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1. Welcome!
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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 statements
import matplotlib.pyplot as plt
import pandas as pd
import numpy as npHidden output
# Read in the data set
office_episodes = pd.read_csv("datasets/office_episodes.csv")
# Display office_episodes
office_episodes# Extract the relevant information
ep_no = office_episodes["episode_number"] # Episode numbers
views = office_episodes["viewership_mil"] # Viewership in millions
has_guests = office_episodes["has_guests"] # Boolean for whether or not pisode has a guest
# Add column: rating_colour
ratings_red = office_episodes["scaled_ratings"] < 0.25 # Episodes with rating < 0.25
ratings_orange = np.logical_and(0.25 <= office_episodes["scaled_ratings"], office_episodes["scaled_ratings"] < 0.50) # Episodes with 0.25 <= ratings < 0.50
ratings_lgreen = np.logical_and(0.50 <= office_episodes["scaled_ratings"], office_episodes["scaled_ratings"] < 0.75) # Episodes with 0.50 <= ratings < 0.75
ratings_dgreen = office_episodes["scaled_ratings"] >= 0.75 # Episodes with ratings >= 0.75
# Make an empty column for rating_color
office_episodes["rating_colour"] = ""
# Assign the colours to this column
office_episodes["rating_colour"] = np.where(ratings_red, "red", office_episodes["rating_colour"])
office_episodes["rating_colour"] = np.where(ratings_orange, "orange", office_episodes["rating_colour"])
office_episodes["rating_colour"] = np.where(ratings_lgreen, "lightgreen", office_episodes["rating_colour"])
office_episodes["rating_colour"] = np.where(ratings_dgreen, "darkgreen", office_episodes["rating_colour"])
# Get the corresponding series data type
rating_colour = office_episodes["rating_colour"]
# Sizing system
office_episodes["sizing_system"] = np.where(office_episodes["has_guests"], 250, 25)
sizing_system = office_episodes["sizing_system"]# Plotting
fig = plt.figure()
# Set figure parameters
plt.rcParams['figure.figsize'] = [11, 7]
# Scatter plot
plt.scatter(ep_no, views, s=sizing_system, c=rating_colour)
# Labels
plt.xlabel("Episode Number")
plt.ylabel("Viewership (Millions)")
plt.title("Popularity, Quality, and Guest Appearances on the Office")# Question 2
max_views = np.max(office_episodes["viewership_mil"]) # Maximum viewership
max_views_ind = np.where(office_episodes["viewership_mil"] == max_views) # Index for max_views
row_top_star = office_episodes.iloc[max_views_ind[0]] # Row in df that contains the top stars
top_star = row_top_star[["guest_stars"]].iloc[0][0].split(",")[0] # Find one of the stars from the top episode
top_star