Python Mini-Projects, Part 2

Picking up from Part 1: you've built a calculator, a contact book, and a word frequency analyzer. Part 2 levels up. The data dashboard is a real exploratory data analysis (EDA) workflow in pandas — the same shape as the first 30 minutes of every Kaggle notebook. The CLI quiz game is the most complex project in the track: three cooperating classes, a decorator for timing, and a leaderboard with JSON persistence. After that, you're ready for the capstone where you'll call your first LLM API.

Learning Objectives

After this lesson, you will be able to:

Use pandas (DataFrames, groupby, correlation) to compute and present a multi-section text dashboard
Design a CLI quiz game with three cooperating classes (Question, Quiz, Leaderboard)
Apply the decorator pattern to add cross-cutting behavior (timing) without modifying core logic
Use random.shuffle, json persistence, and OOP encapsulation in a single coherent program

Prerequisites

Before You Start

Review

#Project 4: Data Dashboard

#Problem Statement

Build a data analysis dashboard that loads a CSV dataset, computes summary statistics, identifies patterns, and generates a text-based report. This project simulates the kind of exploratory data analysis (EDA) that every data scientist does before building ML models.

You will work with a student performance dataset and answer questions like: What is the average score? Which subjects are hardest? Is there a correlation between study hours and grades?

#Concepts Used

pandas — DataFrames, groupby, describe (from the NumPy & Pandas lesson)
Functions — organized analysis pipeline (from the Functions lesson)
String formatting — clean report output
Dictionaries — aggregating results

#Starter Code

pythonplayground.py · Pyodide

# PROJECT 4: Data Dashboard
# Analyze student performance data and generate a report

import pandas as pd

# --- Create sample dataset (in a real project, you would load a CSV) ---
data = {
  "student_id": list(range(1, 21)),
  "name": [
      "Alice", "Bob", "Charlie", "Diana", "Eve",
      "Frank", "Grace", "Henry", "Iris", "Jack",
      "Karen", "Leo", "Mia", "Noah", "Olivia",
      "Pete", "Quinn", "Rachel", "Sam", "Tina"
  ],
  "math_score": [92, 78, 95, 65, 88, 72, 91, 83, 76, 98,
                 69, 87, 94, 71, 85, 79, 90, 68, 93, 82],
  "science_score": [88, 82, 90, 70, 95, 68, 87, 79, 73, 96,
                    72, 84, 91, 75, 89, 77, 93, 65, 88, 80],
  "english_score": [85, 90, 88, 78, 82, 75, 94, 86, 80, 92,
                    77, 81, 89, 83, 91, 73, 86, 79, 84, 88],
  "study_hours": [8, 5, 9, 3, 7, 4, 8, 6, 4, 10,
                  3, 7, 9, 4, 7, 5, 8, 3, 9, 6],
  "attendance_pct": [95, 80, 98, 65, 90, 70, 92, 85, 72, 99,
                     68, 88, 96, 75, 93, 78, 95, 60, 97, 83],
}

df = pd.DataFrame(data)

# TODO 1: Basic dataset overview
def dataset_overview(df):
  # Print: number of students, number of columns
  # Print: column names and their data types
  # Print: first 5 rows
  pass

# TODO 2: Compute summary statistics for each subject
def subject_statistics(df):
  # For each subject (math, science, english):
  #   Print: mean, median, std, min, max
  # Hint: use df[column].describe() or individual methods
  pass

# TODO 3: Find top and bottom performers
def top_bottom_performers(df, n=3):
  # Calculate average score across all subjects for each student
  # Print the top N and bottom N students by average score
  pass

# TODO 4: Analyze study hours vs performance
def study_hours_analysis(df):
  # Group students by study hours ranges:
  #   Low (1-4 hours), Medium (5-7 hours), High (8-10 hours)
  # For each group, compute average total score
  # Print the comparison
  pass

# TODO 5: Attendance analysis
def attendance_analysis(df):
  # Group students by attendance:
  #   Low (&lt;75%), Medium (75-89%), High (90%+)
  # For each group, compute average total score
  # Print the comparison
  pass

# TODO 6: Generate a full text report
def generate_report(df):
  # Call all the above functions in order
  # Add section headers and separators for readability
  pass

# --- Run the dashboard ---
print("=" * 60)
print("     STUDENT PERFORMANCE DASHBOARD")
print("=" * 60)
generate_report(df)

Tests · Fill in each analysis function, then run generate_report. You should see a multi-section text dashboard with statistics and insights.

#Challenge Extensions

Grade distribution histogram. A text-based histogram showing how many students fall in each grade range (A: 90-100, B: 80-89, C: 70-79, D: 60-69)
Subject correlation. Compute the correlation between each pair of subjects
Pass/fail analysis. Define a passing score (e.g., 70) and report the pass rate for each subject
Percentile ranks. For each student, compute their percentile rank in each subject
Export report. Save the entire report to a text file using file I/O

#Project 5: CLI Quiz Game

#Problem Statement

Build a command-line quiz game with multiple-choice questions loaded from a data structure. The game tracks scores, provides feedback on each answer, and maintains a leaderboard. This project brings together OOP design patterns, decorators for timing, file I/O for persistence, and the random module for shuffling.

This is the most complex project in the lesson. It requires you to think about design: how to organize classes, what methods to expose, how data flows through the system.

#Concepts Used

OOP — Question, Quiz, and Leaderboard classes (from the Classes & OOP lesson)
Decorators — timing decorator for quiz duration (from the Decorators & Generators lesson)
File I/O — loading questions and saving the leaderboard (from the File I/O lesson)
random — shuffling questions and answer order
Data structures — lists, dicts, and sorting for the leaderboard

#Starter Code

pythonplayground.py · Pyodide

# PROJECT 5: CLI Quiz Game
# An OOP-based quiz game with scoring and a leaderboard

import json
import random
import time

# TODO 1: Write a timer decorator
def timer(func):
  # Use time.time() before and after calling func
  # Print the elapsed time
  # Return the result of func
  pass

# TODO 2: Implement the Question class
class Question:
  def __init__(self, text, options, correct_index, explanation):
      pass

def display(self, number):
      pass

def check_answer(self, answer_index):
      pass

# TODO 3: Implement the Quiz class
class Quiz:
  def __init__(self, questions, player_name):
      pass

def shuffle_questions(self):
      pass

def run(self):
      pass

def get_results(self):
      pass

# TODO 4: Implement the Leaderboard class
class Leaderboard:
  def __init__(self):
      self.entries = []

def add_entry(self, name, score, total):
      pass

def display(self, top_n=5):
      pass

def save_to_file(self, filename):
      pass

def load_from_file(self, filename):
      pass

# --- Sample Questions ---
questions_data = [
  {"text": "What is O(1)?", "options": ["constant", "linear", "log", "quadratic"], "correct_index": 0, "explanation": "O(1) is constant time — same number of operations regardless of input size."},
  {"text": "Which keyword defines a class?", "options": ["def", "class", "struct", "object"], "correct_index": 1, "explanation": "The 'class' keyword."},
  {"text": "What does 'self' represent?", "options": ["the class", "the parent class", "the current instance", "unused"], "correct_index": 2, "explanation": "self refers to the specific instance."},
  {"text": "Which is LIFO?", "options": ["Queue", "Stack", "Linked List", "Hash Map"], "correct_index": 1, "explanation": "Stacks are Last-In, First-Out."},
]

questions = [Question(**q) for q in questions_data]

print("=== PYTHON KNOWLEDGE QUIZ ===\n")
quiz = Quiz(questions, "Player1")
quiz.shuffle_questions()
quiz.run()

results = quiz.get_results()
print("\nScore: " + str(results["score"]) + "/" + str(results["total"]))

board = Leaderboard()
board.add_entry(results["name"], results["score"], results["total"])
board.add_entry("Alice", 4, 4)
board.add_entry("Bob", 2, 4)
board.display()

Tests · Fill in the decorator and all 3 classes, then run to see a simulated quiz session with scoring and a leaderboard.

#Challenge Extensions

Question categories. Add a category field and let the player choose
Difficulty levels — easy/medium/hard with point multipliers
Streak bonus — bonus points for consecutive correct answers
Timed questions — per-question time limit with point penalty
Question bank file — load questions from JSON instead of hardcoding

#Design Wins You Just Practiced

#1. Decorator Pattern Adds Cross-Cutting Behavior

The @timer decorator can wrap any function. Quiz logic stays focused on quiz behavior; timing is a separate, reusable wrapper. This is the same pattern FastAPI uses for route registration, Flask for view functions, and @functools.lru_cache for memoization.

#2. Three-Class Decomposition

Question owns the what (text, options, correct answer). Quiz owns the flow (running through questions, scoring, results). Leaderboard owns the persistence (sorting, saving, loading). None of these classes need to know much about the others' internals — they communicate through small interfaces.

#3. Pipeline Architecture in the Dashboard

generate_report is a pipeline: each section function is independent and writes to stdout. Adding a new analysis is one new function plus one line in generate_report. This is the same shape as a real ML pipeline (load → clean → engineer features → train → evaluate).

#Next Stop: The Capstone

You've now shipped five working programs across Parts 1 and 2. The next lesson — Capstone: Build Your First AI Script — uses everything you've practiced (functions, OOP, file I/O, error handling, decomposition) to build a text analyzer that calls a real LLM API. Same engineering muscles, real ML payoff.

Quick Check1 / 4

In the Data Dashboard, why is each analysis function separate rather than one giant function?

Python Mini-Projects, Part 2

Learning Objectives

After this lesson, you will be able to:

Use pandas (DataFrames, groupby, correlation) to compute and present a multi-section text dashboard
Design a CLI quiz game with three cooperating classes (Question, Quiz, Leaderboard)
Apply the decorator pattern to add cross-cutting behavior (timing) without modifying core logic
Use random.shuffle, json persistence, and OOP encapsulation in a single coherent program

Prerequisites

Before You Start

Review

#Project 4: Data Dashboard

#Problem Statement

You will work with a student performance dataset and answer questions like: What is the average score? Which subjects are hardest? Is there a correlation between study hours and grades?

#Concepts Used

pandas — DataFrames, groupby, describe (from the NumPy & Pandas lesson)
Functions — organized analysis pipeline (from the Functions lesson)
String formatting — clean report output
Dictionaries — aggregating results

#Starter Code

pythonplayground.py · Pyodide

# PROJECT 4: Data Dashboard
# Analyze student performance data and generate a report

import pandas as pd

df = pd.DataFrame(data)

# TODO 1: Basic dataset overview
def dataset_overview(df):
  # Print: number of students, number of columns
  # Print: column names and their data types
  # Print: first 5 rows
  pass

# TODO 6: Generate a full text report
def generate_report(df):
  # Call all the above functions in order
  # Add section headers and separators for readability
  pass

# --- Run the dashboard ---
print("=" * 60)
print("     STUDENT PERFORMANCE DASHBOARD")
print("=" * 60)
generate_report(df)

Tests · Fill in each analysis function, then run generate_report. You should see a multi-section text dashboard with statistics and insights.

#Challenge Extensions

Grade distribution histogram. A text-based histogram showing how many students fall in each grade range (A: 90-100, B: 80-89, C: 70-79, D: 60-69)
Subject correlation. Compute the correlation between each pair of subjects
Pass/fail analysis. Define a passing score (e.g., 70) and report the pass rate for each subject
Percentile ranks. For each student, compute their percentile rank in each subject
Export report. Save the entire report to a text file using file I/O

#Project 5: CLI Quiz Game

#Problem Statement

This is the most complex project in the lesson. It requires you to think about design: how to organize classes, what methods to expose, how data flows through the system.

#Concepts Used

OOP — Question, Quiz, and Leaderboard classes (from the Classes & OOP lesson)
Decorators — timing decorator for quiz duration (from the Decorators & Generators lesson)
File I/O — loading questions and saving the leaderboard (from the File I/O lesson)
random — shuffling questions and answer order
Data structures — lists, dicts, and sorting for the leaderboard

#Starter Code

pythonplayground.py · Pyodide

# PROJECT 5: CLI Quiz Game
# An OOP-based quiz game with scoring and a leaderboard

import json
import random
import time

# TODO 1: Write a timer decorator
def timer(func):
  # Use time.time() before and after calling func
  # Print the elapsed time
  # Return the result of func
  pass

# TODO 2: Implement the Question class
class Question:
  def __init__(self, text, options, correct_index, explanation):
      pass

def display(self, number):
      pass

def check_answer(self, answer_index):
      pass

# TODO 3: Implement the Quiz class
class Quiz:
  def __init__(self, questions, player_name):
      pass

def shuffle_questions(self):
      pass

def run(self):
      pass

def get_results(self):
      pass

# TODO 4: Implement the Leaderboard class
class Leaderboard:
  def __init__(self):
      self.entries = []

def add_entry(self, name, score, total):
      pass

def display(self, top_n=5):
      pass

def save_to_file(self, filename):
      pass

def load_from_file(self, filename):
      pass

questions = [Question(**q) for q in questions_data]

print("=== PYTHON KNOWLEDGE QUIZ ===\n")
quiz = Quiz(questions, "Player1")
quiz.shuffle_questions()
quiz.run()

results = quiz.get_results()
print("\nScore: " + str(results["score"]) + "/" + str(results["total"]))

board = Leaderboard()
board.add_entry(results["name"], results["score"], results["total"])
board.add_entry("Alice", 4, 4)
board.add_entry("Bob", 2, 4)
board.display()

Tests · Fill in the decorator and all 3 classes, then run to see a simulated quiz session with scoring and a leaderboard.

#Challenge Extensions

Question categories. Add a category field and let the player choose
Difficulty levels — easy/medium/hard with point multipliers
Streak bonus — bonus points for consecutive correct answers
Timed questions — per-question time limit with point penalty
Question bank file — load questions from JSON instead of hardcoding

#Design Wins You Just Practiced

#1. Decorator Pattern Adds Cross-Cutting Behavior

#2. Three-Class Decomposition

#3. Pipeline Architecture in the Dashboard

#Next Stop: The Capstone

Quick Check1 / 4

In the Data Dashboard, why is each analysis function separate rather than one giant function?

Python Mini-Projects, Part 2

#Project 4: Data Dashboard

#Problem Statement

#Concepts Used

#Starter Code

#Challenge Extensions

#Project 5: CLI Quiz Game

#Problem Statement

#Concepts Used

#Starter Code

#Challenge Extensions

#Design Wins You Just Practiced

#1. Decorator Pattern Adds Cross-Cutting Behavior

#2. Three-Class Decomposition

#3. Pipeline Architecture in the Dashboard

#Next Stop: The Capstone

What to Build Next (after the capstone)

Your Reflection

Continue Learning

Discussion

Python Mini-Projects, Part 2

#Project 4: Data Dashboard

#Problem Statement

#Concepts Used

#Starter Code

#Challenge Extensions

#Project 5: CLI Quiz Game

#Problem Statement

#Concepts Used

#Starter Code

#Challenge Extensions

#Design Wins You Just Practiced

#1. Decorator Pattern Adds Cross-Cutting Behavior

#2. Three-Class Decomposition

#3. Pipeline Architecture in the Dashboard

#Next Stop: The Capstone

What to Build Next (after the capstone)

Your Reflection

Continue Learning

Discussion