Convert JSON to CSV for Excel in 3 Seconds (Free Online Tool)

• Nested structures: Objects within objects, arrays of arrays

• Data types: Preserves strings, numbers, booleans, null

• APIs: Standard format for web services

• Developer-friendly: Easy to parse in code

• Hierarchical data: Parent-child relationships

Example JSON:

```json

{

"users": [

{"name": "Alice", "age": 30, "active": true},

{"name": "Bob", "age": 25, "active": false}

]

}

```

• Spreadsheet compatibility: Opens directly in Excel/Sheets

• Human-readable: Simple tabular format

• Universal support: Every database and analytics tool reads CSV

• Smaller file size: No markup overhead

• Easy editing: Non-technical users can modify

Equivalent CSV:

```

name,age,active

Alice,30,true

Bob,25,false

```

✅ When you need JSON to CSV:

• Importing API data into Excel for analysis

• Sharing data with non-developers

• Creating charts and pivot tables from JSON

• Bulk importing into databases (MySQL, PostgreSQL)

• Email marketing list exports

• Financial reporting from JSON APIs

• Converting app data exports for spreadsheets

Sources of JSON data:

• API responses: Copy from browser network tab or Postman

• Database exports: MongoDB, Firebase, CouchDB export as JSON

• Application data: Export from web apps, mobile apps

• JSON files: Drag and drop .json files

Common mistake: Ensure you have VALID JSON. Use a JSON validator first if you encounter errors.

1. Copy your JSON data

2. Open online JSON to CSV converter

3. Paste into input field

4. Tool automatically detects structure

5. Preview CSV output appears

Look for these converter features:

• Client-side processing (data doesn't leave browser)

• Array flattening (converts nested arrays)

• Header row generation (column names from JSON keys)

• Delimiter options (comma, tab, semicolon, pipe)

Delimiter choice:

• Comma (,): Standard CSV—use for most cases

• Tab (\t): TSV format—good for data with commas

• Semicolon (;): European CSV standard

• Pipe (|): When data contains commas and quotes

Nested data handling:

• Flatten: Convert `{"address": {"city": "NYC"}}` to `address.city` column

• JSON stringify: Keep nested objects as JSON strings

• Ignore: Skip nested properties (only top-level fields)

Header options:

• Include header row (recommended)

• Use custom column names

• Quote all fields (safer for special characters)

1. Click "Convert" or "Download CSV"

2. Save .csv file to your computer

3. Open in Excel, Google Sheets, or database tool

4. Verify data imported correctly

Excel import tips:

• Double-click CSV to open in Excel

• Use "Data → From Text/CSV" for import control

• Check delimiter settings if columns look wrong

• Set data types (numbers, dates) after import

Simple array conversion:

JSON:

```json

[

{"id": 1, "name": "Alice", "email": "alice@example.com"},

{"id": 2, "name": "Bob", "email": "bob@example.com"}

]

```

CSV:

```

id,name,email

1,Alice,alice@example.com

2,Bob,bob@example.com

```

This is the ideal structure—converts perfectly to tabular format.

Complex nested structure:

JSON:

```json

[

{

"name": "Alice",

"address": {

"city": "NYC",

"zip": "10001"

}

}

]

```

Flattened CSV:

```

name,address.city,address.zip

Alice,NYC,10001

```

Good converters automatically flatten nested objects into dot-notation columns.

Problem structure:

JSON:

```json

[

{

"name": "Alice",

"skills": ["Python", "JavaScript", "SQL"]

}

]

```

Option 1: Array to string (simplest)

CSV:

```

name,skills

Alice,"Python; JavaScript; SQL"

```

Option 2: One row per array item (denormalized)

CSV:

```

name,skill

Alice,Python

Alice,JavaScript

Alice,SQL

```

Option 3: Multiple columns (fixed-size arrays)

CSV:

```

name,skill1,skill2,skill3

Alice,Python,JavaScript,SQL

```

Choose based on how you'll use the data. Option 1 is easiest; Option 2 is best for databases.

If JSON is one object instead of array:

JSON:

```json

{

"user": "Alice",

"age": 30,

"active": true

}

```

Solution: Wrap in array first:

```json

[

{"user": "Alice", "age": 30, "active": true}

]

```

Then convert. Or use horizontal format:

```

user,age,active

Alice,30,true

```

Cause: JSON objects are unordered; CSV column order varies.

Solution:

• Use converter with column ordering feature

• Rearrange columns in Excel after import

• Specify column order in converter settings

• Use schema/template if available

Cause: Commas, quotes, newlines in data conflict with CSV delimiters.

Problem data:

```json

{"company": "Smith, Jones & Associates"}

```

Correct CSV (quoted):

```

"Smith, Jones & Associates"

```

Solutions:

• Enable "quote all fields" option

• Use different delimiter (tab, pipe)

• Escape special characters

• Use RFC 4180 compliant converter

Cause: Inconsistent JSON structure—not all objects have same properties.

JSON:

```json

[

{"name": "Alice", "age": 30, "city": "NYC"},

{"name": "Bob", "age": 25}

]

```

CSV:

```

name,age,city

Alice,30,NYC

Bob,25,

```

Notice Bob's row has empty city cell. This is correct—converter should add empty values for missing properties.

Cause: CSV stores everything as text; Excel must infer data types.

Solutions:

• Use "Data → Text to Columns" in Excel to set types

• Import via "Data → From Text/CSV" with type specification

• Remove leading zeros if they're being preserved incorrectly

• For large numbers, use apostrophe prefix: `'1234567890123456` (prevents scientific notation)

• Preparing data for API requests

• Converting spreadsheet data for web apps

• Importing into MongoDB or other JSON databases

• Creating configuration files from spreadsheets

• Building mock data for development

1. Export your spreadsheet as CSV

2. Use online CSV to JSON converter

3. Choose output format (array of objects, nested structure)

4. Download JSON file

5. Validate JSON before using

Most converters handle both directions with the same tool.

Install jq:

• Mac: `brew install jq`

• Linux: `apt-get install jq`

• Windows: Download from jq website

Convert array of objects to CSV:

```bash

jq -r '.[] | [.name, .age, .email] | @csv' input.json > output.csv

```

With headers:

```bash

(echo "name,age,email"; jq -r '.[] | [.name, .age, .email] | @csv' input.json) > output.csv

```

Pros: Fast, scriptable, handles huge files

Cons: Requires command-line knowledge

```python

import json

import csv

# Load JSON

with open('data.json') as f:

data = json.load(f)

# Write CSV

with open('output.csv', 'w', newline='') as f:

writer = csv.DictWriter(f, fieldnames=data[0].keys())

writer.writeheader()

writer.writerows(data)

```

Best for: Custom transformations, scheduled jobs, complex data processing