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Exercise

Unit testing a data pipeline with fixtures

You've learned in the last video that unit testing can help to instill more trust in your data pipeline, and can even help to catch bugs throughout development. In this exercise, you'll practice writing both fixtures and unit tests, using the pytest library and assert.

The transform function that you'll be building unit tests around is shown below for reference. pandas has been imported as pd, and the pytest() library is loaded and ready for use.

def transform(raw_data):
    raw_data["tax_rate"] = raw_data["total_taxes_paid"] / raw_data["total_taxable_income"]
    raw_data.set_index("industry_name", inplace=True)
    return raw_data

Instructions 1/2

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  • Create a pytest fixture called clean_tax_data.
  • Apply the transform() function to the raw_data dataset, and save the result in the clean_data variable and return it.