Data types
Numbers, booleans, dates and nulls can become plain text when moved into loosely typed formats.
JSON Tools
Excel to JSON Converter Online Free
Convert Excel XLSX files into structured JSON format. Supports nested objects, arrays, dot notation columns and API-ready JSON output.
Spreadsheet conversion guide
Design spreadsheet columns for reliable JSON output
Excel to JSON turns worksheet rows into objects, usually using the first row as property names. Clean headers and consistent cell types are essential. Dot notation or indexed paths can reconstruct nested structures, but they must be used consistently and tested with empty cells, repeated child records and multiple worksheets.
✓ Browser processing
✓ No account required
✓ Free to use
✓ Developer friendly
Step-by-step
A reliable workflow
1
Choose the workbook and worksheet.
2
Confirm that the header row contains unique property names.
3
Select nesting and output options.
4
Convert and validate representative JSON records.
Data transformation
Preserve structure, types and business meaning
Converting structured data is not only a formatting task. Arrays, nested objects, dates, numbers, null values and duplicate keys may map differently between JSON, CSV, XML, YAML and spreadsheets. A successful export should preserve the meaning needed by the destination system, not merely create a file that opens.
Nested structure
Tabular formats require flattening, multiple sheets or repeated rows to represent nested arrays and objects.
Schema expectations
Valid syntax does not guarantee that required fields, allowed values or relationships are correct.
Character encoding
UTF-8 handling is essential for names, symbols and multilingual data.
What the operation can change
- Flattening can turn nested paths into column names.
- CSV cannot natively preserve objects, arrays or strong data types.
- Spreadsheet software may reinterpret identifiers, dates and leading zeros.
- Round trips between formats may not reproduce the original structure exactly.
Domain-specific verification
- Compare record counts and required field counts.
- Inspect leading zeros, large numbers, dates, nulls and Unicode text.
- Validate against a schema or import a small sample into the destination system.
- Keep the original source file and document any flattening convention used.
Before processing
Input checks that prevent bad conversions
- 01Use one unique header for every column and remove merged header cells before conversion.
- 01Decide whether dot-notation headers describe nested objects, arrays or literal property names.
- 01Format identifiers, postcodes and codes as text when leading zeros must be preserved.
After processing
Verify the result before integration
- ✓Verify the first and final spreadsheet rows against the generated objects.
- ✓Check blank cells, formulas, dates and large numbers for expected JSON values.
- ✓Validate nested objects and arrays with the target API or a JSON Schema.
Troubleshooting
Common conversion failures
Dates are unexpected numbers
Excel may store dates as serial values; standardise the source column or convert it deliberately.
Nested arrays are malformed
Check numeric indexes in headers such as items.0.sku and ensure related columns use the same path.
A code lost leading zeros
Format the source column as text and avoid automatic numeric inference.
Practical use cases
API seed data
Create JSON fixtures from a maintained workbook.
Product catalogues
Convert product, pricing and category rows into objects.
ERP integration
Prepare structured imports from business-maintained spreadsheets.
Prototype content
Turn editorial or configuration tables into application data.
Common mistakes
- ⚠Using duplicate or blank headers
- ⚠Mixing numbers, text and dates in one column
- ⚠Merging cells inside the data table
- ⚠Assuming formatted display values equal raw cell values
Professional tips
- ✓Use machine-friendly headers
- ✓Keep one record per row
- ✓Store identifiers as text
- ✓Validate nested output and record counts
Continue the workflow
Frequently asked questions
Which row becomes the JSON keys?+
The first data header row is normally used as object property names.
How can I create nested objects?+
Use supported path-style headers such as customer.name consistently, then review the generated structure.
How can I create arrays?+
Supported indexed headers such as items.0.product can create arrays, but repeating child tables may be better handled as separate sheets.
Why are Excel dates difficult?+
Excel stores dates as serial values with formatting. Confirm the converter produces the required ISO or display format.
Can multiple sheets be converted?+
Where supported, sheets can become separate datasets or properties. Confirm how relationships are represented.
How are blank cells handled?+
They may become empty strings, nulls or omitted properties depending on options. Choose a rule that matches the consumer.
Is my data uploaded to a server?+
The supported FormatForge data tools process input in your browser. Avoid pasting secrets, production credentials or personal data into any online utility.
Can I use the tool on mobile?+
Yes. For large files, deeply nested data or long configuration files, a desktop browser normally provides more memory and a better editing area.
What should I do before using converted output?+
Validate the result, compare representative records and test it in a non-production environment before using it in an application, pipeline or deployment.
Why can large files take longer?+
Parsing, flattening, validating and rendering large nested structures uses your device memory and processor.