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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).
# Cargar paquetes
import pandas as pd
import matplotlib.pyplot as plt
office_df=pd.read_csv("datasets/office_episodes.csv")

# Colores
colores = []

# Iterar
for lab, row in office_df.iterrows() :
    if row["scaled_ratings"]<0.25 :
        colores.append("red")
    elif row["scaled_ratings"]<0.5 :
        colores.append("orange")
    elif row["scaled_ratings"]<0.75 :
        colores.append("lightgreen")   
    else:
        colores.append("darkgreen")
Hidden output 
#Tamaños
size=[]

#iterar
for ind, row in office_df.iterrows():
    if row["has_guests"]==True:
        size.append(250)
    else:
        size.append(25)
fig=plt.figure()
plt.rcParams['figure.figsize'] = [11, 7]

plt.scatter(x=office_df["episode_number"],
            y=office_df["viewership_mil"]
           ,c=colores,
            s=size
           )
plt.title("Popularity, Quality, and Guest Appearances on the Office")
plt.xlabel("Episode Number")
plt.ylabel("Viewership (Millions)")
plt.show()


#Nombre de un guest star en el episodio más visto

office_df.loc[office_df["viewership_mil"].idxmax(), "guest_stars"]
#office_df.loc[office_df["viewership_mil"]>20]["guest_stars"]

top_star="Jessica Alba"
Hidden output 
#añadir colores a df
office_df["colores"]=colores
office_df["size"]=size
#separar los df entre invitados
no_office_df=office_df[office_df["has_guests"]==False]
yes_office_df=office_df[office_df["has_guests"]==True]
#Bonus Step
fig=plt.figure()
plt.rcParams['figure.figsize'] = [11, 7]

plt.scatter(x=no_office_df["episode_number"],
            y=no_office_df["viewership_mil"]
           ,c=no_office_df["colores"],
            s=no_office_df["size"]
           )

plt.scatter(x=yes_office_df["episode_number"],
            y=yes_office_df["viewership_mil"]
           ,c=yes_office_df["colores"],
            s=yes_office_df["size"],
            marker="*"
           )
plt.title("Popularity, Quality, and Guest Appearances on the Office")
plt.xlabel("Episode Number")
plt.ylabel("Viewership (Millions)")
plt.show()
Hidden output