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Data validation and preprocessing debugging

concept · part of Troubleshooting

Troubleshooting data quality issues that affect model performance. Use data validation tools like Great Expectations or Pandera to check incoming data for consistency, completeness, and validity. Debug preprocessing pipelines by systematically isolating each step to ensure transformations work as intended.

import great_expectations as ge

df = ge.from_pandas(incoming_data)
df.expect_column_values_to_be_in_set('units', ['mg', 'ml'])
df.expect_column_values_to_not_be_null('feature_1')
print("Data validation complete. Issues identified and flagged.")

• Use df.validate() to run all expectations and obtain a validation result object.
• Expectations can be grouped into suites for reusable checks across different datasets.
• The ge.from_pandas() method creates a Great Expectations DataFrame wrapper that tracks expectations and validation history.

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