JSONEyeX 0.0.10

A lightweight and efficient JSON validation tool for Python.

# JSONEyeX: Effortless JSON Data Validation in Python

JSONEyeX is a robust and user-friendly Python package that streamlines the process of validating JSON data against predefined schemas. Whether you're building web APIs, managing data pipelines, or developing in JSON-heavy environments, JSONEyeX ensures your data adheres to the specified format and types, enhancing the reliability and efficiency of your projects.

Key Features:

• Comprehensive validation: Handles various data types, nested structures, and custom validations.
• Clear error messages: Simplifies debugging with informative and descriptive feedback.
• Seamless integration: Easily integrates into existing Python projects.

Ideal Use Cases:

• Data cleansing and verification in web APIs.
• Ensuring data integrity in data pipelines and processing tasks.
• Rapid development in projects with extensive JSON data usage.

Get Started with JSONEyeX:

1. Install JSONEyeX using pip install JSONEyeX.
2. Define your JSON schema using Python dictionaries.
3. Validate your data against the schema with jsonvalidator function.

Example 1: Validating a Simple JSON Object:

schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "number"},
        "address": {
            "type": "object",
            "properties": {
                "street": {"type": "string"},
                "city": {"type": "string"}
            },
            "required": ["street", "city"]
        }
    },
    "required": ["name", "age", "address"]
}

data = {
    "name": "John Doe",
    "age": 30,
    "address": {
        "street": "123 Main St",
        "city": "Anytown"
    }
}

from JSONEyeX import jsonvalidator

try:
    validator = jsonvalidator(schema, data)
    print("Validation successful! Your data conforms to the expected format.")
except ValueError as e:
    print(f"Validation error: {e}")

Example 2: Handling Unknown Properties in Nested Objects

While defining nested JSON objects in your schema, sometimes you might not know or need to specify every possible property. JSONEyeX allows you to handle these unknown properties gracefully. Consider the "material_grade" object in our example schema. It has an empty "properties" list, indicating that any additional properties beyond "manufacturing_process" will be accepted without validation. However, note that the "material_grade" object itself is still required through the schema's "required" field.

schema = {
    "type": "object",
    "properties": {
        "tolerance": {"type": "string"},
        "quantity": {"type": "number"},
        "surface_finish": {"type": "string"},
        "material_grade": {
            "type": "object",
            "properties": {},
            "required": [] # No specific properties required, but object itself is mandatory
        },
        "material_group": {
            "type": "object",
            "properties": {
                "data": {"type": "string"}
            },
            "required": ["data"]
        }
    },
    "required": [
        "tolerance",
        "quantity",
        "surface_finish",
        "material_grade"
    ]
}

data = {
    "tolerance": "0.1mm",
    "quantity": 100,
    "surface_finish": "matte",
    "material_grade": {"manufacturing_process": "3D printing", "additional_property": "value"},
    "material_group": {"data": "Sample material group data"}
}