fakedata-python 2.0.0

The fakedata package generates realistic synthetic user profiles for machine learning, deep learning, data analysis, and data science workflows.

fakedata

A high-performance, zero-dependency synthetic data generation engine, available for both Node.js and Python. Designed specifically for machine learning, data science, and analytics workflows, providing 100% data parity across platforms.

Overview

fakedata has been completely rebuilt from the ground up to serve as an ML-ready synthetic data engine. It generates deeply interconnected user profiles with 109 flat columns across 13 domains (Health, Financial, Employment, Digital Footprint, etc.), making it the perfect tool for training models, benchmarking pipelines, or simulating realistic databases.

Machine Learning Power Features:

• Seed Reproducibility: Generate byte-for-byte identical datasets across runs (and languages!) using seed.
• Schema Overrides: Force specific distributions (e.g., age ranges, income brackets, genders) using schema.
• Locale-Aware Generation: Support for 8 culture-specific name sets and phone formats (en, in, jp, kr, de, br, ar, fr).
• Missing Data Simulation: Automatically inject realistic nulls using missing_rate to test your data imputation pipelines.
• Anomaly Injection: Inject fraud/outlier profiles (e.g., impossible geography, credit fraud, income spikes) using anomaly_rate.
• Time-Series Data: Generate chronological activity logs (logins, page views, purchases) per user for behavioral modeling.
• Pipeline Ready: Export directly to CSV, JSON, or Flat objects (perfect for pandas.DataFrame).

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Node.js / TypeScript Implementation

Installation

npm install @abhay557/fakedata

Quick Start

const { data } = require('@abhay557/fakedata');

// Generate deterministic users with a 5% missing data rate (null injection)
const users = data.users(1000, { seed: 42, missing_rate: 0.05 });

// Export directly to CSV format
const csvString = data.usersToCSV(1000, { seed: 42 });

// Time-series activity data
const ts = data.userTimeSeries({ days: 30, eventsPerDay: 8 });
console.log(`Generated ${ts.activity.length} events for ${ts.user.fullName}`);

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Python Implementation

Installation

pip install fakedata-python

Quick Start

import fakedata.data as data
import pandas as pd

# Generate 10,000 highly correlated users deterministically
users = data.users(10000, {"seed": 42})

# Or export directly to a Pandas DataFrame
df = pd.DataFrame(data.users_flat(10000, {"seed": 42}))
print(df.head())

# Create time-series activity data
ts = data.user_time_series({"days": 30, "events_per_day": 8})
print(f"Generated {len(ts['activity'])} events for {ts['user']['fullName']}")

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Advanced Features Reference

Both Python and JS/TS expose the same underlying engine options.

1. Configuration Options

Pass an options dictionary/object to data.user(options) or data.users(n, options):

const options = {
    seed: 42,              // Number: Ensures deterministic, byte-for-byte identical output
    missing_rate: 0.05,    // Float (0-1): 5% chance of any leaf field being null
    locale: 'jp',          // String: 'en', 'in', 'jp', 'kr', 'de', 'br', 'ar', 'fr'
    anomaly_rate: 0.05,    // Float (0-1): 5% of users will have injected fraud anomalies
    days: 30,              // Number: Days of time-series activity to generate
    eventsPerDay: 8,       // Number: Average events per day for time-series logs
    
    // Schema Constraints (force specific data distributions)
    schema: {
        age: { min: 25, max: 40 },           // Can also use { exact: 30 }
        gender: "female",                    // "male", "female", or "non-binary"
        employment: { status: "employed" }, 
        education: { level: "Master's" },
        financial: { annualIncome: { min: 60000, max: 120000 } },
        health: { medicalCondition: "Diabetes" },
        address: { country: "Japan" },
        height: { min: 160, max: 180 },
        weight: { min: 50, max: 80 }
    }
}

2. Supported API Methods

Method (JS)	Method (Python)	Description
data.user(opts?)	data.user(opts=None)	Generate a single complex user profile.
data.users(n, opts?)	data.users(n, opts=None)	Generate an array/list of n users.
data.userTimeSeries(opts)	data.user_time_series(opts)	Returns { user, activity } containing chronological event logs.
data.usersFlat(n, opts?)	data.users_flat(n, opts=None)	Returns flat dicts/objects, perfect for pandas.DataFrame ingestion.
data.usersToCSV(n, opts?)	data.users_to_csv(n, opts=None)	Returns a fully formatted CSV string (109 columns).
data.usersToJSON(n, opts?)	data.users_to_json(n, opts=None)	Returns a pretty-printed JSON string.

3. Locale-Aware Name Generation

Supports 8 locales with culturally accurate first names, last names, and country/phone codes:

• 'in': Aarav Sharma, Priya Patel (+91, India)
• 'jp': Haruto Tanaka, Sakura Sato (+81, Japan)
• 'kr': Minjun Kim, Seo-yeon Park (+82, South Korea)
• 'de': Lukas Müller, Mia Schmidt (+49, Germany)
• 'br': Miguel Silva, Alice Santos (+55, Brazil)
• 'ar': Mohammed Al-Ahmed, Fatima Khalil (+966, Saudi Arabia)
• 'fr': Gabriel Martin, Emma Dubois (+33, France)
• 'en': James Smith, Mary Johnson (+1, United States)

4. Time-Series Activity Data

Generate chronological behavioral logs for users. Event types include login, page_view, purchase, search, click, logout, api_call, upload, download, and comment.

const ts = data.userTimeSeries({ seed: 42, days: 30, eventsPerDay: 8 });
// ts.user → Full user profile
// ts.activity → [{ timestamp, type, page, duration, device, ip, success, amount?, query? }]

5. Anomaly Injection Engine (Fraud Detection)

When anomaly_rate is > 0, fakedata injects ML-detectable fraud patterns into the dataset. Affected users receive a special _anomaly flag object indicating the fraud type.

Anomaly Type	Effect
income_spike	Income multiplied 5-15x
credit_fraud	Credit score = 100-200 or 850-999, DTI = 10-60
session_anomaly	Sessions/week = 200-700, avg session = 500-1500 min
age_outlier	Age = 1, 2, 3, 115, 120, or 130
geo_impossible	Coordinates = (0,0), IP = 0.0.0.0
velocity_attack	Total sessions = 50k-150k, last login = now
data_mismatch	Age=12 + employed + 30yr experience + $500k income
health_outlier	BMI = 8-9 or 75-80, BP = extreme values

6. The User Profile Schema (109 Correlated Fields)

Each generated user contains highly realistic, correlated data. For example, age determines education graduation year, which impacts employment salary, which impacts credit score, which impacts housing status and health/BMI metrics.

identity(9) → personal(6) → network(3) → address(7) → demographics(5)
→ education(7) → employment(10) → financial(8) → health(16)
→ social(9) → digitalFootprint(15) → bank(5) → lifestyle(9)

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License

Distributed under the MIT License. See LICENSE for more information.

Maintainer: abhay557

• Project Commit History - https://github.com/abhay557/random-api.xyz