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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.pyplot as plt
import numpy as np
import pandas as pd
office_data = pd.read_csv("datasets/office_episodes.csv")
office_data.head()
office_data.tail()
office_data.info()
office_data.describe()
office_data.loc[office_data.guest_stars.isna(), ["guest_stars", "has_guests"]].describe()
office_data.loc[office_data.guest_stars.notna(), ["guest_stars", "has_guests"]].describe()
def colorize(rating):
    """Define color of the point based on rating."""
    if rating < .25:
        return "red"

    if (.25 <= rating < .5):
        return "orange"

    if (.5 <= rating < .75):
        return "lightgreen"

    if (rating >= .75):
        return "darkgreen"


def marker_size(has_guest):
    """Define marker size based on guest existance."""
    if has_guest:
        return 250
    return 25
fig, ax = plt.subplots(figsize=(11, 7))
colors = office_data.scaled_ratings.apply(colorize).tolist()
markersizes = office_data.has_guests.apply(marker_size).tolist()
most_viewed = office_data.loc[
    office_data.viewership_mil == office_data.viewership_mil.max()
]
top_star = most_viewed.guest_stars.item().split(", ")[0]
ax.scatter(
    office_data.episode_number,
    office_data.viewership_mil,
    c=colors,
    s=markersizes,
)

ax.annotate(
    top_star,
    xy=(most_viewed.episode_number, most_viewed.viewership_mil),
    xytext=(most_viewed.episode_number, most_viewed.viewership_mil-1)
)

plt.title("Popularity, Quality, and Guest Appearances on the Office")
plt.xlabel("Episode Number")
plt.ylabel("Viewership (Millions)")
fig.show()