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# Person Sampling in Data Designer

Person sampling in Data Designer allows you to generate synthetic person data for your datasets. There are two distinct approaches, each with different capabilities and use cases.

## Overview

Data Designer provides two ways to generate synthetic people:

1. **Faker-based sampling** - Quick, basic PII generation for testing or when realistic demographic distributions are not relevant for your use case
2. **Nemotron-Personas datasets** - Demographically accurate, rich persona data

***

## Approach 1: Faker-Based Sampling

### What It Does

Uses the Faker library to generate random personal information. The data is basic and not demographically accurate, but is useful for quick testing, prototyping, or when realistic demographic distributions are not relevant for your use case.

### Features

* Gives you access to person attributes that Faker exposes
* Quick to set up with no additional downloads
* Generates random names, emails, addresses, phone numbers, etc.
* Supports [all Faker-supported locales](https://faker.readthedocs.io/en/master/locales.html)
* **Not demographically grounded** - data patterns don't reflect real-world demographics

### Usage Example

```python
import data_designer.config as dd

config_builder.add_column(
    dd.SamplerColumnConfig(
        name="customer",
        sampler_type=dd.SamplerType.PERSON_FROM_FAKER,
        params=dd.PersonFromFakerSamplerParams(
            locale="en_US",
            age_range=[25, 65],
            sex="Female",
        ),
    )
)
```

For mor details, see the documentation for [`SamplerColumnConfig`](/code-reference/topic-overviews/column-configs#data_designer.config.column_configs.SamplerColumnConfig) and [`PersonFromFakerSamplerParams`](/code-reference/topic-overviews/sampler-params#data_designer.config.sampler_params.PersonFromFakerSamplerParams).

***

## Approach 2: Nemotron-Personas Datasets

### What It Does

Uses curated Nemotron-Personas datasets from NVIDIA GPU Cloud (NGC) to generate demographically accurate person data with rich personality profiles and behavioral characteristics.

The NGC datasets are extended versions of the [open-source Nemotron-Personas datasets on HuggingFace](https://huggingface.co/collections/nvidia/nemotron-personas), with additional fields and enhanced data quality.

Supported locales:

* `en_US`: United States
* `en_IN`: India (English)
* `en_SG`: Singapore (English)
* `fr_FR`: France (French)
* `hi_Deva_IN`: India (Devanagari script)
* `hi_Latn_IN`: India (Latin script)
* `ja_JP`: Japan
* `ko_KR`: South Korea (Korean)
* `pt_BR`: Brazil (Portuguese)

### Features

* **Demographically accurate personal details**: Names, ages, sex, marital status, education, occupation based on census data
* **Rich persona details**: Comprehensive behavioral profiles including:
  * Big Five personality traits with scores
  * Cultural backgrounds and narratives
  * Skills and hobbies
  * Career goals and aspirations
  * Context-specific personas (professional, financial, healthcare, sports, arts, travel, culinary, etc.)
* Consistent, referenceable attributes across your dataset
* Grounded in real-world demographic distributions

### Prerequisites

To use the extended Nemotron-Personas datasets with Data Designer, you need to download them [from NGC](https://catalog.ngc.nvidia.com/search?orderBy=scoreDESC\&query=nemotron+personas) and move them to the Data Designer managed assets directory.

See below for step-by-step instructions.

### Nemotron-Personas Datasets Setup Instructions

#### Step 0: Obtain an NGC API Key and install the NGC CLI

To download the Nemotron-Personas datasets from NGC, you will need to obtain an NGC API key and install the NGC CLI.

1. **NGC API Key**: Obtain from [NVIDIA GPU Cloud](https://ngc.nvidia.com/)
2. **NGC CLI**: [NGC CLI](https://org.ngc.nvidia.com/setup/installers/cli)

#### Step 1: Set Your NGC API Key

```bash
export NGC_API_KEY="your-ngc-api-key-here"
```

#### Step 2 (option 1): Download Nemotron-Personas Datasets via the Data Designer CLI

Once you have the NGC CLI and your NGC API key set up, you can download the datasets via the Data Designer CLI.

You can pass the locales you want to download as arguments to the CLI command:

```bash
data-designer download personas --locale en_US --locale ja_JP
```

Or you can use the interactive mode to select the locales you want to download:

```bash
data-designer download personas
```

#### Step 2 (option 2): Download Nemotron-Personas Datasets Directly

Use the NGC CLI to download the datasets:

```bash
# For Nemotron-Personas USA
ngc registry resource download-version "nvidia/nemotron-personas/nemotron-personas-dataset-en_us"

# For Nemotron-Personas IN
ngc registry resource download-version "nvidia/nemotron-personas/nemotron-personas-dataset-hi_deva_in"
ngc registry resource download-version "nvidia/nemotron-personas/nemotron-personas-dataset-hi_latn_in"
ngc registry resource download-version "nvidia/nemotron-personas/nemotron-personas-dataset-en_in"

# For Nemotron-Personas FR
ngc registry resource download-version "nvidia/nemotron-personas/nemotron-personas-dataset-fr_fr"

# For Nemotron-Personas JP
ngc registry resource download-version "nvidia/nemotron-personas/nemotron-personas-dataset-ja_jp"

# For Nemotron-Personas KR
ngc registry resource download-version "nvidia/nemotron-personas/nemotron-personas-dataset-ko_kr"

# For Nemotron-Personas SG
ngc registry resource download-version "nvidia/nemotron-personas/nemotron-personas-dataset-en_sg"

# For Nemotron-Personas BR
ngc registry resource download-version "nvidia/nemotron-personas/nemotron-personas-dataset-pt_br"
```

Then move the downloaded dataset to the Data Designer managed assets directory:

```bash
mkdir -p ~/.data-designer/managed-assets/datasets/
mv nemotron-personas-dataset-*/*.parquet ~/.data-designer/managed-assets/datasets/
```

#### Step 3: Use PersonSampler in Your Code

```python
import data_designer.config as dd

config_builder.add_column(
    dd.SamplerColumnConfig(
        name="customer",
        sampler_type=dd.SamplerType.PERSON,
        params=dd.PersonSamplerParams(
            locale="en_US",
            sex="Female",
            age_range=[25, 45],
            with_synthetic_personas=True,
        ),
    )
)
```

For more details, see the documentation for [`SamplerColumnConfig`](/code-reference/topic-overviews/column-configs#data_designer.config.column_configs.SamplerColumnConfig) and [`PersonSamplerParams`](/code-reference/topic-overviews/sampler-params#data_designer.config.sampler_params.PersonSamplerParams).

### Available Data Fields

**Core Fields (all locales):**

| Field             | Type           | Notes                                                                  |
| ----------------- | -------------- | ---------------------------------------------------------------------- |
| `uuid`            | UUID           | Unique identifier                                                      |
| `first_name`      | string         |                                                                        |
| `middle_name`     | string         |                                                                        |
| `last_name`       | string         |                                                                        |
| `sex`             | enum           | "Male" or "Female"                                                     |
| `birth_date`      | date           | Derived: year, month, day                                              |
| `street_number`   | int            |                                                                        |
| `street_name`     | string         |                                                                        |
| `unit`            | string         | Address line 2                                                         |
| `city`            | string         |                                                                        |
| `region`          | string         | Alias: state                                                           |
| `district`        | string         | Alias: county                                                          |
| `postcode`        | string         | Alias: zipcode                                                         |
| `country`         | string         |                                                                        |
| `phone_number`    | PhoneNumber    | Derived: area\_code, country\_code, prefix, line\_number               |
| `marital_status`  | string         | Values: never\_married, married\_present, separated, widowed, divorced |
| `education_level` | string or None |                                                                        |
| `bachelors_field` | string or None |                                                                        |
| `occupation`      | string or None |                                                                        |
| `email_address`   | string         |                                                                        |
| `national_id`     | string         |                                                                        |

**France-Specific Fields (`fr_FR`):**

* `household_type` - Household composition (e.g., single person, couple with/without children)
* `monthly_income_eur` - Estimated monthly income in euros
* `first_name_heritage` - Cultural origin of the first name
* `name_heritage` - Cultural, linguistic, or geographic origin of the surname
* `is_first_gen_immigrant` - Whether the individual is a first-generation immigrant to France

**Japan-Specific Fields (`ja_JP`):**

* `area`

**Korea-Specific Fields (`ko_KR`):**

* `economic_activity_status` - Employment / economic activity status
* `family_type` - Household / family composition type
* `housing_type` - Dwelling type (apartment, detached home, etc.)
* `housing_tenure` - Owned vs rented, etc.
* `income_bracket` - Income range
* `military_status` - Military service status
* `drinking_status` - Drinking frequency / status
* `smoking_status` - Smoking frequency / status
* `blood_pressure_status` - Blood pressure health indicator
* `blood_sugar_status` - Blood sugar health indicator
* `bmi_status` - BMI health indicator
* `waist_status` - Waist-circumference health indicator

**Brazil-Specific Fields (`pt_BR`):**

* `race` - Census-reported race

**Singapore-Specific Fields (`en_SG`):**

* `industry` - Industry of employment
* `preferred_english_name` - Preferred English-form name

**English Locales Shared Fields (`en_US`, `en_SG`):**

* `ethnic_background` - Self-identified ethnic background

**Religion Fields (`en_IN`, `hi_Deva_IN`, `hi_Latn_IN`, `en_SG`, `pt_BR`):**

* `religion` - Census-reported religion

**India Locales Fields (`en_IN`, `hi_Deva_IN`, `hi_Latn_IN`):**

* `education_degree` - Census-reported education degree
* `first_language` - Native language
* `second_language` - Second language (if applicable)
* `third_language` - Third language (if applicable)
* `zone` - Urban vs rural

**With Synthetic Personas Enabled:**

* Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) with t-scores and labels
* Cultural background narratives
* Skills and competencies
* Hobbies and interests
* Career goals
* Context-specific personas (professional, financial, healthcare, sports, arts & entertainment, travel, culinary, etc.)

*Japan-specific persona fields (`ja_JP`):*

* `aspects`
* `digital_skill`

*Korea-specific persona fields (`ko_KR`):*

* `family_persona`

*Religious persona fields (`en_IN`, `hi_Deva_IN`, `hi_Latn_IN`, `en_SG`, `pt_BR`):*

* `religious_persona`
* `religious_background`

*India-locales persona fields (`en_IN`, `hi_Deva_IN`, `hi_Latn_IN`):*

* `linguistic_persona`
* `linguistic_background`

### Configuration Parameters

| Parameter                 | Type                         | Description                                                                                                                                 |
| ------------------------- | ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
| `locale`                  | str                          | Language/region code - must be one of: "en\_US", "en\_IN", "en\_SG", "fr\_FR", "hi\_Deva\_IN", "hi\_Latn\_IN", "ja\_JP", "ko\_KR", "pt\_BR" |
| `sex`                     | str (optional)               | Filter by "Male" or "Female"                                                                                                                |
| `city`                    | str or list\[str] (optional) | Filter by specific city or cities within locale                                                                                             |
| `age_range`               | list\[int] (optional)        | Two-element list \[min\_age, max\_age] (default: \[18, 114])                                                                                |
| `with_synthetic_personas` | bool (optional)              | Include rich personality profiles (default: False)                                                                                          |
| `select_field_values`     | dict (optional)              | Custom field-based filtering (e.g., `{"state": ["NY", "CA"], "education_level": ["bachelors"]}`)                                            |