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


#use pandas to read the CSV file as a dataframe and assign it to a variable

office_df = pd.read_csv('datasets/office_episodes.csv')
list(office_df)
#create a empyt list for colors

colors = []

#create a for loop to append the colors into the list

for lab, row in office_df.iterrows():
    if row['scaled_ratings'] < 0.25 :
        colors.append('red')
    elif row['scaled_ratings'] < 0.50:
        colors.append('orange')
    elif row['scaled_ratings'] < 0.75:
        colors.append('lightgreen')
    else :
        colors.append('darkgreen')
    
print(colors[0:10])
#create a sizes empty list for guest and non-guest episodes

sizes = []

#create a for loop to fill in the sizes in the list

for lab, row in office_df.iterrows():
    if row['has_guests'] == True :
        sizes.append(250)
    else :
        sizes.append(25)
        
print(sizes[0:10])
#add sizes and colors as columns into the dataframe

office_df['colors'] = colors
office_df['sizes'] = sizes
list(office_df)
#create fig object
fig =plt.figure()

#use the figure size parameters given

plt.rcParams['figure.figsize'] = [11,7]

#create scatterplot function and add color parameter and size parameter

plt.scatter(office_df['episode_number'], 
            office_df['viewership_mil'], 
            c=colors,
           s=sizes)

#create title and labels

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

#create 2 new dataframe subsets, one with guests and another with non-guests

guest_df = office_df[office_df['has_guests'] == True]
non_guest_df =office_df[office_df['has_guests'] == False]

#create fig object
fig =plt.figure()

#use the figure size parameters given

plt.rcParams['figure.figsize'] = [11,7]

#create new scatter plot with the 2 dataframes and create a marker parameter for the dataframe with guests stars

plt.scatter(non_guest_df['episode_number'],
           non_guest_df['viewership_mil'],
           c=non_guest_df['colors'],
           s=non_guest_df['sizes'])
plt.scatter(guest_df['episode_number'],
           guest_df['viewership_mil'],
           c=guest_df['colors'],
           s=guest_df['sizes'],
           marker ='*')

plt.title('Popularity, Quality, and Guest Appearances on the Office')
plt.xlabel('Episode Number')
plt.ylabel('Viewership (Millions)')
# subset from a boolian dataframe the stars in the episode with the highest viewership


star_office_df =office_df[office_df['viewership_mil'] > 20]['guest_stars']
top_star = 'Jack Black'