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[ver2] Investigating Netflix Movies and Guest Stars in The Office
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  • 1. Welcome!

    Markdown.

    The Office! What started as a British mockumentary series about office culture in 2001 has since spawned ten other variants across the world, including an Israeli version (2010-13), a Hindi version (2019-), and even a French Canadian variant (2006-2007). Of all these iterations (including the original), the American series has been the longest-running, spanning 201 episodes over nine seasons.

    In this notebook, we will take a look at a dataset of The Office episodes, and try to understand how the popularity and quality of the series varied over time. To do so, we will use the following dataset: datasets/office_episodes.csv, which was downloaded from Kaggle here.

    This dataset contains information on a variety of characteristics of each episode. In detail, these are:

    datasets/office_episodes.csv
    • episode_number: Canonical episode number.
    • season: Season in which the episode appeared.
    • episode_title: Title of the episode.
    • description: Description of the episode.
    • ratings: Average IMDB rating.
    • votes: Number of votes.
    • viewership_mil: Number of US viewers in millions.
    • duration: Duration in number of minutes.
    • release_date: Airdate.
    • guest_stars: Guest stars in the episode (if any).
    • director: Director of the episode.
    • writers: Writers of the episode.
    • has_guests: True/False column for whether the episode contained guest stars.
    • scaled_ratings: The ratings scaled from 0 (worst-reviewed) to 1 (best-reviewed).
    # Use this cell to begin your analysis, and add as many as you would like!
    # Import pandas and matplotlib.pyplot
    %matplotlib inline
    import pandas as pd
    import matplotlib.pyplot as plt
    
    # Read in the csv as a DataFrame
    office_df = pd.read_csv('datasets/office_episodes.csv', parse_dates=['release_date'])
    
    # Initiatlize two empty lists
    cols = []
    sizes = []
    
    # Iterate through the DataFrame, and assign colors based on the rating
    for ind, row in office_df.iterrows():
        if row['scaled_ratings'] < 0.25:
            cols.append('red')
        elif row['scaled_ratings'] < 0.50:
            cols.append('orange')
        elif row['scaled_ratings'] < 0.75:
            cols.append('lightgreen')
        else:
            cols.append('darkgreen')
    
    # Iterate through the DataFrame, and assign a size based on whether it has guests        
    for ind, row in office_df.iterrows():
        if row['has_guests'] == False:
            sizes.append(25)
        else:
            sizes.append(250)
    
    # For ease of plotting, add our lists as columns to the DataFrame
    office_df['colors'] = cols
    office_df['sizes'] = sizes
    
    # Split data into guest and non_guest DataFrames
    non_guest_df = office_df[office_df['has_guests'] == False]
    guest_df = office_df[office_df['has_guests'] == True]
    
    # Set the figure size and plot style        
    plt.rcParams['figure.figsize'] = [11, 7]
    plt.style.use('fivethirtyeight')
    
    # Create the figure
    fig = plt.figure()
    
    # Create two scatter plots with the episode number on the x axis, and the viewership on the y axis
    
    # Create a normal scatter plot for regular episodes
    plt.scatter(x=non_guest_df.episode_number, y=non_guest_df.viewership_mil, \
                     # Assign our color list as the colors and set marker and size
                     c=non_guest_df['colors'], s=25)
    
    # Create a starred scatterplot for guest star episodes
    plt.scatter(x=guest_df.episode_number, y=guest_df.viewership_mil, \
                     # Assign our color list as the colors and set marker and size
                     c=guest_df['colors'], marker='*', s=250)
    
    # Create a title
    plt.title("Popularity, Quality, and Guest Appearances on the Office", fontsize=28)
    
    # Create an x-axis label
    plt.xlabel("Episode Number", fontsize=18)
    
    # Create a y-axis label
    plt.ylabel("Viewership (Millions)", fontsize=18)
    
    # Show the plot
    plt.show()
    
    # Get the most popular guest star
    print(office_df[office_df['viewership_mil'] > 20]['guest_stars'])
    top_star = 'Jessica Alba'