5 Ways to Convert JSON to Excel (Ranked by Ease of Use)

Need to convert JSON to Excel but overwhelmed by options?

You're not alone. There are dozens of tools claiming to be "the best JSON to Excel converter," but which one actually fits your needs?

I tested 5 popular methods and ranked them by ease of use, speed, security, and cost.

TL;DR: If you just need quick results without coding, skip to Method 4.

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Quick Comparison Table

Now let's break down each method in detail.

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Method 1: Excel Power Query

Rating: 6/10
Best For: Power Query experts who already use it daily

How It Works

Excel has a built-in feature called Power Query (formerly Get & Transform Data) that can import JSON files.

Steps:

1. Open Excel → Data tab
2. Get Data → From File → From JSON
3. Select your JSON file
4. Power Query Editor opens
5. Click "To Table" if needed
6. Expand nested objects manually
7. Close & Load

Pros ✅

• Built into Excel – No additional software needed
• Refreshable – Can update data with one click
• Free – Included with Excel 2016+

Cons ❌

• Steep learning curve – Requires learning M language for complex transformations
• Manual nesting expansion – You must click "expand" for each nested level
• Cryptic error messages – "Expression.Error: The key didn't match any rows in the table" (what does that even mean?)
• Performance issues – Slow with files over 10MB

Real-World Example

You have JSON from a Salesforce export:

{
  "records": [
    {
      "Id": "001",
      "Account": {
        "Name": "Acme Corp",
        "Industry": "Tech"
      }
    }
  ]
}

Steps in Power Query:

1. Import JSON → See "List" or "Record"
2. Click "To Table"
3. Click expand icon on "Column1"
4. Select "records"
5. Click expand icon on "Account"
6. Select "Name" and "Industry"
7. Finally get your data

Time: 5-10 minutes for a moderately nested JSON.

When to Use Power Query

✅ You already know M language
✅ You need automated weekly refreshes
✅ JSON structure is simple (1-2 levels of nesting)

❌ Skip if you're new to Excel or need a one-time conversion.

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Method 2: Python + Pandas

Rating: 7/10
Best For: Data scientists and Python developers

How It Works

Python's pandas library has a json_normalize() function that flattens nested JSON into DataFrames, which can then be exported to Excel.

Code Example:

import pandas as pd
import json

# Load JSON file
with open('data.json', 'r') as f:
    data = json.load(f)

# Flatten nested JSON
df = pd.json_normalize(data['records'])

# Export to Excel
df.to_excel('output.xlsx', index=False)

Pros ✅

• Very powerful – Can handle any JSON structure
• Scriptable – Automate conversions in data pipelines
• Unlimited file size – Can process 100MB+ files
• Customizable – Full control over transformations

Cons ❌

• Requires programming skills – Not accessible to non-developers
• Setup time – Install Python, pandas, openpyxl
• Learning curve – Understanding DataFrames, file I/O, error handling
• Not reusable – Each JSON structure needs custom code

Real-World Example

Converting HubSpot deals JSON:

import pandas as pd
import json

# Load HubSpot JSON export
with open('hubspot_deals.json', 'r') as f:
    data = json.load(f)

# Flatten the 'deals' array
df = pd.json_normalize(
    data['deals'],
    sep='_'  # Use underscore instead of dot notation
)

# Export to Excel
df.to_excel('hubspot_deals.xlsx', index=False)
print(f"Exported {len(df)} deals to Excel")

Time: 5-10 minutes to write + debug script.

When to Use Python

✅ You're already a Python developer
✅ You need to automate conversions (e.g., nightly data pipeline)
✅ Files are 50MB+ and other tools crash
✅ You need complex transformations beyond simple flattening

❌ Skip if you're not comfortable with code or need a quick one-time conversion.

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Method 3: Online Converters (Upload-Based)

Rating: 5/10
Best For: Users who don't care about data privacy and have small files

How It Works

Dozens of websites offer "JSON to Excel converter" where you upload your file to their server, they convert it, and you download the result.

Examples: ConvertCSV, JSON-CSV.com, OnlineJSONTools, etc.

Steps:

1. Google "JSON to Excel converter"
2. Click first result
3. Upload JSON file
4. Wait for conversion
5. Download Excel file

Pros ✅

• Very easy – No software installation
• No technical skills – Just upload and click
• Works on any device – Even mobile phones

Cons ❌

• Privacy risk – Your data is uploaded to unknown servers
• File size limits – Usually 5-10MB maximum
• Slow – Upload + processing + download takes time
• Inconsistent quality – Some converters break on nested JSON
• Ads everywhere – Many sites are cluttered with ads
• Potential data leaks – What happens to your uploaded files?

Real-World Security Concern

You're converting Stripe transaction data (contains customer emails, payment info). You upload to RandomConverter.com.

Questions:

• Where is this data stored?
• Is it encrypted?
• Do they sell it to third parties?
• Is it GDPR/HIPAA compliant?

Most online converters don't answer these questions.

When to Use Online Converters

✅ Non-sensitive public data (e.g., weather data, sample files)
✅ Files under 5MB
✅ You're on a work computer without admin rights

❌ Never use for: Customer data, financial records, healthcare info, proprietary business data

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Method 4: JsonExport (Recommended ⭐)

Rating: 9/10
Best For: 95% of use cases — fast, secure, no coding required

How It Works

JsonExport is a client-side web app that processes JSON entirely in your browser. No uploads. No servers.

Key Difference: Unlike Method 3, your data never leaves your computer. Everything happens locally.

Steps:

1. Go to jsonexport.com
2. Upload JSON file (or paste JSON)
3. Instantly see preview
4. Click "Download Excel"

Time: 30 seconds.

Pros ✅

• 100% private – Client-side processing (GDPR/HIPAA compliant)
• No file size limit – Handles 100MB+ files smoothly
• Free to use – No signup required, no premium tiers
• No coding required – Visual interface
• Auto-unescape – Fixes double-encoded JSON automatically
• Three view modes:
  • Flat View – Dot notation (customer.name)
  • Table View – Optimized for Excel pivot tables
  • Nested View – Parent-child relationships

Cons ❌

• No API – Designed for manual use (not scriptable)
• Browser-based – Requires modern browser (Chrome, Firefox, Safari, Edge)

Real-World Example

Converting Google Analytics 4 JSON export (15MB):

1. Upload: Drag ga4_export.json into JsonExport
2. Preview: Instantly see 50,000 rows of event data
3. Choose View: Select "Table View" for easier analysis
4. Download: Click "Download Excel" → Get .xlsx file

Total time: 45 seconds.

Result: Clean Excel file with columns like:

• event_name
• event_timestamp
• user_properties.device
• event_params.page_location

All nested objects flattened automatically.

When to Use JsonExport

✅ One-time conversions – Quick weekly/monthly reports
✅ Sensitive data – Financial, healthcare, customer info
✅ Large files – 10MB+ exports from APIs
✅ No coding skills – Business analysts, marketers, PMs
✅ Nested JSON – APIs from Salesforce, HubSpot, Stripe, Shopify

Bottom line: If you're not a developer and need results in under 1 minute, use JsonExport.

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Method 5: Manual Copy-Paste

Rating: 2/10
Best For: Absolutely nothing (but people still do it)

How It Works

1. Open JSON in text editor
2. Copy values manually
3. Paste into Excel cells one by one

Why This Is Terrible

• ❌ Incredibly slow – 30 minutes for 100 rows
• ❌ Error-prone – Easy to copy wrong values
• ❌ Not scalable – Impossible for 1,000+ rows
• ❌ Loses data structure – No way to preserve relationships

When to Use Manual Copy-Paste

✅ You have 5 rows and 30 minutes to waste
✅ Literally every other method failed
✅ You enjoy repetitive tasks

Otherwise, don't do this.

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Decision Matrix: Which Method Should You Use?

Scenario 1: Non-Technical Data Analyst

Your Situation:

• You don't know Python
• Power Query intimidates you
• You get JSON files from Salesforce/HubSpot weekly
• Files are 5-20MB

Recommendation: JsonExport (Method 4)

Why: Easy, secure, handles large files. No learning curve.

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Scenario 2: Python Developer

Your Situation:

• Comfortable with Python
• Need to automate conversions (nightly job)
• Files are 100MB+
• Complex transformations needed

Recommendation: Python + Pandas (Method 2)

Why: Scriptable, handles huge files, full control.

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Scenario 3: Excel Power User

Your Situation:

• Already use Power Query daily
• Need refreshable data connections
• Same JSON structure every week
• Have time to set up once

Recommendation: Power Query (Method 1)

Why: Built into Excel, refreshable, you already know it.

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Scenario 4: Quick Public Data Conversion

Your Situation:

• Non-sensitive public data (weather, sports stats)
• Files under 5MB
• Need one-time conversion

Recommendation: Online Converter (Method 3) or JsonExport (Method 4)

Why: Both are fast. JsonExport is still better for privacy.

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Advanced: Handling Edge Cases

Edge Case 1: Double-Encoded JSON

Some APIs return JSON as a string (double-encoded):

{
  "data": "{\"name\":\"John\",\"age\":30}"
}

Solutions:

• ❌ Power Query: Requires custom M code
• ❌ Python: Need json.loads() twice
• ✅ JsonExport: Auto-detects and unescapes automatically

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Edge Case 2: Nested Arrays

JSON with arrays inside arrays:

{
  "orders": [
    {
      "id": 1,
      "items": [
        {"product": "Shirt", "qty": 2},
        {"product": "Pants", "qty": 1}
      ]
    }
  ]
}

Problem: How do you represent this in Excel?

Solutions:

• Flat View: Each item becomes a separate row
• Nested View: Parent table (Orders) + child table (Items)

Best Tool: JsonExport's Nested View handles this perfectly.

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Edge Case 3: Files Over 50MB

Solutions:

• ❌ Online Converters: File size limit (5-10MB)
• ❌ Power Query: Slow/crashes
• ✅ Python: Handles 100MB+ with streaming
• ⚠️ JsonExport: Works up to ~50MB (browser memory limit)

Recommendation: For 50MB+, use Python.

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Cost Comparison

* Assumes you already have Excel. Stand-alone Excel license costs $159.99/year.

Winner: All free tools are tied. But JsonExport combines "free" with "easy" and "private."

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FAQ

Q: Which method is fastest?

A: For one-time conversions: JsonExport (30 seconds). For automation: Python (once script is written).

Q: Which is most secure?

A: JsonExport and Python (both process locally). Never use upload-based online converters for sensitive data.

Q: Can I batch-convert multiple files?

A:

• Python: Yes (with a script loop)
• Power Query: Yes (using folder import)
• JsonExport: No (one file at a time)

Q: What if I have a weird JSON structure?

A: Python offers the most flexibility. But try JsonExport first — it handles 95% of edge cases.

Q: Do I need Excel installed?

A: All methods export to .xlsx files, which can be opened in Google Sheets, LibreOffice, or Numbers. Excel not required.

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Conclusion: The Best Method Depends on Your Needs

Here's my final recommendation:

1. Start with JsonExport (Method 4) – Works for 95% of use cases
2. If you need automation: Learn Python (Method 2)
3. If you already know Power Query: Use it (Method 1)
4. Avoid online upload tools for sensitive data (Method 3)
5. Never manually copy-paste (Method 5)

My Personal Ranking (2026)

1. JsonExport – Best balance of ease, security, and features (9/10)
2. Python + Pandas – Best for automation and huge files (7/10)
3. Power Query – Good for Excel experts (6/10)
4. Online Converters – Only for non-sensitive public data (5/10)
5. Manual Copy-Paste – Just... no (2/10)

Try JsonExport Now (Free, No Signup)

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Related Guides:

• How to Fix '[object Object]' in Excel
• JSON to Excel for Weekly Reports
• Best Tool for Large JSON Files (10MB+)