Handle Missing Values

Handling Missing Values

Real data often has missing values. Create clean_records(records) that:

Filters out records with empty amounts
Converts amount strings to floats
Sets empty categories to "other"

script.py

Loading PYTHON engine...

def clean_records(records):
    """Clean records: filter empty amounts, convert types."""
    cleaned = []
    for record in records:
        # Skip if amount is empty
        if record.get("amount", "") == "":
            continue
        
        # Create clean record
        clean = {
            "description": record.get("description", "Unknown"),
            "amount": float(record["amount"]),
            "category": record.get("category", "") or "other"
        }
        cleaned.append(clean)
    
    return cleaned

# Test data with problems
records = [
    {"description": "Coffee", "amount": "5.00", "category": "meals"},
    {"description": "Unknown", "amount": "", "category": ""},
    {"description": "Taxi", "amount": "25.00", "category": ""},
    {"description": "Lunch", "amount": "15.00", "category": "meals"},
]

cleaned = clean_records(records)
print(f"Original: {len(records)}, Cleaned: {len(cleaned)}")
for r in cleaned:
    print(f"  {r['description']}: $" + f"{r['amount']} ({r['category']})")

Downloading Python… (one-time, ~10 MB)

This runs entirely in your browser and is cached for next time.