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Data Normalizer

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

Enter any data that needs normalization - CSV, JSON, XML, Excel spreadsheets, and other formats

Select the format of your input data

Select the format for your output data

Select which normalization operations to perform

Key Facts

Category
Data Processing
Input Types
textarea, select, checkbox
Output Type
text
Sample Coverage
4
API Ready
Yes

Overview

The Data Normalizer is a powerful utility designed to clean and standardize inconsistent datasets. By applying programmatic logic, it transforms messy CSV, JSON, XML, or spreadsheet data into a uniform, professional format ready for analysis or integration.

When to Use

  • •When merging datasets from multiple sources that use different formatting conventions.
  • •When preparing raw data for database imports or API consumption that requires strict schema adherence.
  • •When cleaning up exported files containing whitespace errors, empty rows, or inconsistent quote styles.

How It Works

  • •Paste your raw data into the input field and select the source format or use auto-detection.
  • •Choose your desired output format, such as JSON or CSV, to match your target system requirements.
  • •Select specific normalization options like trimming whitespace, removing empty rows, or standardizing headers.
  • •Process the data to generate a clean, structured output ready for immediate use.

Use Cases

Standardizing inconsistent CSV exports from legacy accounting software for import into a modern CRM.
Cleaning up JSON API responses by removing null values and trimming whitespace before frontend integration.
Converting mixed-format XML and TSV files into a unified CSV structure for spreadsheet analysis.

Examples

1. Standardizing CRM Export

Data Analyst
Background
A CRM export contained inconsistent whitespace, empty rows, and mixed quote styles, making it impossible to import into the company database.
Problem
The raw data was too messy for the database schema to accept.
How to Use
Pasted the CSV data, selected 'Trim whitespace', 'Remove empty rows', and 'Standardize quote characters'.
Example Config
trimWhitespace: true, removeEmptyRows: true, standardizeQuotes: true
Outcome
A clean, uniform CSV file that imported into the database without errors.

2. JSON API Response Cleanup

Web Developer
Background
An external API returned JSON data with inconsistent data types and missing fields, causing errors in the application frontend.
Problem
The application required strict data types and consistent field presence.
How to Use
Input the JSON response, enabled 'Auto-detect and convert data types' and 'Fill missing values'.
Example Config
detectDataTypes: true, fillMissingValues: true
Outcome
A standardized JSON object with consistent types and default values for missing fields.

Try with Samples

json, csv, xml
Web Data Serialization Rust Samples
Web Rust data serialization examples including JSON serialization, deserialization, and XML parsing
preferred input family json,xml
json, xml
Windows Serialization - C# Samples
Comprehensive C# serialization examples for Windows platform including JSON, XML, binary serialization, and data transformation
preferred input family json,xml
json, xml
CSV Samples
Sample CSV files with various data types, sizes, and complexity levels
preferred input family json,csv
json, csv
Windows Serialization C++ Samples
Windows C++ data serialization examples including JSON serialization/deserialization and XML parsing
preferred input family json,xml
json, xml

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FAQ

What file formats are supported?

The tool supports CSV, JSON, XML, TSV, and SSV formats.

Can I remove empty rows automatically?

Yes, simply check the 'Remove empty rows' option in the normalization settings.

Does this tool handle data type conversion?

Yes, by selecting 'Auto-detect and convert data types', the tool will attempt to identify and format numbers, booleans, and strings correctly.

Is my data stored on your servers?

No, all data processing is performed locally in your browser to ensure your information remains private.

Can I standardize column headers?

Yes, the 'Standardize column headers' option ensures your data follows a consistent naming convention.

API Documentation

Request Endpoint

POST /en/api/tools/data-normalizer

Request Parameters

Parameter Name Type Required Description
inputData textarea Yes Enter any data that needs normalization - CSV, JSON, XML, Excel spreadsheets, and other formats
inputFormat select Yes Select the format of your input data
outputFormat select Yes Select the format for your output data
normalizationOptions checkbox No Select which normalization operations to perform

Response Format

{
  "result": "Processed text content",
  "error": "Error message (optional)",
  "message": "Notification message (optional)",
  "metadata": {
    "key": "value"
  }
}
Text: Text

AI MCP Documentation

Add this tool to your MCP server configuration: 

{
  "mcpServers": {
    "elysiatools-data-normalizer": {
      "name": "data-normalizer",
      "description": "Pure code-based data format normalization tool that cleans and standardizes messy data using programmatic logic",
      "baseUrl": "https://elysiatools.com/mcp/sse?toolId=data-normalizer",
      "command": "",
      "args": [],
      "env": {},
      "isActive": true,
      "type": "sse"
    }
  }
}

You can chain multiple tools, e.g.: `https://elysiatools.com/mcp/sse?toolId=png-to-webp,jpg-to-webp,gif-to-webp`, max 20 tools.

If you encounter any issues, please contact us at [email protected]