JSON to CSV Guide: Convert Nested JSON to Spreadsheet in Seconds

Why JSON → CSV Isn't as Simple as It Looks

JSON and CSV are fundamentally different data models:

• JSON is hierarchical. Objects contain objects contain arrays contain objects. Arbitrary depth. Variable structure per record.
• CSV is flat. Rows and columns. Every row has the same columns in the same order. Two dimensions only.

Converting JSON to CSV means flattening a tree into a table. This is lossy — information gets discarded or restructured. The question isn't whether you can convert, it's what you lose in the process.

How Nested JSON Gets Flattened Into Columns

Consider this JSON:

{
  "users": [
    {
      "id": 1,
      "name": "Alice",
      "address": {
        "city": "New York",
        "zip": "10001"
      }
    }
  ]
}

The converter must flatten address.city and address.zip into columns. It does this by concatenating the key path with a separator (usually dot or underscore):

This works for one level of nesting. For deeper nesting (user.orders[0].items[2].sku), the column names become unwieldy. Most converters cap at 2-3 levels of depth and either ignore deeper fields or JSON-stringify them into a single cell.

Arrays in JSON: The Biggest Conversion Headache

Arrays are the hard part. What do you do with an array field like "tags": ["javascript", "react", "css"]?

Three approaches, each with tradeoffs:

• JSON-stringify the array → Cell contains ["javascript","react","css"]. Preserves all data but the CSV cell isn't spreadsheet-friendly (you can't filter by "javascript" easily).
• Create separate columns → tags_0, tags_1, tags_2. Spreadsheet-friendly but breaks when arrays have different lengths (one record has 2 tags, another has 15).
• Comma-join into one cell → Cell contains "javascript, react, css". Readable, filterable, but breaks if values contain commas (the CSV delimiter).

Our JSON to CSV converter defaults to JSON-stringify for arrays (preserves all data) with the option to comma-join or expand into columns.

Common Conversion Problems (And Fixes)

1. Inconsistent keys across records

Record 1 has {"name": "Alice", "email": "alice@..."}. Record 2 has {"name": "Bob", "phone": "555-..."}. The CSV needs columns for both email and phone. The converter must scan all records first to build the complete column list before writing any rows.

2. Top-level JSON that isn't an array

If the JSON is {"results": [{"id": 1}, {"id": 2}]}, the converter needs to find the array. Most tools let you specify the path (results) or auto-detect the first array.

3. CSV escaping

If a JSON string value contains a comma, double-quote, or newline, it must be escaped in CSV output. A value like He said, "Hello" becomes "He said, ""Hello""" in proper CSV. Missing escaping creates misaligned columns that silently corrupt data.

4. Unicode and encoding

JSON is always UTF-8. CSV has no inherent encoding — it's just bytes. If you open a UTF-8 CSV in Excel without the BOM, non-ASCII characters (é, 中文, 😊) garble. Always add BOM for Excel compatibility, or open via Data → Import and specify UTF-8.

Free JSON to CSV Tools — Plus the Reverse

• JSON to CSV — Paste JSON, get CSV. Handles nested objects, arrays, and inconsistent records. Download as .csv file.
• CSV to JSON — The reverse. Paste CSV, get JSON array of objects. Useful when someone sends you a spreadsheet and you need JSON for an API or config.
• JSON Formatter — If the JSON is minified and you can't even see the structure, format it first, then convert.
• CSV Cleaner — After conversion, clean the CSV: trim whitespace, standardize nulls, strip BOM characters.