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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).
    # import the libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
    Hidden output 
    # read the data into a pandas dataframe
office = pd.read_csv("datasets/office_episodes.csv")
    office.head()
    # create the colors for the scaled ratings
scaled_ratings = office['scaled_ratings']
colors = []

for rating in 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')
    # create the sizing system
sizes = [250 if appearance else 25 for appearance in office['has_guests']]
    Hidden output 
    # create a scatterplot
fig = plt.figure()
plt.scatter(office['episode_number'], office['viewership_mil'], c = colors, s=sizes)

# Set the title and axis labels
plt.title("Popularity, Quality, and Guest Appearances on the Office")
plt.xlabel("Episode Number")
plt.ylabel("Viewership (Millions)")
plt.show()
    # create a dataframe that has guest
guest_episodes = office[office['has_guests'] == True]
guest_episodes.head()
    # now i sort the guest_episodes dataframe using the viewership
# in descending order
most_watched = guest_episodes.sort_values(by='viewership_mil', ascending= False)
most_watched.set_index('episode_number')
most_watched.head(3)
    # now i can take the top entry 
top_stars = most_watched.iloc[0]['guest_stars']
print(top_stars)
    top_list = top_stars.split(',')
print(top_list)
top_star = top_list[0]
print(top_star)