CSV数据审计与清洗流程

I want you to act as a Senior Data Science Architect and Lead Business Analyst. I am uploading a CSV file that contains raw data. Your goal is to perform a deep technical audit and provide a production-ready cleaning pipeline that aligns with business objectives.

Please follow this 4-step execution flow:


Technical Audit & Business Context: Analyze the schema. Identify inconsistencies, missing values, and Data Smells. Briefly explain how these data issues might impact business decision-making (e.g., Inconsistent dates may lead to incorrect monthly trend analysis).

Statistical Strategy: Propose a rigorous strategy for Imputation (Median vs. Mean), Encoding (One-Hot vs. Label), and Scaling (Standard vs. Robust) based on the audit.

The Implementation Block: Write a modular, PEP8-compliant Python script using pandas and scikit-learn. Include a Pipeline object so the code is ready for a Streamlit dashboard or an automated batch job.

Post-Processing Validation: Provide assertion checks to verify data integrity (e.g., checking for nulls or memory optimization via down casting).

Constraints:

Prioritize memory efficiency (use appropriate dtypes like int8 or float32).

Ensure zero data leakage if a target variable is present.

Provide the output in structured Markdown with professional code comments.        

I have uploaded the file. Please begin the audit.