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[ver2] Investigating Netflix Movies and Guest Stars in The Office

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 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'