Categories

Data Cleaner

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

Custom separator for CSV/TSV/SSV formats

Key Facts

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

Overview

The Data Cleaner is a powerful utility designed to streamline your data management by automatically fixing spelling errors, normalizing formats, removing duplicates, and filling in missing values to ensure your datasets are consistent and ready for analysis.

When to Use

  • When preparing raw datasets for import into spreadsheets or database systems.
  • When merging multiple data sources that contain inconsistent formatting or duplicate entries.
  • When cleaning up exported logs or lists that require standardized text casing and date formats.

How It Works

  • Paste your raw data into the input field and select the appropriate input format (e.g., CSV, JSON, or line-by-line).
  • Toggle the specific cleaning options you need, such as removing duplicates, trimming whitespace, or fixing spelling errors.
  • Configure your preferred output settings, including text case styles, date formats, and number standardization.
  • Click process to generate your cleaned data in your chosen output format, such as a formatted table or JSON.

Use Cases

Standardizing customer contact lists by normalizing phone number formats and removing duplicate entries.
Cleaning up messy survey results by fixing spelling errors and converting all text to a consistent case.
Preparing financial data exports by standardizing decimal separators and filling in missing values for accurate reporting.

Examples

1. Standardizing Customer Mailing List

Marketing Coordinator
Background
A marketing team received a customer list from multiple regions with inconsistent capitalization, extra spaces, and several duplicate email addresses.
Problem
The list is unusable for email campaigns due to formatting errors and redundant entries.
How to Use
Paste the list into the tool, enable 'Remove Duplicate Records', 'Trim Whitespace', and set 'Text Case Style' to 'Title Case'.
Example Config
removeDuplicates: true, trimWhitespace: true, caseStyle: 'titlecase'
Outcome
A clean, deduplicated list with uniform capitalization, ready for import into the email marketing platform.

2. Cleaning Financial Export Data

Data Analyst
Background
An analyst exported transaction logs that contain mixed date formats and inconsistent number separators (commas vs. decimals).
Problem
The data cannot be imported into the accounting software because the formats are not recognized.
How to Use
Upload the data, select 'CSV' format, set 'Date Format Standardization' to 'YYYY-MM-DD', and 'Number Format' to 'Decimal'.
Example Config
dateFormat: 'yyyy-mm-dd', numberFormat: 'decimal'
Outcome
A standardized CSV file where all dates and numbers follow a consistent format, allowing for seamless integration into the accounting system.

Try with Samples

csv, video, text

Related Hubs

FAQ

Can I process CSV files with this tool?

Yes, select 'CSV' as your input format and specify a custom separator if your file uses something other than a standard comma.

Does the tool automatically detect date formats?

Yes, the tool includes an 'Auto Detect' feature for date standardization, or you can manually select a specific format like YYYY-MM-DD.

Will this tool remove empty rows from my data?

Yes, by enabling the 'Remove Empty Records' option, the tool will automatically filter out blank lines or rows from your input.

Can I change the casing of my text data?

Yes, you can use the 'Text Case Style' setting to convert your data to lowercase, uppercase, title case, or sentence case.

Is my data stored on your servers?

No, this tool processes your data locally in your browser to ensure your information remains private and secure.

API Documentation

Request Endpoint

POST /en/api/tools/data-cleaner

Request Parameters

Parameter Name Type Required Description
data textarea Yes -
format select Yes -
fixSpelling checkbox No -
standardizeFormat checkbox No -
removeDuplicates checkbox No -
fillMissing checkbox No -
trimWhitespace checkbox No -
removeEmpty checkbox No -
separator text No Custom separator for CSV/TSV/SSV formats
outputFormat select No -
caseStyle select No -
dateFormat select No -
numberFormat select No -

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-cleaner": {
      "name": "data-cleaner",
      "description": "Clean and standardize data by fixing spelling errors, standardizing formats, removing duplicates, and filling missing values",
      "baseUrl": "https://elysiatools.com/mcp/sse?toolId=data-cleaner",
      "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]