Data Processing

Merge multiple JSON files with various merging strategies (deep merge, overwrite, combine arrays, etc.)

Data Processing

Merge multiple markdown files with smart header level adjustment and table of contents generation

Data Processing

Merge multiple text files with various strategies (concatenate, interleave, etc.)

Data Processing

Merge multiple XML files into a single file with options for handling root elements and namespaces

Data Processing

Merge multiple YAML files with various strategies (deep merge, overwrite, combine arrays, etc.)

Data Processing

Remove duplicate columns from CSV data with flexible detection strategies. Perfect for cleaning datasets, removing redundant information, and optimizing data structure. Features: - Detect columns with identical headers - Find columns with identical data content - Support for case-sensitive/insensitive matching - Multiple removal strategies available - Preserve data integrity - Support for large datasets - Fast and efficient processing Common Use Cases: - Clean up merged datasets - Remove redundant data columns - Optimize data for analysis - Prepare data for machine learning - Reduce file size and complexity - Standardize data format

Data Processing

Add headers to CSV data that lacks column names. Perfect for data import from databases, API responses, or numeric datasets that need proper column identification. Features: - Add custom headers to header-less data - Auto-generate intelligent headers - Support for various header naming conventions - Preview headers before applying - Multiple header format options - Support for existing data detection - Batch processing capabilities Common Use Cases: - Fix database exports without headers - Process API response data - Prepare numeric datasets for analysis - Standardize data column naming - Create proper CSV structures - Data format normalization

Data Processing

Remove headers from CSV data to create clean header-less files. Perfect for database imports, data processing pipelines, API integrations, and systems that require header-less CSV format. Features: - Remove first row (header) from CSV data - Remove multiple header rows - Skip empty lines before removing headers - Preserve data integrity - Support various CSV separators - Preview before removal - Data validation options - Batch processing capabilities Common Use Cases: - Prepare data for database imports - Clean up API response data - Remove metadata from exported files - Create header-less data for machine learning - Prepare data for systems that don't use headers - Extract pure data values from structured files

Data Analysis

Comprehensive data distribution analysis with normality tests, outlier detection, and goodness-of-fit assessments

Data Processing

Scale and normalize features using various methods for machine learning preprocessing and data standardization

Data Processing

Comprehensive missing value detection, analysis, and intelligent handling with multiple strategies

Data Analysis

Comprehensive normality testing using multiple statistical methods