Complete JSON to CSV Conversion Guide | 3 Methods to Export Excel-Readable Format in 1 MinuteJSON 轉 CSV 完整指南 | 3 種方法 1 分鐘匯出 Excel 可讀格式

Exported JSON data is a mess, your manager wants it in Excel-readable format? Chinese characters become gibberish, columns don't align, don't know how to handle complex structures? Don't panic! This article teaches you 3 practical methods, from one-click online tools to Python automation, even frontend JavaScript direct export. Whether you're a data analyst, backend engineer, or product manager, you'll find the perfect solution in 1 minute!

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Why Convert JSON to CSV?為什麼要將 JSON 轉換為 CSV？

Differences Between JSON and CSVJSON 和 CSV 的差異

JSON and CSV are two completely different data formats:

JSON (JavaScript Object Notation):
- Hierarchical structure, supports nested objects and arrays
- Suitable for data exchange between programs
- Human-readable but not user-friendly for non-technical people

CSV (Comma-Separated Values):
- Flat table structure, one row per record
- Can be opened directly in Excel, Google Sheets
- Simple and intuitive, suitable for data analysis and reports

Three Common Conversion Scenarios三種常見轉換場景

Scenario 1: Reports for Non-Technical Staff
Marketing department needs to view user data from API responses—JSON is too complex, converting to CSV for Excel is most intuitive.

Scenario 2: Data Analysis and Visualization
Import backend API data into analysis tools like Tableau, Power BI—CSV is the most universal format.

Scenario 3: System Integration and Data Migration
Old system exports JSON, new system only accepts CSV—need to batch convert thousands of records.

Preparation Before Conversion轉換前的準備

Before starting conversion, recommend using JSON Parser to validate data format. Confirm:
- JSON syntax is correct (no extra commas, brackets matched)
- Data structure is consistent (every object in array has same fields)
- No special characters or encoding issues

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Method 1: Quick Conversion with Online Tools方法 1：使用線上工具快速轉換

Tool Master JSON Parser (Recommended)Tool Master JSON Parser（推薦）

The fastest method is using online tools—completely free and no installation required:

Steps:
1. Go to JSON Parser
2. Paste JSON data
3. Click "Convert to CSV" button
4. Download CSV file

Advantages:
- ✅ 100% local processing, data not uploaded to server
- ✅ Auto-detect character encoding (UTF-8 BOM)
- ✅ Supports automatic flattening of complex nested structures
- ✅ Customizable delimiters (comma, semicolon, tab)

Image Description:
  slug: "json-to-csv-tool-interface"
  Scene Description: "Web browser displaying JSON to CSV online tool interface, left side shows JSON input area, right side shows converted table preview"
  Visual Focus: "Tool interface input box, convert button, and result preview area, presenting clear before-after comparison"
  Must-Have Elements: "Computer screen, browser window, JSON code, CSV table, convert button, download button"
  Avoid Elements: "Abstract icons (like gears, arrows, lightbulbs), cartoon characters, 3D models"
  Color Scheme: "White background, blue theme (#2563eb), green success message (#10b981), black text"
  Text to Display: "Interface must include 'Convert to CSV', 'Download File' and other English button text"
  Atmosphere: "Professional, efficient, clean work environment"
  Composition: "Horizontal split composition, left-right comparison showing before/after data format"
  Dimensions: "1200x630px, 16:9 ratio"
  Photography Style: "Screen capture composite, sharp and clear, UI elements clearly identifiable"
  Detail Requirements: "JSON code must be syntactically correct with proper indentation, CSV table must include column headers and at least 3 rows of data"
  People: "No people required"
  Quality Requirements: "High resolution (at least 72 DPI), clear readable text, accurate colors"

Other Online Tool Options其他線上工具選項

ConvertCSV:
- Supports multiple format conversions (JSON, CSV, XML, YAML)
- Provides API service (paid)

JSON-CSV.com:
- Simple interface, intuitive operation
- But doesn't support complex nested structures

Selection Advice:
If data contains sensitive information (like customer personal data, financial data), strongly recommend using Tool Master's local processing tool to ensure data security.

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Method 2: Python Automation Processing方法 2：Python 自動化處理

Using Built-in csv Module使用內建 csv 模組

For simple JSON arrays, Python built-in modules can handle it:

import json
import csv

# Read JSON file
with open('data.json', 'r', encoding='utf-8') as f:
    data = json.load(f)

# Write CSV
with open('output.csv', 'w', encoding='utf-8-sig', newline='') as f:
    if data:
        writer = csv.DictWriter(f, fieldnames=data[0].keys())
        writer.writeheader()
        writer.writerows(data)

print("✅ Conversion complete!")

Key Points:
- encoding='utf-8-sig': Solves Chinese character gibberish in Excel
- newline='': Avoids extra blank lines on Windows
- DictWriter: Automatically handles dictionary to table conversion

Using pandas for Complex Structures使用 pandas 處理複雜結構

For nested JSON (objects containing arrays or sub-objects), pandas is more powerful:

import pandas as pd
import json

# Read JSON
with open('nested_data.json', 'r', encoding='utf-8') as f:
    data = json.load(f)

# Method 1: Normalize nested structure
df = pd.json_normalize(data)

# Method 2: Keep nested structure (convert to string)
df = pd.DataFrame(data)
df = df.applymap(lambda x: str(x) if isinstance(x, (dict, list)) else x)

# Save as CSV
df.to_csv('output.csv', index=False, encoding='utf-8-sig')

json_normalize Example:

Original JSON:

[
  {
    "name": "John",
    "age": 30,
    "address": {
      "city": "New York",
      "district": "Manhattan"
    }
  }
]

Converted CSV:

name,age,address.city,address.district
John,30,New York,Manhattan

Batch Processing Multiple Files批量處理多個檔案

import os
import glob
import pandas as pd

# Read all JSON files in folder
for file in glob.glob('data/*.json'):
    df = pd.read_json(file)
    output = file.replace('.json', '.csv')
    df.to_csv(output, index=False, encoding='utf-8-sig')
    print(f"✅ {file} conversion complete")

Image Description:
  slug: "python-json-csv-batch"
  Scene Description: "Computer terminal window showing Python script batch converting multiple JSON files to CSV, includes progress bar and success messages"
  Visual Focus: "Python code execution results in terminal window, green checkmarks and file name list"
  Must-Have Elements: "Computer screen, black terminal window, Python code, green success checkmarks, file path list"
  Avoid Elements: "Abstract icons (like gears, arrows, lightbulbs), cartoon characters, 3D models"
  Color Scheme: "Dark background (#1e1e1e), green success message (#10b981), white/gray text"
  Text to Display: "Terminal must include 'Conversion complete', 'Processing' and other English messages"
  Atmosphere: "Professional development environment, strong technical feel"
  Composition: "Single terminal window centered, command shown above, output results below"
  Dimensions: "1200x630px, 16:9 ratio"
  Photography Style: "Screen capture, sharp and clear, code font clearly readable"
  Detail Requirements: "Python code must be syntactically correct, show at least 3 files' successful conversion messages"
  People: "No people required"
  Quality Requirements: "High resolution (at least 72 DPI), clear readable text, high contrast"

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Method 3: JavaScript Frontend Conversion方法 3：JavaScript 前端轉換

Using json2csv Package (Node.js)使用 json2csv 套件（Node.js）

In Node.js environment:

const fs = require('fs');
const { Parser } = require('json2csv');

// Read JSON
const data = JSON.parse(fs.readFileSync('data.json', 'utf8'));

// Set fields
const fields = ['name', 'age', 'city'];
const opts = { fields };

// Convert
const parser = new Parser(opts);
const csv = parser.parse(data);

// Save
fs.writeFileSync('output.csv', '\uFEFF' + csv);
console.log('✅ Conversion complete!');

Key Points:
- '\uFEFF': UTF-8 BOM, solves Chinese character gibberish in Excel
- fields: Can customize field order and filtering

Direct Export in Browser Frontend瀏覽器前端直接匯出

Let users download CSV directly on webpage:

function jsonToCSV(jsonData) {
  const array = typeof jsonData !== 'object' ? JSON.parse(jsonData) : jsonData;

  // Get field names
  const headers = Object.keys(array[0]);

  // Build CSV content
  let csv = headers.join(',') + '\n';

  array.forEach(obj => {
    const row = headers.map(header => {
      const value = obj[header];
      // Handle values containing commas or newlines
      return typeof value === 'string' && (value.includes(',') || value.includes('\n'))
        ? `"${value}"`
        : value;
    });
    csv += row.join(',') + '\n';
  });

  return csv;
}

// Trigger download
function downloadCSV(csv, filename) {
  const blob = new Blob(['\uFEFF' + csv], { type: 'text/csv;charset=utf-8;' });
  const link = document.createElement('a');
  link.href = URL.createObjectURL(blob);
  link.download = filename;
  link.click();
}

// Usage example
const jsonData = [
  { name: 'John Doe', age: 25, city: 'New York' },
  { name: 'Jane Smith', age: 30, city: 'Los Angeles' }
];

const csv = jsonToCSV(jsonData);
downloadCSV(csv, 'export.csv');

Integration into React Application整合到 React 應用程式

import React from 'react';

function ExportButton({ data }) {
  const handleExport = () => {
    const csv = jsonToCSV(data);
    downloadCSV(csv, `export_${new Date().getTime()}.csv`);
  };

  return (
    <button onClick={handleExport} className="btn-export">
      Export CSV
    </button>
  );
}

export default ExportButton;

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Handling Complex JSON Structures (Nested Objects and Arrays)處理複雜 JSON 結構（巢狀物件和陣列）

Flattening Nested Objects展平巢狀物件

Original Data:

{
  "user": {
    "name": "John",
    "profile": {
      "age": 30,
      "address": {
        "city": "New York",
        "zipcode": "10001"
      }
    }
  }
}

Flattening Strategy 1: Dot Separation (Recommended)

user.name,user.profile.age,user.profile.address.city,user.profile.address.zipcode
John,30,New York,10001

Python Implementation:

def flatten_json(nested_json, prefix=''):
    result = {}
    for key, value in nested_json.items():
        new_key = f"{prefix}.{key}" if prefix else key
        if isinstance(value, dict):
            result.update(flatten_json(value, new_key))
        else:
            result[new_key] = value
    return result

# Usage
flat_data = [flatten_json(item) for item in data]
df = pd.DataFrame(flat_data)

Flattening Strategy 2: Underscore Separation

user_name,user_profile_age,user_profile_address_city
John,30,New York

Handling Array Fields處理陣列欄位

Original Data:

[
  {
    "name": "John",
    "skills": ["Python", "JavaScript", "SQL"]
  }
]

Method 1: Convert to String (Simple)

name,skills
John,"Python, JavaScript, SQL"

Method 2: Explode into Multiple Rows (Suitable for analysis)

name,skill
John,Python
John,JavaScript
John,SQL

Python Implementation:

# Method 1
df['skills'] = df['skills'].apply(lambda x: ', '.join(x) if isinstance(x, list) else x)

# Method 2
df = df.explode('skills')

Image Description:
  slug: "json-structure-flattening"
  Scene Description: "Two-column comparison diagram, left showing nested JSON tree structure, right showing flattened table format, with conversion arrow in middle"
  Visual Focus: "Visual comparison of JSON hierarchical indentation and CSV flat table, conversion arrow"
  Must-Have Elements: "JSON code (with indentation), CSV table (with column headers), middle conversion arrow, comparison layout"
  Avoid Elements: "Abstract icons (like gears, lightbulbs), cartoon characters, 3D models"
  Color Scheme: "White background, JSON side uses dark gray (#374151), CSV side uses blue (#2563eb), arrow uses green (#10b981)"
  Text to Display: "Must include labels like 'Nested Structure', 'After Flattening' in English"
  Atmosphere: "Educational diagram, clear and easy to understand"
  Composition: "Left-right split comparison composition, middle arrow prominent"
  Dimensions: "1200x630px, 16:9 ratio"
  Photography Style: "Infographic style, concise and clear, focused key points"
  Detail Requirements: "JSON must include at least 2 levels of nesting, CSV table at least 3 columns 3 rows"
  People: "No people required"
  Quality Requirements: "High resolution (at least 72 DPI), clear readable text, obvious contrast"

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🎯 Try Tool Master's JSON Tool Combination!

Solving complex data conversion problems requires multiple tools working together:

Tool	Function	Use Case
JSON Parser	Validate, format, fix errors	Check data format before conversion
Unit Converter	Unified unit processing	Convert numeric units in CSV
Color Shower	Color code conversion	Process data containing color codes

💡 Practical Workflow:
1. First use JSON parser to validate format
2. Convert to CSV
3. Use unit converter to standardize numeric units
4. Finally import into Excel for analysis

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Chinese Encoding Problem Solutions中文編碼問題解決方案

Why Chinese Characters Become Gibberish in Excel為什麼中文在 Excel 中變成亂碼

Excel on Windows defaults to Big5 or ANSI encoding, while modern JSON data is all UTF-8, causing gibberish when opened directly.

Solution 1: Add UTF-8 BOM解決方案 1：添加 UTF-8 BOM

BOM (Byte Order Mark) is a special marker at the beginning of file, telling Excel this is a UTF-8 file.

Python Implementation:

# Method 1: Add BOM when writing
df.to_csv('output.csv', index=False, encoding='utf-8-sig')

# Method 2: Add BOM to existing file
with open('input.csv', 'r', encoding='utf-8') as f:
    content = f.read()
with open('output.csv', 'w', encoding='utf-8-sig') as f:
    f.write(content)

JavaScript Implementation:

const csv = jsonToCSV(data);
const csvWithBOM = '\uFEFF' + csv;  // \uFEFF is UTF-8 BOM

Solution 2: Excel Import Wizard解決方案 2：Excel 匯入精靈

If file cannot be modified, use Excel's built-in import feature:

1. Open Excel → Data tab
2. Click From Text/CSV
3. After selecting file, choose "65001: Unicode (UTF-8)" for File Origin
4. Click Load

Solution 3: Google Sheets解決方案 3：Google Sheets

Simplest method: upload to Google Sheets, it automatically detects UTF-8 encoding.

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CSV Format Optimization TipsCSV 格式優化技巧

Handling Special Characters處理特殊字元

Three types of characters in CSV need special handling:

1. Comma (,):
Values containing commas must be wrapped in double quotes.

name,address
John,"New York, Manhattan, 5th Avenue"

2. Newline (\n):
Preserving newlines requires double quote wrapping.

name,description
Product A,"Line 1
Line 2
Line 3"

3. Double Quote ("):
Double quotes need escaping with two double quotes.

name,quote
John,"He said: ""Hello"""

Python Auto-Handling:

import csv
writer = csv.DictWriter(f, fieldnames=headers, quoting=csv.QUOTE_MINIMAL)
# QUOTE_MINIMAL: Only quote when necessary
# QUOTE_ALL: Quote all fields

Custom Delimiters自訂分隔符號

Some regions use different delimiters:

Semicolon (;): Common in Europe (because decimal point uses comma)

df.to_csv('output.csv', sep=';', encoding='utf-8-sig')

Tab (\t): Handle data containing both commas and semicolons

df.to_csv('output.tsv', sep='\t', encoding='utf-8-sig')

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Real-World Use Cases (Data Analysis, Report Generation, System Integration)實際應用案例（資料分析、報表生成、系統整合）

Case 1: E-commerce Order Report案例 1：電商訂單報表

Requirement: Daily fetch order data from API (JSON), convert to CSV for finance department reconciliation.

JSON Example:

[
  {
    "order_id": "A001",
    "customer": "John Doe",
    "items": [
      {"product": "Product A", "price": 100, "qty": 2},
      {"product": "Product B", "price": 200, "qty": 1}
    ],
    "total": 400
  }
]

Python Script (Auto-execute daily):

import requests
import pandas as pd
from datetime import datetime

# Fetch data from API
response = requests.get('https://api.example.com/orders')
data = response.json()

# Flatten items array
rows = []
for order in data:
    for item in order['items']:
        rows.append({
            'Order ID': order['order_id'],
            'Customer': order['customer'],
            'Product': item['product'],
            'Unit Price': item['price'],
            'Quantity': item['qty'],
            'Subtotal': item['price'] * item['qty']
        })

df = pd.DataFrame(rows)
today = datetime.now().strftime('%Y%m%d')
df.to_csv(f'orders_{today}.csv', index=False, encoding='utf-8-sig')

Case 2: Social Media Data Analysis案例 2：社群媒體資料分析

Requirement: Fetch post data from Instagram API, import into Tableau for analysis.

Key Points:
- Flatten hashtags array (convert to comma-separated string)
- Convert timestamp to readable date format
- Remove emoji to avoid encoding issues

df['hashtags'] = df['hashtags'].apply(lambda x: ', '.join(x) if x else '')
df['created_at'] = pd.to_datetime(df['timestamp'], unit='s')
df['caption'] = df['caption'].apply(lambda x: x.encode('ascii', 'ignore').decode('ascii'))

Case 3: Multilingual Product CatalogCase 3: Multilingual Product Catalog

Requirement: Export product data from website backend (JSON), convert to multilingual CSV for translation agency.

Image Description:
  slug: "json-csv-real-world-use"
  Scene Description: "Office scene, an Asian female professional sitting at computer, screen showing Excel opened CSV report, focused expression analyzing data"
  Visual Focus: "Excel spreadsheet on computer screen, including order data, charts, etc., person operating mouse"
  Must-Have Elements: "Modern office desk, computer screen, Excel spreadsheet, keyboard, mouse, coffee cup, notebook"
  Avoid Elements: "Abstract icons (like gears, arrows, lightbulbs), cartoon characters, 3D models"
  Color Scheme: "Natural lighting, light wood office desk, white/light gray walls, green plant accents"
  Text to Display: "Excel spreadsheet must include English column headers (like 'Order ID', 'Customer Name', 'Amount')"
  Atmosphere: "Professional, efficient, modern office environment"
  Composition: "45-degree side angle shot, person on right side, screen content clearly visible"
  Dimensions: "1200x630px, 16:9 ratio"
  Photography Style: "Natural light professional commercial photography, moderate depth of field, focus on screen and person"
  Detail Requirements: "Excel spreadsheet shows at least 5 columns 10 rows of data, including column headers and data charts"
  People: "25-35 year old Asian female, professional business casual attire, short hair or ponytail, focused work expression"
  Quality Requirements: "High resolution (at least 72 DPI), natural colors, soft lighting, no obvious shadows"

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Common Errors and SolutionsCommon Errors and Solutions

Error 1: KeyError (Field Not Exist)Error 1: KeyError (Field Not Exist)

Error Message:

KeyError: 'email'

Cause: Inconsistent object fields in JSON array.

Solution:

# Use get() method to set default value
rows = []
for item in data:
    rows.append({
        'name': item.get('name', 'Not Provided'),
        'email': item.get('email', 'Not Provided'),
        'phone': item.get('phone', 'Not Provided')
    })

Error 2: UnicodeDecodeError (Encoding Error)Error 2: UnicodeDecodeError (Encoding Error)

Error Message:

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff

Cause: File encoding is not UTF-8.

Solution:

# Try different encodings
encodings = ['utf-8', 'big5', 'gbk', 'latin1']
for enc in encodings:
    try:
        with open('data.json', 'r', encoding=enc) as f:
            data = json.load(f)
        print(f"✅ Using encoding: {enc}")
        break
    except:
        continue

Error 3: CSV Columns Misaligned in ExcelError 3: CSV Columns Misaligned in Excel

Cause: Data contains unescaped commas or newlines.

Solution:

# Use pandas to auto-handle escaping
df.to_csv('output.csv', index=False, encoding='utf-8-sig', quoting=csv.QUOTE_NONNUMERIC)

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Summary: Choosing the Best Conversion MethodSummary: Choosing the Best Conversion Method

Method	Use Case	Advantages	Limitations
Online Tool	One-time conversion, small files (<10MB)	Zero code, instantly available	Not suitable for automation
Python	Batch processing, automated scheduling	Powerful, customizable	Requires programming basics
JavaScript	Frontend web export	Great user experience	Browser memory limits

Recommended Process:
1. Small Test: First test with JSON Parser online tool
2. Verify Format: Confirm conversion results meet requirements
3. Automate Deployment: Write Python script, schedule daily execution

If encountering complex syntax errors, recommend first reading Complete JSON Syntax Error Solutions to fix data, then proceed with conversion. For readers wanting to dive deeper into JSON processing, refer to Complete JSON Parser Guide for more advanced techniques.

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Reference ResourcesReference Resources

Official Documentation:
- Python csv Module Official Documentation
- pandas.DataFrame.to_csv
- RFC 4180 - CSV Format Standard

Recommended Tools:
- Tool Master JSON Parser - Free online tool, local processing protects privacy
- json2csv NPM Package - Most popular Node.js conversion package

Further Reading:
- Complete JSON Parser Guide - 7 Essential Tips from Beginner to Expert
- JSON Formatter Best Practices - Learn How to Optimize JSON Format
- JSON Beautifier Usage Guide - Beautify JSON Data to Improve Readability
- JSON Schema Validation Practical Guide - Build Automated Validation Systems
- Complete JSON Syntax Error Solutions - 30+ Error Examples with Fixes
- JSON Tree Viewer Visualization Guide - Visualize Complex JSON Structures

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