🧩 Data Processing

Transform, clean, and process datasets

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TXT File Merger

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

Data Processing

Markdown Merger

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

Data Processing

CSV File Merger

Merge multiple CSV files into a single file with options for header handling and deduplication

Data Processing

YAML File Merger

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

Data Processing

XML File Merger

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

Data Processing

JSON File Merger

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

Data Processing

CSV Transformer

Transform and process CSV data with column operations, calculations, and data type conversions. Supports renaming columns, adding calculated columns, removing columns, converting data types, calculating values, and filtering rows. Operation examples: • Rename column: [{"type": "rename", "column": "old_name", "new_name": "new_name"}] • Add calculated column: [{"type": "add_column", "new_column": "total", "formula": "price * quantity"}] • Remove column: [{"type": "remove_column", "remove_column": "column_to_remove"}] • Convert data type: [{"type": "convert_type", "convert_column": "age", "target_type": "number"}] • Calculate values: [{"type": "calculate", "target_column": "total", "expression": "price * tax + shipping"}] • Filter rows: [{"type": "filter_values", "filter_column": "status", "operator": "equals", "value": "active"}]

Data Processing

CSV Filter

Filter CSV data by column values with multiple conditions and operators. Supports 12 filter operators including equals, contains, greater_than, less_than, and empty value checks. Additional Filters examples: [{"column": "age", "operator": "greater_than", "value": "25"}] [{"column": "status", "operator": "equals", "value": "active"}, {"column": "score", "operator": "greater_equal", "value": "80"}] [{"column": "name", "operator": "contains", "value": "john"}, {"column": "email", "operator": "is_not_empty"}]

Data Processing

CSV Sorter

Sort CSV data by one or multiple columns with ascending/descending order options

Data Processing

CSV Splitter

Split CSV content by specified number of rows per file. Perfect for processing large datasets, dividing data for analysis, batch processing, and managing file size limits. Features: - Split CSV by row count - Support multiple output formats - Preserve header row in each split - Flexible output format options - Support for large datasets - Fast and efficient processing Common Use Cases: - Split large CSV files for processing - Divide data for parallel processing - Create manageable data chunks - Export data in different formats - Prepare data for batch operations - Manage file size limitations

Data Processing

BOM Character Remover

Remove BOM (Byte Order Mark) characters from text and file content. Perfect for cleaning up text files that have encoding issues, fixing CSV imports, and preparing data for processing. Features: - Detect and remove UTF-8 BOM (EF BB BF) - Detect and remove UTF-16 BOM (FE FF or FF FE) - Detect and remove UTF-32 BOM (00 00 FE FF or FF FE 00 00) - Support multiple input formats - Visual BOM character display - Detailed detection report - Support for batch text processing Common Use Cases: - Fix CSV file import errors - Clean up text file encoding issues - Prepare data for JSON parsing - Fix XML parsing problems - Resolve API data encoding conflicts - Standardize text data format

Data Processing

Data Noise Injection

Inject various types of noise into text data for testing purposes. Perfect for stress testing data processing systems, testing data quality algorithms, and creating realistic test datasets. Features: - Character-level noise injection - Word-level noise injection - Numeric data noise - Formatting noise - Whitespace noise - Special character noise - Configurable intensity levels - Realistic noise patterns Common Use Cases: - Test data validation systems - Stress test parsing algorithms - Evaluate error handling - Test data cleaning algorithms - Create realistic messy data - Benchmark data processing performance

Data Processing