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Validation rule

As explained in Starting Data Analytics with Generative AI and Python

A validation rule is a defined condition that data must satisfy to be considered acceptable.

A validation rule defines a condition that data must satisfy to be considered acceptable. Rules express expected values, types, boundaries, or relationships between tables, using domain knowledge rather than universal thresholds. section 2.5.4

Useful validation rules check:

  • Whether values fall within expected ranges or deviations from typical distributions. section 2.5.4
  • Whether variables have suitable data types for reliable downstream operations. section 2.5.4
  • Whether records remain consistent across multiple tables. section 2.5.4

Foreign-key validation

Compare order identifiers with payment identifiers, isolate order records missing from the payment table, and investigate or correct the violations instead of silently accepting them.

How Starting Data Analytics with Generative AI and Python covers it

How deep it goes
Defines upfront expectations, checks types and cross-table consistency, then revises rules after exploratory analysis.
Examples
Uses expected ranges, distribution deviations, data-type checks, and missing foreign-key relationships between order and payment tables.
What it assumes
Assumes dataset and domain knowledge for distinguishing typical values, while permitting uncertain boundaries to use deviations or percentiles.
What you can do after
Apply it via dataframe checks to flag unexpected values, investigate anomalies, and correct or handle violations.
How it connects
Connects domain knowledge, data types, exploratory data analysis, and foreign-key consistency.

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This explainer was generated by Manning's knowledge graph from the text of Starting Data Analytics with Generative AI and Python. The section links point to the parts of the book it draws on.