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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).

    Importing the required modules for analysis:

    import matplotlib.pyplot as plt
    import pandas as pd
    Hidden output

    Loading the .csv file and defining/saving the dataframe; defining the sorting algorithm, coloring for each rating bracket, sizing data points

    office_episodes = pd.read_csv('datasets/office_episodes.csv')
    colors = []
    for rating in office_episodes['scaled_ratings']:
        if rating < 0.25:
            colors.append('red')
        elif rating < 0.5:
            colors.append('orange')
        elif rating < 0.75:
            colors.append('lightgreen')
        else:
            colors.append('darkgreen')
    
    size = []
    shape = []
    for guest in office_episodes['guest_stars'].isnull():
        if guest == True:
            size.append(25)
        else:
            size.append(250)

    Design a scatter plot

    fig = plt.figure()
    plt.scatter(office_episodes['episode_number'], office_episodes['viewership_mil'], color = colors, s = size)
    plt.xlabel('Episode Number')
    plt.ylabel('Viewership (Millions)')
    plt.title("Popularity, Quality, and Guest Appearances on the Office")
    plt.show()

    Select the guest stars in the most watched episode

    sorted_episodes_by_viewership = office_episodes.sort_values(by='viewership_mil', ascending=False)
    top_stars_most_viewed = sorted_episodes_by_viewership.iloc[0][9]
    top_star = top_stars_most_viewed.split(',')[0]
    print(top_star)