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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 matplotlib and set up project
import matplotlib.pyplot as plt
import pandas as pd

office_df = pd.read_csv("datasets/office_episodes.csv")
#create colors list and push colors to it corresponding with data elements

colors = []

for ind, row in office_df.iterrows():
    if row['scaled_ratings'] < 0.25:
        colors.append('red')
    elif row['scaled_ratings'] < 0.50:
        colors.append('orange')
    elif row['scaled_ratings'] < 0.75:
        colors.append('lightgreen')
    else:
        colors.append('darkgreen')
#specify sizes

sizes = []

for ind, row in office_df.iterrows():
    if row['has_guests'] == False:
        sizes.append(25)
    else:
        sizes.append(250)
# For ease of plotting, add lists as columns to the DataFrame

office_df["colors"] = colors
office_df["sizes"] = sizes
Hidden output 
# 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]
Hidden output 
# Create a normal scatter plot for regular episodes
plt.rcParams['figure.figsize'] = [11, 7]
plt.style.use('fivethirtyeight')

fig = plt.figure()


             
plt.scatter(x=non_guest_df["episode_number"], 
    y=non_guest_df["viewership_mil"], 
    c=non_guest_df["colors"], 
    s=non_guest_df["sizes"])
# Create a starred scatterplot for guest star episodes
plt.scatter(x=guest_df.episode_number, y=guest_df.viewership_mil, \
                 # Assign our color list as the colors and set marker and size
                 c=guest_df['colors'], marker='*', s=250)

plt.title("Popularity, Quality, and Guest Appearances on the Office", fontsize=28)
plt.xlabel("Episode Number", fontsize=18)
plt.ylabel("Viewership (Millions)", fontsize=18)
plt.show()
# Get the most popular guest star
print(office_df[office_df['viewership_mil'] > 20]['guest_stars'])
top_star = 'Jessica Alba'