Investigating Netflix Movies and Guest Stars in The Office
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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 pandas as pd
import numpy as np
import matplotlib.pyplot as plt
    Hidden output 
    # First, import the data
episodes = pd.read_csv('datasets/office_episodes.csv')
    Hidden output 
    # There needs to be a color assigned to every episode, based on its scaled ratings value.
# Colors will be stared in an empty list
colors = []

# Create an empty list to store colors
for ind, row in episodes.iterrows():
    
    if (row['scaled_ratings'] < 0.25):
        
        colors.append("red")
    
    elif ((row['scaled_ratings'] >= 0.25) & (row['scaled_ratings'] < 0.50)):
        
        colors.append("orange")
        
    elif ((row['scaled_ratings'] >= 0.50) & (row['scaled_ratings'] < 0.75)):
        
        colors.append("lightgreen")
        
    else:
        
        colors.append('darkgreen')
        
# A similar rule will be established for size
sizes=[]

for ind, row in episodes.iterrows():
    
    if (row['has_guests']==True):
        
        sizes.append(250)
        
    else:
        
        sizes.append(25)
    Hidden output 
    # Set a decent size for the scatterplot
plt.rcParams['figure.figsize'] = [11, 7]

# Create a scatter plot for episode rating

# Initialize fig object and add an ax
fig = plt.figure(tight_layout=True)

# Create a scatterlpot
plt.scatter(
    x=episodes['episode_number'], 
    y=episodes['viewership_mil'],
    c=colors,
    s=sizes
)

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

# Show command
plt.show()


# Guest star in the most watched episode
top_star = episodes[episodes['viewership_mil']==np.max(episodes['viewership_mil'])]['guest_stars'].item().split(',')[0]
print(top_star)