Clean Mixed Data Files

I have several datasets—some in Excel/CSV worksheets, others stored in a small SQL-based database, and a batch of plain text files—that need thorough cleaning. The information is a blend of text strings (names, addresses, comments) and numerical fields (prices, counts, dates expressed as numbers). Right now there are duplicates, inconsistent date and number formats, stray characters, and a few obvious outliers.

The job is strictly about data cleaning rather than basic re-entry: consolidate the sources, remove duplicates, fix or flag invalid values, standardise formats, and return a set of tidy, analysis-ready files. Python (pandas), SQL, or even advanced Excel techniques are all acceptable as long as the final output is accurate and reproducible.

Deliverables (all required):
• A cleaned master file in CSV (plus the native format you worked in, if different)
• A brief log or script documenting every significant transformation so I can rerun or audit the process
• A short summary noting remaining anomalies, if any, that need human review

I’ll provide the raw files as soon as we start; let me know if any clarifications are needed before you begin.