JSON to XLSX runs through our table engine, creating one worksheet whose column order follows first-seen keys.
From real runs
| Element | Result | What happens |
|---|---|---|
| Top-level records | Kept | A JSON array of objects becomes one Excel row per object. |
| Column order | Changed | Columns are ordered by first-seen key order as records are scanned, then late keys are appended to the right. |
| Nested objects | Changed | Flattening matches JSON to CSV behavior, using dot.path columns for nested objects. |
| Arrays | Changed | Arrays become JSON strings or indexed columns depending on the array mode, so product variants can remain readable. |
| Workbook shape | Changed | The output is a single-sheet XLSX workbook, not one sheet per nested collection. |
| Unicode | Kept | Unicode text is written into the workbook cells without CSV encoding concerns. |
| JSON types | Changed | Booleans, numbers, strings, and nulls map to worksheet cell values; object structure beyond the table is flattened or stringified. |
A JSON array of objects becomes one Excel row per object.
Columns are ordered by first-seen key order as records are scanned, then late keys are appended to the right.
Flattening matches JSON to CSV behavior, using dot.path columns for nested objects.
Arrays become JSON strings or indexed columns depending on the array mode, so product variants can remain readable.
The output is a single-sheet XLSX workbook, not one sheet per nested collection.
Unicode text is written into the workbook cells without CSV encoding concerns.
Booleans, numbers, strings, and nulls map to worksheet cell values; object structure beyond the table is flattened or stringified.
We converted our JSON sample (557 B) with Table engine on 2026-10-20. It took 1 ms and produced a 17 KB Excel XLSX file, 31× larger than the input. The engine raised no warnings.
Input · sample.json · 557 B
Array of 4 objects; keys: company, stage, arr, owner, close_date
Output · sample.xlsx · 17 KB
Workbook with 1 sheet (json); first sheet 5 rows, 25 cells, 0 formulas, header filter, fitted column widths
pandas json_normalize
python - <<'PY'
import json
import pandas as pd
with open("products.json", encoding="utf-8") as fh:
data = json.load(fh)
pd.json_normalize(data).to_excel("products.xlsx", index=False, sheet_name="Products")
PYArrays normally need custom expansion or stringification before writing to Excel.
jq plus csvkit workflow
jq -r '(map(keys) | add | unique) as $cols | $cols, map([.[ $cols[] ]])[] | @csv' in.json > out.csvThis creates CSV, not XLSX; open it in Excel or use Python when a workbook file is required.
The HTTP API and the MCP server run the exact engine this page uses (Table engine); the response carries the same warnings the web converter shows. Check support first with GET /api/v1/pairs?from=json&to=xlsx.
curl -X POST https://filetypeconverters.com/api/v1/convert \
-H "Authorization: Bearer $FTC_API_KEY" \
-F "file=@input.json" -F "from=json" -F "to=xlsx" \
-F 'options={"encoding":"utf-8"}' \
--output output.xlsx| Key | Applies to | Type | Default |
|---|---|---|---|
| encoding | Reading JSON | utf-8 · latin1 | utf-8 |
| flattenSeparator | Reading JSON | string | . |
| arrayMode | Reading JSON | json · explode | json |
| sheetName | Writing Excel XLSX | string |
No. It creates one sheet. Nested objects are flattened or stringified instead of becoming separate worksheets.
Columns follow first-seen key order. Keys that appear only in later records are appended after the earlier columns.
Arrays are changed into JSON strings or indexed columns. Excel does not have a native nested array cell type.