle

data = {'name': 'Alice', 'age': 30, 'city': 'New York'}

# Serialize Python object to binary file
with open('data.pkl', 'wb') as f:
    pickle.dump(data, f)

# Deserialize Python object from binary file
with open('data.pkl', 'rb') as f:
    loaded_data = pickle.load(f)
    print(loaded_data)

---

6. Custom Serialization with pickle

Implementing custom serialization using pickle.

Copy

import pickle

class Person:
    def __init__(self, name, age, city):
        self.name = name
        self.age = age
        self.city = city

    def __repr__(self):
        return f'Person(name={self.name}, age={self.age}, city={self.city})'

    def __getstate__(self):
        # Custom serialization logic
        return (self.name, self.age, self.city)

    def __setstate__(self, state):
        # Custom deserialization logic
        self.name, self.age, self.city = state

person = Person("Alice", 30, "New York")

# Serialize custom object
with open('person.pkl', 'wb') as f:
    pickle.dump(person, f)

# Deserialize custom object
with open('person.pkl', 'rb') as f:
    loaded_person = pickle.load(f)
    print(loaded_person)

---

7. YAML Serialization with PyYAML

Using PyYAML for YAML serialization.

Copy

import yaml

data = {"name": "Alice", "age": 30, "city": "New York"}

# Serialize to YAML string
yaml_data = yaml.dump(data)
print(yaml_data)

# Save to a file
with open('data.yaml', 'w') as f:
    yaml.dump(data, f)

---

8. YAML Deserialization with PyYAML

Deserializing YAML into Python objects.

Copy

import yaml

yaml_data = """
name: Alice
age: 30
city: New York
"""

# Deserialize YAML string to Python object
data = yaml.safe_load(yaml_data)
print(data)

# Read from a file
with open('data.yaml', 'r') as f:
    data_from_file = yaml.safe_load(f)
    print(data_from_file)

---

9. MessagePack: Efficient Binary Serialization

Using msgpack for efficient binary serialization (requires the msgpack library).

Copy

import msgpack

data = {"name": "Alice", "age": 30, "city": "New York"}

# Serialize to MessagePack
packed_data = msgpack.packb(data)

# Save to a file
with open('data.msgpack', 'wb') as f:
    f.write(packed_data)

# Deserialize from MessagePack
with open('data.msgpack', 'rb') as f:
    unpacked_data = msgpack.unpackb(f.read())
    print(unpacked_data)

---

10. Custom JSON Serialization with json

Customizing the JSON serialization by overriding the default method.

Copy

import json

class Person:
    def __init__(self, name, age, city):
        self.name = name
        self.age = age
        self.city = city

    def __repr__(self):
        return f'Person(name={self.name}, age={self.age}, city={self.city})'

    def to_dict(self):
        return {"name": self.name, "age": self.age, "city": self.city}

# Custom serialization function
def person_serializer(obj):
    if isinstance(obj, Person):
        return obj.to_dict()
    raise TypeError("Type not serializable")

person = Person("Alice", 30, "New York")

# Serialize custom object
json_data = json.dumps(person, default=person_serializer)
print(json_data)

# Deserialize back to Person object
data = json.loads(json_data)
person_obj = Person(**data)
print(person_obj)

---

Conclusion:

These snippets cover a variety of serialization techniques in Python, from standard methods like JSON and XML to more efficient options like MessagePack and custom serialization with pickle. Depending on the use case, you can choose the appropriate format for storing and transmitting data, such as for storage, APIs, or inter-process communication.