I receive a fresh batch of data every day that blends numeric columns with free-text fields. To keep my internal dashboards accurate, that raw feed has to be cleaned and returned the same day. For the numeric side you will: • remove any duplicate rows • fill or otherwise handle missing values in a sensible way (I will share my preferred rules) • normalise the figures so the ranges are consistent across files For the text fields you will: • strip out special characters and errant line breaks • standardise dates, casing and other format quirks • run a quick typo-correction pass so key terms stay uniform I normally work in CSV and Excel, but if you prefer Python (pandas), R, Power Query or a similar tool that’s fine—just make sure the cleaned file drops straight back into my workflow with the same column order and naming. Daily deliverable: a cleaned dataset plus a short change log noting what was fixed or flagged. Turnaround is within the same business day of receiving the raw file. If this cadence suits you and you have a proven routine for mixed-data cleaning, I’m ready to get started.