CSV Cleanup and Table Reshaping Tools

Auto-detect CSV delimiter/encoding (UTF-8/GBK), normalize the table, and import to XLSX/CSV

Reorder, remove, and rearrange CSV columns with customizable column positions

Select specific columns from CSV data by column names or indices. Perfect for extracting relevant data from large CSV files with many columns.

Group CSV data by specified columns with aggregation options. Perfect for summarizing and analyzing large datasets by categories, dates, or other criteria.

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"}]

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

Transpose CSV data by converting rows to columns, with support for various delimiters and output formats

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

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

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"}]

Compare two CSV or XLSX datasets and export a PDF report with row, column, and cell-level differences