All-in-One Tools Collection
A broad collection of online tools for development, AI, design, and productivity tasks.
Showing page 182 of 183 (12 tools on this page)
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"}]
Date Adder
Add or subtract time periods from a date (years, months, days, hours, minutes, seconds)
Daily Calorie Needs Calculator
Calculate daily calorie needs based on activity level and goals
Date Difference Calculator
Calculate the difference between two dates in various time units
Data Storage Unit Converter
Convert between different data storage units (bit, byte, KB, MB, GB, TB, etc.)
CSV to Excel Converter
Convert CSV data to Excel format with customizable parsing and formatting options
CSV to JSON Converter
Convert CSV data to JSON format with customizable parsing options
CSV to XML Converter
Convert CSV data to XML format with customizable formatting options
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
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
Foreign Key Validator
Validate foreign key relationships between multiple datasets. Perfect for checking data integrity, finding orphaned records, and ensuring referential consistency across related tables. Features: - Validate foreign key relationships - Find orphaned records - Check referential integrity - Support multiple key formats - Cross-table validation - Missing key detection - Duplicate key analysis - Relationship mapping Common Use Cases: - Database integrity checks - Data migration validation - ETL process verification - Referential consistency checks - Data quality assurance - Relationship analysis
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