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 and matplotlib.pyplot
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
# Read in the csv as a DataFrame
office_df = pd.read_csv('datasets/office_episodes.csv', parse_dates=['release_date'])
# Initiatlize two empty lists
cols = []
sizes = []
# 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')
# 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)
# For ease of plotting, add our lists as columns to the DataFrame
office_df['colors'] = cols
office_df['sizes'] = sizes
# 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]
# Set the figure size and plot style
plt.rcParams['figure.figsize'] = [11, 7]
plt.style.use('fivethirtyeight')
# Create the figure
fig = plt.figure()
# Create two scatter plots with the episode number on the x axis, and the viewership on the y axis
# Create a normal scatter plot for regular episodes
plt.scatter(x=non_guest_df.episode_number, y=non_guest_df.viewership_mil, \
# Assign our color list as the colors and set marker and size
c=non_guest_df['colors'], s=25)
# Create a starred scatterplot for guest star episodes
plt.scatter(x=guest_df.episode_number, y=guest_df.viewership_mil, \
# Assign our color list as the colors and set marker and size
c=guest_df['colors'], marker='*', s=250)
# Create a title
plt.title("Popularity, Quality, and Guest Appearances on the Office", fontsize=28)
# Create an x-axis label
plt.xlabel("Episode Number", fontsize=18)
# Create a y-axis label
plt.ylabel("Viewership (Millions)", fontsize=18)
# Show the plot
plt.show()
# Get the most popular guest star
print(office_df[office_df['viewership_mil'] > 20]['guest_stars'])
top_star = 'Jessica Alba'