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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 pandas as pd
import matplotlib.pyplot as plt

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

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

office_df.head()
Hidden output 
office_df.info()
cols_tes = []

for ind, row in office_df.iterrows():
    print(office_df['episode_title'])
cols = []

# Iterate through the DataFrame, and assign colors based on the rating
for ind, 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')
        
cols[:10]
fig = plt.figure()

plt.scatter(x=office_df['episode_number'], y=office_df['viewership_mil'])
plt.show()
fig = plt.figure()

# get colors from previous code
plt.scatter(x=office_df['episode_number'], y=office_df['viewership_mil'], c=cols)
plt.show()
sizes = []

# Iterate through the DataFrame, and assign a size based on whether it has guests        
for ind, row in office_df.iterrows():
    if row['has_guests'] == False:
        sizes.append(25)
    else:
        sizes.append(250)
        
sizes[:10]        
fig = plt.figure()

# add sizes
plt.scatter(x=office_df['episode_number'], y=office_df['viewership_mil'], 
            c=cols, s=sizes)
plt.show()
fig = plt.figure()

# add sizes
plt.scatter(x=office_df['episode_number'], y=office_df['viewership_mil'], 
            c=cols, s=sizes)

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

plt.show()
fig = plt.figure()

# not best option to have release_year as the x-axis (string not date form)
plt.scatter(x=office_df['release_date'], y=office_df['viewership_mil'], 
            c=cols, s=sizes)

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

plt.show()
# parsing dates
office_df = pd.read_csv('datasets/office_episodes.csv', parse_dates=['release_date'])

office_df.info()
fig = plt.figure()

# not best option to have release_year as the x-axis (string not date form)
plt.scatter(x=office_df['release_date'], y=office_df['viewership_mil'], 
            c=cols, s=sizes)

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

plt.show()
# adding new fields
office_df['colors'] = cols
office_df['sizes'] = sizes

office_df.info()
# Split data into guest and non_guest DataFrames
non_guest_df = office_df[office_df['has_guests'] == False]
guest_df = office_df[office_df['has_guests'] == True]
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