Data Cleaner | Online Free Tool | Elysia Tools

Tool usage guide

Learn when to use this tool, what it supports, and how real users apply it.

Key facts

Category: Data Processing
Input types: textarea, select, checkbox, text
Output type: text
Sample coverage: 4
API ready: Yes

Overview

Data Cleaner cleans and standardizes raw datasets by fixing spelling mistakes, removing duplicate entries, trimming extra whitespace, filling missing fields, and standardizing dates, numbers, and text case styles across multiple delimited formats and JSON.

When to use

When preparing raw CSV, TSV, or JSON records with inconsistent formatting for database ingestion.
When deduplicating customer lists or transaction rows while removing empty lines and extra spaces.
When harmonizing mixed date structures, number conventions, and letter casing across merged text files.

How it works

1Enter or paste raw data and select its input structure, such as Line by Line, CSV, TSV, SSV, or JSON array.
2Enable cleaning options including duplicate removal, whitespace trimming, empty row elimination, spelling fixes, and missing value replacement.
3Select formatting standards for date representations, number delimiters, text case rules, or custom field separators.
4Choose the desired output format (CSV, formatted table, JSON, or simple list) and review the cleaned results along with an optional summary report.

Use cases

Customer List Sanitization: Trim whitespace, standardize names to Title Case, and remove duplicate entries across contact tables.
Financial Log Normalization: Harmonize conflicting number decimal separators and convert varied date entries into standard ISO formats.
Survey Data Preparation: Fill missing fields, eliminate blank response rows, and output uniform JSON arrays for downstream processing.

Examples

1. Standardizing Customer Contact Exports

Marketing Operations Specialist

Background

A marketing specialist exported contact lists containing duplicate entries, trailing whitespace, and inconsistent letter casing.

Problem

The list needs to be deduplicated, formatted with uniform Title Case, and cleaned of blank rows before loading into an email tool.

How to use

Paste the raw text, set Input Format to CSV, check Remove Duplicates, Trim Whitespace, and Remove Empty Records, and set Text Case Style to Title Case.

Input Format: CSV, Remove Duplicates: true, Trim Whitespace: true, Remove Empty Records: true, Text Case Style: Title Case, Output Format: CSV

Outcome

Outputs a clean, deduplicated CSV table with uniform Title Case formatting and no empty records.

2. Converting Delimited Sales Logs to JSON

Data Analyst

Background

Sales logs from multiple regional branches use tab separators with conflicting date styles and European comma decimal formats.

Problem

Downstream analytics require standard YYYY-MM-DD dates, standard decimal points, and JSON array formatting.

How to use

FAQ

What input formats can I clean with this tool?

The tool accepts Line by Line, CSV (comma-separated), TSV (tab-separated), SSV (space-separated), and JSON array formats.

Can I convert data from one format to another while cleaning?

Yes, you can input data in one format (such as TSV) and select a different output format like CSV, Formatted Table, JSON, or Simple List.

Which date formats can be standardized?

You can auto-detect date formats or standardize entries into YYYY-MM-DD, MM-DD-YYYY, DD-MM-YYYY, or ISO 8601.

How does the tool handle missing values and whitespace?

Selecting Trim Whitespace strips leading and trailing spaces, while Fill Missing Values populates absent fields and Remove Empty Records discards blank rows.

What does the Include Report option do?

Enabling Include Report adds a human-readable summary detailing the cleaning modifications applied directly before the processed data.

Input Format: TSV, Date Format Standardization: YYYY-MM-DD, Number Format Standardization: Decimal (1,234.56), Output Format: JSON Format

Data Cleaner

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Learn when to use this tool, what it supports, and how real users apply it.

Key facts

Overview

When to use

How it works

Use cases

Examples

1. Standardizing Customer Contact Exports

2. Converting Delimited Sales Logs to JSON

FAQ

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