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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 pandas as pd
import matplotlib.pyplot as plt
Hidden output 
netflix_data = pd.read_csv('datasets/office_episodes.csv')
Hidden output 
netflix_data.info()
netflix_data.head()
colors = []

for lab, row in netflix_data.iterrows():
    if row['scaled_ratings'] < 0.25:
        colors.append('red')
    elif 0.25 <= row['scaled_ratings'] < 0.50:
        colors.append('orange')
    elif 0.50 <= row['scaled_ratings'] < 0.75:
        colors.append('lightgreen')
    else:
        colors.append('darkgreen')
        
colors[:10]
sizes = []
for lab, row in netflix_data.iterrows():
    if row['has_guests']:
        sizes.append(250)
    else:
        sizes.append(25)
sizes[:10]

fig = plt.figure(figsize=(11,7))

plt.scatter(x= netflix_data.episode_number, y= netflix_data.viewership_mil,
            c = colors, s = sizes, marker='x')
plt.xlabel('Episode Number')
plt.ylabel('Viewership (Millions)')
plt.title('Popularity, Quality, and Guest Appearances on the Office')
plt.show();
max_view_time = max(netflix_data['viewership_mil'])
most_watched_episode = netflix_data.loc[netflix_data['viewership_mil'] == max_view_time]

top_stars = most_watched_episode[['guest_stars']]
top_stars
top_star = 'Cloris Leachman'
top_star