JSON to Excel Converter

Convert JSON to Excel Format

This tool converts JSON data into Excel spreadsheets that you can open in Microsoft Excel, Google Sheets, or any spreadsheet application. It handles arrays of objects, nested data structures, and various data types automatically.

How to Use

1. Paste your JSON — Input should be an array of objects
2. Configure options — Set sheet name and flattening preferences
3. Preview the result — Click Preview to see how your data will look
4. Download — Click Download Excel to get your .xls file

JSON to Excel vs JSON to CSV

Both formats export tabular data, but Excel offers advantages for certain use cases:

Use Excel when you need formatting, multiple sheets, or proper data types. Use CSV for maximum compatibility or smaller files.

Handling Nested JSON

When your JSON contains nested objects, the converter flattens them using dot notation for column headers:

// Input
{
  "name": "Alice",
  "address": {
    "city": "New York",
    "zip": "10001"
  }
}

// Excel columns
| name  | address.city | address.zip |
|-------|--------------|-------------|
| Alice | New York     | 10001       |

Arrays within objects

Arrays are converted to comma-separated strings:

// Input
{ "name": "Alice", "skills": ["Python", "JavaScript"] }

// Excel output
| name  | skills              |
|-------|---------------------|
| Alice | Python, JavaScript  |

Data Type Handling

The converter preserves data types where possible:

• Numbers — Stored as numeric values (sortable, summable)
• Booleans — Converted to 1/0 for Excel compatibility
• Strings — Stored as text
• Null/undefined — Empty cells
• Objects — Flattened or stringified

Example Conversion

Input JSON:

[
  {
    "id": 1,
    "name": "Alice Johnson",
    "department": "Engineering",
    "salary": 95000,
    "active": true
  },
  {
    "id": 2,
    "name": "Bob Smith",
    "department": "Marketing",
    "salary": 65000,
    "active": true
  }
]

This produces an Excel file with columns: id, name, department, salary, active — with proper numeric formatting for id and salary.

Programmatic Conversion

JavaScript (with SheetJS)

import * as XLSX from 'xlsx';

const data = [{ name: "Alice", age: 30 }];
const ws = XLSX.utils.json_to_sheet(data);
const wb = XLSX.utils.book_new();
XLSX.utils.book_append_sheet(wb, ws, "Sheet1");
XLSX.writeFile(wb, "output.xlsx");

Python (with pandas)

import pandas as pd
import json

data = json.loads('[{"name": "Alice", "age": 30}]')
df = pd.DataFrame(data)
df.to_excel("output.xlsx", index=False)

Related Tools

• Excel to JSON — Convert Excel back to JSON
• JSON to CSV — Simpler tabular format
• JSON to Table — Preview as HTML table
• JSON Flatten — Flatten nested objects first
• JSON Validator — Validate JSON before converting

Frequently Asked Questions

Can I convert nested JSON to Excel?

Yes! Enable "Flatten nested objects" to convert nested structures into dot-notation columns (e.g., address.city). Arrays become comma-separated values.

What's the maximum file size?

The conversion runs entirely in your browser, so there's no server limit. However, very large datasets (10,000+ rows) may slow down your browser. For massive files, consider using a programming library.

Does it preserve data types?

Yes. Numbers remain numeric (sortable, usable in formulas), booleans become 1/0, and strings remain text. This is an advantage over CSV which treats everything as text.

Can I customize column order?

Column order follows the order of keys in your first JSON object. To reorder, restructure your JSON or reorder columns in Excel after export.

Why .xls instead of .xlsx?

We use Excel XML format (.xls) for browser compatibility without external dependencies. This format opens in all modern spreadsheet applications including Excel 2007+, Google Sheets, and LibreOffice.

Common Mistakes & Pro Tips

• Excel silently coerces strings into dates and numbers — Values like 03/04 are interpreted as a date, and a string of digits becomes a number that may lose leading zeros (007 becomes 7) or switch to scientific notation past 15 digits. A good converter writes such fields as explicit text cells, but if yours doesn't, format the JSON value with a leading apostrophe or set the column type before importing to preserve the exact string.
• Excel's 15-significant-digit limit truncates large integers — Excel stores numbers as IEEE 754 doubles, so any integer longer than 15 significant digits — such as a 64-bit ID, a credit-card number, or a Twitter/X snowflake ID — loses its trailing digits silently (e.g. 1234567890123456789 may become 1234567890123450000). Keep these as text strings in your JSON so they survive the round trip unchanged.
• One sheet, flat columns: nested data needs flattening first — An .xlsx worksheet is a flat grid, so nested objects and arrays must be flattened (dot-notation columns like contact.email) or serialized into a single cell. Arrays of objects map naturally to rows and columns; deeply nested or ragged JSON does not. Decide on a flattening strategy before converting so columns line up across all records.
• A real .xlsx is a zipped XML package, not a CSV with a different name — Genuine .xlsx files are ZIP archives of XML parts (the Open Packaging Convention), which is why they preserve cell types, multiple sheets, and formatting that CSV cannot. Renaming a .csv to .xlsx does not create a valid workbook and Excel will warn about a corrupt or mismatched format. Use a converter that emits the actual spreadsheet binary.
• Leading +, =, -, or @ can trigger formula injection — If a cell value starts with =, +, -, or @, Excel may treat it as a formula, which is both a display bug and a CSV-injection security risk when the data is untrusted. Safe converters prefix such values with a quote or write them as text. Be cautious importing user-supplied JSON that contains strings like =cmd|... into a spreadsheet.