How to Convert JSON to CSV or Excel — 3 Methods, Step by Step

There are three good ways to convert JSON to CSV or Excel, and each is best for a different situation. A browser tool takes 30 seconds for one-off jobs. Excel Power Query handles recurring imports with a saved transform. Python pandas automates the process for pipelines and batch work.

Method 1: Browser Tool (30 Seconds, No Setup)

Best for: One-time conversions, quick spot checks, sensitive data you do not want to upload anywhere.

1. Open the JSON to CSV Converter
2. Paste your JSON into the input area (or load a .json file from your device)
3. Click Convert
4. Review the CSV preview — check that columns look correct and nesting was flattened properly
5. Click Download CSV
6. Open the downloaded .csv file in Excel, Google Sheets, or any spreadsheet tool

That is it. No Python. No Power Query. No terminal. The conversion runs entirely in your browser — your data never touches a server.

When this method falls short: If you need to repeat this exact conversion weekly (use Power Query), or if you are converting hundreds of files (use Python).

Method 2: Excel Power Query (Recurring Imports)

Best for: Regular imports from the same JSON source where you want a saved, refreshable transform.

Requirements: Excel 2016 or later (Windows or Mac). Earlier versions do not have Power Query.

1. Open Excel and create a new workbook
2. Go to Data tab → Get Data → From File → From JSON
3. Select your .json file and click Import
4. The Power Query Editor opens. You will see your data, but nested objects appear as "Record" and arrays as "List"
5. Expand nested objects: Click the expand icon (two-arrow icon) on any column header showing "Record." Select which nested fields to include. Click OK.
6. Expand arrays: Right-click any column showing "List" and select Expand to New Rows. Each array item becomes its own row.
7. Repeat steps 5-6 until all nesting is resolved and you see actual values in every column
8. Click Close & Load to load the data into your worksheet

The power move: This transform is now saved. Next time you get an updated JSON file, right-click the table in Excel, click Refresh, and it re-applies the same flattening to the new data. No manual work.

When this method falls short: If Excel does not recognize your JSON structure (irregular or deeply nested), if your file exceeds 1M rows, or if you need this on a machine without Excel.

Method 3: Python pandas (Automation & Pipelines)

Best for: Automated workflows, batch processing, files too large for browser or Excel, custom transformation logic.

Requirements: Python 3.x with pandas installed (pip install pandas).

Basic conversion (flat JSON):

• Import pandas and json
• Load JSON: data = json.load(open("input.json"))
• Convert: df = pd.DataFrame(data)
• Save: df.to_csv("output.csv", index=False)

Nested JSON (the common case):

• Use pd.json_normalize(data) instead of pd.DataFrame(data)
• json_normalize automatically flattens nested objects with dot notation
• For arrays of objects within records, use the record_path parameter to specify which array to expand
• Use the meta parameter to carry parent fields into expanded rows

When this method falls short: Initial setup time (install Python, install pandas). Overkill for a one-time conversion of a small file. But unbeatable for repeated or large-scale work.

Decision Matrix: Which Method to Use

Scenario	Best Method	Why
One JSON file, need CSV now	Browser tool	Zero setup, 30 seconds, done
Same API export every week	Excel Power Query	Save the transform, click Refresh next time
50 JSON files to convert	Python script	Loop through files programmatically
500MB+ JSON file	Python or jq	Streams data, no memory issues
Sensitive data (API keys, PII)	Browser tool	Data never leaves your device
Custom field extraction	Python	Full control over which fields to include
Non-technical team member	Browser tool	No coding, no Excel Power Query knowledge needed
Daily automated pipeline	Python + cron/scheduler	Runs unattended on a schedule

Common Gotchas Across All Methods

• Excel truncates long numbers: IDs like 123456789012345678 get rounded to 123456789012346000. Import as text to preserve them.
• Date parsing: ISO 8601 dates (2026-04-02T14:30:00Z) may not auto-parse in Excel. Format the column as a date after import, or convert the timestamp format in your script.
• Null vs empty: JSON null and empty string "" are different values but may both appear as blank cells in CSV. If the distinction matters, check before converting.
• Unicode characters: Ensure your CSV editor reads UTF-8. Excel sometimes defaults to ANSI encoding on Windows, which garbles accented characters and emojis.

Related Tools

• JSON to CSV Converter — the browser tool for Method 1
• JSON Formatter — validate and pretty-print JSON before converting
• Excel Viewer — preview your CSV output without opening Excel
• CSV to Excel — convert CSV output to .xlsx format
• CSV to JSON — reverse the process when you need JSON from a spreadsheet
• Merge CSV — combine multiple output files