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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!
# %matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
    Hidden output 
    df = pd.read_csv("datasets/office_episodes.csv")
    Hidden output 
    df.head()
    df.info()
    def getColour(rating):
    if( rating < 0.25):
        return "red"
    elif (rating >= 0.25 and rating < 0.5):
        return "orange"
    elif (rating >= 0.50 and rating < 0.75):
        return "lightgreen"
    elif (rating >= 0.75):
        return "darkgreen"
    Hidden output 
    colors = df["scaled_ratings"].apply(getColour)
colors
    sizes = df["has_guests"].apply(lambda x : 250 if x else 25)
sizes
    plt.rcParams['figure.figsize'] = [11, 7]
fig = plt.figure()
plt.scatter(df["episode_number"], df["viewership_mil"], c = colors, s = sizes)
plt.xlabel("Episode Number")
plt.ylabel("Viewership (Millions)")
plt.title("Popularity, Quality, and Guest Appearances on the Office")
plt.show()
    top_star = df[df["viewership_mil"] == max(df["viewership_mil"])]["guest_stars"].iloc[0]
    Hidden output 
    top_star