data-importer

# Data Importer

Transform product data from any source into Saleor's config.yml format.

## Core Workflow

1. **Detect format** - CSV, Excel, JSON, or unknown
2. **Extract columns** - Read headers and sample data
3. **Map interactively** - User confirms field mappings
4. **Transform** - Convert to Saleor schema
5. **Validate** - Check for issues before output

## File Handling

### Excel (.xlsx)
Excel files need conversion. Ask user to export as CSV from Excel/Sheets, or attempt:
```bash
python3 -c "import pandas as pd; pd.read_excel('$FILE').to_csv('${FILE%.xlsx}.csv', index=False)"
```

### CSV
Read directly:
```bash
head -1 "$FILE"  # Headers
head -5 "$FILE"  # Sample rows
```

## Field Mapping Strategy

**Don't assume column names.** Instead:
1. Show user all columns with sample values
2. Ask which column maps to each Saleor field
3. Mark unmapped columns as potential attributes

### Required Saleor Fields

| Field | Source Options |
|-------|----------------|
| `product.name` | Any "name", "title", or descriptive column |
| `product.slug` | Generate from name, or use ID/handle column |
| `variant.sku` | Any unique ID column (SKU, External ID, Code) |
| `productType` | From "type" column or user-specified |

### Optional Fields

| Field | Notes |
|-------|-------|
| `price` | If missing, import as catalog-only |
| `quantity` | If missing, skip stocks |
| `category` | From category/country/region column |
| `description` | If present |
| Attributes | Any unmapped columns become product attributes |

## Output Structure

```yaml
productTypes:
  - name: "[from type column or user input]"
    isShippingRequired: [ask user]
    productAttributes: [unmapped columns]

categories:
  - name: "[from category column]"
    slug: "[generated]"

products:
  - name: "[from name column]"
    slug: "[generated or from ID]"
    productType: "[reference]"
    variants:
      - sku: "[from SKU column]"
        attributes: {[mapped attributes]}
        channelListings: [if price ex