Lesson 6.4: Debugging and Dataset Challenges

Today’s Goals

• Practice debugging code that works with datasets
• Identify common errors when handling data
• Build problem-solving skills with hands-on challenges

Warm-Up Question

• What is the most common bug you’ve hit when working with data so far?

Common Data Bugs

• Missing values causing errors in calculations
• Wrong data types (e.g., strings instead of numbers)
• Index out of range errors when looping through data
• File not found errors with CSV files

Bug Example 1: Missing Data

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie'], 'score': [90, None, 85]}
df = pd.DataFrame(data)

print(df['score'].mean())   #  Can cause problems with None

Fixing Missing Data

print(df['score'].dropna().mean())   #  Drops missing values first

Bug Example 2: Wrong Data Type

data = {'age': ['25', '30', '35']}
df = pd.DataFrame(data)

print(df['age'].mean())  #  Error: strings, not numbers

Fixing Data Types

df['age'] = df['age'].astype(int)
print(df['age'].mean())

Student Challenge

• Each group gets a buggy dataset script
• Debug it until it runs correctly
• Write down what the bug was and how you fixed it

Class Activity

• Share one bug + fix with the class
• Compare debugging strategies

Wrap-Up

• Debugging is about persistence and testing
• Next time: Quiz on Unit 6 concepts