Exemples
Exemples pratiques montrant differents cas d'usage du convertisseur JSON vers CSV.
Exemples de base
Conversion simple de tableau
JSON d'entree:
[
{ "name": "Alice", "age": 28, "city": "New York" },
{ "name": "Bob", "age": 32, "city": "Los Angeles" },
{ "name": "Charlie", "age": 25, "city": "Chicago" }
]
CSV de sortie:
name,age,city
Alice,28,New York
Bob,32,Los Angeles
Charlie,25,Chicago
Conversion d'un objet unique
JSON d'entree:
{
"product": "Laptop",
"price": 1299.99,
"category": "Electronics",
"inStock": true,
"tags": ["portable", "gaming", "work"]
}
CSV de sortie:
product,price,category,inStock,tags
Laptop,1299.99,Electronics,true,"[""portable"",""gaming"",""work""]"
Exemples concrets
Donnees produit e-commerce
JSON d'entree (catalogue produit):
[
{
"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 sortie (aplati):
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
Donnees de reponse API
JSON d'entree (API de gestion des utilisateurs):
{
"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 sortie (tableau users uniquement):
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
Exemples avances
Donnees financieres
JSON d'entree (donnees boursieres):
[
{
"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 sortie:
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}"
Donnees d'enquete
JSON d'entree (enquete de satisfaction client):
[
{
"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 sortie:
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
Gestion des caracteres speciaux
Donnees internationales
JSON d'entree (contenu 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 sortie (encodage 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
Donnees avec guillemets et virgules
JSON d'entree:
[
{
"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 sortie (avec echappement des guillemets):
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"
Exemples de configuration
Format CSV europeen
Parametres:
- Delimiteur : point-virgule (;)
- Encodage : UTF-8
- Inclure les en-tetes : Oui
- Echappement des guillemets : Oui
JSON d'entree:
[
{ "name": "Müller", "city": "München", "price": "1.234,56" },
{ "name": "Schmidt", "city": "Köln", "price": "2.345,67" }
]
CSV de sortie:
name;city;price
Müller;München;1.234,56
Schmidt;Köln;2.345,67
Valeurs separees par tabulation (TSV)
Parametres:
- Delimiteur : tabulation
- Encodage : UTF-8
- Inclure les en-tetes : Oui
- Echappement des guillemets : Non
JSON d'entree:
[
{ "gene": "BRCA1", "mutation": "c.5266dupC", "significance": "Pathogenic" },
{ "gene": "TP53", "mutation": "c.742C>T", "significance": "Likely Pathogenic" }
]
TSV de sortie:
gene mutation significance
BRCA1 c.5266dupC Pathogenic
TP53 c.742C>T Likely Pathogenic
Exemples de gestion des erreurs
Entree JSON invalide
Entree (virgule manquante):
[
{"name": "John" "age": 30}
]
Sortie d'erreur:
Invalid JSON format. Please check your input.
Donnees vides
Entree:
[]
Sortie:
(Empty CSV - no data to convert)
Types de donnees mixtes
JSON d'entree:
[
{ "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 sortie:
id,name,active,price,tags
1,Product A,true,99.99,
2,Product B,false,149.99,"[""electronics""]"
3,Product C,true,,"[""gadget"",""portable""]"
Exemples de performance
Grand jeu de donnees
JSON d'entree (1000 enregistrements):
[
// ... 1000 user records
{
"id": 1,
"name": "User 1",
"email": "user1@example.com",
"department": "Engineering",
"salary": 75000,
"hireDate": "2020-01-15"
}
// ... 999 more records
]
Temps de traitement : ~50 ms pour 1000 enregistrements Taille de sortie : fichier CSV d'environ 150 Ko Utilisation memoire : ~2 Mo pendant le traitement
Optimisation memoire
Pour les tres grands jeux de donnees, pensez a traiter par lots :
// 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`);
});