1. Welcome!
.
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.
- 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 pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [11, 7]
# Importing office dataset
df_office = pd.read_csv('datasets/office_episodes.csv')
# first five rows of office dataset
df_office.head()# summary of office dataset
df_office.info()
plt.scatter(df_office['episode_number'], df_office['viewership_mil'])
plt.show()# adding an array of colors to our plot
cols = []
for ind, row in df_office.iterrows() :
if row['scaled_ratings'] < 0.25 :
cols.append('red')
elif row['scaled_ratings'] >= 0.25 and row['scaled_ratings'] < 0.50 :
cols.append('orange')
elif row['scaled_ratings'] >= 0.50 and row['scaled_ratings'] < 0.75 :
cols.append('lightgreen')
else :
cols.append('darkgreen')# list of colors corresponding to our links to the plot
print(cols)# adding colors to our plot
plt.scatter(df_office['episode_number'], df_office['viewership_mil'], c = cols)
plt.show()# size list
sizes = []
for ind, row in df_office.iterrows() :
if row['has_guests'] == True :
sizes.append(250)
else :
sizes.append(25)
# list of sizes corresponding to our links to the plot
print(sizes)# Adding sizes to our plot
plt.scatter(df_office['episode_number'], df_office['viewership_mil'], c = cols, s = sizes)
plt.show()# Adding a title, x - axis lable and y-axis label
plt.scatter(df_office['episode_number'], df_office['viewership_mil'], c = cols, s = sizes)
plt.title('Popularity, Quality, and Guest Appearances on the Office')
plt.xlabel('Episode Number')
plt.ylabel('Viewership (Millions)')
plt.show()df_office['colors'] = cols
df_office['sizes'] = sizes
df_office.info()
# getting non_guest data
df_non_guest = df_office[df_office['has_guests'] == False]
# getting guest dataframe
df_guest = df_office[df_office['has_guests'] == True]
# Differenciating guest appearances with size and a star
fig = plt.figure()
# Adding a plot sytle from 'ggplot'
plt.style.use('ggplot')
plt.scatter(df_non_guest['episode_number'],
df_non_guest['viewership_mil'],
c = df_non_guest['colors'],
s = df_non_guest['sizes']
)
plt.scatter(df_guest['episode_number'],
df_guest['viewership_mil'],
c = df_guest['colors'],
s = df_guest['sizes'],
marker = '*'
)
plt.title('Popularity, Quality, and Guest Appearances on the Office')
plt.xlabel('Episode Number')
plt.ylabel('Viewership (Millions)')
plt.show()