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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).
Hidden output 
# Use this cell to begin your analysis, and add as many as you would like!
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
Hidden output 
df = pd.read_csv("datasets/office_episodes.csv")
Hidden output 
df.head()
def get_color(rating):
    if rating<0.25:
        return "red"
    elif 0.50>rating>=0.25:
        return "orange"
    elif 0.75>rating>=0.50:
        return "lightgreen"
    return "darkgreen"
Hidden output 
df["color"] = df.scaled_ratings.apply(get_color)
Hidden output 
def get_size(has_guest):
    if has_guest:
        return 250
    return 25
Hidden output 
df["marker_size"] = df.has_guests.apply(get_size)
Hidden output 
fig = plt.figure()
plt.scatter(df.episode_number, df.viewership_mil, color = df.color, s = df.marker_size)
plt.title("Popularity, Quality, and Guest Appearances on the Office")
plt.xlabel("Episode Number")
plt.ylabel("Viewership (Millions)")
plt.show()
max_viewership = df.viewership_mil.max()
top_stars = df[df.viewership_mil == max_viewership].guest_stars.values[0]
top_star = top_stars.split(",")[0]
Hidden output 
top_star