Data Processing Tools - Online Utilities for Data Transformation

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AI Data Normalizer

AI-powered data format normalization tool that intelligently cleans and standardizes messy data using advanced AI analysis

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Array Analyzer

Comprehensive array analysis tool with statistics, data types, and frequency analysis

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Array Deduplicator

Remove duplicate elements from arrays with various deduplication methods

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Array Extractor

Extract elements of specific types from arrays (numbers, strings, booleans, objects, etc.)

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Array Filter

Filter out elements of specific types from arrays (remove numbers, strings, booleans, etc.)

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Array Flattener

Flatten multidimensional arrays into one-dimensional arrays with customizable depth

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Array Generator

Generate various types of arrays including numeric sequences, random arrays, test data, and date sequences

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Array Operations

Perform set operations (intersection, union, difference) on two arrays

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Array Sorter

Sort arrays with various sorting methods and maintain original input format

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Assign In Object

Assign own and inherited properties using lodash _.assignIn

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Assign Object

Assign source properties to a target object using lodash _.assign

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Chunk Array

Split array into chunks of specified size using lodash _.chunk

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Coefficient of Variation Calculator

Calculate the coefficient of variation (CV) for numerical columns to measure relative variability

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Column Calculator

Perform calculations on columns and add new rows

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Column Remover

Remove specified columns from table data

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Column Renamer

Rename column headers in CSV and table data with preview and validation

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Column Reorder

Reorder table columns without changing the data rows

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Compact Array

Remove falsey values from array using lodash _.compact

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Concat Arrays

Concatenate multiple arrays using lodash _.concat

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Count By

Count items by a derived key using lodash _.countBy

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CSV Column Reorderer

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

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CSV Column Selector

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

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CSV Data Grouper

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

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CSV / Excel Diff Tool

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

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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"}]

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CSV File Merger

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

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CSV Row Column Transposer

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

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CSV Sorter

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

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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

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CSV to Database Migration Planner

Infer a relational schema from CSV data and generate create-table plus ALTER migration plans for PostgreSQL, MySQL, SQLite, or SQL Server

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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"}]

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Data Append Merger

Vertically append and merge multiple data tables with intelligent column matching

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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

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Data Boundary Processor

Advanced boundary value processing tool that identifies and handles minimum/maximum values in numerical data. Perfect for data validation, range checking, statistical analysis, and data preprocessing. Features: - Multiple boundary detection methods (absolute, percentile, standard deviation) - Flexible handling strategies (clip, remove, replace, transform) - Custom range validation - Asymmetric boundary handling - Batch processing capabilities - Comprehensive boundary statistics - Data quality assessment - Visual boundary reports Common Use Cases: - Data validation and quality control - Sensor data range checking - Financial data limit enforcement - Statistical data preprocessing - Machine learning feature engineering - Database constraint validation

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Data Cleaner

Clean and standardize data by fixing spelling errors, standardizing formats, removing duplicates, and filling missing values

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Data Column Extractor

Extract specific columns from tabular data with support for various formats and flexible column selection

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Data Crosstab Generator

Advanced crosstab (pivot table) generator that creates powerful cross-tabulation analysis from your data. Perfect for business intelligence, statistical analysis, data exploration, and reporting. Features: - Multiple aggregation functions (sum, count, average, min, max, median) - Flexible row and column grouping - Percentage and ratio calculations - Row/column totals and grand totals - Multi-dimensional analysis - Conditional formatting support - Statistical significance testing - Custom sorting and filtering - Export-ready formatting Common Use Cases: - Sales analysis by region and product - Customer demographics analysis - Financial statement analysis - Survey response analysis - Inventory turnover analysis - Performance metrics tracking

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Data Deduplicator

Remove duplicate rows from CSV files based on multiple column combinations. Perfect for cleaning customer lists, survey responses, and database exports. Features: - Multi-column combination deduplication - Fuzzy matching for similar records - Custom deduplication strategies (keep first, last, or most complete record) - Case-insensitive matching option - Whitespace trimming - Detailed duplicate statistics Common Use Cases: - Remove duplicate customer records - Clean email marketing lists - Eliminate redundant survey responses - Prepare data for analysis

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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

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Data Interpolator

Advanced data interpolation tool that fills missing values and generates data points using various mathematical methods. Perfect for time series analysis, data completion, signal processing, and scientific computing. Features: - Multiple interpolation methods (linear, polynomial, spline, cubic) - Time series interpolation with date/time support - Forward fill and backward fill options - Nearest neighbor interpolation - Custom interpolation parameters - Missing value detection and reporting - Data point generation and densification - Support for multiple columns simultaneously - Interactive interpolation preview Common Use Cases: - Sensor data gap filling - Financial data completion - Scientific experiment data processing - Time series forecasting preparation - Image and signal processing - Statistical data imputation

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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

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Data Normalizer

Pure code-based data format normalization tool that cleans and standardizes messy data using programmatic logic

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Min-Max Normalizer

Normalize numerical data using Min-Max scaling to transform values to a 0-1 range. Perfect for machine learning preprocessing, data analysis, and feature scaling. Features: - Min-Max scaling (0-1 normalization) - Custom range support (e.g., -1 to 1) - Multiple column selection - Automatic data type detection - Handles missing values - Preserves non-numeric columns - Statistical summary included Common Use Cases: - Machine learning feature preparation - Neural network input normalization - Data visualization preprocessing - Comparative analysis across different scales

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Data Outlier Processor

Advanced outlier detection and processing tool that identifies, removes, or replaces anomalous values in numerical data using multiple statistical methods. Perfect for data cleaning, statistical analysis, and machine learning data preparation. Features: - Multiple detection methods (IQR, Z-score, Modified Z-score, Isolation Forest) - Flexible handling strategies (Remove, Replace with mean/median/mode, Cap) - Automatic threshold optimization - Multi-dimensional outlier detection - Visual outlier statistics and reporting - Batch processing capabilities - Custom sensitivity levels - Comprehensive impact analysis Common Use Cases: - Data cleaning and preprocessing - Statistical analysis preparation - Machine learning dataset cleaning - Quality control in manufacturing - Financial anomaly detection - Sensor data validation

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Data Range Limiter

Limit numerical values to specified ranges by clipping, filtering, or marking out-of-bounds values. Perfect for data quality control, sensor data cleaning, business rule enforcement, and data preprocessing. Features: - Range clipping (clip values to min/max boundaries) - Range filtering (remove out-of-bounds rows) - Range marking (flag modified values) - Per-column range configuration - Automatic numeric column detection - Multiple handling strategies - Detailed modification reports - Statistical analysis of changes - Business rule enforcement Common Use Cases: - Sensor data validation and cleaning - Machine learning input preparation - Data quality control and validation - Business constraint enforcement - Outlier management and control - Data preprocessing pipelines

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Z-Score Standardizer

Standardize numerical data using Z-score (standard score) normalization to transform values with mean=0 and standard deviation=1. Perfect for statistical analysis, machine learning feature preprocessing, outlier detection, and data comparison across different scales. Features: - Z-score standardization (mean=0, std=1) - Robust Z-score option (using median and MAD) - Custom scaling to target range - Multiple column selection - Automatic data type detection - Handles missing values intelligently - Preserves non-numeric columns - Comprehensive statistical summary - Outlier detection and reporting Common Use Cases: - Machine learning feature preparation - Statistical hypothesis testing - Outlier detection and removal - Data comparison across different units - Principal Component Analysis (PCA) preprocessing

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Defaults Deep

Apply deep default values using lodash _.defaultsDeep

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Defaults Object

Apply default values to an object using lodash _.defaults

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Difference Arrays

Remove values from array that are present in other arrays using lodash _.difference

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Drop Items

Drop first N items from array using lodash _.drop

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Drop Right Items

Drop last N items from array using lodash _.dropRight

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Drop While

Drop items from array while predicate is true using lodash _.dropWhile

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Duplicate Column Remover

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

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.env Parser

Extract and validate .env style KEY=VALUE configurations with detection of duplicate keys and suspicious spaces/quotes

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Excel Formula Builder

Build Excel formulas with common functions and operators

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Excel Macro Generator

Generate simple VBA macros for Excel automation

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Feature Scaler

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

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Fill Array

Fill array with value from start to end index using lodash _.fill

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Find Key

Find the first matching key using lodash _.findKey

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Flatten Array

Flatten array one level deep using lodash _.flatten

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Flatten Deep Array

Flatten array completely using lodash _.flattenDeep

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Flatten Depth Array

Flatten array to specified depth using lodash _.flattenDepth

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For In Object

Iterate over object properties using lodash _.forIn

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For Own

Iterate over own object properties using lodash _.forOwn

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From Pairs

Convert array of key-value pairs to object using lodash _.fromPairs

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Get Path

Get a value at a path using lodash _.get

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Group By

Group items by a derived key using lodash _.groupBy

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Has In Path

Check if a path exists using lodash _.hasIn

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Has Path

Check if a path exists using lodash _.has

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Head Item

Get the first element of array using lodash _.head

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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

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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

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Index Of

Find the index of a value in array using lodash _.indexOf

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Initial Items

Get all elements except the last using lodash _.initial

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Intersection Arrays

Find common values across multiple arrays using lodash _.intersection

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Invert By

Invert keys grouped by values using lodash _.invertBy

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Invert Object

Invert keys and values using lodash _.invert

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Join Array

Join array items into a single string with a custom separator

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JSON Data Lineage Tracer

Trace JSON field paths, derived dependencies, and transformation history to build a field-level lineage graph

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JSON Flattener

Flatten nested JSON objects into key-value pairs with customizable delimiters and flattening strategies

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JSON Key Extractor

Extract all keys from JSON objects with multiple output formats. Perfect for analyzing JSON structure, documentation generation, and understanding complex nested objects.

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JSON Key Renamer

Batch rename JSON object keys with support for patterns, rules, and transformations. Perfect for API response formatting and data normalization.

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JSON File Merger

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

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JSON Path Extractor

Query JSON with JSONPath or JMESPath-style expressions, inspect matched paths, and highlight extracted values in the original document

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JSON Unflattener

Convert flattened key-value pairs back to nested JSON objects with support for different key formats and data type inference

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JSONata Query & Transform Studio

Preview JSONata-style queries and transformations, compare results across sample payloads, and export the transformed output as JSON, CSV, YAML, or Markdown

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Keys In List

List own and inherited keys using lodash _.keysIn

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Keys List

List own enumerable keys using lodash _.keys

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Last Item

Get the last item from an array using lodash _.last

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Log Parser - Apache/Nginx Access Log Parser

Parse Apache/Nginx access logs using regex patterns and extract structured data

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Log Redactor

Batch redact sensitive data from logs using configurable rules and multiple strategies

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Markdown Merger

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

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Missing Value Handler

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

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Multi-Table Joiner

Merge and join multiple tables with various join types (inner, left, right, full)

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Nth Item

Get the item at a specific index using lodash _.nth

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Object Key-Value Converter

Convert between different object key-value formats and manipulate object structures

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PDF Table Extractor to CSV/JSON

Extract tables from PDFs with OpenDataLoader and export them as structured JSON, flat CSV, or HTML tables

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PII Finder

Scan text/logs for potential PII (Personally Identifiable Information) and annotate positions and types for redaction and compliance checking

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Pull Values

Remove specific values from an array using lodash _.pull

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Remove By Predicate

Remove items that match a predicate using lodash _.remove

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Reverse Array

Reverse an array using lodash _.reverse

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Row Calculator

Perform calculations on each row and add new columns

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Slice Array

Extract a section of an array using lodash _.slice

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Sorted Index

Find the insertion index in a sorted array using lodash _.sortedIndex

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Sorted Unique

Remove duplicates from a sorted array using lodash _.sortedUniq

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Sorted Unique By

Remove duplicates from a sorted array using lodash _.sortedUniqBy

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Structured Log Analyzer

Detect common log formats, extract core fields, infer field types, and export parsed logs as JSON, CSV, or SQL inserts

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SVG Chart to Data Extractor

Reverse engineer SVG charts into data points for line, bar, scatter, and pie-style SVG structures

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Tail Items

Return all but the first array item using lodash _.tail

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Take Items

Take the first N items using lodash _.take

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Take Right Items

Take the last N items using lodash _.takeRight

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Take While

Take items while a predicate matches using lodash _.takeWhile

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TXT File Merger

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

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Uniq Array

Remove duplicates from an array using lodash _.uniq

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Uniq By

Remove duplicates from an array using lodash _.uniqBy with a property path

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Uniq With

Remove duplicates using lodash _.uniqWith and deep equality

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Unzip Arrays

Split grouped arrays using lodash _.unzip

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VBA Code Formatter

Format and beautify VBA code for better readability

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Without Values

Remove specified values from an array using lodash _.without

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XML File Merger

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

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XML Path Extractor

Extract values from XML documents using XPath expressions. Perfect for extracting specific data from complex nested XML structures, API responses, RSS feeds, and configuration files.

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Xor Arrays

Compute the symmetric difference of arrays using lodash _.xor

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YAML File Merger

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

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Zip Arrays

Combine arrays by index using lodash _.zip

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Zip Object

Create an object from keys and values using lodash _.zipObject

Lodash

53 Tools

Access 53 online Lodash tools for data manipulation and object transformation. Secure server-side processing with automatic file deletion after 6 hours.

Array

59 Tools

Access 59 online tools to manipulate, convert, and format arrays. Process data securely on our servers with automatic file deletion after 6 hours.

CSV

62 Tools

Access 62 professional CSV tools to convert, format, and manipulate comma-separated data. Secure server-side processing with automatic file deletion after 6 hours.

JSON

83 Tools

Access 83 online JSON tools for formatting, validating, and converting data. Process files securely on our servers with automatic deletion after 6 hours.

SQL

35 Tools

Access 35 online SQL tools for database management, query formatting, and data conversion. Process files securely without software installation.

Files

50 Tools

Access 50 professional tools for file conversion, validation, and processing. Secure server-side processing with automatic file deletion after 6 hours.