Analytics of Guest Stars in The Office

The Office! What started as a British documentary 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.

Kaggle is where the data was extracted.

The dataset contains information on a variety of characteristics of each episode. In detail, these are:

The first step is to import the required libraries and load the data into pandas data frame as show below. Plotly was the main library used to generative interactive visual insights.

import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
 import numpy as np
 import plotly.express as px
 plt.rcParams['figure.figsize']=[11,7]
 plt.style.use("ggplot")

Data was imported using pandas and the date column set to be datetime.

# Here we imported the data, and set the Date column to be: datetime not object
 office_df=pd.read_csv('the_office_series.csv',parse_dates=['Date'])
 df=office_df.copy()

The function below used to generate hind sights on the data before making further analysis. For instance, we ca observe that GuestStars is the only variable with both null and non null values. In this case the are 29 non null data points.

df.info()
 <class 'pandas.core.frame.DataFrame'>
 RangeIndex: 188 entries, 0 to 187
 Data columns (total 12 columns):
  # Column Non-Null Count Dtype
 --- ------ -------------- -----
  0 Unnamed: 0 188 non-null int64
  1 Season 188 non-null int64
  2 EpisodeTitle 188 non-null object
  3 About 188 non-null object
  4 Ratings 188 non-null float64
  5 Votes 188 non-null int64
  6 Viewership 188 non-null float64
  7 Duration 188 non-null int64
  8 Date 188 non-null datetime64[ns]
  9 GuestStars 29 non-null object
  10 Director 188 non-null object
  11 Writers 188 non-null object
 dtypes: datetime64[ns](1), float64(2), int64(4), object(5)
 memory usage: 17.8+ KB

The function below shows that there are 159 null data points on GuestStars variable.

# Check null values in a dataset
 df.isna().sum()
 Unnamed: 0 0
 Season 0
 EpisodeTitle 0
 About 0
 Ratings 0
 Votes 0
 Viewership 0
 Duration 0
 Date 0
 GuestStars 159
 Director 0
 Writers 0
 dtype: int64

Based on insights of non values, another variable created to slice the that so that we have a variable contains data with guest stars and the another without guest stars.

# create 'has_guests' column that contains boolean values for whether there is a guest star or not
 guest=df.GuestStars.isnull()
 has_guest=[False if i else True for i in guest]
 has_guest
 df['has_guests']=has_guest
 df.head()
 
 # change the name of the first column to be episode_number
 df.rename(columns={'Unnamed: 0':'episode_number'},inplace=True)
 df.head()

To see the relationship between viewership and ratings and guest appearance along with the series, we need to separate the data with colors and size as there are at most 3 dimensions we can draw.

Create colors of red if the scaled rating is less than 0.25, orange if less than 0.5, light green if less than 0.75, and dark green if more than 0.75.

Create different sizes of dots; the size of 250 if the episode contains guest star and 25 if does not.

# Below is the interactive chart which shows viewership across the years
 fig = px.scatter(df, x="Date", y="Viewership",title="Scatter plot displays relationship between Years and viewership",
  color="Coloring", size='Size' )
 
 fig.show()

One outlier (episode) with the scaled rating of more than 7.5 and that contains guest stars has 22.5 million viewers. What is that episode?

df[df['viewership'] == max(df['viewership'])]

episode_number 77
 season 5
 episodetitle Stress Relief
 ratings 9.7
 votes 8170
 viewership 22.91
 duration 60
 date 2009-02-01 00:00:00
 gueststars Cloris Leachman, Jack Black, Jessica Alba
 director Jeffrey Blitz
 writers Paul Lieberstein
 scaled_ratings 0.96875
 has_guest True

The scatter plot below show the association between viewership and episode number.

fig = plt.figure()
 # plotting using scatter plots two dataframes, the first one that contains data
 # with existence of guest stars, The other one for data with no guest stars with
 # different markers
 
 plt.scatter(x=non_guests_df.episode_number,y=non_guests_df.Viewership,
  c=non_guests_df.Coloring,
  s=non_guests_df.Size)
 
 plt.scatter(x=guests_df.episode_number,y=guests_df.Viewership,
  c=guests_df.Coloring,
  s=guests_df.Size,marker='*')
 
 
 plt.xlabel('Episode Number')
 plt.ylabel('Viewership (Millions)')
 plt.title('Popularity, Quality, and Guest Appearances on the Office')
 plt.show()

We can observe the 75 episode number with viewership above 22.5 million as an outlier.

How many episodes in each season

# In this case we count the number of episodes in each season
 e=df.groupby(('Season'),as_index=False).count()
 e=e[['Season','episode_number']]
 e.rename(columns={'episode_number':'NoOfEpisodes'},inplace=True)
 fig = px.bar(e, x="Season", y="NoOfEpisodes",title="Season and Number of episods bar chart")
 fig.show()

There are only 6 episodes in Season 1 and 14 in Season 2.

Relationship between season and rating

# Grouping the data by season and calculate the average of the ratings for each season
 rating=df.groupby('Season')['Ratings'].mean()
 
 fig = px.line( rating,
  title = 'Season Vs Rating line graph')
 
 fig.show()

The line graph above shows that season 3 has the most rating on average whilist season 8 has the least rating.

As we know there is an outlier in the `viewership` column that has guest stars. Even an outlier contains, the mean values are almost the same and viewership does not seem to increase if guest stars are present.