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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

office_df = pd.read_csv("datasets/office_episodes.csv") #laod the dataset as a dataframe.

print(office_df[:5]) #check if the dataset is loaded and what it looks like
#filter the dataset to the relevant columns only
filtered_office_df = office_df.loc[:, ["episode_number", "season", "episode_title", 
                                      "viewership_mil", "guest_stars", "has_guests", "scaled_ratings"]]

#check if it was filtered properly
print(filtered_office_df[0:5])
#create an empty list for colors derived from scaled_ratings column
colors = []

#create an empty list for marker size based on if the episode has a guest star
sizes = []

#using for loop to append colors list and sizes list
for key, row in filtered_office_df.iterrows():
    #conditions for colors
    if row["scaled_ratings"] >= 0.75:
        colors.append("darkgreen")
    elif row["scaled_ratings"] >= 0.50 and row["scaled_ratings"] < 75:
        colors.append("lightgreen")
    elif row["scaled_ratings"] >= 0.25 and row["scaled_ratings"] < 50:
        colors.append("orange")
    else:
        colors.append("red")
        
    #conditions for sizes
    if row["has_guests"] == True:
        sizes.append(250)
    else:
        sizes.append(25)
        
#checking if the lists are appended
print(colors[0:10])
print(sizes[0:10])
        
import matplotlib.pyplot as plt 

fig = plt.figure()
plt.rcParams['figure.figsize'] = [15, 10]

#plot scatter and labels
plt.scatter(filtered_office_df["episode_number"], filtered_office_df["viewership_mil"], s=sizes,
            c=colors, marker="*")
plt.title("Popularity, Quality, and Guest Appearances on the Office")
plt.xlabel("Episode Number")
plt.ylabel("Viewership (Millions)")

#show plot
plt.show()
#check row with highest viewership
print(filtered_office_df.loc[filtered_office_df["viewership_mil"] == 
                             max(filtered_office_df["viewership_mil"]) ]) 
#name of one of the guest in the episode with the highest veiwership
top_star = "Jessica Alba"
Hidden output