How to Fix '[object Object]' in Excel When Importing JSON (2026 Guide)

You open your JSON file in Excel, only to see [object Object] everywhere instead of your actual data.

Frustrating, right?

This happens to thousands of data analysts every day. You're not alone, and it's not your fault.

Why Does Excel Show [object Object]?

The [object Object] error appears when Excel tries to display nested JSON objects but can't flatten them automatically.

Example: The Problem

Here's a simple JSON file:

{
  "orders": [
    {
      "id": "ORD-001",
      "customer": {
        "name": "Sarah Johnson",
        "email": "sarah@example.com"
      },
      "total": 299.99
    }
  ]
}

When you open this in Excel using "Get Data → From JSON", the customer column shows [object Object] instead of the actual name and email.

Why? Because customer is a nested object (an object inside an object), and Excel doesn't know how to display it in a flat table cell.

Common Scenarios Where This Happens

1. API Exports – Salesforce, HubSpot, Stripe all return nested JSON
2. Database Exports – MongoDB, PostgreSQL JSONB columns
3. Google Analytics – GA4 API responses have deeply nested structures
4. Survey Tools – Typeform, SurveyMonkey export nested answer arrays
5. E-commerce Platforms – Shopify orders with nested line items

If you've seen [object Object], you're dealing with nested JSON that Excel can't handle natively.

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Method 1: Excel Power Query (For Advanced Users)

Difficulty: Hard
Best For: Users comfortable with Excel's M language
Time: 10-15 minutes per file

Steps:

1. Open Excel → Data tab → Get Data → From File → From JSON
2. Select your JSON file
3. Excel opens Power Query Editor
4. Find the column showing [object Object]
5. Click the expand icon (two arrows) next to the column header
6. Select which fields to expand (e.g., name, email)
7. Click OK
8. Repeat for each nested object
9. Click Close & Load to import to Excel

Problems with This Method:

❌ Learning curve: Power Query M language is complex
❌ Repetitive: You must manually expand each nested level
❌ Breaks easily: Small JSON structure changes require re-doing everything
❌ Time-consuming: 10-15 minutes for complex JSON

Verdict: Good if you're already a Power Query expert, but overkill for most data analysts.

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Method 2: Python + Pandas (For Programmers)

Difficulty: Hard
Best For: Data scientists comfortable with Python
Time: 5-10 minutes to write script

Steps:

1. Install Python and Pandas

   pip install pandas openpyxl
   

2. Write a Python script:

   import pandas as pd
   import json
   
   # Load JSON file
   with open('data.json', 'r') as f:
       data = json.load(f)
   
   # Flatten nested structure
   df = pd.json_normalize(data['orders'])
   
   # Export to Excel
   df.to_excel('output.xlsx', index=False)
   

3. Run the script

4. Open output.xlsx

Problems with This Method:

❌ Requires coding skills: Not everyone knows Python
❌ Setup time: Installing Python, libraries, learning syntax
❌ Not reusable: Each JSON structure needs a new script
❌ Overkill: Too complex for simple one-time conversions

Verdict: Excellent if you're already a Python developer, but unrealistic for most business/data analysts.

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Method 3: JsonExport (Easiest & Fastest)

Difficulty: Easy
Best For: Anyone who needs quick results without coding
Time: 30 seconds

Why This Works:

JsonExport automatically detects nested objects and flattens them using dot notation. The customer object becomes separate columns: customer.name and customer.email.

No manual expansion. No coding. No Power Query.

Step-by-Step Tutorial:

Step 1: Go to JsonExport.com

No signup required. 100% free.

Step 2: Upload Your JSON File

• Click "Upload File" or drag-and-drop
• Or paste JSON text directly into the editor

Step 3: Preview the Data

You'll immediately see a clean table preview. No [object Object] errors.

How? JsonExport's Smart Flattener automatically:

• Detects nested objects
• Converts them to flat columns (customer.name, customer.email)
• Preserves your data structure

Step 4: Choose View Mode (Optional)

Three options:

1. Flat View – All data in one table with dot notation

   • customer.name, customer.email as separate columns
   • Best for simple analysis

2. Table View – Perfect for spreadsheets

   • Similar to Flat but optimized for Excel pivot tables

3. Nested View – Parent-child relationships

   • Shows hierarchical data (e.g., Order → Line Items)
   • Best for complex nested arrays

For most use cases, Flat View or Table View works perfectly.

Step 5: Download as Excel

Click "Download Excel (XLSX)" button.

Done. Your Excel file is ready with no [object Object] errors.

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Real-World Example: Fixing Salesforce JSON

The Problem:

You export Salesforce Contacts. The JSON looks like this:

{
  "records": [
    {
      "Id": "003xx000004TmiQ",
      "Name": "John Smith",
      "Account": {
        "Name": "Acme Corp",
        "Industry": "Technology"
      },
      "Email": "john@acme.com"
    }
  ]
}

What Happens in Excel:

After Using JsonExport:

Perfect! Now you can analyze by Account.Industry or filter by Account.Name.

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Comparison: Which Method Should You Use?

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Advanced: Handling Multiple Levels of Nesting

Some JSON files have 3+ levels of nesting:

{
  "user": {
    "profile": {
      "address": {
        "city": "New York",
        "zip": "10001"
      }
    }
  }
}

Result in Excel: user.profile.address.city, user.profile.address.zip

JsonExport handles this automatically. Power Query requires manual expansion at each level (very tedious).

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FAQ

Q: Can I use this for large JSON files?

A: JsonExport handles files up to 100MB (tested with ~100,000 rows). Files under 50MB process in seconds. For files over 100MB, we recommend Python with streaming - see our large files guide.

Q: Is my data secure?

A: Absolutely. JsonExport processes everything 100% client-side in your browser. Your data never touches our servers. Perfect for sensitive financial or healthcare data.

Q: What if I have nested arrays (not just objects)?

A: Arrays are handled differently:

• Flat View: Arrays are expanded into multiple columns (item.0, item.1, etc.)
• Nested View: Arrays create separate child tables (better for parent-child relationships)

Q: Does this work with double-encoded JSON?

A: Yes! If your JSON is double-encoded (e.g., "{\"name\":\"John\"}" as a string), JsonExport's Auto-Unescape feature detects and fixes it automatically.

Q: Can I undo changes?

A: Yes. JsonExport has full undo/redo history (Ctrl+Z / Ctrl+Y).

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Conclusion: Stop Fighting with [object Object]

The [object Object] error happens because Excel can't natively handle nested JSON. You have three options:

1. Power Query – Complex, time-consuming, requires expertise
2. Python – Requires programming skills
3. JsonExport – 30 seconds, no coding, handles all edge cases

For 95% of data analysts, JsonExport is the fastest solution.

Try JsonExport Now (Free, No Signup)

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Related Guides:

• 5 Ways to Convert JSON to Excel (Ranked by Ease)
• JSON to Excel for Weekly Reports
• Best Tool for Large JSON Files