Faker
Faker is a Python package that generates fake data for you.
Decision gist · record as of 2026-08-14
Yes. Faker is a mature, actively maintained library with no known vulnerabilities, permissive licensing, and low install friction. It's widely used for testing and data anonymization. Install it if you need realistic fake data for tests, prototyping, or anonymization; skip it only if you have no use for generated test data.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or above; earlier versions are not supported.
- Low install friction with a single runtime dependency (tzdata).
- Actively maintained with a release 21 days ago and strong repository signals (19369 stars, last commit 2026-08-03).
License · maintenance · safety
MIT License (permissive) — MIT License (permissive) allows use in commercial and private projects with minimal restrictions—include a copy of the license and you're clear.
last release 2026-07-24 (21 days) · last repo commit 2026-08-03 · 19,369 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 82,257,950 downloads/mo, #406 on PyPI
Alternatives
Verify before relying
pip install Faker
from faker import Faker
fake = Faker()
print(fake.name())
print(fake.address())- Performance characteristics when generating large datasets or with weighting disabled.
- Thread-safety guarantees for concurrent fake data generation.
- Memory footprint when loading multiple locales simultaneously.
What it is and what it does
Faker is a data generation library that produces realistic fake values—names, addresses, emails, phone numbers, dates, and text—across multiple locales. It's commonly used to populate test databases, create realistic fixtures for unit and integration tests, stress-test persistence layers, and anonymize sensitive production data. The library exposes generators as properties on a Faker instance (e.g., `fake.name()`, `fake.address()`), each call returning a new random value. It supports localization by accepting locale codes like `it_IT` or `ja_JP`, and can mix multiple locales in a single generator.
Faker is built around a provider architecture: bundled providers handle common data types (names, addresses, internet data, dates), and custom providers can be added to generate domain-specific fakes. It includes a pytest plugin for fixture integration, a command-line interface for one-off generation, and a `use_weighting` option to control whether generated values match real-world frequency distributions. The package has a single runtime dependency (tzdata) and requires Python 3.10 or above.
Use it for
- Populate test databases with realistic but non-sensitive data before running integration tests.
- Generate mock user profiles and contact information for UI/UX prototyping and demos.
- Create anonymized datasets from production data for safe sharing with development or analytics teams.
- Stress-test database and API performance with large volumes of varied but realistic fake records.
- Build pytest fixtures that generate fresh fake data for each test without hardcoding values.
- Generate command-line examples and documentation with realistic-looking sample data.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Faker is a mature, actively maintained library with no known vulnerabilities, permissive licensing, and low install friction. It's widely used for testing and data anonymization. Install it if you need realistic fake data for tests, prototyping, or anonymization; skip it only if you have no use for generated test data.
Install
faker on PyPI
Before you install
Low install friction with a single runtime dependency (tzdata). Actively maintained with a release 21 days ago and strong repository signals (19369 stars, last commit 2026-08-03). Supports current Python versions (3.10+).
Requires Python 3.10 or above; earlier versions are not supported.
License in practice
MIT License (permissive) allows use in commercial and private projects with minimal restrictions—include a copy of the license and you're clear.
Quickstart
pip install Faker
from faker import Faker
fake = Faker()
print(fake.name())
print(fake.address())Verify before relying
- Performance characteristics when generating large datasets or with weighting disabled.
- Thread-safety guarantees for concurrent fake data generation.
- Memory footprint when loading multiple locales simultaneously.
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagetzdata |
| Maintenance | Actively maintained 21 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 82,257,950 / month, #406 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Software Development :: Libraries :: Python ModulesTopic :: Software Development :: TestingTopic :: Utilities |
Evidence: faker-40.36.0-py3-none-any.whl
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See also django-anon · fake-factory · faker-e164 · jsf · mimesis · pytest-faker · streamlit-faker · faker-enum · faker-edu · faker-nonprofit