If you build apps for the Indian market, you've probably needed mock demographic data for testing, UI previews, or training ML models.
And if you've used standard mock-data generators, you've probably ended up with a profile that looks something like this: A Sikh person named Mohammed Sharma living in Mizoram. ๐
Most fake-data libraries generate impossible demographic combinations because they operate on simple random selection. They pick a first name from list A, a last name from list B, a religion from list C, and a state from list D.
When context matters, that bad data can ruin your testing environment. Thatโs why I built indian-fakedata.
Output Sample (one profile)
{
"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": "EKIPS1361G",
"voterIdNumber": "MHR3140140",
"phoneNumber": "7513110431",
"email": "pushpasharma328@gmail.com",
"state": "Maharashtra",
"stateCode": "MH",
"district": "Aurangabad",
"areaType": "urban",
"addressLine": "469/A, Adarsh Colony, Aurangabad",
"locality": "Adarsh Colony",
"pinCode": "418683",
"religion": "Hindu",
"caste": "Deshastha Brahmin",
"socialCategory": "General",
"motherTongue": "Marathi",
"secondLanguage": "English",
"education": "secondary",
"occupation": "agricultural_labourer",
"employmentSector": "self_employed",
"maritalStatus": "married",
"annualIncomeINR": 194000,
"monthlyExpenditureINR": 15300,
"numberOfChildren": 1,
"dietaryPreference": "non_vegetarian",
"disability": "none",
"isMigrant": true,
"migrationOriginState": "Kerala",
"bankIFSC": "PUNB0314300",
"bankName": "Punjab National Bank",
"bankAccountNumber": "03131130413",
"rationCardType": "APL",
"healthInsurance": "none",
"landOwnershipAcres": 0,
"vehicleRegistration": "MH 01 BG 3908",
"vehicleType": "four_wheeler",
"hasInternetAccess": true,
"hasSmartphone": true,
"usesSocialMedia": true,
"upiId": "pushpa@okicici",
"personality": {
"openness": 53,
"conscientiousness": 47,
"extraversion": 53,
"agreeableness": 46,
"neuroticism": 74
},
"politicalLeaning": "nationalist_right",
"religiosity": "very_religious",
"cognitiveProfile": {
"aptitudeScore": 40,
"numeracyScore": 37,
"literacyScore": 75,
"digitalLiteracyScore": 45,
"financialLiteracyScore": 93
},
"interests": {
"primarySport": "cricket",
"petPreference": "fish",
"entertainment": [
"Bollywood", "TV Serials", "Cricket Matches", "News",
"YouTube", "OTT/Netflix", "Devotional Music"
],
"readingHabit": "rare",
"musicPreference": "Bollywood",
"preferredSocialMedia": "WhatsApp"
},
"habits": {
"tobaccoUse": "none",
"alcoholUse": "none",
"exerciseFrequency": "weekly",
"avgSleepHours": 9.3,
"cooksAtHome": true,
"chronotype": "early_riser"
},
"educationDetails": {
"fieldOfStudy": null,
"institutionType": "private",
"mediumOfInstruction": "English",
"qualificationYear": 2008,
"competitiveExamPercentile": null
},
"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": 0.8033,
"stateGivenReligionProb": 0.1032,
"casteGivenContextProb": 0.0423,
"lastNameGivenCasteProb": 0.2000,
"socialCategoryProb": 0,
"educationProb": 0.2189,
"occupationProb": 0.1800,
"jointProbability": 2.76e-05
},
"generatedAt": "2026-07-12T21:56:35.146855",
"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 <number> |
-s |
Reproducibility seed for RNG | random |
--no-metrics |
Exclude probability metrics from output | included | |
--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 (0โ100) |
--maxAge <n> |
Maximum age (0โ100) |
--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, 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
Node.js / TypeScript
import { generate, generateEnriched } from '@abhay557/indian-fakedata';
const profiles = generate({ count: 10 });
const enriched = generateEnriched({ count: 5, includeOutcomes: true });
Python
from indian_fakedata import generate, generate_enriched
profiles = generate(count=10)
enriched = generate_enriched(count=5, include_outcomes=True)
Data Sources & Real-World Accuracy
Every demographic profile, name distribution, and asset weighting is calibrated against public survey data:
- Census of India 2011 (D-Series & C-Series Tables): Core distributions for religion, state-by-state population metrics, and mother tongue frequencies.
- National Family Health Survey (NFHS-5): State-wise dietary preferences, BMI, blood groups, height/weight by age.
- MSME Census: Community-level occupational sectors, vocational rates, industry divisions.
- UIDAI & RTO Records: Structural syntax for Aadhaar, Voter ID, PAN, IFSC, and RTO registrations.
- CSDS/Lokniti Election Studies: Political leanings and religiosity index biases.
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