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Visualization Module

Visualizer

Source code in src/visualize.py 
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class Visualizer:
    @staticmethod
    def plot_genre_distribution(movies: pd.DataFrame):
        """Plot distribution of movie genres"""
        # Get all unique genres
        all_genres = set()
        for genres_list in movies['genres']:
            if isinstance(genres_list, list):
                all_genres.update(genres_list)

        # Count genre occurrences
        genre_counts = {}
        for genre in all_genres:
            if genre != '(no genres listed)':
                genre_counts[genre] = movies[genre].sum()

        # Plot
        plt.figure(figsize=(12, 8))
        genres = list(genre_counts.keys())
        counts = list(genre_counts.values())

        plt.barh(genres, counts)
        plt.title('Movie Genre Distribution')
        plt.xlabel('Number of Movies')
        plt.tight_layout()
        plt.savefig(f'{vis_dir}/genre_distribution.png', dpi=300, bbox_inches='tight')
        plt.show()

    @staticmethod
    def plot_rating_distribution(ratings: pd.DataFrame):
        """Plot distribution of ratings"""
        plt.figure(figsize=(10, 6))
        sns.histplot(ratings['rating'], bins=10, kde=True)
        plt.title('Rating Distribution')
        plt.xlabel('Rating')
        plt.ylabel('Frequency')
        plt.tight_layout()
        plt.savefig(f'{vis_dir}/rating_distribution.png', dpi=300, bbox_inches='tight')
        plt.show()

    @staticmethod
    def plot_movies_per_year(movies: pd.DataFrame):
        """Plot number of movies released per year"""
        # Extract year from title (assuming format like "Movie Title (Year)")
        movies['year'] = movies['title'].str.extract(r'\((\d{4})\)')
        movies['year'] = pd.to_numeric(movies['year'], errors='coerce')

        # Filter valid years
        movies_with_year = movies.dropna(subset=['year'])
        movies_with_year = movies_with_year[movies_with_year['year'] >= 1900]

        plt.figure(figsize=(12, 6))
        year_counts = movies_with_year['year'].value_counts().sort_index()
        plt.plot(year_counts.index, year_counts.values, linewidth=2)
        plt.title('Number of Movies Released Per Year')
        plt.xlabel('Year')
        plt.ylabel('Number of Movies')
        plt.grid(True, alpha=0.3)
        plt.tight_layout()
        plt.savefig(f'{vis_dir}/movies_per_year.png', dpi=300, bbox_inches='tight')
        plt.show()

    @staticmethod
    def plot_top_rated_movies(df: pd.DataFrame, min_ratings=50):
        """Plot top rated movies with minimum number of ratings"""
        # Calculate average rating and count for each movie
        movie_stats = df.groupby('title').agg({
            'rating': ['mean', 'count']
        }).reset_index()

        # Flatten column names
        movie_stats.columns = ['title', 'avg_rating', 'rating_count']

        # Filter movies with minimum ratings
        popular_movies = movie_stats[movie_stats['rating_count'] >= min_ratings]
        top_movies = popular_movies.nlargest(15, 'avg_rating')

        plt.figure(figsize=(10, 8))
        plt.barh(top_movies['title'], top_movies['avg_rating'])
        plt.title(f'Top 15 Highest Rated Movies (min {min_ratings} ratings)')
        plt.xlabel('Average Rating')
        plt.tight_layout()
        plt.savefig(f'{vis_dir}/top_rated_movies.png', dpi=300, bbox_inches='tight')
        plt.show()

    @staticmethod
    def plot_rating_trends(df: pd.DataFrame):
        """Plot rating trends over time"""
        # Convert timestamp to datetime
        df['datetime'] = pd.to_datetime(df['timestamp'], unit='s')
        df['year'] = df['datetime'].dt.year

        # Calculate average rating per year
        yearly_ratings = df.groupby('year')['rating'].mean()

        plt.figure(figsize=(12, 6))
        plt.plot(yearly_ratings.index, yearly_ratings.values, marker='o', linewidth=2)
        plt.title('Average Rating Trends Over Time')
        plt.xlabel('Year')
        plt.ylabel('Average Rating')
        plt.grid(True, alpha=0.3)
        plt.tight_layout()
        plt.savefig(f'{vis_dir}/rating_trends.png', dpi=300, bbox_inches='tight')
        plt.show()

plot_genre_distribution(movies) staticmethod

Plot distribution of movie genres

Source code in src/visualize.py 
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@staticmethod
def plot_genre_distribution(movies: pd.DataFrame):
    """Plot distribution of movie genres"""
    # Get all unique genres
    all_genres = set()
    for genres_list in movies['genres']:
        if isinstance(genres_list, list):
            all_genres.update(genres_list)

    # Count genre occurrences
    genre_counts = {}
    for genre in all_genres:
        if genre != '(no genres listed)':
            genre_counts[genre] = movies[genre].sum()

    # Plot
    plt.figure(figsize=(12, 8))
    genres = list(genre_counts.keys())
    counts = list(genre_counts.values())

    plt.barh(genres, counts)
    plt.title('Movie Genre Distribution')
    plt.xlabel('Number of Movies')
    plt.tight_layout()
    plt.savefig(f'{vis_dir}/genre_distribution.png', dpi=300, bbox_inches='tight')
    plt.show()

plot_movies_per_year(movies) staticmethod

Plot number of movies released per year

Source code in src/visualize.py 
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@staticmethod
def plot_movies_per_year(movies: pd.DataFrame):
    """Plot number of movies released per year"""
    # Extract year from title (assuming format like "Movie Title (Year)")
    movies['year'] = movies['title'].str.extract(r'\((\d{4})\)')
    movies['year'] = pd.to_numeric(movies['year'], errors='coerce')

    # Filter valid years
    movies_with_year = movies.dropna(subset=['year'])
    movies_with_year = movies_with_year[movies_with_year['year'] >= 1900]

    plt.figure(figsize=(12, 6))
    year_counts = movies_with_year['year'].value_counts().sort_index()
    plt.plot(year_counts.index, year_counts.values, linewidth=2)
    plt.title('Number of Movies Released Per Year')
    plt.xlabel('Year')
    plt.ylabel('Number of Movies')
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(f'{vis_dir}/movies_per_year.png', dpi=300, bbox_inches='tight')
    plt.show()

plot_rating_distribution(ratings) staticmethod

Plot distribution of ratings

Source code in src/visualize.py 
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@staticmethod
def plot_rating_distribution(ratings: pd.DataFrame):
    """Plot distribution of ratings"""
    plt.figure(figsize=(10, 6))
    sns.histplot(ratings['rating'], bins=10, kde=True)
    plt.title('Rating Distribution')
    plt.xlabel('Rating')
    plt.ylabel('Frequency')
    plt.tight_layout()
    plt.savefig(f'{vis_dir}/rating_distribution.png', dpi=300, bbox_inches='tight')
    plt.show()

Plot rating trends over time

Source code in src/visualize.py 
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@staticmethod
def plot_rating_trends(df: pd.DataFrame):
    """Plot rating trends over time"""
    # Convert timestamp to datetime
    df['datetime'] = pd.to_datetime(df['timestamp'], unit='s')
    df['year'] = df['datetime'].dt.year

    # Calculate average rating per year
    yearly_ratings = df.groupby('year')['rating'].mean()

    plt.figure(figsize=(12, 6))
    plt.plot(yearly_ratings.index, yearly_ratings.values, marker='o', linewidth=2)
    plt.title('Average Rating Trends Over Time')
    plt.xlabel('Year')
    plt.ylabel('Average Rating')
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig(f'{vis_dir}/rating_trends.png', dpi=300, bbox_inches='tight')
    plt.show()

plot_top_rated_movies(df, min_ratings=50) staticmethod

Plot top rated movies with minimum number of ratings

Source code in src/visualize.py 
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@staticmethod
def plot_top_rated_movies(df: pd.DataFrame, min_ratings=50):
    """Plot top rated movies with minimum number of ratings"""
    # Calculate average rating and count for each movie
    movie_stats = df.groupby('title').agg({
        'rating': ['mean', 'count']
    }).reset_index()

    # Flatten column names
    movie_stats.columns = ['title', 'avg_rating', 'rating_count']

    # Filter movies with minimum ratings
    popular_movies = movie_stats[movie_stats['rating_count'] >= min_ratings]
    top_movies = popular_movies.nlargest(15, 'avg_rating')

    plt.figure(figsize=(10, 8))
    plt.barh(top_movies['title'], top_movies['avg_rating'])
    plt.title(f'Top 15 Highest Rated Movies (min {min_ratings} ratings)')
    plt.xlabel('Average Rating')
    plt.tight_layout()
    plt.savefig(f'{vis_dir}/top_rated_movies.png', dpi=300, bbox_inches='tight')
    plt.show()

create_all_visualizations()

Create all visualizations

Source code in src/visualize.py 
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def create_all_visualizations():
    """Create all visualizations"""
    print("Creating visualizations...")

    viz = Visualizer()

    print("1. Genre distribution...")
    viz.plot_genre_distribution(movies)

    print("2. Rating distribution...")
    viz.plot_rating_distribution(ratings)

    print("3. Movies per year...")
    viz.plot_movies_per_year(movies)

    print("4. Top rated movies...")
    viz.plot_top_rated_movies(df)

    print("5. Rating trends...")
    viz.plot_rating_trends(df)

    print("All visualizations saved!")