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 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 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

Header Adder

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

Header Remover

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