README.md

---
license: cc-by-nc-4.0
task_categories:
  - feature-extraction
  - text-classification
language:
  - en
tags:
  - fragrance
  - perfume
  - cosmetics
  - recommendation-system
  - e-commerce
  - retail
  - fragrantica
size_categories:
  - 10<n<100
configs:
  - config_name: fragrances
    data_files: fragrances.csv
    default: true
    sep: "|"
  - config_name: brands
    data_files: brands.csv
    sep: "|"
  - config_name: perfumers
    data_files: perfumers.csv
    sep: "|"
  - config_name: notes
    data_files: notes.csv
    sep: "|"
  - config_name: accords
    data_files: accords.csv
    sep: "|"
dataset_info:
  - config_name: fragrances
    features:
      - name: pid
        dtype: int64
      - name: url
        dtype: string
      - name: brand
        dtype: string
      - name: name
        dtype: string
      - name: year
        dtype: int64
      - name: gender
        dtype: string
      - name: collection
        dtype: string
      - name: main_photo
        dtype: string
      - name: info_card
        dtype: string
      - name: user_photoes
        dtype: string
      - name: video_url
        dtype: string
      - name: accords
        dtype: string
      - name: notes_pyramid
        dtype: string
      - name: perfumers
        dtype: string
      - name: description
        dtype: string
      - name: rating
        dtype: string
      - name: appreciation
        dtype: string
      - name: price_value
        dtype: string
      - name: gender_votes
        dtype: string
      - name: longevity
        dtype: string
      - name: sillage
        dtype: string
      - name: season
        dtype: string
      - name: time_of_day
        dtype: string
      - name: by_designer
        dtype: string
      - name: in_collection
        dtype: string
      - name: reminds_of
        dtype: string
      - name: also_like
        dtype: string
      - name: news_ids
        dtype: string
      - name: reviews_count
        dtype: int64
      - name: pros_cons
        dtype: string
  - config_name: brands
    features:
      - name: id
        dtype: string
      - name: name
        dtype: string
      - name: url
        dtype: string
      - name: logo_url
        dtype: string
      - name: country
        dtype: string
      - name: main_activity
        dtype: string
      - name: website
        dtype: string
      - name: parent_company
        dtype: string
      - name: description
        dtype: string
      - name: brand_count
        dtype: int64
  - config_name: perfumers
    features:
      - name: id
        dtype: string
      - name: name
        dtype: string
      - name: url
        dtype: string
      - name: photo_url
        dtype: string
      - name: status
        dtype: string
      - name: company
        dtype: string
      - name: also_worked
        dtype: string
      - name: education
        dtype: string
      - name: web
        dtype: string
      - name: perfumes_count
        dtype: int64
      - name: biography
        dtype: string
  - config_name: notes
    features:
      - name: id
        dtype: string
      - name: name
        dtype: string
      - name: url
        dtype: string
      - name: latin_name
        dtype: string
      - name: other_names
        dtype: string
      - name: group
        dtype: string
      - name: odor_profile
        dtype: string
      - name: main_icon
        dtype: string
      - name: alt_icons
        dtype: string
      - name: background
        dtype: string
      - name: fragrance_count
        dtype: int64
  - config_name: accords
    features:
      - name: id
        dtype: string
      - name: name
        dtype: string
      - name: bar_color
        dtype: string
      - name: font_color
        dtype: string
      - name: fragrance_count
        dtype: int64
---

# FragDB v3.1 — Fragrantica Fragrance Database (Sample)

The most comprehensive structured fragrance database available. This is a **free sample** containing 10 fragrances with related brands, perfumers, notes, and accords.

## What's New in v3.1

- **New field**: `video_url` — YouTube video URLs for fragrances
- **Removed field**: `ownership` — no longer included (was empty in source data)
- **Total**: 67 data fields across 5 files (30 fragrance fields)

## Dataset Description

FragDB is a relational database of the fragrance industry containing:

| File | Records | Fields | Description |
|------|---------|--------|-------------|
| `fragrances.csv` | 10 | 30 | Iconic fragrances with notes, accords, ratings |
| `brands.csv` | 10 | 10 | Luxury brand profiles |
| `perfumers.csv` | 10 | 11 | Master perfumer profiles |
| `notes.csv` | 10 | 11 | Fragrance notes with Latin names, odor profiles |
| `accords.csv` | 10 | 5 | Scent accords with display colors |

### Full Database

| | Sample | Full Database |
|---|--------|---------------|
| Fragrances | 10 | **121,539** |
| Brands | 10 | **7,316** |
| Perfumers | 10 | **2,828** |
| Notes | 10 | **1,825** |
| Accords | 10 | **92** |
| **Total Records** | 50 | **133,600** |

Full database available at [fragdb.net](https://fragdb.net)

## Quick Start

### Using Hugging Face Datasets

```python
from datasets import load_dataset

# Load all files
fragrances = load_dataset("FragDBnet/fragrance-database", "fragrances")
brands = load_dataset("FragDBnet/fragrance-database", "brands")
perfumers = load_dataset("FragDBnet/fragrance-database", "perfumers")
notes = load_dataset("FragDBnet/fragrance-database", "notes")
accords = load_dataset("FragDBnet/fragrance-database", "accords")
```

### Using Pandas

```python
import pandas as pd

fragrances = pd.read_csv('fragrances.csv', sep='|')
brands = pd.read_csv('brands.csv', sep='|')
perfumers = pd.read_csv('perfumers.csv', sep='|')
notes = pd.read_csv('notes.csv', sep='|')
accords = pd.read_csv('accords.csv', sep='|')

# Join fragrances with brands
fragrances['brand_id'] = fragrances['brand'].str.split(';').str[1]
df = fragrances.merge(brands, left_on='brand_id', right_on='id', suffixes=('', '_brand'))

print(df[['name', 'name_brand', 'country', 'rating']])
```

## Data Structure

### fragrances.csv (30 fields)

#### Identity & Basic Info
| Field | Description | Example |
|-------|-------------|---------|
| `pid` | Unique fragrance ID | `9828` |
| `url` | Direct link to fragrance page | URL |
| `brand` | Brand name and ID reference | `Creed;b1` |
| `name` | Fragrance name | `Aventus` |
| `year` | Release year | `2010` |
| `gender` | Target gender | `for men`, `for women`, `for women and men` |
| `collection` | Collection within brand | Text |

#### Media
| Field | Description | Format |
|-------|-------------|--------|
| `main_photo` | Main product image | URL |
| `info_card` | Info card image | URL |
| `user_photoes` | User-submitted photos | Semicolon-separated URLs |
| `video_url` | YouTube video | URL (new in v3.1) |

#### Composition
| Field | Description | Format |
|-------|-------------|--------|
| `accords` | Scent accords with strength | `a1:100;a2:67;a3:45` (join with accords.csv) |
| `notes_pyramid` | Notes by layer | `top(name,id,url,opacity,weight;...)middle(...)base(...)` |
| `perfumers` | Perfumer names and IDs | `Erwin Creed;p1;Olivier Creed;p2` |
| `description` | Fragrance description | HTML text |

#### Ratings & Votes (Structured Format: category:votes:percent)
| Field | Description | Format |
|-------|-------------|--------|
| `rating` | Average rating & vote count | `4.33;24561` |
| `appreciation` | Love/like/ok/dislike/hate | `love:5000:45.2;like:3000:27.1;...` |
| `price_value` | Price perception votes | `way_overpriced:6658:30;overpriced:2844:13;...` |
| `gender_votes` | Gender suitability votes | `female:149:2;unisex:866:10;male:7977:88` |
| `longevity` | Duration votes | `very_weak:784:5;weak:1459:10;moderate:5869:40;...` |
| `sillage` | Projection votes | `intimate:1816:12;moderate:8139:55;strong:4289:29;...` |
| `season` | Seasonal suitability | `winter:4439:44.39;spring:9760:97.60;...` |
| `time_of_day` | Day/night suitability | `day:10000:100;night:6893:68.93` |

#### Related Fragrances
| Field | Description | Format |
|-------|-------------|--------|
| `by_designer` | Same brand fragrances | Semicolon-separated PIDs |
| `in_collection` | Same collection fragrances | Semicolon-separated PIDs |
| `reminds_of` | Similar fragrances | Semicolon-separated PIDs |
| `also_like` | Recommended fragrances | Semicolon-separated PIDs |

#### New in v3.0
| Field | Description |
|-------|-------------|
| `reviews_count` | Total number of user reviews |
| `pros_cons` | AI-generated pros/cons summary with vote counts |

### notes.csv (11 fields) — NEW in v3.0

| Field | Description | Example |
|-------|-------------|---------|
| `id` | Unique note identifier | `n1` |
| `name` | Note name | `Lavender` |
| `url` | Fragrantica note page | URL |
| `latin_name` | Latin/scientific name | `Lavandula angustifolia` |
| `other_names` | Alternative names | `English Lavender, True Lavender` |
| `group` | Note category | `Flowers`, `Woods`, `Citrus` |
| `odor_profile` | Scent description | `Fresh, herbal, floral...` |
| `main_icon` | Primary icon image URL | URL |
| `alt_icons` | Alternative icons | Semicolon-separated URLs |
| `background` | Background/splash image | URL |
| `fragrance_count` | Number of fragrances | `12229` |

### accords.csv (5 fields) — NEW in v3.0

| Field | Description | Example |
|-------|-------------|---------|
| `id` | Unique accord identifier | `a1` |
| `name` | Accord name | `woody` |
| `bar_color` | Display bar color (hex) | `#774414` |
| `font_color` | Text color (hex) | `#FFFFFF` |
| `fragrance_count` | Number of fragrances | `45892` |

### brands.csv (10 fields)

| Field | Description | Example |
|-------|-------------|---------|
| `id` | Unique brand identifier | `b1` |
| `name` | Brand name | `Creed` |
| `url` | Fragrantica brand page | URL |
| `logo_url` | Brand logo image | URL |
| `country` | Country of origin | `France` |
| `main_activity` | Primary business | `Fragrance house` |
| `website` | Official website | `https://www.creed.com` |
| `parent_company` | Parent company | `Kering` |
| `description` | Brand description | HTML text |
| `brand_count` | Number of fragrances | `847` |

### perfumers.csv (11 fields)

| Field | Description | Example |
|-------|-------------|---------|
| `id` | Unique perfumer identifier | `p1` |
| `name` | Perfumer name | `Alberto Morillas` |
| `url` | Fragrantica perfumer page | URL |
| `photo_url` | Perfumer photo | URL |
| `status` | Professional status | `Master Perfumer` |
| `company` | Current company | `Firmenich` |
| `also_worked` | Previous companies | `Quest International, Givaudan` |
| `education` | Education | `ISIPCA` |
| `web` | Personal website | URL |
| `perfumes_count` | Number of fragrances | `538` |
| `biography` | Biography | HTML text |

## Parsing Examples (v3.0)

### Parse v3.0 voting format
```python
def parse_votes(votes_str):
    """Parse v3.0 voting format: category:votes:percent"""
    result = {}
    for item in votes_str.split(';'):
        parts = item.split(':')
        if len(parts) >= 3:
            result[parts[0]] = {
                'votes': int(parts[1]),
                'percent': float(parts[2])
            }
    return result

longevity = parse_votes(row['longevity'])
# {'very_weak': {'votes': 784, 'percent': 5.0}, 'weak': {...}, ...}
```

### Parse v3.0 accords format with join
```python
def parse_accords(accords_str, accords_df):
    """Parse v3.0 accords format: id:percent and join with reference"""
    result = []
    for item in accords_str.split(';'):
        accord_id, percent = item.split(':')
        accord_info = accords_df[accords_df['id'] == accord_id].iloc[0]
        result.append({
            'name': accord_info['name'],
            'percent': int(percent),
            'bar_color': accord_info['bar_color'],
            'font_color': accord_info['font_color']
        })
    return result
```

### Parse notes pyramid with opacity/weight
```python
import re

def parse_notes_pyramid(pyramid_str):
    """Parse v3.0 notes pyramid with opacity and weight"""
    result = {'top': [], 'middle': [], 'base': []}
    for layer in ['top', 'middle', 'base']:
        match = re.search(rf'{layer}\(([^)]+)\)', pyramid_str)
        if match:
            for note in match.group(1).split(';'):
                parts = note.split(',')
                result[layer].append({
                    'name': parts[0],
                    'id': parts[1] if len(parts) > 1 else None,
                    'url': parts[2] if len(parts) > 2 else None,
                    'opacity': float(parts[3]) if len(parts) > 3 else None,
                    'weight': float(parts[4]) if len(parts) > 4 else None
                })
    return result
```

## Use Cases

- **Recommendation Systems** — Build "if you like X, try Y" engines using accords, notes, and also_like data
- **Market Analysis** — Analyze trends by brand, country, year, or perfumer
- **NLP** — Process descriptions, odor profiles, and pros/cons data
- **Collection Apps** — Build fragrance tracking and discovery apps
- **E-commerce** — Enrich product catalogs with detailed fragrance data
- **Data Visualization** — Create accord charts with actual display colors from accords.csv

## File Format

- **Format**: CSV (pipe `|` delimited)
- **Encoding**: UTF-8
- **Quote Character**: `"` (double quote)

## Links

- **Full Database**: [fragdb.net](https://fragdb.net)
- **GitHub**: [github.com/FragDB/fragrance-database](https://github.com/FragDB/fragrance-database)
- **Kaggle**: [kaggle.com/datasets/eriklindqvist/fragdb-fragrance-database](https://www.kaggle.com/datasets/eriklindqvist/fragdb-fragrance-database)

## License

This sample is released under the **CC BY-NC 4.0 License**. Free for non-commercial use with attribution.

The full database requires a commercial license — see [fragdb.net](https://fragdb.net) for details.

## Citation

```bibtex
@dataset{fragdb2026,
  title={FragDB Fragrantica Fragrance Database},
  author={FragDB},
  year={2026},
  version={3.1},
  url={https://fragdb.net},
  note={Sample dataset with 5 files, 67 fields}
}
```