Testing Data Science Code with pytest

Testing Data Science Code with pytest – Complete Guide 2026

Testing is the safety net that turns fragile notebooks into reliable production pipelines. This article shows exactly how data scientists should write, organize, and run tests for data loading, feature engineering, model training, and validation using pytest.

TL;DR

• Use pytest for all data science tests
• Test data loading, transformation, and model output
• Use fixtures and parametrization for efficiency
• Aim for 80%+ test coverage on pipelines

1. Basic pytest Example

import pytest
import polars as pl

def test_feature_engineering():
    df = pl.DataFrame({"amount": [100, 200]})
    result = df.with_columns((pl.col("amount") * 1.1).alias("taxed"))
    assert result["taxed"].to_list() == [110, 220]

Conclusion

In 2026, untested data science code is considered irresponsible. pytest is the industry standard — start testing your pipelines today and never ship broken code again.

Next steps:

• Add a tests/ folder to your current project and write your first data pipeline test