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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).
# Importing Packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Hidden output 
# Creating DataFrame from the csv file.
data = pd.read_csv("datasets/office_episodes.csv")

# Episode Number Column
e_num = np.array(data["episode_number"])

# Viewership Column
viewership = np.array(data["viewership_mil"])

# Scaled Ratings Columns
ratings = np.array(data["scaled_ratings"])

# Adding new Column named "sizes" with the Same Value as "has_guests"
data["sizes"] = data["has_guests"]
# Updating "sizes" column who has False value with 25
data.loc[data["sizes"] == False, "sizes"] = 25
# Updating "sizes" column who has True value with 250
data.loc[data["sizes"] == True, "sizes"] = 250
# Creating NumPy array from "sizes" column and changing its datatype to int32
sizes = np.array(data["sizes"]).astype("int32")

# Initializing colors 
colors = []

# Adding colors in "colors" list
for r in ratings:
    if r < 0.25:
        colors.append("red")
    elif r < 0.50:
        colors.append("orange")
    elif r < 0.75:
        colors.append("lightgreen")
    else:
        colors.append("darkgreen")
fig = plt.figure()
plt.scatter(x = e_num, y = viewership, s = sizes, c = colors)
plt.title("Popularity, Quality, and Guest Appearances on the Office")
plt.xlabel("Episode Number")
plt.ylabel("Viewership (Millions)")
plt.show()
row_lab = data["viewership_mil"][data["viewership_mil"] == data["viewership_mil"].max()].index[0]
top_star = data["guest_stars"][77]

top_star
Hidden output