[Week 4] Bringing Data to Life: Control Flow and Functions 👨‍💻

Hello! Welcome to Week 4.

Last time, we learned to store data using Python variables and data structures.

Today, we will go a step further and learn how to 'process' and 'manipulate' stored data.

Use if statements to select data meeting particular conditions,

Process hundreds or tens of thousands of data items using for loops,

Learn how to turn this entire process into a reusable component called a function (def).

🚦 Module 1: Code That Follows Conditions — if Statements

• What is a conditional statement?

  • Syntax that executes a block of code only when a specified condition is True.

• Basic structure

  if 조건식:
      # Code to run when the condition is True
  elif 다른_조건식:
      # Code to run when the other condition is True
  else:
      # Code to run when all conditions above are False

• Applications in machine learning

  • ex1) "If the rating is at least 9.0, classify it as a recommended movie."
  • ex2) "If the data contain missing values, fill them with a specified value."
  • Essential for data preprocessing and defining model rules.

# Example: Decide whether to recommend a movie based on its rating
movie_rating = 9.1

# 'If' movie_rating is greater than or equal to 9.0
if movie_rating >= 9.0:
    print("✨ 강력 추천하는 영화입니다!")
# 'Otherwise'
else:
    print("볼만한 영화입니다.")

✨ 강력 추천하는 영화입니다!

# Example: Decide whether to recommend a movie based on its rating
movie_rating = 8.5

# 'If' movie_rating is greater than or equal to 9.0
if movie_rating >= 9.0:
    print("✨ 강력 추천하는 영화입니다!")
# 'Otherwise'
else:
    print("볼만한 영화입니다.")

# Change movie_rating to 8.5 and run again!

볼만한 영화입니다.

# A more detailed recommendation system
movie_rating = 8.8

if movie_rating >= 9.0:
    print("✨ 강력 추천! (9.0점 이상)")
# 'Else if' the rating is 8.5 or higher
elif movie_rating >= 8.5:
    print("👍 추천! (8.5점 이상)")
# 'In all other cases'
else:
    print("🤔 보통. (8.5점 미만)")

👍 추천! (8.5점 이상)

🔁 Module 2: All the Data at Once! for Loops

• What Is a Loop?

  • For all data in a structure such as a list or dictionary,
  • it repeats the same task in sequence.

• Basic structure

  for 아이템을_담을_변수 in 데이터_리스트:
      # Code to run for each list item

• Uses in Machine Learning

  • Essential for large-scale data processing, such as loading and processing millions of images individually,
  • or calculating the mean or sum of an entire dataset.

# The movie dataset created in Week 3
my_movie_dataset = [
    { "title": "인셉션",
     "director": "크리스토퍼 놀란",
      "rating": 9.29 },
    { "title": "기생충",
     "director": "봉준호",
      "rating": 9.07 },
    { "title": "어바웃 타임",
     "director": "리차드 커티스",
      "rating": 9.27 }
]

# Iterate through the dataset (list), placing each movie's information (dictionary) in movie
print("--- 모든 영화 제목 출력 ---")
for movie in my_movie_dataset:
    # Retrieve and print the value of the 'title' key from each movie (dictionary)
    print(movie['title'])

--- 모든 영화 제목 출력 ---
인셉션
기생충
어바웃 타임

# Combine loops and conditionals to select only 'movies rated 9.2 or higher'

print("\n--- 평점 9.2 이상인 추천 영화 ---")
for movie in my_movie_dataset:
    # 'If' the current movie's rating is greater than or equal to 9.2
    if movie['rating'] >= 9.2:
        # Print that movie's title and rating
        print(f"제목: {movie['title']}, 평점: {movie['rating']}")

--- 평점 9.2 이상인 추천 영화 ---
제목: 인셉션, 평점: 9.29
제목: 어바웃 타임, 평점: 9.27

⚙️ Module 3: Reusable Code Components — Functions (def)

• What is a function?

  • A block of code that performs a specific task
  • and can be named and reused.

• Basic structure

  def 함수이름(입력값1, 입력값2, ...):
      # Code to run
      return 결과값

• Applications in machine learning

  • Implement data preprocessing as a function to apply the same process to multiple datasets.

  • A model itself can be viewed as a function.

    [Input (data) → Processing (prediction) → Output (result)]

  • Organize complex code by functionality to make it easier to maintain.

# A simple function that adds two numbers
def add_numbers(x, y):
    """
    이 함수는 두 개의 숫자 x와 y를 입력받아,
    그 합을 계산하여 결과값으로 돌려줍니다.
    """
    result = x + y
    return result

# Call (use) the function
sum_result = add_numbers(5, 3)
print(f"5와 3의 합은? {sum_result}")

another_sum = add_numbers(100, 200)
print(f"100과 200의 합은? {another_sum}")

5와 3의 합은? 8
100과 200의 합은? 300

# Turn Module 2's 'rating-based movie recommendation' logic into a function.

def recommend_movies(dataset, min_rating):
    """
    영화 데이터셋과 최소 평점을 입력받아,
    조건에 맞는 영화 제목 리스트를 결과로 돌려줍니다.
    """
    recommended_list = [] # An empty list for recommended movies
    for movie in dataset:
        if movie['rating'] >= min_rating:
            recommended_list.append(movie['title']) # Append to the list
    return recommended_list


# Use the function to recommend movies rated 9.0 or higher
my_best_movies = recommend_movies(my_movie_dataset, 9.0)
print(f"평점 9.0 이상 추천 영화: {my_best_movies}")

# Reuse the same function to recommend movies rated 9.1 or higher
my_good_movies = recommend_movies(my_movie_dataset, 9.1)
print(f"평점 9.1 이상 추천 영화: {my_good_movies}")

평점 9.0 이상 추천 영화: ['인셉션', '기생충', '어바웃 타임']
평점 9.1 이상 추천 영화: ['인셉션', '어바웃 타임']