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Investigating Netflix Movies and Guest Stars in The Office

The Office

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).

First, we have to import important libraries to begin processing.

import pandas as pd
import matplotlib.pyplot as plt

In addition, we can set the figure size parameters to be able to see a larger version of our plot using this code.

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

Then, we read the dataset and display some informations about it and the last five rows as example using tail function without parameters.

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

office_df.info()
office_df.tail()


In this notebook, we will initiate this process by creating an informative plot of the episode data provided.

First, we will create a matplotlib scatter plot of the data where episode number is plotted along the x-axis and viewership(in millions) is plotted along the y-axis.

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

Then, we will add a color scheme to our plot reflecting the scaled ratings of each episode, such that:

  • Ratings < 0.25 are colored "red"

  • Ratings >= 0.25 and < 0.50 are colored "orange"

  • Ratings >= 0.50 and < 0.75 are colored "lightgreen"

  • Ratings >= 0.75 are colored "darkgreen"

To do so, we need to initialize a list calles cols and fill it with colors based on the rating of each episode and add a parameter called c to our scatter function call.

cols = []

for i, row in office_df.iterrows():
    if row['scaled_ratings'] < 0.25:
        cols.append('red')
    elif row['scaled_ratings'] < 0.5:
        cols.append('orange')
    elif row['scaled_ratings'] < 0.75:
        cols.append('lightgreen')
    else:
        cols.append('darkgreen')
        
print(cols[-1:-5:-1])

['darkgreen', 'darkgreen', 'darkgreen', 'orange']

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

To make our plot more readable we should introduce a sizing system, such that episodes with guest appearances have a marker size of 250 and episodes without are sized 25.

So we will implement a list calles sizes that contains sizes and also add the parameter s to our function.

sizes = []

for i, row in office_df.iterrows():
    if row['has_guests']: sizes.append(250)
    else: sizes.append(25)

office_df['size'] = sizes

sizes[-1:-5:-1]

[250, 25, 250, 25]


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

Finally, we have to add a title reading "Popularity, Quality, and Guest Appearances on the Office", an x-axis label reading "Episode Number" and a y-axis label reading "Viewership (Millions)".

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

Bonus Step!

We can use different marker to visualize different data points and differentiate guest appearances not just with size, but also with a star!

office_df['sizes'] = sizes
office_df['cols'] = cols
non_guest_df = office_df[office_df['has_guests'] == False]
guest_df = office_df[office_df['has_guests'] == True]

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

plt.scatter(x = guest_df['episode_number'],
            y = guest_df['viewership_mil'],
            c = guest_df['cols'],
            s = guest_df['sizes'],
            marker = '*')

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


That's it !!




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1 Comment


Data Insight
Data Insight
Oct 18, 2021

Your GitHub link should be an hyperlink.

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