Excel To CSV

📊 Convert Excel files (XLSX, XLS) to CSV format. Handle multiple sheets, large files, and batch processing. Perfect for ETL pipelines and data integration.

🎯 What This Actor Does

A robust Excel to CSV converter that:

• Converts XLSX and XLS files to standard CSV format
• Handles multiple sheets - extract all or specific sheets
• Processes large files - memory-efficient streaming
• Preserves data types - dates, numbers, text formatted correctly
• Batch processing - convert multiple files at once

🚀 Use Cases

Use Case	Description
ETL Pipelines	Transform Excel exports for data warehouses
Data Migration	Convert legacy Excel databases
API Integration	Excel → CSV → JSON for APIs
Reporting	Standardize financial reports
Data Analysis	Prepare data for Pandas, R, SQL
Automation	Process daily Excel email attachments

📥 Input Options

Upload Directly

Drag and drop your Excel file in the Apify Console.

Provide URL

{
    "fileUrl": "https://example.com/report.xlsx"
}

Batch Processing

{
    "fileUrls": [
        "https://example.com/report-q1.xlsx",
        "https://example.com/report-q2.xlsx",
        "https://example.com/report-q3.xlsx"
    ]
}

⚙️ Configuration

File Input

Parameter	Type	Description
file	string	Upload file directly
fileUrl	string	URL to Excel file
fileUrls	array	Multiple file URLs

Sheet Selection

Parameter	Type	Default	Description
allSheets	boolean	true	Convert all sheets
sheets	array	[]	Specific sheet names or indices

CSV Options

Parameter	Type	Default	Description
delimiter	string	,	Field separator: , ; \t `
includeHeaders	boolean	true	First row is headers
dateFormat	string	YYYY-MM-DD	Date formatting (dayjs)
skipEmptyRows	boolean	true	Omit blank rows

Output Options

Parameter	Type	Default	Description
outputToDataset	boolean	false	Also push rows as JSON

📤 Output

Dataset (Metadata)

{
    "fileName": "sales-report.xlsx",
    "sheetName": "Q1 Sales",
    "sheetIndex": 0,
    "rowCount": 1523,
    "columnCount": 12,
    "csvUrl": "https://api.apify.com/v2/key-value-stores/.../records/sales_q1.csv",
    "status": "success",
    "convertedAt": "2024-01-15T10:30:00.000Z"
}

Key-Value Store (CSV Files)

Download CSV files directly from the Key-Value Store.

🚀 Quick Start

Using Apify Console

1. Upload your Excel file or enter URL
2. Configure sheet and CSV options
3. Click Start
4. Download CSVs from Storage → Key-Value Store

Using API

curl -X POST "https://api.apify.com/v2/acts/YOUR_USERNAME~excel-to-csv/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "fileUrl": "https://example.com/data.xlsx",
    "allSheets": true,
    "delimiter": ","
  }'

Using JavaScript

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_TOKEN' });

const run = await client.actor('YOUR_USERNAME/excel-to-csv').call({
    fileUrl: 'https://example.com/quarterly-report.xlsx',
    allSheets: true,
    delimiter: ',',
    dateFormat: 'YYYY-MM-DD'
});

// Get conversion results
const { items } = await client.dataset(run.defaultDatasetId).listItems();

for (const item of items) {
    console.log(`Sheet: ${item.sheetName}`);
    console.log(`Rows: ${item.rowCount}`);
    console.log(`Download: ${item.csvUrl}`);
}

Using Python

from apify_client import ApifyClient
import pandas as pd

client = ApifyClient('YOUR_TOKEN')

# Convert Excel to CSV
run = client.actor('YOUR_USERNAME/excel-to-csv').call(run_input={
    'fileUrl': 'https://example.com/data.xlsx'
})

# Get CSV URLs
items = client.dataset(run['defaultDatasetId']).list_items().items

# Load into pandas
for item in items:
    if item['status'] == 'success':
        df = pd.read_csv(item['csvUrl'])
        print(f"Loaded {item['sheetName']}: {len(df)} rows")

💡 Advanced Examples

Extract Specific Sheets

{
    "fileUrl": "https://example.com/workbook.xlsx",
    "allSheets": false,
    "sheets": ["Summary", "Data", "0"]
}

Note: You can use sheet names or zero-based indices

European CSV Format

{
    "fileUrl": "https://example.com/report.xlsx",
    "delimiter": ";",
    "dateFormat": "DD.MM.YYYY"
}

Output as JSON Dataset

{
    "fileUrl": "https://example.com/customers.xlsx",
    "outputToDataset": true,
    "includeHeaders": true
}

This adds each row as a JSON object to the Dataset:

{
    "_sheet": "Customers",
    "_file": "customers.xlsx",
    "Name": "John Doe",
    "Email": "john@example.com",
    "SignupDate": "2024-01-15"
}

📊 Supported Formats

Format	Extension	Support
Excel 2007+	.xlsx	✅ Full
Excel 97-2003	.xls	✅ Full
OpenDocument	.ods	✅ Full
CSV (input)	.csv	✅ Full
Numbers	.numbers	⚠️ Limited

💰 Cost Estimation

File Size	Sheets	Approx. Time	Compute Units
1 MB	3	~5 seconds	~0.002
10 MB	5	~15 seconds	~0.008
50 MB	10	~45 seconds	~0.03
100 MB	20	~2 minutes	~0.08

🔧 Technical Details

• Node.js: 22.x
• Library: SheetJS (xlsx)
• Max File Size: ~200MB recommended
• Memory: 512MB-2GB depending on file size

⚠️ Limitations

• Formulas: Values only (not formula text)
• Formatting: Lost in CSV conversion
• Merged Cells: Unmerged, value in first cell
• Images/Charts: Not extracted
• Password Protected: Not supported

❓ FAQ

How are dates handled?

Dates are converted using the dateFormat parameter (default: YYYY-MM-DD). Uses dayjs formatting.

What about number formatting?

Numbers are extracted as raw values. Currency symbols and formatting are removed.

Can I convert password-protected files?

No, password-protected Excel files are not currently supported.

What's the maximum file size?

Recommended max is ~200MB. Larger files may timeout or run out of memory.

🔗 Integration Pipeline

// 1. Fetch Excel from email/S3/API
const excelUrl = await fetchLatestReport();

// 2. Convert to CSV
const convertRun = await client.actor('YOUR_USERNAME/excel-to-csv').call({
    fileUrl: excelUrl,
    outputToDataset: true
});

// 3. Load into database
const { items } = await client.dataset(convertRun.defaultDatasetId).listItems();
await database.insertMany(items);

// 4. Notify completion
await sendSlackNotification(`Imported ${items.length} rows`);

📄 License

MIT License - see LICENSE for details.