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:
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.
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).
First we have to import the pandas and matplotlib libraries
import pandas as pd import matplotlib.pyplot as plt plt.rcParams['figure.figsize'] = [11, 7]
Read the csv file as DataFrame office_df and display the first five rows.
office_df = pd.read_csv('datasets/office_episodes.csv') office_df.head()
For data visualisation we define colors based on the given statements to reflect the different ratings
cols = for i, row in office_df.iterrows(): if row['scaled_ratings'] < 0.25: cols.append('red') elif row['scaled_ratings'] < 0.50: cols.append('orange') elif row['scaled_ratings'] < 0.75: cols.append('lightgreen') else: cols.append('darkgreen')
Sizing the guest appearance with marker size 250 and those without guest marker size as 25, as per the instruction
size =  for i, row in office_df.iterrows(): if row['has_guests']==True: size.append(250) else: size.append(25)
We create columns for color and size also define DataFrame for with guest and without guest
office_df['colors']=cols office_df['size']=size with_guest_df = office_df[office_df['has_guests'] == True] no_guest_df = office_df[office_df['has_guests'] == False]
For data visualization
fig=plt.figure() plt.style.use('fivethirtyeight') plot_1=plt.scatter(data=no_guest_df,x="episode_number",y="viewership_mil",c='colors',s='size') plot_2=plt.scatter(data=with_guest_df,x="episode_number",y="viewership_mil",c='colors',s='size',marker='*')
We add title, xlabel and ylabel. And display the plot
plt.title("Popularity, Quality, and Guest Appearances on the Office") plt.xlabel("Episode Number") plt.ylabel("Viewership (Millions)") plt.show()
Now to get the most watched episode
To view the top star person
# to view the top star persontop_stars=office_df_most_watched['guest_stars']top_stars #OUTPUT Cloris Leachman, Jack Black, Jessica Alba Name: guest_stars, dtype: object