CSV Validator & Cleaner
Validate CSV structure, detect uneven rows, normalize delimiters, and clean up your data. Perfect for fixing broken CSVs, standardizing formats, and preparing data for import.
100% Client-Side Processing
Your CSV data is processed entirely in your browser. No data is sent to any server.
Table of Contents
What is CSV Validation?
CSV (Comma-Separated Values) validation is the process of checking whether a CSV file conforms to proper CSV structure and format. A valid CSV should have consistent structure, proper quoting, and uniform delimiters throughout.
Common CSV structure requirements:
- Consistent number of columns across all rows
- Proper delimiter usage (commas, semicolons, tabs, etc.)
- Correctly quoted fields when they contain special characters
- Properly escaped quotes within quoted fields
- Consistent line endings (handled automatically by most tools)
Validating CSV before importing or processing helps prevent data corruption, parsing errors, and ensures your data structures are correctly formatted.
Common CSV Issues
❌ Uneven Rows
Different rows have different numbers of columns, which can cause import failures.
❌ Invalid
Name,Email,Age
John,john@example.com,30
Jane,jane@example.com
Bob,bob@example.com,35,Extra✅ Valid
Name,Email,Age
John,john@example.com,30
Jane,jane@example.com,25
Bob,bob@example.com,35❌ Mixed Delimiters
Using different delimiters (commas, semicolons, tabs) in the same file causes parsing errors.
❌ Invalid
Name,Email,Age
John;john@example.com;30
Jane,jane@example.com,25✅ Valid
Name,Email,Age
John,john@example.com,30
Jane,jane@example.com,25❌ Unclosed Quotes
Fields with unclosed quotes cause parsing to fail or misinterpret data.
❌ Invalid
Name,Description
John,"Great person
Jane,"Awesome person"✅ Valid
Name,Description
John,"Great person"
Jane,"Awesome person"⚠️ Empty Rows
Empty rows can cause issues during import or data processing, though they're sometimes intentional.
⚠️ Has Empty Rows
Name,Email
John,john@example.com
Jane,jane@example.com✅ Cleaned
Name,Email
John,john@example.com
Jane,jane@example.comFeatures
🔍 Structure Validation
Validates CSV structure by checking that all rows have the same number of columns. Detects uneven rows and provides precise line numbers for easy fixing.
📏 Uneven Row Detection
Identifies rows with incorrect column counts. Shows exactly how many columns each row has versus the expected count, making it easy to spot missing or extra fields.
🔧 Delimiter Normalization
Automatically detects the delimiter used in your CSV (comma, semicolon, tab, pipe) and normalizes it to a consistent delimiter. Handles mixed delimiters and standardizes the format.
🧹 Empty Row Removal
Optionally removes empty rows from your CSV. Perfect for cleaning up data before import or processing.
✅ Quote Validation
Checks for properly closed quotes in CSV fields. Detects unclosed quotes that can cause parsing errors.
📊 Statistics
Provides detailed statistics about your CSV including total rows, column count, empty rows, and detected delimiter type.
Use Cases
- Data Import Preparation: Validate and clean CSV files before importing into databases, spreadsheets, or applications
- Data Migration: Fix CSV issues when migrating data between systems or formats
- Data Quality Assurance: Ensure CSV files meet quality standards before processing
- Format Standardization: Normalize delimiters and structure across multiple CSV files
- Debugging Import Errors: Identify and fix CSV structure issues that cause import failures
- Data Cleaning: Remove empty rows and fix formatting issues in CSV data
- Compliance Checking: Verify CSV files meet specific format requirements
Best Practices
Validate Before Import
Always validate CSV files before importing them into databases or applications. This helps catch structure issues early and prevents data corruption.
Use Consistent Delimiters
Stick to one delimiter type throughout your CSV file. While different regions may prefer commas or semicolons, consistency is key for reliable parsing.
Quote Fields with Special Characters
Quote fields that contain commas, quotes, newlines, or other special characters. This ensures they're parsed correctly.
Remove Empty Rows
Clean up empty rows before processing. While sometimes intentional, they often cause issues during import and data analysis.
Check Row Consistency
Ensure all rows have the same number of columns. Uneven rows are one of the most common causes of CSV import failures.
Use Headers
Include header rows in your CSV to make data structure clear. Headers should match the number of columns in data rows.
Frequently Asked Questions
The tool automatically detects and supports common delimiters: comma (,), semicolon (;), tab (\t), pipe (|), and colon (:). It automatically detects which delimiter is used in your CSV and can normalize mixed delimiters to a consistent format.
An uneven row is a row that has a different number of columns than expected. For example, if your CSV has 3 columns in the header and most rows, but one row has only 2 columns or 4 columns, that row is "uneven." This usually indicates missing data or extra delimiters and can cause import failures.
Yes. The tool properly handles quoted fields, including fields that contain commas, quotes, or newlines. It validates that quotes are properly closed and handles escaped quotes (double quotes) within quoted fields.
Yes. The "Remove empty rows" option is a checkbox that you can toggle. If unchecked, empty rows will be preserved in the cleaned CSV output. This is useful when empty rows are intentionally used for formatting or grouping.
No. All CSV validation and cleaning happens entirely in your browser using JavaScript. Your CSV data never leaves your device. This tool is designed with privacy as a top priority.
The tool automatically detects the most likely delimiter based on the frequency of delimiter characters in your CSV. If it detects the wrong delimiter, you may see an "uneven rows" error. Check your CSV manually to confirm the correct delimiter, and ensure all rows use the same delimiter consistently.
This tool works in your browser, so very large files (hundreds of MBs) may cause performance issues. For extremely large files, consider using command-line tools or splitting the file. Most typical CSV files work fine.