Convert JSON to Excel: Quick and Easy Methods
What it is
Converting JSON to Excel transforms structured JSON data (objects/arrays) into spreadsheet rows and columns so it’s easier to view, analyze, and share.
Quick methods (no code)
- Online converters: Upload JSON, download XLSX/CSV. Fast for small files.
- Spreadsheet import: In Excel: Data → Get Data → From File → From JSON. Google Sheets: use Apps Script or paste JSON into a converter add-on.
- CSV intermediate: Use an online tool or a simple script to convert JSON → CSV, then open in Excel.
Easy code-based methods
- Python (pandas):
python
import pandas as pd df = pd.json_normalize(your_json)# your_json: list/dict df.toexcel(“output.xlsx”, index=False)
- Node.js (json2xls):
js
const json2xls = require(‘json2xls’); const fs = require(‘fs’); fs.writeFileSync(‘output.xlsx’, json2xls(jsonData), ‘binary’);
- Power Query (Excel): Use Get Data → From JSON, then transform and load.
Handling nested JSON
- Use json_normalize (pandas) or Power Query’s Expand Record/Expand List features to flatten nested objects/arrays into columns.
Tips for large files
- Convert to CSV in streaming mode (avoid loading entire file into memory).
- Split very large JSON arrays into chunks before converting.
- Use command-line tools (jq) to preprocess and extract required fields.
Common pitfalls
- Inconsistent object keys → missing columns.
- Deeply nested arrays → require manual flattening decisions.
- Large numeric/precision issues when exporting to CSV/Excel.
Quick workflow (recommended)
- Inspect JSON structure.
- Decide which fields to include and how to flatten nested parts.
- Use pandas/json_normalize or Excel Power Query for robust results.
- Validate output and format columns in Excel.
Example resources: pandas.json_normalize docs, Excel Power Query JSON import.
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