How to Save API Response JSON to Excel — No Code, Just Paste

You have a JSON response from an API — maybe from Postman, your browser's network tab, or a curl command — and you need it in Excel. The fastest way is to paste the JSON array into a free browser converter and download a formatted .xlsx file. No Python script, no Power Query, no Office 365 license required. This guide shows exactly how.

The 30-Second Method: Paste JSON, Get Excel

If your API returns a JSON array of objects (the most common format), this works in three steps:

1. Copy the JSON response. In Postman, click the response body and select all. In your browser's network tab, right-click the response and choose "Copy response body." In curl, pipe the output to your clipboard.
2. Paste into the converter. Open wildandfreetools.com/spreadsheet-tools/json-to-excel/ and paste your JSON into the text area.
3. Download the .xlsx. Click Convert and save the file. Open it in Excel, Google Sheets, or any spreadsheet application.

The converter maps each JSON key to a column header and each object in the array to a row. For a response with 500 records and 20 fields, you will get a spreadsheet with 500 rows and 20 columns, ready for filtering and analysis.

Which API Response Formats Convert Correctly

The converter works best with the most common API response format: a flat array of objects.

[
  {"id": 1, "name": "Alice", "email": "[email protected]", "status": "active"},
  {"id": 2, "name": "Bob", "email": "[email protected]", "status": "inactive"}
]

This maps perfectly: id, name, email, status become columns. Each object becomes a row.

Nested objects are handled by flattening with dot notation. An API that returns {"user": {"id": 1, "email": "[email protected]"}} produces columns named user.id and user.email.

Wrapped responses (where the data is inside a key like {"data": [...], "total": 500}) need one extra step: copy just the array portion, not the entire response wrapper. In Postman you can use a simple script to extract the array, or just manually copy from the first [ to the last ].

Arrays within objects (like a user with multiple addresses) get stringified into a single cell, which keeps the row structure intact even if it means some cells contain JSON text.

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How to Copy JSON From Postman, curl, and Browser DevTools

Depending on your workflow, here is how to get the JSON response:

Postman: Send your request, click the "Body" tab in the response panel. Either highlight the response and copy, or use the "Copy to Clipboard" icon. If the response is paginated, you may need to copy each page separately and combine the arrays.

Browser DevTools (Network tab): Open DevTools (F12 or Cmd+Option+I), go to the Network tab, find your API request, click it, and open the "Response" subtab. Right-click and choose "Copy response body" or manually select and copy.

curl: Run curl https://api.example.com/endpoint and pipe to clipboard. On Mac: curl ... | pbcopy. On Linux: curl ... | xclip. On Windows PowerShell: curl ... | Set-Clipboard.

REST clients (Insomnia, Bruno, HTTPie): All have copy-response options in the response panel.

When the Paste-and-Download Approach Is Not Enough

The browser converter is the right tool for one-off exports — you need this response in Excel once, or a few times, and you do not want to write code. But there are cases where Python or another scripting approach makes more sense:

• Pagination: If the API returns data across 50 pages of 100 records each, pasting 50 separate responses is impractical. A Python script that loops through pages and builds a single JSON array — then converts it — is better.
• Scheduled exports: If you need this data in Excel every Monday morning, a Python script with pandas and a scheduler is more reliable than manual copy-paste.
• Authentication: APIs that require OAuth 2.0 or complex authentication headers are easier to handle in code.

For the 80% case — a developer or analyst who has the response open in Postman and needs a spreadsheet right now — the browser converter is significantly faster than writing even a simple Python script. See also: JSON to Excel in Python vs Browser Tool for a full comparison.

How the Converter Handles Numbers, Dates, and Booleans

One advantage of this tool over simple copy-paste into Excel is that data types are preserved:

• Numbers: JSON numeric values (integers and floats) are stored as Excel numbers, so SUM, AVERAGE, and other formulas work correctly.
• Booleans: JSON true and false values are stored as Excel TRUE and FALSE, making it easy to filter by status flags.
• Dates: ISO 8601 date strings (like "2025-01-15") are stored as Excel date values, so date sorting and calculations work correctly.
• Null values: JSON null becomes an empty cell in Excel.

If you have used Excel's "Get Data from Web" feature or Power Query to import API data, you know the data type mapping can be unreliable and requires manual configuration. The browser converter handles this automatically.

Frequently Asked Questions

What if the API response is not a JSON array — for example, it is wrapped in an object?

Most REST APIs return data wrapped in an object like {"data": [...], "total": 500, "page": 1}. You need to copy just the array portion. In Postman, you can use a test script to extract the array: var data = pm.response.json().data; — then copy the result. Or manually copy from the opening bracket [ to the closing bracket ].

Is it safe to paste API response data — especially data with user records?

The converter runs 100% in your browser. Nothing is sent to any server. That said, standard data handling practices still apply — do not paste sensitive production data on any computer where you would not want it to appear in clipboard history or browser history.

Can I convert multiple API responses at once?

Not directly — the converter processes one JSON array at a time. If you need to combine multiple responses (from pagination, for example), combine the JSON arrays manually first: copy all the objects from each response into one large array, paste that, and convert.

The response has 10,000+ rows. Will this still work?

Yes, for most cases. The converter runs in the browser using available memory. Tens of thousands of rows typically work fine on modern hardware. For very large datasets (100,000+ rows), Python or a dedicated ETL tool would be more reliable.