Column Dependency Checker
Discover hidden relationships and dependencies between columns in your CSV data with advanced statistical analysis and functional dependency detection
Smart Analysis
AI-powered detection of functional and statistical dependencies
Relationship Mapping
Visualize complex data relationships and dependencies
Statistical Insights
Correlation analysis and data quality metrics
Upload Your Data
Upload your CSV file or paste your data to start analyzing column dependencies and relationships
Upload your CSV or Excel file
Drag and drop your file here, or click to browse. We'll clean up all the whitespace issues automatically.
✅ Supported formats:
📊 CSV files (.csv) • 📋 Excel files (.xlsx, .xls)
📏 Maximum file size:
55MB - Perfect for large datasets
How to Column Dependency Analysis
- 1Upload your CSV file or paste your data using the input methods above
- 2Review the dataset overview showing total rows, columns, and basic statistics
- 3Click 'Analyze Column Dependencies' to start the comprehensive analysis
- 4Examine the discovered dependencies categorized by type (functional, partial, statistical)
- 5Review column statistics to understand data distribution and quality
- 6Click on specific dependencies to see detailed examples and relationships
- 7Use insights for database normalization, data modeling, and quality improvement
Analysis Features
- Functional dependency detection for database normalization
- Statistical correlation analysis between numeric columns
- Partial dependency identification for data quality insights
- Column statistics including unique values and null counts
- Interactive dependency exploration with detailed examples
- Data type detection and validation
- Relationship strength scoring and ranking
- Export-ready analysis results for documentation
Best Practices for Column Dependency Analysis Conversion
- Ensure your CSV has meaningful column headers for accurate dependency detection
- Include at least 10-20 rows of data for reliable statistical analysis
- Clean your data beforehand to remove obvious inconsistencies
- Consider data types when interpreting correlation results
- Use functional dependencies to identify potential primary keys
- Review partial dependencies to optimize database normalization
- Validate statistical correlations with domain knowledge
Frequently Asked Questions
Everything you need to know about column dependency analysis
Dependency Analysis FAQ
Our tool detects three main types: Functional dependencies (where one column uniquely determines another), Partial dependencies (where there's a strong but not complete relationship), and Statistical correlations (mathematical relationships between numeric columns).
The accuracy depends on your data quality and size. For reliable results, we recommend datasets with at least 20-50 rows. Functional dependencies are detected with 95%+ accuracy, while statistical correlations use standard correlation coefficients.
We support CSV files (.csv) and Excel files (.xlsx, .xls). You can also paste CSV data directly into the text area. Maximum file size is 55MB.
Functional dependencies help identify primary keys and support database normalization. Partial dependencies may indicate the need for data cleaning or restructuring. Use these insights to optimize your database schema.
The strength percentage indicates how reliable the dependency is. For functional dependencies, it shows what percentage of source values uniquely determine target values. For correlations, it represents the correlation coefficient.
Yes, the tool can process files up to 55MB. For very large datasets, consider sampling your data first to get initial insights, then apply findings to the full dataset.
Discover hidden relationships and dependencies between columns in your CSV data with advanced statistical analysis and functional dependency detection.
- Tool
- Column Dependency Checker
- Category
- All CSV tools
- Availability
- Browser-based tool