@abhay557/indian-fakedata

v2.0.3

Published

2 days ago

A generator for realistic Indian demographic data based on Census 2011 statistics.

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Indian Fake Data Generator

A library that generates culturally accurate, statistically consistent mock Indian demographic profiles backed by Census 2011 data.

This repository provides two native implementations:

A Node.js / TypeScript package (@abhay557/indian-fakedata)
A Python package (indian-fakedata)

Unlike traditional mock generators that produce impossible demographic combinations (such as a Sikh named Mohammed Sharma from Mizoram), this library correctly links variables together so that every generated person makes logical sense based on real-world statistical correlations.

Output Sample (one profile, `seed = 7`)

Sample generated by the Python implementation. The TypeScript implementation is independently deterministic: the same seed is reproducible within one implementation, but the two runtimes draw RNG streams differently.

{
  "id": "33553413-2014-4616-9361-511204427271",
  "firstName": "Pushpa",
  "lastName": "Sharma",
  "fatherName": "Santosh Sharma",
  "motherName": "Geeta Sharma",
  "spouseName": "Sanjay Sharma",
  "gender": "female",
  "age": 34,
  "dateOfBirth": "1992-06-11",
  "bloodGroup": "O+",
  "heightCm": 152.9,
  "weightKg": 65.9,
  "bmi": 28.2,
  "aadhaarNumber": "500233102039",
  "panNumber": "EKIPS1361D",
  "voterIdNumber": "MHR0314014",
  "phoneNumber": "7501311043",
  "email": "[email protected]",
  "state": "Maharashtra",
  "stateCode": "MH",
  "district": "Solapur",
  "areaType": "urban",
  "addressLine": "245/D, MG Road, Solapur",
  "locality": "MG Road",
  "pinCode": "412519",
  "religion": "Hindu",
  "caste": "Deshastha Brahmin",
  "socialCategory": "General",
  "motherTongue": "Marathi",
  "secondLanguage": "Hindi",
  "education": "secondary",
  "occupation": "agricultural_labourer",
  "employmentSector": "self_employed",
  "maritalStatus": "married",
  "annualIncomeINR": 194000,
  "monthlyExpenditureINR": 15600,
  "numberOfChildren": 1,
  "dietaryPreference": "vegetarian",
  "disability": "none",
  "isMigrant": true,
  "migrationOriginState": "Andhra Pradesh",
  "bankIFSC": "SBIN0331430",
  "bankName": "State Bank of India",
  "bankAccountNumber": "00313113041",
  "rationCardType": "APL",
  "healthInsurance": "none",
  "landOwnershipAcres": 0,
  "vehicleRegistration": "MH 02 BB 2481",
  "vehicleType": "four_wheeler",
  "hasInternetAccess": true,
  "hasSmartphone": true,
  "usesSocialMedia": true,
  "upiId": "pushpa@okicici",
  "personality": {
    "openness": 54,
    "conscientiousness": 62,
    "extraversion": 60,
    "agreeableness": 70,
    "neuroticism": 58
  },
  "personalityTraits": {
    "summary": "An outgoing, people-oriented person who is practical, disciplined and kind-hearted. They feel things deeply and care about those around them.",
    "strengths": [
      "prefers familiar routines",
      "organized and punctual",
      "compassionate and helpful"
    ],
    "weaknesses": [
      "worries about small things",
      "needs company to feel energised",
      "perfectionist, can be rigid"
    ],
    "traitLabels": [
      "practical",
      "disciplined",
      "outgoing",
      "kind-hearted",
      "sensitive"
    ],
    "communicationStyle": "polite_indirect",
    "decisionStyle": "analytical",
    "socialBehavior": "outgoing"
  },
  "politicalLeaning": "nationalist_right",
  "religiosity": "very_religious",
  "cognitiveProfile": {
    "aptitudeScore": 74,
    "numeracyScore": 68,
    "literacyScore": 75,
    "digitalLiteracyScore": 53,
    "financialLiteracyScore": 71
  },
  "interests": {
    "primarySport": "cricket",
    "petPreference": "birds",
    "entertainment": [
      "Bollywood",
      "TV Serials",
      "Cricket Matches",
      "News",
      "YouTube",
      "OTT/Netflix"
    ],
    "readingHabit": "occasional",
    "musicPreference": "Bollywood",
    "preferredSocialMedia": "WhatsApp"
  },
  "habits": {
    "tobaccoUse": "smoking",
    "alcoholUse": "none",
    "exerciseFrequency": "weekly",
    "avgSleepHours": 9.3,
    "cooksAtHome": true,
    "chronotype": "early_riser"
  },
  "educationDetails": {
    "fieldOfStudy": null,
    "institutionType": "private",
    "mediumOfInstruction": "English",
    "qualificationYear": 2008,
    "competitiveExamPercentile": null
  },
  "educationTimeline": [
    {
      "level": "primary",
      "stageName": "Primary School",
      "institutionName": "Infant Jesus, Solapur",
      "institutionType": "private",
      "boardOrUniversity": "CBSE",
      "startYear": 1997,
      "endYear": 2003,
      "status": "completed",
      "score": "69.8%"
    },
    {
      "level": "middle",
      "stageName": "Middle School",
      "institutionName": "St. Peter's, Solapur",
      "institutionType": "private",
      "boardOrUniversity": "CBSE",
      "startYear": 2003,
      "endYear": 2006,
      "status": "completed",
      "score": "86.3%"
    },
    {
      "level": "secondary",
      "stageName": "Secondary School",
      "institutionName": "St. Agnes, Solapur",
      "institutionType": "private",
      "boardOrUniversity": "CBSE",
      "startYear": 2006,
      "endYear": 2008,
      "status": "completed",
      "score": "78.3%"
    }
  ],
  "moviePreferences": {
    "genres": [
      "Comedy",
      "Romance",
      "Action"
    ],
    "favoriteLanguages": [
      "Marathi",
      "Hindi"
    ],
    "anime": true,
    "animePreferences": [
      "Shonen action"
    ],
    "favoriteAnimeTitles": [
      "Demon Slayer",
      "One Piece"
    ],
    "primaryPlatform": "ott",
    "watchFrequency": "weekly"
  },
  "culturalProfile": {
    "entrepreneurialScore": 32,
    "academicOrientation": 64,
    "artisticInclination": 41,
    "militaryTradition": 37,
    "agriculturalRootedness": 21,
    "artisanTradition": 1,
    "bureaucraticOrientation": 50,
    "socialActivism": 13,
    "communityBonding": 67,
    "migrationTendency": 24,
    "careerPreference": "business_trade",
    "familyStructure": "nuclear_family",
    "savingsOrientation": 65,
    "riskAppetite": 12
  },
  "householdSize": 1,
  "householdAssets": {
    "hasRadioTransistor": false,
    "hasTelevision": true,
    "hasComputer": true,
    "hasPhone": true,
    "hasBicycle": true,
    "hasScooter": true,
    "hasCar": true,
    "bankingService": true,
    "treatedWaterSource": true,
    "latrineFacility": true,
    "numberOfRooms": 2,
    "roofMaterial": "concrete",
    "wallMaterial": "burnt_brick",
    "cookingFuel": "lpg",
    "lightingSource": "electricity",
    "drinkingWaterSource": "tap_treated"
  },
  "probabilityMetrics": {
    "nationalReligionFreq": 0.803301791826052,
    "stateGivenReligionProb": 0.10324714506000403,
    "casteGivenContextProb": 0.04225352112676056,
    "lastNameGivenCasteProb": 0.2,
    "socialCategoryProb": 0.35211267605633797,
    "educationProb": 0.21890547263681592,
    "occupationProb": 0.18000000000000002,
    "jointProbability": 2.7617147096522784e-05
  },
  "generatedAt": "2026-08-03T21:45:13.883314",
  "seed": 7
}

Installation

Node.js / TypeScript

npm install @abhay557/indian-fakedata

Requires Node.js >= 18.

Python

pip install indian-fakedata

Requires Python 3.8+.

CLI Usage (Both Languages)

Both packages ship with the indian-fakedata CLI binary. The arguments are identical across both versions!

# Node.js
npx @abhay557/indian-fakedata [options]

# Python (or globally installed Node package)
indian-fakedata [options]

Run with no arguments to display the full help menu.

Core Options

| Flag | Alias | Description | Default | |------|-------|-------------|---------| | --count <n> | -c | Number of profiles to generate | 100 | | --output <path> | -o | File path to save output | stdout | | --format <fmt> | -f | Output format: json, jsonl, csv | json | | --seed <value> | -s | Reproducibility seed (number or string, e.g. 011) | random | | --no-metrics | | Exclude probability metrics from output | included | | --family | | Generate a full family (head + spouse + parents + children + siblings) from one seed | off | | --help | -h | Show help screen | |

Demographic Constraints

Filter generated profiles to specific demographic slices:

| Flag | Values | |------|--------| | --religion <string> | Hindu, Muslim, Christian, Sikh, Buddhist, Jain | | --state <string> | e.g. Maharashtra, Tamil Nadu, Punjab | | --gender <gender> | male, female, other | | --caste <string> | e.g. Brahmin, Maratha, Jat | | --socialCategory <cat> | SC, ST, OBC, General | | --areaType <type> | urban, rural | | --minAge <n> | Minimum age (0–100) | | --maxAge <n> | Maximum age (0–100) | | --education <level> | illiterate, primary, secondary, graduate, etc. | | --occupation <sector> | cultivator, other_worker, non_worker, etc. | | --maritalStatus <status> | never_married, married, widowed, etc. |

Enrichment Layers (Progressive Depth)

| Flag | Description | |------|-------------| | --enrich | Enable ALL enrichment layers (outcomes + narrative:all + persona) | | --outcomes | [Layer 2] Add credit score, health risk, employment outcome, education attainment | | --bias <0-1> | Bias dial for outcome simulation. 0.0 = pure meritocracy, 1.0 = max historical discrimination. Default: 0.3 | | --narrative <type> | [Layer 3] Generate realistic Indian text documents. Repeat for multiple types: loan_application, medical_consultation, school_enrollment, ration_card_application, hinglish_conversation, all | | --persona | [Layer 4] Generate LLM-ready agent persona (system prompt + full roleplay prompt, beliefs, memory seeds) |

Quick Examples

# 1000 profiles as CSV
indian-fakedata -c 1000 -f csv -o profiles.csv

# 50K Tamil Nadu Hindus as JSONL
indian-fakedata -c 50000 -f jsonl -o tn_data.jsonl --state "Tamil Nadu" --religion Hindu

# All enrichment layers with moderate bias
indian-fakedata -c 100 --enrich --bias 0.3 -f jsonl -o enriched.jsonl

# SC community fairness audit
indian-fakedata -c 5000 --outcomes --bias 0.5 --socialCategory SC -f jsonl -o sc_bias.jsonl

Programmatic API

User / Family / Persona (faker-style)

Both runtimes expose ergonomic entry points built on top of generate:

import { generateUser, generateUsers, generateFamily, generatePersona } from '@abhay557/indian-fakedata';

// One user — same shape as the "Output Sample" above. Seed may be a
// number (7) or string ('011'); string seeds are hashed deterministically.
const user = generateUser({ seed: 7 });

// Many users driven by a single seed
const users = generateUsers({ count: 5, seed: '011' });

// A highly educated female IT professional from Karnataka
const dev = generateUser({ highlyEducated: true, gender: 'female', constraints: { state: 'Karnataka' } });

// Full relational household from one seed — spouse, parents, children,
// siblings all share the head's state/religion/caste/surname and keep
// age-consistent relationships. Fully reproducible for the same seed.
const family = generateFamily({ seed: '011' });
family.spouse?.lastName;   // === family.head.lastName
family.children.map(c => c.age); // younger than head

// User + LLM-ready agent persona (system prompt, beliefs, memory seeds)
const { user: u, persona } = generatePersona({ seed: '011' });
persona.systemPrompt; // ready to inject into any LLM system role
persona.fullPrompt;   // complete self-contained roleplay prompt: identity,
                      // education timeline, personality traits, movie/anime
                      // preferences, habits, beliefs, behaviour rules

from indian_fakedata import generate_user, generate_users, generate_family, generate_persona

user = generate_user(seed=7)
users = generate_users(count=5, seed="011")
dev = generate_user(highly_educated=True, gender="female", constraints={"state": "Karnataka"})
family = generate_family(seed="011")
out = generate_persona(seed="011")   # {"user": ..., "persona": ...}
out["persona"]["fullPrompt"]         # complete roleplay prompt (see above)

Core Node.js / TypeScript

import { generate, generateEnriched } from '@abhay557/indian-fakedata';

const profiles = generate({ count: 10 });
const enriched = generateEnriched({ count: 5, includeOutcomes: true });

Core Python

from indian_fakedata import generate, generate_enriched

profiles = generate(count=10)
enriched = generate_enriched(count=5, include_outcomes=True)

See TUTORIAL.md for comprehensive, side-by-side code snippets including data exporting, streams, and narratives.

Data Sources & Real-World Accuracy

The generator is calibrated against publicly available survey data. The bundled distributions are approximations derived from published reports, not raw census tables — actual census microdata (team/data/*.xlsx) is provided for reference but is not compiled into the package at build time.

Census of India 2011 (D-Series & C-Series Tables): Reference material for religion shares, state populations, and mother tongue frequencies; distributions are hand-calibrated approximations.
National Family Health Survey (NFHS-5): Dietary preferences, BMI, blood groups, height/weight-by-age published statistics.
MSME Census: Community-level occupational sectors, vocational rates, industry divisions.
UIDAI & RTO Records: Structural syntax for Aadhaar, Voter ID, PAN, IFSC, and RTO registrations (Aadhaar uses a true Verhoeff checksum; PAN's 10th character is self-consistent but not the official check digit).
CSDS/Lokniti Election Studies: Political leanings and religiosity index biases.

Note: All data is synthetic mock data. Names, IDs, and numbers are randomly generated and do not correspond to any real individuals.

The 4 Data Layers

| Layer | Name | Description | |-------|------|-------------| | 1 | Core Demographics | State, gender, religion, caste, names, languages, biological markers, address | | 2 | Socio-Economic Outcomes | CIBIL credit score, health risk, literacy, employment vulnerability (configurable bias) | | 3 | Narrative Documents | Loan applications, OPD records, Hinglish WhatsApp chats, school admissions | | 4 | Agent Persona Prompts | LLM-ready system prompts + full roleplay prompts (education timeline, personality traits, movie/anime preferences), worldview beliefs, stress responses, memory seeds |

Scripts (For Contributors)

| Command | Description | |---------|-------------| | npm run build | Compile TypeScript to dist/ | | npm run dev | Run src/index.ts via tsx | | npm run cli | Run src/cli.ts via tsx | | npm test | Run vitest test suite | | npm run demo | Run demo script | | npm run lint | Type-check without emitting |

For AI Agents

SKILL.md teaches AI coding agents how to correctly use this library: install commands, the full API matrix (TS/Python), CLI reference, seed semantics, output shape, verification assertions, and common mistakes.