🧩 Data & Tables
Structured data cleanup, analysis, transformation, and table workflows
Column Calculator
Perform calculations on columns and add new rows
Data & TablesMulti-Table Joiner
Merge and join multiple tables with various join types (inner, left, right, full)
Data & TablesData Append Merger
Vertically append and merge multiple data tables with intelligent column matching
Data & TablesOutlier Detector
Detect outliers in numerical data using various statistical methods including IQR, Z-score, and modified Z-score
Data & TablesMin-Max Normalizer
Normalize numerical data using Min-Max scaling to transform values to a 0-1 range. Perfect for machine learning preprocessing, data analysis, and feature scaling. Features: - Min-Max scaling (0-1 normalization) - Custom range support (e.g., -1 to 1) - Multiple column selection - Automatic data type detection - Handles missing values - Preserves non-numeric columns - Statistical summary included Common Use Cases: - Machine learning feature preparation - Neural network input normalization - Data visualization preprocessing - Comparative analysis across different scales
Data & TablesData Range Limiter
Limit numerical values to specified ranges by clipping, filtering, or marking out-of-bounds values. Perfect for data quality control, sensor data cleaning, business rule enforcement, and data preprocessing. Features: - Range clipping (clip values to min/max boundaries) - Range filtering (remove out-of-bounds rows) - Range marking (flag modified values) - Per-column range configuration - Automatic numeric column detection - Multiple handling strategies - Detailed modification reports - Statistical analysis of changes - Business rule enforcement Common Use Cases: - Sensor data validation and cleaning - Machine learning input preparation - Data quality control and validation - Business constraint enforcement - Outlier management and control - Data preprocessing pipelines
Data & TablesData Interpolator
Advanced data interpolation tool that fills missing values and generates data points using various mathematical methods. Perfect for time series analysis, data completion, signal processing, and scientific computing. Features: - Multiple interpolation methods (linear, polynomial, spline, cubic) - Time series interpolation with date/time support - Forward fill and backward fill options - Nearest neighbor interpolation - Custom interpolation parameters - Missing value detection and reporting - Data point generation and densification - Support for multiple columns simultaneously - Interactive interpolation preview Common Use Cases: - Sensor data gap filling - Financial data completion - Scientific experiment data processing - Time series forecasting preparation - Image and signal processing - Statistical data imputation
Data & TablesZ-Score Standardizer
Standardize numerical data using Z-score (standard score) normalization to transform values with mean=0 and standard deviation=1. Perfect for statistical analysis, machine learning feature preprocessing, outlier detection, and data comparison across different scales. Features: - Z-score standardization (mean=0, std=1) - Robust Z-score option (using median and MAD) - Custom scaling to target range - Multiple column selection - Automatic data type detection - Handles missing values intelligently - Preserves non-numeric columns - Comprehensive statistical summary - Outlier detection and reporting Common Use Cases: - Machine learning feature preparation - Statistical hypothesis testing - Outlier detection and removal - Data comparison across different units - Principal Component Analysis (PCA) preprocessing
Data & TablesData Deduplicator
Remove duplicate rows from CSV files based on multiple column combinations. Perfect for cleaning customer lists, survey responses, and database exports. Features: - Multi-column combination deduplication - Fuzzy matching for similar records - Custom deduplication strategies (keep first, last, or most complete record) - Case-insensitive matching option - Whitespace trimming - Detailed duplicate statistics Common Use Cases: - Remove duplicate customer records - Clean email marketing lists - Eliminate redundant survey responses - Prepare data for analysis
Data & TablesData Outlier Processor
Advanced outlier detection and processing tool that identifies, removes, or replaces anomalous values in numerical data using multiple statistical methods. Perfect for data cleaning, statistical analysis, and machine learning data preparation. Features: - Multiple detection methods (IQR, Z-score, Modified Z-score, Isolation Forest) - Flexible handling strategies (Remove, Replace with mean/median/mode, Cap) - Automatic threshold optimization - Multi-dimensional outlier detection - Visual outlier statistics and reporting - Batch processing capabilities - Custom sensitivity levels - Comprehensive impact analysis Common Use Cases: - Data cleaning and preprocessing - Statistical analysis preparation - Machine learning dataset cleaning - Quality control in manufacturing - Financial anomaly detection - Sensor data validation
Data & TablesRegression Analyzer
Advanced regression analysis tool for performing linear regression analysis, calculating regression statistics, and making predictions. Perfect for statistical modeling, trend analysis, forecasting, and understanding relationships between variables. Features: - Simple linear regression (y = mx + b) - Multiple linear regression support - Regression coefficients calculation - Statistical significance testing - R-squared and adjusted R-squared - Residual analysis and diagnostics - Prediction intervals and confidence intervals - Outlier detection in regression - Model validation metrics - Visual regression diagnostics - Data transformation support Common Use Cases: - Sales forecasting and trend analysis - Financial modeling and risk assessment - Scientific research and hypothesis testing - Quality control and process optimization - Marketing analytics and ROI analysis - Medical and biological research
Data & TablesCorrelation Analyzer
Advanced correlation analysis tool that calculates correlation coefficients between variables to measure the strength and direction of their linear relationships. Perfect for statistical analysis, financial modeling, scientific research, and data exploration. Features: - Multiple correlation methods (Pearson, Spearman, Kendall) - Correlation matrix generation - Statistical significance testing (p-values) - Confidence intervals calculation - Heatmap visualization - Scatter plot matrix generation - Missing value handling strategies - Outlier detection and handling - Group analysis capabilities - Detailed statistical reports Common Use Cases: - Financial market analysis and risk assessment - Scientific research and hypothesis testing - Customer behavior and marketing analysis - Healthcare and medical data analysis - Quality control and process optimization - Educational performance evaluation
Data & Tables