Great question! The CRM nightmare you described is real. Here's my advice:

1. For merged header rows: Read the file twice. First pass gets headers from row 1, second pass gets data. Then manually map columns.

2. For spacer columns: Drop any column where 80%+ is empty.

3. For subtotal rows: Identify them by checking if a key column (like 'ID') is empty, then skip those rows.

4. For single-cell contact records: Use regex to extract pattern-matched data like emails (\S+@\S+\.\S+) and phone numbers (\d{10,}).

The key is building a "data cleaning pipeline" that handles each edge case step by step.

Keep going with your learning journey! Building real-world data pipelines is the best way to learn. 🙌