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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!
#Importing Libs
import pandas as pd
import matplotlib.pyplot as plt
Hidden output 
#Increase plot size
plt.rcParams['figure.figsize'] = [11, 7]
# importing data
office_df = pd.read_csv('datasets/office_episodes.csv',parse_dates=["release_date"])
office_df.head()
office_df.info()
# creating sizing list
sizes = []
for ind, row in office_df.iterrows() :
    if row['has_guests'] == False:
        sizes.append(25)
    else :
        sizes.append(250)
# creating colours list
cols = []
# looping through colours list
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')
#Creating new df with star marker
office_df['colors'] = cols
office_df['sizes'] = sizes
office_df.info()
#splitting df to guests and non guests
non_guest_df = office_df[office_df['has_guests'] == False]
guest_df = office_df[office_df['has_guests'] == True]

# creating first scatter plot
fig = plt.figure()
plt.style.use('fivethirtyeight')
plt.scatter(x=non_guest_df['episode_number'],
            y=non_guest_df['viewership_mil'],
            c=non_guest_df['colors'],
            s=non_guest_df['sizes'],
            )
plt.scatter(x=guest_df['episode_number'],
            y=guest_df['viewership_mil'],
            c=guest_df['colors'],
            s=guest_df['sizes'],
            marker='*'
            )
plt.title('Popularity, Quality, and Guest Appearances on the Office')
plt.xlabel('Episode Number')
plt.ylabel('Viewership (Millions)')
plt.show()
#top_star
office_df[office_df['viewership_mil'] == office_df['viewership_mil'].max()]['guest_stars']
top_star = 'Jack Black'
Hidden output