How to Convert JSON to CSV in Seconds

JSON (JavaScript Object Notation) is a nested, tree-like format built for APIs and configuration. CSV (Comma-Separated Values) is a flat, tabular format built for spreadsheets and databases.

Converting between them means flattening the tree — turning objects into columns, arrays of records into rows, and property names into headers.

CSV doesn’t have built-in types for nested objects or arrays. Those values need a deliberate representation before they fit into a flat table.

Not every JSON document needs to become CSV. Convert when you need to:

• Open records in Excel, Google Sheets, or another spreadsheet.
• Send a report to a teammate who works with tabular data.
• Import records into a legacy system that accepts CSV.
• Analyze rows in a tool that expects a table as input.

Keep the original JSON when:

• ×The hierarchy itself is important and flattening would lose meaning.
• ×Arrays or nested objects must remain structured for another API.
• ×You need to preserve types or relationships that a flat file cannot express.

Think of CSV as a useful export view of the data, not always a complete replacement for the source.

The naive approach is to loop through the array and join each object’s values with commas:

const headers = Object.keys(data[0])
const rows = data.map((obj) =>
  Object.values(obj).join(',')
)
const csv = [headers.join(','), ...rows].join('\n')

That shortcut works only for very simple data. It breaks as soon as values contain delimiters, quotes, line breaks, or nested types.

1. Values contain commas

   “New York, NY” becomes two columns unless the field is quoted.

2. Values contain quotes

   A quote inside a quoted field must be escaped correctly or a CSV reader may end the field too early.

3. Nested objects

   JavaScript string coercion can turn an object into “[object Object]”, which is not useful data.

4. Arrays inside objects

   Joining values blindly can lose array boundaries or produce ambiguous cells.

5. Unicode and line breaks

   Files should be encoded consistently, and line breaks inside a field must be quoted to keep records intact.

Reliable CSV conversion requires choosing columns, flattening values, escaping fields, and handling empty or inconsistent records.

For a quick export, use TEO ToolKit’s JSON to CSV converter. There’s no setup: open the tool, paste a JSON array, and convert.

1. 1

   Open the JSON to CSV tool

   Open JSON to CSV
2. 2

   Paste your JSON array

   Use a list of objects with consistent fields for a straightforward table. The sample above is ready to try.
3. 3

   Convert and review the CSV

   Check the column order and values, then copy or download the result for your spreadsheet or import workflow.

[
  { "name": "Ada",
    "city": "London" },
  { "name": "Grace",
    "city": "New York, NY" }
]

That’s it. Review the output, then copy the CSV, download it as a file, or paste it straight into Google Sheets.

Treat the output as an export and verify important data before importing it into another system, especially when the source contains irregular records.

Real JSON is rarely flat. Consider a record with an address object:

[
  {
    "name": "Ada Lovelace",
    "address": {
      "city": "London",
      "country": "UK"
    }
  }
]

One common approach is to flatten nested keys into dotted column names. The same record could become:

name,address.city,address.country
Ada Lovelace,London,UK

Flattening preserves individual values while making the hierarchy visible in the headers. For arrays, decide whether each item should become a separate row, a joined cell, or its own related file. Pick a rule that the receiving spreadsheet or importer can understand.

Assuming every row has the same keys

API records may omit optional fields. Build the column set from all records or explicitly define the schema, then leave a cell empty when a record has no value.

Forgetting CSV quoting rules

Fields containing commas, double quotes, or newlines should be wrapped in double quotes. A quote within a quoted field is escaped by doubling it. For example, "She said ""hello""".

Confusing empty values with missing fields

An empty string, a missing property, and a JSON null can mean different things. Decide how each should appear in the CSV and document that convention.

Skipping a review before import

Open a sample of the output and verify headers, row counts, special characters, and nested data before using it for reporting or bulk imports.