Clean Financial Data with Pandas

I need a solid, repeatable Python workflow that cleans a large financial transactions file (≈200 k rows) entirely with Pandas. My priorities are: • remove duplicates and impossible values • standardise dates, currency symbols and decimal separators • impute or flag missing entries in key fields such as amount, account ID, cost centre • expose any outliers so I can double-check them later Please deliver: 1. a well-commented .py script or Jupyter notebook built around Pandas (NumPy and built-in libs are fine, avoid heavyweight extras) 2. the cleaned CSV / XLSX ready for downstream analysis 3. a short README describing each major cleaning step and how to rerun the process on future files Acceptance criteria: the script must run end-to-end from raw file to tidy output without manual intervention and leave a simple log of actions performed. If something in the data prevents full automation, surface a clear warning rather than silently skipping it. I will share a sample dataset as soon as we start so you can prove the routine works before moving on to the full file. Three days should be enough for an experienced Pandas user, but let me know early if you see any blockers.

Project ID: 40520458