Python Standard Library Can Quickly Audit CSV Files for Duplicate IDs and Blank Cells

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A lightweight Python script using only the standard library can perform a first-pass quality check on CSV files by counting rows, detecting duplicate IDs, and identifying missing cell values. The approach uses csv.DictReader and collections.Counter to build a simple profile of any given CSV without requiring third-party dependencies. A synthetic seven-row dataset demonstrates the method, correctly flagging one duplicate ID and one blank value in the amount column. The author deliberately scopes the tool to observable facts, noting it does not handle numeric validation, schema checks, or production-safety concerns. The runnable code and test files are publicly available on GitHub, with an expanded HTTP API version offered separately.

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