Daily Mixed Data Cleaning

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.