CSV Viewer
How It Works
- 1
Upload CSV File or Paste Data
Upload CSV files from your computer or paste raw CSV text with comma-separated values. Supports headers, quoted fields with embedded commas ("Smith, John"), escaped quotes, multi-line values, and various delimiters (comma, semicolon, tab, pipe). Handles UTF-8 encoding and BOM markers automatically.
- 2
Parser Detects Format and Structure
The RFC 4180-compliant parser auto-detects delimiters by analyzing first 10 rows, identifies header row based on data type consistency, handles quoted fields with embedded delimiters, processes escaped quotes (""quoted"" → "quoted"), and validates row length consistency to detect malformed CSV.
- 3
Render Interactive Table with Features
Displays data in sortable HTML table with click-to-sort columns (numeric, alphabetic, date-aware), real-time search filtering across all fields, pagination for large datasets (1000+ rows), column statistics (sum, average, min, max for numeric columns), and cell selection for copying specific data ranges.
Text Editor vs CSV Viewer
| Feature | Text Editor | CSV Viewer |
|---|---|---|
| Data Visualization | Text editor (raw CSV) | Interactive table with formatting |
| Column Sorting | Manual reorganization | Click header to sort instantly |
| Search/Filter | Ctrl+F (limited) | Real-time row filtering |
| Large Files | Slow loading, crashes | Virtual scrolling, smooth |
| Data Analysis | Manual calculation | Auto column statistics |
| Export Options | None | JSON, Excel, clipboard |
CSV Viewing Examples
Example: CSV Data Visualization
id,name,email,department,salary,hire_date
101,John Smith,[email protected],Engineering,95000,2022-03-15
102,Sarah Johnson,[email protected],Marketing,78000,2021-07-22
103,Mike Chen,"[email protected], [email protected]",Sales,82000,2023-01-10
104,Emily Davis,[email protected],Engineering,105000,2020-11-05Interactive Table View:
┌─────┬──────────────┬────────────────────────────────┬─────────────┬─────────┬────────────┐
│ ID │ Name │ Email │ Department │ Salary │ Hire Date │
├─────┼──────────────┼────────────────────────────────┼─────────────┼─────────┼────────────┤
│ 101 │ John Smith │ [email protected] │ Engineering │ $95,000 │ 2022-03-15 │
│ 102 │ Sarah Johnson│ [email protected] │ Marketing │ $78,000 │ 2021-07-22 │
│ 103 │ Mike Chen │ [email protected], mike.chen... │ Sales │ $82,000 │ 2023-01-10 │
│ 104 │ Emily Davis │ [email protected] │ Engineering │$105,000 │ 2020-11-05 │
└─────┴──────────────┴────────────────────────────────┴─────────────┴─────────┴────────────┘
Column Statistics:
Salary:
Average: $90,000
Min: $78,000 (Sarah Johnson)
Max: $105,000 (Emily Davis)
Total: $360,000
Department Distribution:
Engineering: 2 employees (50%)
Marketing: 1 employee (25%)
Sales: 1 employee (25%)
Sort Options:
✓ Sort by Salary (descending)
✓ Sort by Hire Date (oldest first)
✓ Sort by Name (alphabetical)
Filter Features:
✓ Search: "Engineering" (shows 2 rows)
✓ Salary range: $80,000-$100,000 (shows 2 rows)
✓ Date range: After 2022-01-01 (shows 2 rows)
Export Options:
✓ Export to JSON
✓ Export to Excel (XLSX)
✓ Copy to clipboardKey Changes:
The viewer transforms raw CSV text into interactive table with automatic column detection and type inference. The header row (id, name, email, department, salary, hire_date) becomes clickable column headers for sorting. Quoted fields like '[email protected], [email protected]' are correctly parsed despite containing commas—the quotes indicate a single field value. Numeric values (salary) are right-aligned and formatted with currency symbols for readability. Date values are parsed and formatted consistently. The viewer handles edge cases: embedded commas in quotes, newlines in fields (if present), and escaped quotes. Sorting by salary column orders rows numerically (not alphabetically), showing Emily Davis ($105,000) first when sorted descending. Filtering by department='Engineering' instantly shows matching rows. Column statistics calculate sum, average, min, max for numeric columns—useful for quick data analysis without Excel. Data analysts use CSV viewers to preview large datasets, validate data imports, and perform quick exploratory analysis before loading into databases or analytics tools.
Frequently Asked Questions
What delimiters and edge cases does the CSV viewer handle?
The viewer automatically detects and parses multiple delimiter types: commas, semicolons, tabs, and pipes. It handles complex edge cases including quoted fields with embedded delimiters (e.g., "Smith, John" as single field), escaped quotes within quoted fields (""quoted"" becomes "quoted"), multi-line field values enclosed in quotes, mixed line endings (CRLF vs LF), UTF-8 BOM markers, and empty fields. The parser follows RFC 4180 CSV specification while being lenient with malformed data, making it robust for real-world CSV files exported from Excel, databases, or custom scripts.
Can the viewer handle large CSV files with millions of rows?
Yes. The viewer uses virtual scrolling and lazy rendering to efficiently display large datasets. Files with 100,000+ rows are paginated client-side, rendering only visible rows to maintain 60fps scrolling performance. Memory usage scales with file size, not row count—a 50MB CSV file (approximately 500,000 rows) uses ~100MB browser memory. For files exceeding 100MB, consider splitting them or using streaming parsers. The tool performs best with files under 50MB, where sorting, filtering, and search operations remain instant. No server uploads occur; all processing runs in-browser using Web Workers for non-blocking parsing.
How does the viewer differ from Excel for CSV analysis?
Unlike Excel, the CSV viewer runs entirely in-browser with zero installation, supports instant preview without opening desktop software, preserves original CSV formatting (Excel may auto-convert dates or strip leading zeros), handles UTF-8 and international characters correctly by default, and never modifies the source file. Excel offers advanced features like formulas, pivot tables, and charts, but the viewer excels at quick data inspection, validation before import, and sharing CSV previews via URL. For data analysts, the viewer provides instant column statistics (sum, average, min, max) and JSON export for API integration—features requiring VBA macros or plugins in Excel.