JSONDetective 🔍

A powerful tool for analyzing and understanding JSON schemas. Built to handle large, complex JSON files by automatically detecting and abstracting patterns in your data.

Key features:

• Automatically recognizes and normalizes date formats in both keys and values
• Detects optional fields by analyzing multiple instances
• Abstracts repeated patterns into clean, readable schemas

Quick Start

# Install
pip install jsondetective
# Use
jsondetective data.json

Pattern Recognition Example

Given a JSON with repeated date patterns like:

{
"2021-08-24": {"views": 100, "likes": 20},
"2021-08-25": {"views": 150, "likes": 30},
"2021-08-26": {"views": 200, "likes": 40}
}

JSONDetective recognizes the pattern and abstracts it as:

{
"yyyy-mm-dd_1": {
"type": "object",
"properties": {
"views": {"type": "integer"},
"likes": {"type": "integer"}
}
}
}

Note: The _1 suffix indicates the nesting level in the JSON structure.

Complex Structure Example

It also handles nested structures with various data types and patterns:

{
"users": [
{
"id": "123",
"joined_date": "2024-01-15",
"last_active": "2024-03-20T15:30:00Z",
"activity": {
"2024-03-19": {"posts": 5},
"2024-03-20": {"posts": 3}
},
"preferences": {
"theme": "dark",
"notifications": true
}
}
],
// many more users...
}

Produces this clean schema:

{
"users": {
"type": "array",
"items": {
"id": {
"type": "string",
"examples": ["123"]
},
"joined_date": {
"type": "string",
"format": "yyyy-mm-dd"
},
"last_active": {
"type": "string",
"format": "datetime"
},
"activity": {
"type": "object",
"properties": {
"yyyy-mm-dd_2": {
"type": "object",
"properties": {
"posts": {"type": "integer"}
}
}
}
},
"preferences": {
"type": "object",
"properties": {
"theme": {
"type": "string",
"optional": true
},
"notifications": {
"type": "boolean"
}
}
}
}
}
}

Features

• Intelligent Pattern Detection:
  • Recognizes date formats in both keys and values
  • Abstracts repeated structures
  • Identifies optional fields
• Schema Intelligence:
  • Detects data types
  • Identifies nested structures
  • Provides example values
• Experimental: Python dataclass generation (beta feature)

Advanced Usage

Experimental Python Dataclass Generation

# Print dataclass to console
jsondetective data.json -d
# Save to file
jsondetective data.json -d -o my_dataclasses.py
# Custom class name
jsondetective data.json -d -c MyDataClass

CLI Options

jsondetective [JSON_FILE] [OPTIONS]
Options:
-d, --create-dataclass Generate Python dataclass code
-o, --output-path PATH Save dataclass to file
-c, --class-name TEXT Name for the root dataclass (default: Root)
--help Show this message and exit

Why Use JSONDetective?

• Pattern Recognition: Automatically detects and abstracts repeated patterns
• Date Handling: Intelligent date format recognition and normalization
• Large Files: Efficiently processes and summarizes large JSON structures
• Clear Output: Clean, readable schema representation
• Time Saving: No manual inspection of large JSON files needed