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Generate realistic synthetic user profiles including names, addresses, photos, email addresses, and demographic attributes. Used in data engineering for creating test datasets, populating development databases, stress-testing data pipelines, and building anonymised sample data for analytics workflows in Python.
Engineers call `requests.get('https://randomuser.me/api/?results=1000')` to generate batches of realistic fake user profiles with names, addresses, emails, and photos. This replaces manual test data creation and avoids using real PII in development environments.
Random User Generator produces safe synthetic PII for testing AI systems that handle personal data — train entity recognition models on realistic names and addresses, or populate RAG demo systems with fake user profiles without risking real privacy exposure.
# pip install requests pandas
import requests, pandas as pd
resp = requests.get("https://randomuser.me/api/", params={"results": 100})
users = resp.json()["results"]
df = pd.DataFrame([{
"name": f"{u['name']['first']} {u['name']['last']}",
"email": u["email"],
"country": u["location"]["country"]
} for u in users])
print(df.head())Official dataset source
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