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:

Faker-based sampling - Quick, basic PII generation for testing or when realistic demographic distributions are not relevant for your use case
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
Not demographically grounded - data patterns don't reflect real-world demographics

Usage Example

from data_designer.essentials import (
    SamplerColumnConfig,
    SamplerType,
    PersonFromFakerSamplerParams,
)

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

For mor details, see the documentation for SamplerColumnConfig and 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, with additional fields and enhanced data quality.

Supported locales:

en_US: United States
ja_JP: Japan
en_IN: India
hi_Deva_IN: India (Devanagari script)
hi_Latn_IN: India (Latin script)

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 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.

NGC API Key: Obtain from NVIDIA GPU Cloud
NGC CLI: NGC CLI

Step 1: Set Your NGC API Key

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:

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:

data-designer download personas

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

Use the NGC CLI to download the datasets:

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

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

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

Step 3: Use PersonSampler in Your Code

from data_designer.essentials import (
    SamplerColumnConfig,
    SamplerType,
    PersonSamplerParams,
)

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

For more details, see the documentation for SamplerColumnConfig and 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

Japan-Specific Fields (ja_JP):

area

India-Specific Fields (en_IN, hi_IN, hi_Deva_IN, hi_Latn_IN):

religion - Census-reported religion
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.)

Configuration Parameters

Parameter	Type	Description
`locale`	str	Language/region code - must be one of: "en_US", "ja_JP", "en_IN", "hi_Deva_IN", "hi_Latn_IN"
`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"]})