Is it safe to auto-convert string numbers in JSON?

Auto-converting strings that look like numbers can be risky if leading zeros are meaningful (e.g. 007). This tool previews all changes before applying. You can target specific fields rather than converting everything globally.

Does the JSON type transformer handle nested JSON?

Yes. The transformer recursively walks all nested objects and arrays, applying type transformations at every level of depth.

How do I convert JSON types programmatically in JavaScript?

Use a recursive coerceTypes function: check each string value and convert it to boolean if it is 'true' or 'false', to Number if it is numeric, to null if it is 'null', and recurse into nested objects and arrays.

Home → JSON Data Type Transformer

JSON Data Type Transformer

Fix type mismatches in JSON. Convert string numbers to integers, normalize boolean strings, and clean up null values — instantly, in your browser.

What Is a JSON Data Type Transformer?

APIs and data pipelines frequently produce JSON with incorrect data types. A common example is a number arriving as a string — "age": "30" instead of "age": 30 — or a boolean serialized as "active": "true" instead of "active": true. This tool automatically detects and corrects those mismatches.

All processing happens entirely in your browser — no data is ever sent to a server. Free to use, no account required.

Supported Type Conversions

String → Number: "123" → 123, "3.14" → 3.14
String → Boolean: "true" / "false" → true / false
String → Null: "null" → null
Number → String: Wrap numeric values in quotes when you need string fields
Boolean → String: Convert true → "true" for systems expecting string booleans
Null normalization: Convert empty strings "" or "null" to proper null
Deep/recursive: Transformations apply to all levels of nested objects and arrays

Common Use Cases

Fixing API responses where a backend returns numbers or booleans as strings
Database exports that serialize all values as strings (e.g. MySQL → JSON exports)
CSV → JSON conversions where every field starts as a string
Preparing data for TypeScript or schema validation that requires strict types
Cleaning legacy JSON from older systems that didn't respect JSON type semantics
Normalizing third-party webhook payloads before storing in a typed database

How to Use

Paste your JSON with mismatched types into the input box above
Select the transformations you want to apply (string→number, string→boolean, etc.)
Click Transform to apply the changes
Review the output and copy it to your clipboard

Frequently Asked Questions

Why does JSON have type mismatches?

Type mismatches commonly occur when JSON is generated from a CSV file, database query result, or system that doesn't preserve native types. For example, SQL exports often serialize everything as strings, and CSV files have no concept of data types at all — every value is a string by default.

Is it safe to auto-convert string numbers?

Auto-converting strings that look like numbers (e.g. "id": "007") can be risky if leading zeros are meaningful. This tool previews all changes so you can review them before applying. You can also target specific fields rather than converting everything.

Does this handle nested JSON?

Yes. The transformer recursively walks all nested objects and arrays, applying transformations at every level of depth.

Can I do this programmatically in JavaScript?

Yes — a simple recursive approach works well:

function coerceTypes(value) {
  if (Array.isArray(value)) return value.map(coerceTypes);
  if (value !== null && typeof value === 'object') {
    return Object.fromEntries(
      Object.entries(value).map(([k, v]) => [k, coerceTypes(v)])
    );
  }
  if (typeof value === 'string') {
    if (value === 'null') return null;
    if (value === 'true') return true;
    if (value === 'false') return false;
    if (!isNaN(value) && value.trim() !== '') return Number(value);
  }
  return value;
}

JSON Data Transformation Examples

JSON transformations reshape data from one structure to another — renaming keys, extracting nested values, pivoting arrays to objects, and changing data types. This is essential for ETL pipelines and API data mapping.

Common Transformation Patterns

// 1. Rename keys
const renamed = data.map(({firstName: first_name, ...rest}) => ({first_name, ...rest}));

// 2. Flatten nested object
const flat = {name: user.name, city: user.address.city, zip: user.address.zip};

// 3. Pick specific fields
const pick = ({id, name, email}) => ({id, name, email});
const subset = users.map(pick);

// 4. Group array by key
const grouped = users.reduce((acc, user) => {
  (acc[user.role] = acc[user.role] || []).push(user);
  return acc;
}, {});

// 5. Pivot object to array
const arr = Object.entries(scoreMap).map(([name, score]) => ({name, score}));

jq Transformation Examples

# Rename keys
jq '[.[] | {id: .userId, full_name: .name}]' input.json

# Filter and transform
jq '[.users[] | select(.active) | {id, name}]' input.json

# Group by field
jq 'group_by(.department) | map({dept: .[0].department, count: length})' input.json

# Flatten nested
jq '[.[] | {name: .name, city: .address.city}]' input.json

Transformation Use Cases

Use Case	Description
API data mapping	Transform external API format to internal schema
ETL pipelines	Extract, transform, load between systems
Frontend normalization	Normalize API responses for state management
CSV preparation	Flatten nested JSON for spreadsheet export
Schema migration	Update JSON structure when schema version changes

JSON Type Coercion: When to Use Each Conversion

Not all type conversions are safe in every context. This table helps you decide which conversions to apply for your specific data source.

Data Source	Safe Conversions	Caution
CSV → JSON	String→Number, String→Boolean, String→Null	Check for leading-zero IDs (e.g. "007")
MySQL export	String→Number, "NULL"→Null	TINYINT(1) used for booleans — safe to convert
Legacy API	String→Boolean ("true"/"false")	Avoid auto-converting numeric strings (IDs)
Form submission	String→Number for numeric fields	Phone/zip fields — keep as strings
Excel/Google Sheets	String→Number, ""→Null	Date strings — keep as strings