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Choose CSV File

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Select Transformation

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Select Columns to Check for Duplicates

Hold Ctrl/Cmd to select multiple columns. Rows will be considered duplicates if they have the same values in all selected columns.

Keep first occurrence (uncheck to keep last)

Select Column to Split

Split Delimiter

New Column Prefix (Optional)

If left empty, new columns will be named [original_column]_1, [original_column]_2, etc.

Select Columns to Remove

Hold Ctrl/Cmd to select multiple columns

Column to Sort By

Sort Ascending

Column to Filter

Filter Type

Filter Value

Select Column to Rename

New Column Name

Select Column

Modification Type

Select columns to trim (hold Ctrl/Cmd to select multiple):

Select the columns where you want to remove leading and trailing whitespace

Select columns to fix (hold Ctrl/Cmd to select multiple):

Select the text columns where you want to fix encoding issues. Leave empty to fix all text columns.

Select Columns to Merge

Hold Ctrl/Cmd to select multiple columns. The columns will be merged in the order selected.

Merge Delimiter

New Column Name

Remove original columns after merging

Operation Type

First Column

Select the first column for the operation

Second Column

Select the second column for the operation

Result Column Name

Remove original columns after calculation

Note: Non-numeric values will be treated as 0. Division by zero will result in NaN.

Select Date Columns

Hold Ctrl/Cmd to select multiple columns containing dates

Date Format Type

Select Format

Automatically detect input date formats (recommended for mixed formats)

Preserve time components if present

Note: Invalid dates will be kept as is. The transformation will try to parse dates intelligently when "Automatically detect" is enabled.

Select Phone Number Columns

Country Format

Output Format

Select Address Columns

Country Format

Format Guidelines:

US: Street number, street name, city, state, and ZIP code
Example: 123 Main Street, New York, NY 10001
UK: House number, street name, city, county (optional), and postcode
Example: 10 Downing Street, London, Greater London, SW1A 2AA
France: Number, street name, postal code, and city
Example: 5 Avenue des Champs-Élysées, 75008 Paris
Germany: Street name, house number, postal code, and city
Example: Unter den Linden 77, 10117 Berlin

Clean Shopify Data

Select the cleaning operations you want to apply. Changes will be highlighted in the preview.

Clean Tags Example: "mens,tshirt, cotton,basics" → "basics, cotton, mens, tshirt"

Clean HTML Content Removes unnecessary HTML tags and fixes formatting

Fix Variant Format Example: "Size:Small/Color:Blue" → "Size: Small / Color: Blue"

Fix Title Case Example: "BASIC T-SHIRT" → "Basic T-Shirt"

Select Company Name Columns

This transformation will:

Standardize company suffixes (LLC, Inc, Corp)
Remove common prefixes (The, A, An)
Fix capitalization
Standardize common variations

SKU Prefix (Optional)

Select Source Columns for SKU Generation First 3 characters from each selected column will be used

Target Column Name

Preview First 5 rows shown

Original Data

Transformed Data

Processing your CSV file...

Our CSV Repair Tools

Clean & Organize

✓ Remove duplicate entries
✓ Fix text encoding issues
✓ Clean up extra spaces
✓ Standardize column names

Transform & Format

✓ Convert date formats
✓ Change text case
✓ Split or merge columns
✓ Calculate between columns

Filter & Sort

✓ Sort by any column
✓ Filter specific rows
✓ Extract matching data
✓ Organize by category

All tools work instantly in your browser - no software to install.

Start Fixing Your CSV

CSV Repair Tutorials

Clean Up Customer Data

Remove duplicates and fix formatting issues

Upload your customer CSV
Remove duplicate entries
Fix text encoding issues
Clean up extra spaces

Read Full Guide

Fix Date Formats

Convert dates to a standard format

Upload your CSV file
Select date columns
Choose target format
Preview and download

Read Full Guide

Extract Specific Data

Filter and sort rows by any criteria

Upload your CSV file
Choose columns to filter
Set filter conditions
Sort results if needed

Read Full Guide

Need help with something specific? Check out our complete guide library.

Frequently Asked Questions

Our CSV repair tool makes it easy to remove duplicates. Simply upload your CSV file, select the "Remove Duplicate Rows" option, and click fix. The tool will automatically identify and remove any duplicate entries, keeping only unique rows in your data.

Yes! Our "Split Column" feature allows you to split any column containing multiple values separated by a delimiter (like a comma). This is perfect for separating full names into first and last names, or breaking down address fields into individual components.

Absolutely! We process your transformations securely and never store your data permanently. Your CSV files are only kept in memory during processing and are automatically deleted once the transformation is complete. We maintain strict data privacy practices to ensure your information remains secure throughout the transformation process.

Fix Your CSV Files Instantly

Fix Your CSV File

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Format Guidelines:

Clean Shopify Data

Preview First 5 rows shown

Original Data

Transformed Data

Our CSV Repair Tools

Clean & Organize

Transform & Format

Filter & Sort

CSV Repair Tutorials

Clean Up Customer Data

Fix Date Formats

Extract Specific Data

Frequently Asked Questions

How do I remove duplicate rows from my CSV file?

Can I split a column that contains multiple values?

Is my data safe when using CSVFix?