Synthetic data has become an exciting approach for experimenting, testing, and training models when real data is limited, sensitive, or difficult to obtain. Let us explore some powerful Python tools that make creating synthetic data even easier and more realistic.
1. Faker: For Quick and Realistic Data
Faker is probably the most popular Python library for generating fake data. It’s simple, fast, and surprisingly versatile. You can generate names, addresses, emails, phone numbers, dates—basically anything that resembles real-world data.
Here’s a quick example:
from faker import Faker
fake = Faker()
for _ in range(5):
print(fake.name(), fake.email(), fake.address())
It’s perfect for generating realistic tabular datasets for testing, prototyping, or anonymizing data. One bonus: Faker supports multiple locales, so you can generate names and addresses from different countries.
2. SDV (Synthetic Data Vault): For Complex Datasets
If you want something more advanced, SDV is a fantastic library. Unlike Faker, which generates random entries, SDV can learn patterns from your real dataset and generate synthetic data that preserves relationships between columns. This makes it ideal for machine learning tasks.
Example workflow with SDV:
from sdv.tabular import GaussianCopula
import pandas as pd
# Load a small sample dataset
data = pd.DataFrame({
'Age': [25, 32, 47, 51],
'Salary': [50000, 60000, 80000, 90000]
})
model = GaussianCopula()
model.fit(data)
synthetic_data = model.sample(5)
print(synthetic_data)
SDV can handle tabular, relational, and time-series data, making it a versatile choice for more realistic synthetic datasets.
3. Mimesis: A Lightweight Alternative
Mimesis is another library similar to Faker but often faster and more structured. It allows you to generate data by categories like personal info, address, numbers, finance, or even custom providers.
from mimesis import Person
from mimesis.locales import Locale
person = Person(Locale.EN)
for _ in range(5):
print(person.full_name(), person.email())
One advantage of Mimesis is its ability to generate structured datasets more efficiently, especially if you need repeatable outputs for testing.
Comparison
- Faker: Quick, simple, great for prototyping or anonymization.
- SDV: Best for realistic synthetic datasets that preserve relationships—ideal for machine learning.
- Mimesis: Lightweight, structured, and fast for repeatable dataset generation.
When experimenting with synthetic data, it’s important to consider the purpose:
Are you testing a prototype?
Protecting sensitive information? Or training a model?
The tool you choose depends on your goal.
Conclusion
Synthetic data is not just “fake” data—it’s a way to safely, efficiently, and creatively experiment with data. Python gives us a rich ecosystem of libraries to make this process smooth and powerful.