Variable Type Identifier
Transform your raw data into structured insights with this powerful type detection and analysis tool. Whether you're working with CSV files, JSON data, or individual values, this tool automatically identifies data types, detects semantic patterns (emails, URLs, dates, credit cards, etc.), and provides comprehensive statistical analysis. Perfect for data scientists, developers, and researchers who need to understand their data structure, validate quality, and generate schemas for multiple platforms. Features advanced outlier detection, field relationship discovery, and actionable data quality recommendations.
Key Features
- 40+ semantic pattern detection including emails, URLs, UUIDs, credit cards, and social media handles
- Advanced statistical analysis with percentiles, skewness, kurtosis, and outlier detection
- Multi-dimensional data quality scoring (completeness, consistency, validity, uniqueness)
- Automatic field relationship and correlation detection
- Schema generation for TypeScript, JSON Schema, SQL, GraphQL, Python, and OpenAPI
- Interactive data visualizations with distribution charts and quality metrics
- CSV and JSON parsing with smart delimiter detection
- Context-aware type detection based on field names
- Enum detection for low-cardinality fields
- Export analysis results and import for continued work
- Real-time analysis progress tracking for large datasets
- Pattern coverage percentage and confidence scoring
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Frequently Asked Questions
What types of data can this variable identifier analyze?
This tool analyzes CSV files, JSON data, and individual values. It detects 40+ semantic patterns including emails, URLs, phone numbers, dates, UUIDs, credit cards, IP addresses, and social media handles. It identifies basic data types (string, number, boolean), statistical types (continuous, categorical), and generates schemas for TypeScript, SQL, GraphQL, Python, and OpenAPI formats.
How does the tool assess data quality?
Data quality is scored across four dimensions: completeness (percentage of non-null values), consistency (uniformity of formats), validity (conformance to expected patterns), and uniqueness (distinct value percentage). Each dimension receives a score and recommendations for improvement. The tool also detects outliers using statistical methods and identifies fields with quality issues.
Can this tool detect relationships between variables?
Yes! The tool automatically detects field relationships and correlations in your dataset. For numeric variables, it calculates correlation coefficients. It also identifies potential foreign key relationships, duplicate patterns, and dependencies between fields. This helps you understand data structure before analysis or database design.
What schema formats can it generate?
The tool generates schemas in six formats: TypeScript interfaces, JSON Schema, SQL CREATE TABLE statements, GraphQL type definitions, Python dataclasses, and OpenAPI 3.0 specifications. Each schema accurately reflects detected types, nullable fields, enum values, and semantic patterns from your data analysis.