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Find the guest stars in "The Office"!

Consistent with Wikipedia,

The Office is an American mockumentarysitcom television series that depicts the everyday work lives of office employees in the Scranton, Pennsylvania, branch of the fictional Dunder Mifflin Paper Company. It aired on NBC from March 24, 2005, to May 16, 2013, spanning a total of nine seasons.

In this, blog post, we are going to use our knowledge in data visualization with matplotlib to search out the guest start within the dataset download from Kaggle. We are also going to use pandas to load and manipulate our dataset and lambda function to simplify certain parts of our code. If you're ready, let's start.

1. Import pandas and matplotlib library

Pandas could be a fast, powerful, flexible and simple to use open source data analysis and manipulation tool, built on top of the Python programing language. We generally import it as follow together with his common prefix:

import pandas as pd

Matplotlib may be a comprehensive library for creating static, animated, and interactive visualizations in Python. To import it, use the subsequent code.

import matplotlib.pyplot as plt

2. Load the Office dataset

After importing pandas and matplotlib, let's load data from the CSV file.

# Read data fron csv file
data = pd.read_csv('office_episodes.csv', parse_dates=['release_date'])

The parameter parse_dates allows pandas to parse the date in string format to pandas data type.

3. Creation of an instance matplotlib.pyplot.figure

matplotlib.pyplot.figure() allow us to define the property of the figure,

in other words to customize our draw.

# Instnciate a figure
fig = plt.figure()

# Set figure params
plt.rcParams['figure.figsize'] = [11, 7]

What do rcParams means? Indeed, on every occasion Matplotlib loads, it defines a runtime configuration (rc) containing the default styles for each plot element we create. This configuration is adjusted at any time using the plt.rc convenience routine. The rc settings are stored in an exceedingly dictionary called matplotlib.rcParams, which is global to the matplotlib package. In our case, we only use a parameter that changes the dimensions of our figure.

4. Define the plotting color for a different group of data

A color scheme reflecting the scaled ratings of every episode is defined as follow:

  • 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"

The python code to define the colour list is:

# Define colors for ploting

for _, row in data.iterrows():
    if row['scaled_ratings'] < 0.25:
    elif row['scaled_ratings'] < 0.5:
    elif row['scaled_ratings'] < 0.75:

data['colors'] = colors

The python code data['colors'] = colors directly add a new column in the original office datasets

5. Defining plotting size for certain group of data

A sizing system of plotting follows the principle that episodes with guest appearances have a marker size of 250 and episodes without are sized 25.

To easily solve the matter, we are going to use a lambda and maps function couple with a ternary operator to do that in one line.

# Define size for ploting
data['size'] = data['has_guests'].map(lambda x: 250 if x == True else 25 )

During this line of code :

  • We access has_guests column of our data frame

  • We apply the map function within the previous result to be able to process and transform all the items in an iterable without using an explicit for loop

  • Add the end, we use a lambda function to apply a transformation in each element of the result and a ternary operator to decide the value to return depending on has_guests value

Coupling lambda function and ternary operator, we write less code than normal. To know more about lambda function in python, I wrote one blog post on that subject.

6. Labels our plot

To better understand our scatter plot, we must label it.

# Set plot title  
plt.title("Popularity, Quality, and Guest Appearances on the Office", color="teal", fontweight="bold", fontsize=18)

# Set plot xlabel
plt.xlabel("Episode Number",color="purple", fontsize=20, fontweight="bold", fontstyle="italic")

# Set plot y label
plt.ylabel("Viewership (Millions)", color="red", fontsize=20, fontweight="bold", fontstyle="italic")

In the process of labeling our plot, we can do more customization by settings:

  • The color of the label with the attribute color

  • The size with the attribute fontsize or size where size is in point (eg: 20, 12,) or relative size ( e.g., 'smaller', 'x-large' )

  • The style with the attribute fontstyle or style where the value can be [ 'normal' | 'italic' | 'oblique' ]

  • The weight with attribute weight or fontweight where values are [ 'normal' | 'bold' | 'heavy' | 'light' | 'ultrabold' | 'ultralight']

7. Create a scatter plot

A scatter plot is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data.We can now draw our scatter plot with the following code.

#  Create a scatter plot
plt.scatter(x=data['episode_number'], y=data['viewership_mil'], c=data['colors'], s=data['size'])

# Show the plot

The plt.scatter() method take a lot of parameters among which:

  • size (s) that is a list of sizes to change the size of values in the plot

  • color (c) that is the list of colors to change the color of the points in the plot

  • Shape

  • Transparency

After complete all these steps, our plot looks like this:

8. Find the guest stars


This final code will provide us names of guest stars.

We can find the entire source code here.


  1. 'The Office' series datasets was downloaded from Kaggle here.

  2. Investigating Appearance of Guest Stars in 'The Office' Series here.

  3. DataCamp's Unguided Project: "Investigating Netflix Movies and Guest Stars in The Office" here.

  4. Matplotlib text properties and layouts, here

  5. The_Office_(American_TV_series) (Wikipedia) here

  6. Matplotlib backend management docs here

  7. Matplotlib Configurations and Stylesheets here

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Data Insight
Data Insight
17 oct. 2021

Check your report for grammatical and spelling errors.