records = parse_csv('sales.csv')
for r in records:
    print(f"Name: '{r['name']}' | Region: '{r['region']}' | Status: '{r['status']}'")

If you run that, you'll see it. Some names have trailing spaces. 'Alice Chen' vs ' Alice Chen '. Same person. Three different representations. That's the problem.

Maya beat you to it. This is what Week 4 is all about. Yesterday you parsed the CSV. Today you clean it.

The Cleaning Pipeline

You've got six records from the CSV. They look right, but they're not. Here's what we need to fix:

1. Names: Strip spaces, title-case them. ' alice chen ' becomes 'Alice Chen'
2. Amounts: Convert to float. If empty or invalid, use 0.0
3. Regions: Strip spaces, upper-case them. 'west' becomes 'WEST'
4. Status: Strip spaces, lower-case them. 'Confirmed' becomes 'confirmed'

Python has string methods that do each of these. A string method is a function built into every string that you call with a dot:

text = '  hello  '
text.strip()  # '  hello  ' → 'hello'
text.upper()  # 'hello' → 'HELLO'
text.lower()  # 'HELLO' → 'hello'
text.title()  # 'hello world' → 'Hello World'

strip() — Remove Leading and Trailing Whitespace

Imagine a name came from the CSV with spaces:

name = '  Alice Chen  '
name.strip()  # 'Alice Chen'

That's all it does. It removes spaces (and tabs, newlines, anything whitespace) from the beginning and end. It doesn't touch spaces inside the string.

text = '  hello   world  '
text.strip()  # 'hello   world' — spaces inside stay

Yes, and then chain other methods on the result:

name = '  Alice Chen  '
cleaned = name.strip().title()  # 'Alice Chen'

This is called "chaining." Each method returns a string, so you can call another method on that result.

upper() and lower() — Normalize Case

For regions, we want consistent casing. Whether the CSV says 'west', 'West', or 'WEST', we normalize to 'WEST':

region = 'west'
region.upper()  # 'WEST'

region = 'West'
region.upper()  # 'WEST'

For status, we normalize to lowercase:

status = 'Confirmed'
status.lower()  # 'confirmed'

status = 'CONFIRMED'
status.lower()  # 'confirmed'

That's exactly what it is. In a real company, you'd call it "standardization" in a meeting, but yeah — you're just making the letters line up so comparisons work.

title() — Capitalize Each Word

Names look better when each word is capitalized:

name = 'alice chen'
name.title()  # 'Alice Chen'

Useful for people's names or job titles.

Putting It Together: clean_record()

Now we write a function that cleans all four fields:

def clean_record(record):
    name = record.get('name', '').strip().title()
    amount_str = record.get('amount', '').strip()
    try:
        amount = float(amount_str) if amount_str else 0.0
    except ValueError:
        amount = 0.0
    region = record.get('region', '').strip().upper()
    status = record.get('status', '').strip().lower()
    return {
        'name': name,
        'amount': amount,
        'region': region,
        'status': status
    }

Let's trace through one record:

raw_record = {'name': '  alice chen  ', 'amount': '1250.50', 'region': 'west', 'status': 'Confirmed'}
cleaned = clean_record(raw_record)
print(cleaned)
# {'name': 'Alice Chen', 'amount': 1250.5, 'region': 'WEST', 'status': 'confirmed'}

Notice the amount is now a float (1250.5, not '1250.50'). If the amount field was empty:

raw_record = {'name': 'Bob', 'amount': '', 'region': 'east', 'status': 'pending'}
cleaned = clean_record(raw_record)
print(cleaned['amount'])  # 0.0

If it's invalid (non-numeric):

raw_record = {'name': 'Carol', 'amount': 'N/A', 'region': 'north', 'status': 'pending'}
cleaned = clean_record(raw_record)
print(cleaned['amount'])  # 0.0  (safe default)

Your Challenge

Write clean_record(record) that takes a dict (from parse_csv()) and returns a cleaned dict with:

• name: stripped and title-cased
• amount: converted to float (0.0 if empty or invalid)
• region: stripped and upper-cased
• status: stripped and lower-cased

Test it with a few records from parse_csv('sales.csv'):

records = parse_csv('sales.csv')
for record in records:
    cleaned = clean_record(record)
    print(cleaned)