# 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

> Canonical page: https://elysiatools.com/en/tools/data-range-limiter

- **Category:** Data Processing

- **Keywords:** range, limit, clip, filter, bounds, validation, data quality, constraints, clipping, outlier control

## Overview

The Data Range Limiter is a powerful utility designed to enforce numerical constraints on your datasets by clipping, filtering, or flagging values that fall outside defined boundaries. It ensures data consistency and quality, making it an essential tool for cleaning sensor outputs, preparing machine learning inputs, and enforcing strict business rules across your CSV files.

## Inputs

- **CSV Data** (textarea): CSV data with headers and numeric values to be processed
- **Range Configuration (JSON)** (textarea): JSON object specifying min/max ranges for each column. Example: {"age": {"min": 18, "max": 65}}
- **Target Columns (Optional)** (textarea): Specify which columns to apply range limits to. Leave empty to auto-detect numeric columns.
- **Handling Strategy** (select)
- **Auto-detect Reasonable Ranges** (checkbox): Automatically suggest reasonable ranges based on data distribution
- **Preserve Original Columns** (checkbox): Keep original values with "_original" suffix
- **Mark Modified Values** (checkbox): Add flags to indicate which values were modified
- **Include Statistics** (checkbox): Generate detailed statistics about modifications

## When to use

- When you need to clean sensor data by removing or capping extreme outliers.
- When preparing datasets for machine learning models that require strictly normalized input ranges.
- When enforcing business constraints, such as ensuring age, salary, or temperature values remain within logical limits.

## How it works

- Upload your CSV data and define your range constraints using a simple JSON configuration.
- Select a handling strategy: 'Clip' to force values to boundaries, 'Filter' to remove invalid rows, or 'Mark' to flag discrepancies.
- Optionally enable auto-detection to identify numeric columns and generate statistical reports on the modifications performed.
- Download your processed, validated dataset with optional modification flags for easy auditing.

## Use cases

- Sensor data validation and cleaning to remove noise and hardware errors.
- Machine learning input preparation to ensure feature values stay within expected bounds.
- Data quality control to identify and rectify entries that violate business logic or safety constraints.

## Frequently asked questions

### What is the difference between clipping and filtering?

Clipping adjusts out-of-bounds values to the nearest allowed minimum or maximum, while filtering removes the entire row containing the invalid value.

### Can I apply different ranges to different columns?

Yes, the JSON configuration allows you to specify unique min and max values for each individual column in your dataset.

### What happens if I don't specify target columns?

The tool will automatically detect all numeric columns in your CSV and apply the range constraints to them.

### Can I keep my original data for comparison?

Yes, by enabling 'Preserve Original Columns', the tool will retain your original values with an '_original' suffix in the output.

### Does this tool support non-numeric data?

The tool is designed for numeric range enforcement; non-numeric columns are ignored during the range processing phase.

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## Samples

- [CSV Samples](https://elysiatools.com/en/samples/csv-samples): Sample CSV files with various data types, sizes, and complexity levels
- [Python Samples](https://elysiatools.com/en/samples/python): Essential Python code examples and Hello World demonstrations
- [WebRTC Real-Time Communication Samples](https://elysiatools.com/en/samples/webrtc-samples): Comprehensive WebRTC samples for peer-to-peer audio/video communication, data channels, screen sharing, and signaling server implementation
- [Distributed Tracing Samples](https://elysiatools.com/en/samples/distributed-tracing-samples): Comprehensive distributed tracing examples using Jaeger, OpenTelemetry, and other modern observability tools for microservices architecture