---

name: json-data-handling description: Working effectively with JSON data structures. user-invocable: false disable-model-invocation: true updated_at: 2025-10-30T17:00:00Z tags: [json, data, parsing, serialization] progressive_disclosure: entry_point: summary: "Working effectively with JSON data structures." when_to_use: "When working with data, databases, or data transformations." quick_start: "1. Review the core concepts below. 2. Apply patterns to your use case. 3. Follow best practices for implementation."

JSON Data Handling

Working effectively with JSON data structures.

Python

Basic Operations

import json

# Parse JSON string
data = json.loads('{"name": "John", "age": 30}')

# Convert to JSON string
json_str = json.dumps(data)

# Pretty print
json_str = json.dumps(data, indent=2)

# Read from file
with open('data.json', 'r') as f:
    data = json.load(f)

# Write to file
with open('output.json', 'w') as f:
    json.dump(data, f, indent=2)

Advanced

# Custom encoder for datetime
from datetime import datetime

class DateTimeEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, datetime):
            return obj.isoformat()
        return super().default(obj)

json_str = json.dumps({'date': datetime.now()}, cls=DateTimeEncoder)

# Handle None values
json.dumps(data, skipkeys=True)

# Sort keys
json.dumps(data, sort_keys=True)

JavaScript

Basic Operations

// Parse JSON string
const data = JSON.parse('{"name": "John", "age": 30}');

// Convert to JSON string
const jsonStr = JSON.stringify(data);

// Pretty print
const jsonStr = JSON.stringify(data, null, 2);

// Read from file (Node.js)
const fs = require('fs');
const data = JSON.parse(fs.readFileSync('data.json', 'utf8'));

// Write to file
fs.writeFileSync('output.json', JSON.stringify(data, null, 2));

Advanced

// Custom replacer
const jsonStr = JSON.stringify(data, (key, value) => {
  if (typeof value === 'bigint') {
    return value.toString();
  }
  return value;
});

// Filter properties
const filtered = JSON.stringify(data, ['name', 'age']);

// Handle circular references
const getCircularReplacer = () => {
  const seen = new WeakSet();
  return (key, value) => {
    if (typeof value === 'object' && value !== null) {
      if (seen.has(value)) return;
      seen.add(value);
    }
    return value;
  };
};
JSON.stringify(circularObj, getCircularReplacer());

Common Patterns

Validation

from jsonschema import validate

schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "number", "minimum": 0}
    },
    "required": ["name", "age"]
}

# Validate
validate(instance=data, schema=schema)

Deep Merge

def deep_merge(dict1, dict2):
    result = dict1.copy()
    for key, value in dict2.items():
        if key in result and isinstance(result[key], dict) and isinstance(value, dict):
            result[key] = deep_merge(result[key], value)
        else:
            result[key] = value
    return result

Nested Access

# Safe nested access
def get_nested(data, *keys, default=None):
    for key in keys:
        try:
            data = data[key]
        except (KeyError, TypeError, IndexError):
            return default
    return data

# Usage
value = get_nested(data, 'user', 'address', 'city', default='Unknown')

Transform Keys

# Convert snake_case to camelCase
def to_camel_case(snake_str):
    components = snake_str.split('_')
    return components[0] + ''.join(x.title() for x in components[1:])

def transform_keys(obj):
    if isinstance(obj, dict):
        return {to_camel_case(k): transform_keys(v) for k, v in obj.items()}
    elif isinstance(obj, list):
        return [transform_keys(item) for item in obj]
    return obj

Best Practices

✅ DO

# Use context managers for files
with open('data.json', 'r') as f:
    data = json.load(f)

# Handle exceptions
try:
    data = json.loads(json_str)
except json.JSONDecodeError as e:
    print(f"Invalid JSON: {e}")

# Validate structure
assert 'required_field' in data

❌ DON'T

# Don't parse untrusted JSON without validation
data = json.loads(user_input)  # Validate first!

# Don't load huge files at once
# Use streaming for large files

# Don't use eval() as alternative to json.loads()
data = eval(json_str)  # NEVER DO THIS!

Streaming Large JSON

import ijson

# Stream large JSON file
with open('large_data.json', 'rb') as f:
    objects = ijson.items(f, 'item')
    for obj in objects:
        process(obj)

Remember

• Always validate JSON structure
• Handle parse errors gracefully
• Use schemas for complex structures
• Stream large JSON files
• Pretty print for debugging