Python JSON

Python JSON Tutorial

JSON (JavaScript Object Notation) is a lightweight data format used for storing and exchanging data. Python provides the json module to encode and decode JSON data.

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1. Importing the JSON Module

Python has a built-in json module that makes working with JSON easy.

import json

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2. Converting Python Data to JSON (Serialization)

Serialization (or encoding) is converting Python objects (dict, list, etc.) into a JSON string using json.dumps().

Example: Convert Python Dictionary to JSON

import json

data = {
    "name": "Alice",
    "age": 25,
    "city": "New York"
}

json_data = json.dumps(data, indent=4)  # Convert to JSON string
print(json_data)

✅ indent=4 makes JSON readable.

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3. Converting JSON to Python Data (Deserialization)

Deserialization (decoding) is converting a JSON string back into a Python object using json.loads().

Example: Convert JSON String to Python Dictionary

json_string = '{"name": "Alice", "age": 25, "city": "New York"}'

python_data = json.loads(json_string)  # Convert to dictionary
print(python_data["name"])  # Output: Alice

✅ json.loads() converts JSON string to Python dictionary.

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4. Working with JSON Files

4.1 Writing JSON Data to a File

data = {"name": "Alice", "age": 25, "city": "New York"}

with open("data.json", "w") as file:
    json.dump(data, file, indent=4)

✅ json.dump() writes JSON data to a file.

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4.2 Reading JSON Data from a File

with open("data.json", "r") as file:
    loaded_data = json.load(file)

print(loaded_data)  # Output: {'name': 'Alice', 'age': 25, 'city': 'New York'}

✅ json.load() reads JSON data from a file.

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5. Handling Nested JSON Data

JSON data can contain nested objects and lists.

Example: Working with Nested JSON

nested_json = '''
{
    "name": "Alice",
    "age": 25,
    "address": {
        "city": "New York",
        "zip": "10001"
    },
    "skills": ["Python", "Django", "Machine Learning"]
}
'''

data = json.loads(nested_json)

print(data["address"]["city"])  # Output: New York
print(data["skills"][0])  # Output: Python

✅ Use indexing to access nested JSON elements.

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6. Handling JSON Errors

If JSON is invalid, Python will raise a JSONDecodeError.

try:
    invalid_json = '{"name": "Alice", age: 25}'  # Missing quotes around 'age'
    data = json.loads(invalid_json)
except json.JSONDecodeError as e:
    print("Error:", e)

✅ Always use try-except when loading JSON to handle errors.

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Conclusion

The json module in Python is useful for storing, exchanging, and processing structured data. It is widely used in APIs, configuration files, and data storage.

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