Project: Investigating Guest Stars in The Office

In this project, 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.

In first time we must import this two libraries pandas and matplotlib

import pandas as pd
import matplotlib.pyplot as plt

Now, to get the data and show its summary we use the code below:

plt.rcParams['figure.figsize'] = [11, 7]
office_df = pd.read_csv('datasets/office_episodes.csv')
office_df.head()

Output:

In this project we want create a matplotlib scatter plot of the data that contains specified attributes, so before creating this nuage we must analyze the data.

for each episode a color scheme reflecting the scaled ratings :

• 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"

cols =[]

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')
cols

and a sizing system, such that episodes with guest appearances have a marker size of 250 and episodes without are sized 25

sizes = []

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

Here we define each variable with its informations

office_df['colors'] = cols
office_df['sizes'] = sizes

office_df.info()
non_guest_df = office_df[office_df['has_guests'] == False]
guest_df = office_df[office_df['has_guests'] == True]

Now we will plot the figure of the data as below with

• A title, reading "Popularity, Quality, and Guest Appearances on the Office"

• An x-axis label reading "Episode Number"

• A y-axis label reading "Viewership (Millions)"

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()

Finally, to show the most-watched Office episode we can use this code:

office_df[office_df['viewership_mil'] == office_df['viewership_mil'].max()]['guest_stars']

The result:

Thank you for regarding!

You can find the complete source code here Github