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JSON Data Handling

JSON数据处理

Working effectively with JSON data structures.
高效处理JSON数据结构。

Python

Python

Basic Operations

基础操作

python
import json
python
import json

Parse JSON string

Parse JSON string

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

Convert to JSON string

Convert to JSON string

json_str = json.dumps(data)
json_str = json.dumps(data)

Pretty print

Pretty print

json_str = json.dumps(data, indent=2)
json_str = json.dumps(data, indent=2)

Read from file

Read from file

with open('data.json', 'r') as f: data = json.load(f)
with open('data.json', 'r') as f: data = json.load(f)

Write to file

Write to file

with open('output.json', 'w') as f: json.dump(data, f, indent=2)
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with open('output.json', 'w') as f: json.dump(data, f, indent=2)
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Advanced

高级操作

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Custom encoder for datetime

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)
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

Handle None values

json.dumps(data, skipkeys=True)
json.dumps(data, skipkeys=True)

Sort keys

Sort keys

json.dumps(data, sort_keys=True)
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json.dumps(data, sort_keys=True)
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JavaScript

JavaScript

Basic Operations

基础操作

javascript
// 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));
javascript
// 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

高级操作

javascript
// 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());
javascript
// 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

常见模式

Validation

数据校验

python
from jsonschema import validate

schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "number", "minimum": 0}
    },
    "required": ["name", "age"]
}
python
from jsonschema import validate

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

Validate

Validate

validate(instance=data, schema=schema)
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validate(instance=data, schema=schema)
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Deep Merge

深度合并

python
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
python
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

嵌套访问

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

Usage

Usage

value = get_nested(data, 'user', 'address', 'city', default='Unknown')
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value = get_nested(data, 'user', 'address', 'city', default='Unknown')
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Transform Keys

键名转换

python
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python
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Convert snake_case to camelCase

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
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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
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Best Practices

最佳实践

✅ DO

✅ 推荐做法

python
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python
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Use context managers for files

Use context managers for files

with open('data.json', 'r') as f: data = json.load(f)
with open('data.json', 'r') as f: data = json.load(f)

Handle exceptions

Handle exceptions

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

Validate structure

Validate structure

assert 'required_field' in data
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assert 'required_field' in data
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❌ DON'T

❌ 不推荐做法

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Don't parse untrusted JSON without validation

Don't parse untrusted JSON without validation

data = json.loads(user_input) # Validate first!
data = json.loads(user_input) # Validate first!

Don't load huge files at once

Don't load huge files at once

Use streaming for large files

Use streaming for large files

Don't use eval() as alternative to json.loads()

Don't use eval() as alternative to json.loads()

data = eval(json_str) # NEVER DO THIS!
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data = eval(json_str) # NEVER DO THIS!
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Streaming Large JSON

大JSON文件流处理

python
import ijson
python
import ijson

Stream large JSON file

Stream large JSON file

with open('large_data.json', 'rb') as f: objects = ijson.items(f, 'item') for obj in objects: process(obj)
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with open('large_data.json', 'rb') as f: objects = ijson.items(f, 'item') for obj in objects: process(obj)
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Remember

注意事项

  • Always validate JSON structure
  • Handle parse errors gracefully
  • Use schemas for complex structures
  • Stream large JSON files
  • Pretty print for debugging
  • 始终校验JSON结构
  • 优雅处理解析错误
  • 复杂结构使用Schema
  • 对大JSON文件使用流处理
  • 调试时使用格式化输出