CSV Lint & Formatter
Lint and format CSV data in your browser. Auto-detects delimiters, reports column mismatches and duplicate headers with line numbers. Great for data validation.
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How to Use
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- 1
Enter CSV Data
Paste or type CSV data in the input area. The delimiter will be automatically detected.
- 2
Review Lint Results
Statistics and error list are displayed in real-time. Errors include line numbers and types.
- 3
Format and Output
Select formatting options and click the 'Format' button to generate formatted CSV output.
Input
About CSV Lint & Formatter
CSV Lint & Formatter validates CSV data for structural errors and reformats it directly in your browser. Auto-detection handles comma, tab, semicolon, and pipe delimiters, so you can paste a Google Sheets export, a PostgreSQL COPY output, or a European-locale semicolon-separated file without adjusting settings. Column count mismatches, unclosed quotes, and duplicate headers are flagged with exact line numbers, which is essential when hunting down import failures in database migration scripts, ETL pipelines, or Shopify/Salesforce bulk import tools.
Key Features
- Automatic delimiter detection (comma, tab, semicolon, pipe)
- Syntax error detection (column mismatch, unclosed quotes, empty rows, duplicate headers)
- Statistics display (total rows, data rows, columns, errors, empty rows)
- CSV formatting (remove empty rows, trim cells, align columns)
- Client-side processing via JavaScript (PapaParse)
Common Use Cases
- Validate a Google Sheets or Excel export before bulk-importing into PostgreSQL, MySQL, or a Shopify/Salesforce data loader
- Debug column count mismatch errors in a Python pandas read_csv() or Node.js csv-parse pipeline by finding the offending line numbers
- Check a PostgreSQL COPY or AWS Redshift COPY source file for unclosed quotes and trailing empty rows before running the import
- Validate semicolon-delimited exports from European-locale ERP systems (SAP, Dynamics) where commas are used as decimal separators
- Pre-process test fixture CSV files for database seeding scripts — trim whitespace and remove blank rows before committing to the repo
Frequently Asked Questions
What delimiters are supported?
Comma (,), tab, semicolon (;), and pipe (|) are supported. The auto-detection feature identifies the optimal delimiter, but you can also select one manually.
What errors can be detected?
Column count mismatches (rows with fewer or more fields than the header), unclosed quotes, empty rows, and duplicate header names. Each error shows the exact line number, which directly maps to the line reported in pandas read_csv() errors, PostgreSQL COPY errors, or csv-parse exceptions.
What formatting options are available?
Three formatting options: remove empty rows, trim leading/trailing whitespace in cells, and align column widths with padding. Each can be toggled independently.
Is my input data sent to a server?
No. Parsing and formatting run locally via PapaParse in your browser. It is safe to validate confidential exports such as customer records or financial data.