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Spreadsheet Converter

Convert spreadsheets between popular formats while preserving data and formulas. Supports Excel (XLSX/XLS), CSV, and ODS formats.

Spreadsheet Converter

Convert spreadsheets between popular formats while preserving data and formulas. Supports Excel (XLSX/XLS), CSV, and ODS formats.

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Maximum file size: 50MB

Why Use This Tool?

✓ Convert Excel to CSV for database import and legacy system compatibility - transform XLSX spreadsheets into plain CSV for importing into SQL databases, CRM systems, accounting software requiring CSV input, enable data migration from modern Excel to older systems accepting only comma-separated format, eliminate 'unsupported file type' errors when uploading data to web applications, APIs, data warehouses expecting standardized CSV structure

Supported Formats

  • Excel (XLSX/XLS) - Microsoft Excel spreadsheets
  • CSV - Comma Separated Values
  • ODS - OpenDocument Spreadsheet

Common Questions

  • Q: What happens to formulas when converting Excel to CSV or vice versa? Excel to CSV conversion: CSV format stores only values (plain text), not formulas - =SUM(A1:A10) becomes the calculated result '450' in CSV, formula is lost permanently. Other losses: formatting (colors, fonts), multiple sheets (CSV = single sheet), charts, macros, cell comments, merged cells. CSV exists for data exchange not spreadsheet features. CSV to Excel conversion: plain values import as numbers/text in Excel, no formulas created (how would converter know '450' was originally a SUM?), but you can add formulas afterward in Excel. Excel to Excel (XLSX ↔ XLS ↔ ODS): formulas typically preserved if formula syntax compatible between versions, complex Excel-specific functions may not translate to ODS, some newer Excel 365 functions don't exist in older XLS format. Best practice: if formulas matter, stay in Excel-compatible formats (XLSX, ODS), only use CSV as final export for data consumption not ongoing analysis.
  • Q: How do I handle special characters, unicode, and international text in CSV conversions? CSV encoding challenges: CSV is plain text but text can be encoded multiple ways - UTF-8 (supports all languages, emojis, special characters), Windows-1252/Latin-1 (Western European only), ASCII (English only). Excel defaults: Windows Excel creates CSV in Windows-1252 (breaks international characters), Mac Excel uses UTF-8 (better). Import problems: CSV with Chinese/Arabic/emoji characters saved in Windows-1252 shows gibberish when opened, UTF-8 CSV may show mojibake if app expects Windows-1252. Solutions: (1) Always save CSV as UTF-8 with BOM (Byte Order Mark) for Excel compatibility, (2) Use XLSX instead of CSV when international characters critical (Excel stores Unicode natively), (3) When importing CSV, specify UTF-8 encoding explicitly, (4) Test with sample data containing accents, Asian characters before full conversion. Excel workarounds: save as 'CSV UTF-8 (Comma delimited)' not plain 'CSV', open CSV in Notepad first to verify characters display correctly indicating proper encoding.
  • Q: Can I convert multi-sheet Excel workbooks or does conversion only handle single sheets? Multi-sheet conversion scenarios: Excel workbook with 5 sheets converted to CSV: only active/first sheet exports (CSV specification = single table), other sheets lost, must export each sheet separately to individual CSV files. Excel to ODS: all sheets preserved (ODS supports multiple sheets), formulas and most formatting carries over. CSV to Excel: imports as single sheet (CSV has no sheet concept), but can import multiple CSV files as separate sheets in same workbook using import wizard. Workflow for multi-sheet workbook to CSV: (1) Open Excel workbook, (2) Select first sheet, save as CSV, (3) Select second sheet, save as different CSV filename, (4) Repeat for each sheet, (5) Result: 5 CSV files instead of 1 workbook. Reverse: import each CSV as separate sheet. Better format for multi-sheet: if retaining sheet organization matters, convert to XLSX or ODS not CSV. Only use CSV when single-table data export is goal (database import, single dataset sharing).
  • Q: Will charts, images, and conditional formatting survive the conversion? Feature preservation by conversion path: Excel to CSV: only cell values survive, formatting (colors, fonts, borders, conditional formatting) completely lost, charts disappear, images removed, only naked data remains. CSV to Excel: imports as plain unformatted data, no formatting inherited from CSV (CSV has no formatting metadata). Excel to ODS: most formatting preserved (fonts, colors, borders generally work), conditional formatting may translate with caveats (different condition syntax), charts often preserve but complex chart types may simplify, images usually embed correctly. ODS to Excel: similar to reverse, common features work, advanced features may degrade. XLS to XLSX (same vendor): very high fidelity, charts and images reliably preserve, formatting nearly 100% accurate, macros may require update to new format. Best practice: if visual presentation matters (charts, formatting), use XLSX or ODS, only use CSV when you need pure data (importing to database, sending to data pipeline, loading into analytics tool treating data programmatically not visually).
  • Q: How should I handle CSV delimiter issues - commas, semicolons, tabs? CSV delimiter confusion: 'CSV' means Comma-Separated but different regions use different delimiters - US/UK: comma (,), European countries: semicolon (;) because they use comma as decimal separator (1,5 not 1.5), Tab-delimited: tabs (\t) between columns. Problems: US CSV opened in German Excel shows all data in column A (expecting semicolons), European CSV in US Excel has values split incorrectly. Detection: most modern tools auto-detect delimiter (try comma, if columns don't split properly try semicolon, then tab), but auto-detection fails with ambiguous data. Export solutions: (1) Specify delimiter explicitly when saving CSV (Excel: 'CSV UTF-8' vs 'CSV semicolon'), (2) Use TSV (Tab-Separated Values) to avoid comma/semicolon ambiguity, (3) For international distribution use XLSX avoiding delimiter issues entirely. Import solutions: use import wizard specifying delimiter manually, preview data before import to verify correct column splits. Industry practices: data science/programming tools (Python, R) expect comma-delimited, European business software often expects semicolon, safest: ask recipient what delimiter they expect.

Pro Tips & Best Practices

  • 💡 Save working Excel version before converting to CSV to preserve formulas: Irreversible conversion trap: Excel to CSV loses formulas forever, once converted can't recover original formulas from calculated values. Workflow: (1) Keep master Excel file (.xlsx) with all formulas, formatting, multiple sheets, (2) Save copy as CSV only when needed for specific system import, (3) Never delete Excel original after CSV export, (4) Treat CSV as disposable export format not source of truth. Version control: use meaningful filenames - 'SalesData_2024_MASTER.xlsx' (keep forever) vs 'SalesData_2024_export.csv' (for one-time import). Horror story: converted quarterly financial model to CSV for import, deleted Excel original to save space, later needed to update formula - had to recreate entire model from scratch. Cloud storage is cheap, recreating complex formulas is expensive. Archive Excel originals, generate CSV exports on demand.
  • 💡 Verify data integrity after conversion by spot-checking calculations and formats: Conversion verification checklist: (1) Row count matches (original Excel: 5,482 rows → converted file: 5,482 rows), (2) Column count matches (12 columns → 12 columns), (3) Sample calculations correct (total revenue cell shows same value), (4) Dates display correctly (2024-01-15 not 45307 Excel serial number), (5) Special characters intact (names with accents, unicode symbols), (6) Number precision maintained (scientific notation, decimals). Automated testing: sum original column, sum converted column, compare totals - if different, data corruption occurred. Common issues: dates become numbers (Excel serial dates to CSV), leading zeros dropped (ZIP codes 01234 becomes 1234), large numbers switch to scientific notation (credit card 1234567890123456 becomes 1.23E+15). Fix before distributing: wrong data in production systems causes cascading problems.
  • 💡 Use XLSX over legacy XLS for better compatibility and larger file support: XLS vs XLSX differences: XLS (Excel 97-2003): 65,536 row limit, 256 column limit, larger file sizes (inefficient binary format), compatibility with ancient Excel versions. XLSX (Excel 2007+): 1,048,576 row limit (16x more), 16,384 column limit (64x more), smaller files (ZIP-compressed XML), better corruption recovery, modern standard. When to use XLS: recipient explicitly requires it (old Excel 2003), legacy system only imports XLS (rare now), otherwise avoid. When to use XLSX: default choice for new spreadsheets, large datasets (over 60k rows), better compatibility with Google Sheets, LibreOffice, modern systems. File size example: same data - XLS: 15MB, XLSX: 3MB (5x smaller). Row limit example: analytics export with 100k rows fails in XLS (row limit), works fine in XLSX. Industry migration: XLS deprecated since 2007 (17+ years), assume XLSX unless proven otherwise. Conversion: if you have old XLS files, convert to XLSX for future-proofing.
  • 💡 Include headers in first row when creating CSV for database imports: CSV header row conventions: first row contains column names (ID, Name, Email, Price) in database-import-friendly format, subsequent rows contain data. Header benefits: (1) Database import tools auto-map columns to database fields, (2) Recipient knows what each column represents without separate documentation, (3) Data visualization tools use headers as axis labels, (4) Programmatic processing (Python pandas) treats first row as column names. Header formatting: use underscores not spaces (first_name not 'First Name'), avoid special characters, keep concise (max 30 chars), match database field names exactly if known. Bad headers: 'Customer First Name (required)', good: 'first_name'. No header row: causes confusion (what is column 3?), requires separate documentation, import tools assign generic names (Column1, Column2), error-prone manual mapping. Verification: open CSV in text editor, confirm first line looks like headers not data ('Name,Email' not 'John,john@email.com'). Some systems accept 'no header' CSV but most expect headers - when in doubt include them.
  • 💡 Escape special characters properly - quotes, commas, newlines in CSV data: CSV escaping rules: (1) Fields containing comma: wrap in double quotes ('Last, First' becomes '"Last, First"'), (2) Fields containing double quote: escape with another quote (He said "hi" becomes '"He said ""hi""'), (3) Fields containing newline: wrap in quotes (multiline address becomes '"123 Main St\nApt 4"'). Consequences of improper escaping: commas in data split into additional columns (customer note 'red, blue, green' becomes three columns), quotes break parsing (premature field termination), newlines create phantom rows. Excel handles automatically: when saving as CSV, Excel escapes automatically (you don't manually add quotes), but if generating CSV programmatically or editing manually must follow rules. CSV libraries: use csv module in Python, write.csv in R, fputcsv in PHP - they handle escaping correctly, never manually concatenate with commas (asking for escaping bugs). Testing: create CSV with challenging data (company names with commas, addresses with quotes, descriptions with newlines), verify imports correctly. CSV complexity: paradoxically 'simple' format requires careful escaping making it fragile - why binary formats (XLSX) are more robust despite being 'complex'.

When to Use This Tool

  • Database & CRM Import/Export: Convert Excel customer lists, product catalogs, contact databases to CSV format for importing into MySQL, PostgreSQL, Salesforce CRM systems, transform database query exports from raw CSV into formatted Excel spreadsheets for client reporting and stakeholder presentations, migrate data between CRMs, ERPs, business systems requiring specific CSV delimiter and encoding configurations for clean import without data corruption or field mismapping
  • Data Analysis & Processing: Convert CSV data exports from Google Analytics, payment processors, ad platforms into Excel for pivot table analysis and formula calculations, transform Excel financial models into CSV for importing into Python pandas, R statistical tools for advanced analysis and visualization, standardize mixed-format spreadsheet data into consistent XLSX format for team collaboration ensuring formula compatibility and shared calculation accuracy
  • Cross-Platform Collaboration: Convert proprietary XLSX files to ODS format for teams using LibreOffice, Google Sheets avoiding Microsoft Office licensing requirements, transform ODS spreadsheets to XLSX when delivering to Microsoft-centric clients requiring Excel-native format for editing, ensure budget spreadsheets, project plans, inventory trackers work across Windows, Mac, Linux environments without format barriers or compatibility errors requiring expensive software purchases
  • System Integration & Automation: Convert Excel price lists, inventory files, product data to CSV for automated upload to e-commerce platforms like Shopify, WooCommerce, Amazon expecting CSV bulk import, transform accounting system CSV exports into Excel for manual review, adjustments, audit trail documentation before submitting to tax preparers or financial auditors, prepare data feeds in specific CSV format for third-party API integrations, data warehouses, marketing automation platforms
  • Legacy System Support: Convert modern XLSX spreadsheets to older XLS format for compatibility with legacy enterprise software stuck on Excel 2003 or earlier versions, transform outdated XLS files to XLSX enabling editing in Google Sheets, LibreOffice, modern Excel with expanded row limits and improved features, migrate historical spreadsheet archives from proprietary formats to open ODS ensuring long-term accessibility without vendor lock-in
  • Reporting & Documentation: Convert CSV transaction exports, sales reports, analytics data into Excel with proper formatting, headers, summary calculations for executive dashboards and board presentations, transform Excel budget templates, financial projections, business plans into CSV for version control systems (Git) enabling line-by-line diff tracking unlike binary XLSX, prepare standardized report templates in XLSX ensuring consistent formatting, formulas, branding across departmental reporting

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