MLOps Transforms Wage Data with Smart Excel Pipeline

The raw Excel file arrives like an unlabeled drawer in a cabinet of records: messy, inconsistent, and full of promise. One cell holds a dollar figure, another a string with commas and a trailing space. State names are abbreviated differently; occupation codes shift between numeric and text formats.

This is the starting point for any wage analysis, but it should never be the stopping point. You need a single, authoritative version of the truth. A preprocess_wage_data function does exactly that: load the workbook, scrub the columns, coerce numbers out of text, normalize state labels, standardize occupation groups and codes, then enrich the dataset with helper columns like total payroll.

Once that function runs, every subsequent plot, T-test, regression, correlation, or Z-test draws from the same clean DataFrame. No more re-scrubbing in each notebook cell. No more silent mismatches between analysis steps.

The data pipeline solidifies into a reusable, callable artifact. Your notebook, your scripts, your future API, all agree on what “clean data” means.