American Art Gallery Data Cleaning via Native String Manipulation

Cleaned and formatted raw American art gallery catalog records using native Python and standard file handling (csv.reader). Processed unstructured dates, mediums, and titles using built-in string manipulation methods (.split(), .replace()) to produce a clean, standardized dataset without relying on third-party libraries. Key Highlights: File Ingestion: Iterated through raw catalog rows using Python's native csv.reader. String Cleaning: Used .replace() to eliminate unwanted special characters, non-standard formatting, and extra whitespace from text fields. Text Parsing: Applied .split() to break apart combined metadata attributes (such as dates, dimensions, or multi-artist entries) into distinct columns. Tech Stack: Python 3 (Standard Library: csv, String Methods).