Investigating Guest Stars in The Office

Updated: Oct 13, 2021

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 pandas and matplotlib.pyplot
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt

# Read in the csv as a DataFrameoffice_df = pd.read_csv('datasets/office_episodes.csv', parse_dates=['release_date'])

# Initiatlize two empty lists
cols = []
sizes = []
# Iterate through the DataFrame, and assign colors based on the rating
for ind, 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')

Here we just created colors for different portions by the instruction of Datacamp. Four categories.

Ratings < 0.25 are colored "red"
Ratings >= 0.25 and < 0.50 are colored "orange"
Ratings >= 0.50 and < 0.75 are colored "lightgreen"
Ratings >= 0.75 are colored "darkgreen"

# Iterate through the DataFrame, and assign a size based on whether it has guests
for ind, row in office_df.iterrows():
if row['has_guests'] == False:
sizes.append(25)
else:
sizes.append(250)

From our instruction, A sizing system, such that episodes with guest appearances have a marker size of 250 and episodes without are sized 25

# For ease of plotting, add our lists as columns to the DataFrameo
ffice_df['colors'] = colsoffice_df['sizes'] = sizes

# Split data into guest and non_guest DataFrames
non_guest_df = office_df[office_df['has_guests'] == False]
guest_df = office_df[office_df['has_guests'] == True]

For plotting, we split the dataset into two parts. One has Guest Stars others have none.

# Set the figure size and plot style plt.rcParams['figure.figsize'] = [11, 7]
plt.style.use('fivethirtyeight')

Fixed the size and style of the plot.

max_index = office_data['Viewership'].idxmax()
most_popular = office_data.loc[max_index]
most_popular

Season 5
EpisodeTitle Stress Relief
About Dwight's too-realistic fire alarm gives Stanle...
Ratings 9.7
Votes 8170
Viewership 22.91
Duration 60
Date 1 February 2009
GuestStars Cloris Leachman, Jack Black, Jessica Alba
Director Jeffrey Blitz
Writers Paul Lieberstein
Coloring darkgreen
Episodes 78
Name: 77, dtype: object

The most viewed episode title is Stress Relief. Viewed 22.91M. And the GuestStars are Cloris Leachman, Jack Black, Jessica Alba.

# Create the figure
fig = plt.figure()

# Create two scatter plots with the episode number on the x axis, and the viewership on the y axis
# Create a normal scatter plot for regular episodes
plt.scatter(x=non_guest_df.episode_number,y=non_guest_df.viewership_mil, \ c=non_guest_df['colors'], s=25)

# Create a starred scatterplot for guest star episodes
plt.scatter(x=guest_df.episode_number,y=guest_df.viewership_mil, \ c=guest_df['colors'], marker='*', s=250)

# Create a title
plt.title("Popularity, Quality, and Guest Appearances on the Office", fontsize=28)

# Create an x-axis label
plt.xlabel("Episode Number", fontsize=18)

# Create a y-axis label
plt.ylabel("Viewership (Millions)", fontsize=18)

# Show the plot
plt.show()

Draw the plot with the title, label and axis name.

# Get the most popular guest star
print(office_df[office_df['viewership_mil'] > 20]['guest_stars'])
#Output
'Cloris Leachman, Jack Black, Jessica Alba'

Conclusion

In these data, we found that the most popular episode was in Season 5 (episode 78). Viewed 22.91M. The GuestStars are Cloris Leachman, Jack Black, Jessica Alba. Ratings 9.7.

GitHub