Data Randomizer
Shuffle and randomize data for testing, sampling, and anonymization. Randomize CSV rows, JSON arrays, text lines with optional seeded randomization for reproducible results
Randomization Options
Recommended Settings
Pro Tips
- •Use seeded randomization for reproducible results across multiple runs
- •Preserve header row when randomizing CSV to keep column names intact
- •Shuffle values within columns to create realistic but anonymized test data
- •JSON mode recursively randomizes nested arrays and object key order
- •Text mode can shuffle either entire lines or words within each line
Most Popular
Most users shuffle rows with preserved headers for creating randomized test datasets
When to Use This Tool
Create randomized test datasets from production data samples. Shuffle CSV rows to generate realistic test data for development and QA environments while maintaining data structure and types. Useful for testing sorting, pagination, and data processing logic.
Generate random samples from large datasets for statistical analysis, A/B testing, and machine learning training sets. Randomize data order to eliminate bias and create representative samples for surveys, research, and data science projects.
Shuffle quiz questions, survey options, or multiple-choice answers to prevent pattern recognition and bias. Randomize the order of items in educational content, assessments, and user research to ensure fair and unbiased results.
Randomize values within columns to break correlations between related data points while preserving individual column distributions. Useful for creating privacy-preserving datasets that maintain statistical properties without exposing real relationships.
How It Works
Parse input data based on selected type (CSV parser, JSON.parse, or line splitter)
Identify structural elements (headers, arrays, objects, lines)
Apply Fisher-Yates shuffle algorithm for uniform random distribution
Use seeded random number generator if seed is provided for reproducibility
Preserve structural elements as specified (headers, formatting)
Shuffle rows, columns, values, or nested structures based on options
Reconstruct data in original format with randomized order
100% Private
Files never leave your device. All processing happens locally in your browser.
Lightning Fast
Powered by Pure JavaScript for optimal performance on modern browsers.
Secure
No data collection, no tracking, no sign-up required.
Frequently Asked Questions
What's the difference between shuffling rows and randomizing values?
Shuffling rows changes the order of entire rows (keeping each row's data together). Randomizing values shuffles values within each column independently, so row relationships are broken. Use row shuffling for sampling, value randomization for anonymization.
How does seeded randomization work?
A seed is a starting value that produces the same sequence of 'random' numbers every time. With the same seed, you'll always get the same randomization - perfect for reproducible tests. Different seeds produce different randomizations. Without a seed, you get truly random results each time.
Will this preserve my CSV headers?
Yes! When 'Preserve header row' is enabled (default), the first row stays at the top and is never shuffled. This is essential for keeping column names intact when randomizing data rows. Disable this option if your CSV has no headers.
Can I randomize nested JSON structures?
Absolutely! JSON mode recursively traverses the entire structure, shuffling array elements at every level and randomizing object key order throughout. This works for deeply nested objects with arrays of objects containing more arrays.
Is the randomization truly random?
The tool uses the Fisher-Yates shuffle algorithm which provides uniform random distribution (every permutation is equally likely). Without a seed, it uses JavaScript's Math.random() which is cryptographically pseudo-random - not suitable for security but perfect for data shuffling.
What happens if I shuffle columns in CSV?
Column shuffling rearranges the columns (vertical) while keeping row data aligned. If you have Name, Age, City columns, they might become City, Name, Age. The header row is also shuffled to match. This is useful for testing column-order-independent processing.