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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).
    # Use this cell to begin your analysis, and add as many as you would like!
    import pandas as pd
    import matplotlib.pyplot as plt
    
    plt.rcParams['figure.figsize'] = [11, 7]
    
    office_df = pd.read_csv("datasets/office_episodes.csv")
    office_df.head()
    
    Hidden output
    cols =[]
    
    for index, 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")
    cols
    Hidden output
    sizes = []
    
    for index, row in office_df.iterrows():
        if row["has_guests"] == True:
            sizes.append(250)
        else:
            sizes.append(25)
    sizes
    
    office_df["colors"] = cols
    office_df["sizes"] = sizes
    Hidden output
    non_guest_df = office_df[office_df["has_guests"] == False]
    guest_df = office_df[office_df["has_guests"] == True]
    fig = plt.figure()
    plt.style.use('fivethirtyeight')
    
    plt.scatter(x=non_guest_df['episode_number'], 
                y=non_guest_df["viewership_mil"],
               c=non_guest_df["colors"],
               s=non_guest_df["sizes"]
               )
    
    plt.scatter(x=guest_df['episode_number'], 
                y=guest_df["viewership_mil"],
               c=guest_df["colors"],
               s=guest_df["sizes"],
                marker="*"
               )
    plt.title("Popularity, Quality, and Guest Appearances on the Office")
    plt.xlabel("Episode Number")
    plt.ylabel("Viewership (Millions)")
    plt.show()
    
    top_star = office_df[office_df["viewership_mil"] == office_df["viewership_mil"].max()]["guest_stars"]
    print(top_star)