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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).

1- Load the Dataset and the Required Package

# Use this cell to begin your analysis, and add as many as you would like!
import pandas as pd
import matplotlib.pyplot as plt

df=pd.read_csv('datasets/office_episodes.csv')

2- Visualize the Effect of Guest Popularity on the Episodes

colors=[]
for rev in df['scaled_ratings']:
    if rev >=0.75:
        colors.append('darkgreen')
    elif rev>=0.5:
        colors.append('lightgreen')
    elif rev>=0.25:
        colors.append('orange')
    else:
        colors.append('red')
size=[]
for s in df['has_guests']:
    if s:
        size.append(250)
    else:
        size.append(25)
plt.scatter(x=df['episode_number'],y=df['viewership_mil'],color=colors,s=size)
plt.xlabel('Episode Number')
plt.ylabel('Viewership (Millions)')
plt.title('Popularity, Quality, and Guest Appearances on the Office')
plt.show()

3- Finding The Top Guest Star of The Ofiice Serie

display(df.sort_values('viewership_mil',ascending=False).head())
top_star='Cloris Leachman'