Python Data Reprocessing & Findings Report

I have a raw numerical dataset that first needs to be brought into Python, profiled to understand its structure, and then put through a full quality-improvement pipeline. The work involves:

• Cleaning & validation – handle missing values, catch outliers, verify ranges
• Transformation & normalization – apply whatever scaling or encoding is most appropriate for later modelling or dashboard use
• Standardisation & final QA – be sure the final table is tidy, well-labelled, and export-ready

Feel free to use pandas, NumPy, SciPy or any other mainstream libraries; a short, well-commented notebook or script that reproduces each step will be ideal.

Once the processing is complete, I need a concise report (around 4 pages including figures and pictures fine) that zeroes in on the findings and results—summary statistics, notable corrections, before-and-after snapshots, and any key insights surfaced during profiling. The methodology only needs brief mention; the spotlight is on what the cleaned data now tells us.

Deliverables
1. Reproducible Python code or notebook
2. Cleaned, standardised dataset (CSV or Parquet)
3. Findings-focused report in PDF, Markdown, or Jupyter-generated HTML

If this sequence matches your skill set and you can turn it around quickly, I’m ready to share the data and move forward.