**# 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:**
```python
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.
```python
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"}
}
```
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"description": "\n\n**# JSONEyeX: Effortless JSON Data Validation in Python**\n\n**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.\n\n**Key Features:**\n\n* **Comprehensive validation:** Handles various data types, nested structures, and custom validations.\n* **Clear error messages:** Simplifies debugging with informative and descriptive feedback.\n* **Seamless integration:** Easily integrates into existing Python projects.\n\n**Ideal Use Cases:**\n\n* **Data cleansing and verification in web APIs.**\n* **Ensuring data integrity in data pipelines and processing tasks.**\n* **Rapid development in projects with extensive JSON data usage.**\n\n**Get Started with JSONEyeX:**\n\n1. Install `JSONEyeX` using `pip install JSONEyeX`.\n2. Define your JSON schema using Python dictionaries.\n3. Validate your data against the schema with `jsonvalidator` function.\n\n**Example 1: Validating a Simple JSON Object:**\n\n```python\nschema = {\n \"type\": \"object\",\n \"properties\": {\n \"name\": {\"type\": \"string\"},\n \"age\": {\"type\": \"number\"},\n \"address\": {\n \"type\": \"object\",\n \"properties\": {\n \"street\": {\"type\": \"string\"},\n \"city\": {\"type\": \"string\"}\n },\n \"required\": [\"street\", \"city\"]\n }\n },\n \"required\": [\"name\", \"age\", \"address\"]\n}\n\ndata = {\n \"name\": \"John Doe\",\n \"age\": 30,\n \"address\": {\n \"street\": \"123 Main St\",\n \"city\": \"Anytown\"\n }\n}\n\nfrom JSONEyeX import jsonvalidator\n\ntry:\n validator = jsonvalidator(schema, data)\n print(\"Validation successful! Your data conforms to the expected format.\")\nexcept ValueError as e:\n print(f\"Validation error: {e}\")\n```\n\n\n**Example 2: Handling Unknown Properties in Nested Objects**\n\nWhile 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.\n\n```python\nschema = {\n \"type\": \"object\",\n \"properties\": {\n \"tolerance\": {\"type\": \"string\"},\n \"quantity\": {\"type\": \"number\"},\n \"surface_finish\": {\"type\": \"string\"},\n \"material_grade\": {\n \"type\": \"object\",\n \"properties\": {},\n \"required\": [] # No specific properties required, but object itself is mandatory\n },\n \"material_group\": {\n \"type\": \"object\",\n \"properties\": {\n \"data\": {\"type\": \"string\"}\n },\n \"required\": [\"data\"]\n }\n },\n \"required\": [\n \"tolerance\",\n \"quantity\",\n \"surface_finish\",\n \"material_grade\"\n ]\n}\n\ndata = {\n \"tolerance\": \"0.1mm\",\n \"quantity\": 100,\n \"surface_finish\": \"matte\",\n \"material_grade\": {\"manufacturing_process\": \"3D printing\", \"additional_property\": \"value\"},\n \"material_group\": {\"data\": \"Sample material group data\"}\n}\n```\n\n\n\n\n",
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