Excel ⇄ JSON Converter

An API response or a database export is JSON, but the person who needs to look at it — a stakeholder, a support team, a finance analyst — works in spreadsheets. This tool turns a JSON array of objects into a real .xlsx workbook with one column per field, so it opens directly in Excel, Google Sheets, or Numbers with no import wizard.

A spreadsheet is flat, so nested JSON needs a flattening rule. Nested objects become dot-notation columns — {"address":{"city":"Surat"}} becomes a column named address.city. An array of primitives (like ["dev","go"]) is joined into one cell as dev, go; an array of objects is stored as a JSON string in the cell, since there's no clean flat representation for a one-to-many relationship inside a single row.

[
  { "name": "Ravi Kumar", "address": { "city": "Surat", "country": "IN" }, "tags": ["dev", "go"] }
]

becomes a sheet with these columns:

name        | address.city | address.country | tags
Ravi Kumar  | Surat         | IN               | dev, go

• ▸Stakeholder reporting — Turn an API response or analytics export into a spreadsheet a non-technical reviewer can open directly
• ▸QA and data review — Hand a QA team a filterable, sortable spreadsheet view of test data or seed records instead of raw JSON
• ▸Finance and ops handoffs — Export order or transaction data to Excel for reconciliation in a tool finance teams already use
• ▸Bulk-edit prep — Export to Excel, make bulk edits with spreadsheet formulas and fill-down, then convert the edited file back to JSON

The downloaded .xlsx is a standard workbook — no special reader needed. In Python, for example:

# pip install pandas openpyxl
import pandas as pd

df = pd.read_excel("data.xlsx")
print(df.head())

# Back to JSON, if needed further down a pipeline
df.to_json("data.json", orient="records", indent=2)