The Data Cleaner

Welcome to the Real World.

In the Digital Forest, you used magic to transform elements. In the software industry, we do the same thing, but we call it Data Cleaning (or Data Munging).

The Problem: Human input is messy. Users accidentally type spaces, forget to capitalize, or scream in ALL CAPS. The Goal: Clean the data so it looks professional and uniform.

Magic vs. Reality

In this lab, you aren't casting spells. You are writing a Transformation Pipeline.

You will receive raw, dirty data, and your script must output clean, structured data.

The Tools

Python keeps two powerful tools in its belt for text cleaning:

name = "  aLIce  "

# 1. .strip() - Removes leading/trailing whitespace
clean_spaces = name.strip()  # "aLIce"

# 2. .title() - Capitalizes first letter, lowers the rest
proper_case = clean_spaces.title()  # "Alice"

# You can even chain them!
perfect = name.strip().title() # "Alice"

Your Task

You have received a list of new user names from a web form. It's a disaster.

raw_users = [" aLIce ", "BOB", " cindy", "dave "]

Initialize

Create an empty list called clean_users.

Iterate

Write a for loop to go through each name in raw_users.

Clean

Inside the loop, clean the name (remove spaces, fix casing).

Store

Append the cleaned name to your clean_users list.

Verify

Print the clean_users list to see your handiwork.

Suggested Solution

Expand

Solution:

This pattern (Initialize -> Loop -> Process -> Append) is the foundation of almost all data processing scripts.

raw_users = ["  aLIce ", "BOB", "  cindy", "dave  "]
clean_users = []

for name in raw_users:
  # Chain the methods for efficiency!
  cleaned = name.strip().title()
  clean_users.append(cleaned)

print("Original:", raw_users)
print("Cleaned: ", clean_users)