Ejemplos
Ejemplos practicos que muestran distintos casos de uso del convertidor de JSON a CSV.
Ejemplos basicos
Conversion simple de arreglos
JSON de entrada:
[
{ "name": "Alice", "age": 28, "city": "New York" },
{ "name": "Bob", "age": 32, "city": "Los Angeles" },
{ "name": "Charlie", "age": 25, "city": "Chicago" }
]
CSV de salida:
name,age,city
Alice,28,New York
Bob,32,Los Angeles
Charlie,25,Chicago
Conversion de un solo objeto
JSON de entrada:
{
"product": "Laptop",
"price": 1299.99,
"category": "Electronics",
"inStock": true,
"tags": ["portable", "gaming", "work"]
}
CSV de salida:
product,price,category,inStock,tags
Laptop,1299.99,Electronics,true,"[""portable"",""gaming"",""work""]"
Ejemplos reales
Datos de productos de comercio electronico
JSON de entrada (catalogo de productos):
[
{
"id": "P001",
"name": "Wireless Headphones",
"brand": "TechSound",
"price": 199.99,
"currency": "USD",
"availability": {
"inStock": true,
"quantity": 150
},
"specifications": {
"battery": "20 hours",
"connectivity": "Bluetooth 5.0",
"weight": "250g"
},
"reviews": {
"average": 4.5,
"count": 128
}
},
{
"id": "P002",
"name": "Smart Watch",
"brand": "FitTech",
"price": 299.99,
"currency": "USD",
"availability": {
"inStock": false,
"quantity": 0
},
"specifications": {
"battery": "7 days",
"connectivity": "Bluetooth, WiFi",
"weight": "45g"
},
"reviews": {
"average": 4.2,
"count": 89
}
}
]
CSV de salida (aplanado):
id,name,brand,price,currency,availability.inStock,availability.quantity,specifications.battery,specifications.connectivity,specifications.weight,reviews.average,reviews.count
P001,Wireless Headphones,TechSound,199.99,USD,true,150,20 hours,Bluetooth 5.0,250g,4.5,128
P002,Smart Watch,FitTech,299.99,USD,false,0,7 days,"Bluetooth, WiFi",45g,4.2,89
Datos de respuesta de API
JSON de entrada (API de gestion de usuarios):
{
"users": [
{
"id": 1,
"username": "john_doe",
"email": "john@example.com",
"profile": {
"firstName": "John",
"lastName": "Doe",
"avatar": "https://example.com/avatars/john.jpg"
},
"permissions": ["read", "write"],
"lastLogin": "2024-01-15T10:30:00Z",
"active": true
},
{
"id": 2,
"username": "jane_smith",
"email": "jane@example.com",
"profile": {
"firstName": "Jane",
"lastName": "Smith",
"avatar": "https://example.com/avatars/jane.jpg"
},
"permissions": ["read"],
"lastLogin": "2024-01-14T15:45:00Z",
"active": true
}
],
"total": 2,
"page": 1,
"limit": 10
}
CSV de salida (solo el arreglo users):
id,username,email,profile.firstName,profile.lastName,profile.avatar,permissions,lastLogin,active
1,john_doe,john@example.com,John,Doe,https://example.com/avatars/john.jpg,"[""read"",""write""]",2024-01-15T10:30:00Z,true
2,jane_smith,jane@example.com,Jane,Smith,https://example.com/avatars/jane.jpg,"[""read""]",2024-01-14T15:45:00Z,true
Ejemplos avanzados
Datos financieros
JSON de entrada (datos del mercado bursatil):
[
{
"symbol": "AAPL",
"company": "Apple Inc.",
"sector": "Technology",
"price": {
"current": 175.43,
"open": 174.2,
"high": 176.15,
"low": 173.5,
"previousClose": 174.2
},
"volume": 45678900,
"marketCap": 2750000000000,
"dividend": {
"yield": 0.0044,
"amount": 0.24,
"frequency": "quarterly"
},
"analysts": {
"rating": "Buy",
"targetPrice": 185.0,
"recommendations": {
"strongBuy": 12,
"buy": 8,
"hold": 3,
"sell": 1,
"strongSell": 0
}
}
}
]
CSV de salida:
symbol,company,sector,price.current,price.open,price.high,price.low,price.previousClose,volume,marketCap,dividend.yield,dividend.amount,dividend.frequency,analysts.rating,analysts.targetPrice,analysts.recommendations
AAPL,Apple Inc.,Technology,175.43,174.20,176.15,173.50,174.20,45678900,2750000000000,0.0044,0.24,quarterly,Buy,185.00,"{""strongBuy"":12,""buy"":8,""hold"":3,""sell"":1,""strongSell"":0}"
Datos de encuestas
JSON de entrada (encuesta de satisfaccion del cliente):
[
{
"respondentId": "R001",
"timestamp": "2024-01-15T14:30:00Z",
"demographics": {
"age": "25-34",
"gender": "Female",
"location": "California"
},
"responses": {
"satisfaction": 4,
"recommendation": 5,
"comments": "Great product, excellent customer service!"
},
"metadata": {
"surveyVersion": "v2.1",
"completionTime": 180,
"device": "mobile"
}
},
{
"respondentId": "R002",
"timestamp": "2024-01-15T16:45:00Z",
"demographics": {
"age": "35-44",
"gender": "Male",
"location": "Texas"
},
"responses": {
"satisfaction": 3,
"recommendation": 3,
"comments": "Product is okay, but could be better."
},
"metadata": {
"surveyVersion": "v2.1",
"completionTime": 120,
"device": "desktop"
}
}
]
CSV de salida:
respondentId,timestamp,demographics.age,demographics.gender,demographics.location,responses.satisfaction,responses.recommendation,responses.comments,metadata.surveyVersion,metadata.completionTime,metadata.device
R001,2024-01-15T14:30:00Z,25-34,Female,California,4,5,"Great product, excellent customer service!",v2.1,180,mobile
R002,2024-01-15T16:45:00Z,35-44,Male,Texas,3,3,"Product is okay, but could be better.",v2.1,120,desktop
Manejo de caracteres especiales
Datos internacionales
JSON de entrada (contenido multilingue):
[
{
"id": 1,
"title": "Café & Restaurant",
"description": "Un café français avec une cuisine délicieuse",
"location": "Paris, France",
"specialChars": "Café, naïve, résumé, piñata, 北京, 東京"
},
{
"id": 2,
"title": "北京烤鸭店",
"description": "正宗北京烤鸭，传统工艺制作",
"location": "北京, 中国",
"specialChars": "中文，English，Français，Español"
}
]
CSV de salida (codificacion UTF-8):
id,title,description,location,specialChars
1,Café & Restaurant,Un café français avec une cuisine délicieuse,Paris France,Café naïve résumé piñata 北京 東京
2,北京烤鸭店,正宗北京烤鸭传统工艺制作,北京 中国,中文EnglishFrançaisEspañol
Datos con comillas y comas
JSON de entrada:
[
{
"name": "Smith, John",
"quote": "He said \"Hello, World!\" to everyone",
"description": "A person who likes to say \"Hello\" and \"Goodbye\"",
"address": "123 Main St, Apt 4B, New York, NY 10001"
}
]
CSV de salida (con escape de comillas):
name,quote,description,address
"Smith, John","He said ""Hello, World!"" to everyone","A person who likes to say ""Hello"" and ""Goodbye""","123 Main St, Apt 4B, New York, NY 10001"
Ejemplos de configuracion
Formato CSV europeo
Configuracion:
- Delimitador: punto y coma (;)
- Codificacion: UTF-8
- Incluir encabezados: Si
- Escapar comillas: Si
JSON de entrada:
[
{ "name": "Müller", "city": "München", "price": "1.234,56" },
{ "name": "Schmidt", "city": "Köln", "price": "2.345,67" }
]
CSV de salida:
name;city;price
Müller;München;1.234,56
Schmidt;Köln;2.345,67
Valores separados por tabulacion (TSV)
Configuracion:
- Delimitador: Tab
- Codificacion: UTF-8
- Incluir encabezados: Si
- Escapar comillas: No
JSON de entrada:
[
{ "gene": "BRCA1", "mutation": "c.5266dupC", "significance": "Pathogenic" },
{ "gene": "TP53", "mutation": "c.742C>T", "significance": "Likely Pathogenic" }
]
TSV de salida:
gene mutation significance
BRCA1 c.5266dupC Pathogenic
TP53 c.742C>T Likely Pathogenic
Ejemplos de manejo de errores
Entrada JSON invalida
Entrada (falta una coma):
[
{"name": "John" "age": 30}
]
Salida de error:
Invalid JSON format. Please check your input.
Datos vacios
Entrada:
[]
Salida:
(Empty CSV - no data to convert)
Tipos de datos mixtos
JSON de entrada:
[
{ "id": 1, "name": "Product A", "active": true, "price": 99.99, "tags": null },
{ "id": 2, "name": "Product B", "active": false, "price": 149.99, "tags": ["electronics"] },
{ "id": 3, "name": "Product C", "active": true, "price": null, "tags": ["gadget", "portable"] }
]
CSV de salida:
id,name,active,price,tags
1,Product A,true,99.99,
2,Product B,false,149.99,"[""electronics""]"
3,Product C,true,,"[""gadget"",""portable""]"
Ejemplos de rendimiento
Conjunto de datos grande
JSON de entrada (1000 registros):
[
// ... 1000 user records
{
"id": 1,
"name": "User 1",
"email": "user1@example.com",
"department": "Engineering",
"salary": 75000,
"hireDate": "2020-01-15"
}
// ... 999 more records
]
Tiempo de procesamiento: ~50 ms para 1000 registros Tamano de salida: archivo CSV de ~150 KB Uso de memoria: ~2 MB durante el procesamiento
Optimizacion de memoria
Para conjuntos de datos muy grandes, considera procesarlos por bloques:
// Process 10,000 records in chunks of 1000
const chunkSize = 1000;
const chunks = [];
for (let i = 0; i < largeDataset.length; i += chunkSize) {
chunks.push(largeDataset.slice(i, i + chunkSize));
}
// Process each chunk
chunks.forEach((chunk, index) => {
const csv = jsonToCSV(chunk);
downloadCSV(csv, `data_chunk_${index + 1}.csv`);
});