5 Useful Python Scripts for Automated Data Quality Checks

KDnuggets

by Bala Priya C

February 26, 2026

AI-Generated Deep Dive Summary

Data quality is a critical concern in AI and data science, as poor data can lead to flawed analyses and incorrect business decisions. This article highlights five Python scripts designed to automate common data quality checks, addressing issues like missing values, type inconsistencies, duplicates, outliers, and text irregularities. Each script serves a specific purpose: one analyzes missing data patterns, another validates data types, while others detect duplicates, outliers, and categorical inconsistencies. The first script identifies missing data across datasets, calculating completeness scores and generating visual reports to pinpoint gaps. This is crucial for ensuring data integrity before analysis. The second script ensures data types match expectations, validating formats like numeric IDs, dates, emails, and URLs. For instance, it flags text in a numeric field or invalid dates, helping maintain data consistency. The third script detects exact and near-duplicate records using fuzzy string matching algorithms, essential for eliminating duplicates that could skew transaction counts or customer lists. The fourth script identifies outliers using statistical methods, crucial for preventing skewed analysis and improving model accuracy. Finally, the fifth script addresses categorical inconsistencies, such as misspelled names or varied text formats, ensuring uniformity in data. These tools streamline repetitive validation tasks, making them indispensable for AI and data science professionals who rely on high-quality data to build accurate models and drive informed decisions. By automating these checks, organizations can save time, reduce errors, and enhance the reliability of their insights.

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Originally published on KDnuggets on 2/26/2026