💻 PROJECT #2.2: Visualize Our Class

Overview & Setup

In this project, you will work together to create a Pandas DataFrame containing data on each member of the class, which you will later use to create data visualizations using matplotlib. This assignment has two parts:

  1. Collaborative Data Collection: You will work together as a class to design and collect data into a single DataFrame.
  2. Independent Visualization: You will choose an aspect of the data to create your own data visualization using matplotlib.
  1. Go to the CS3 Project 2.2 assignment on Blackbaud and follow the provided GitHub Classroom link.

    📁 Clicking the link generates a private repository for your project with the appropriate starter code. Note that projects are stored within the BWL-CS Organization, so you cannot access it from the “Your Repositories” page!

  2. Open the repository in a Codespace whenever you spend time working on the program, in class or at home.

    ⚠️ Always remember to commit changes after every coding session!

  3. When your project is complete, submit the link to your repository in the CS3 Project 2.2 assignment on Blackbaud.

Instructions

🐼 Part A: Collaborative Data Collection

In this part, the class will work together to design a Pandas DataFrame that contains data on each student. You will be interviewing your classmates to gather this data.

  1. Choose the Columns for the DataFrame:
    • Decide as a class what kinds of data to collect. This could include basic information such as:
      • Name
      • Age
      • Favorite Subject
      • Number of Siblings
      • Favorite Movie Genre
      • Hours Spent on Hobbies per Week
      • GPA
      • Favorite Sports Team
      • Any other creative data you come up with as a class!
  2. Conduct Interviews:
    • Each student will interview the rest of the classmates to fill in the data for the DataFrame.
    • Make sure you are accurate and respectful when collecting information.
  3. Create the DataFrame:
    • Once all the data is collected, combine it into a Pandas DataFrame. This can be done by sharing the data and using Python code to structure it.
    • Example of creating a simple DataFrame: 
      data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [17, 18, 17],
    'Favorite Subject': ['Math', 'History', 'Biology']
}
df = pd.DataFrame(data)
  4. Save the DataFrame:
    • Save the class DataFrame into a CSV file that everyone can access.
    • Example of saving the DataFrame to a file: 
      df.to_csv('class_data.csv', index=False)

📊 Part B: Independent Data Visualization

Now that we have a class DataFrame, you will each work independently to create your own unique data visualizations. You will choose any subset of the data to visualize using matplotlib. You are free to explore any kind of visualization that helps you understand the data better.

Here are some common types of visualizations that matplotlib can create:

  • Bar Chart: Useful for comparing categories of data.
  • Line Plot: Great for showing trends over time or continuous data.
  • Scatter Plot: Useful for showing the relationship between two variables.
  • Pie Chart: Ideal for showing parts of a whole (distribution of categories).
  • Histogram: Perfect for showing the distribution of a single variable.


  1. Choose Your Focus:
    • Decide what part of the class DataFrame you want to visualize. This could be:
      • A comparison of favorite subjects.
      • A bar chart showing the average GPA of students by their favorite sports teams.
      • A line plot tracking the number of siblings each student has by age.
      • A pie chart showing the distribution of favorite movie genres.
  2. Create Your Visualization:
    • Using matplotlib, create your visualization.
      • You can clean or filter the data as needed.
      • Use plt.savefig('figure1.png', bbox_inches='tight') to save your figure as a PNG.
      • Feel free to explore and create as many figures as you want, but check with me to make sure you generate at least ONE type of visualization that no one else in the class is doing!!!
  3. Interpret the Visualization:
    • In a ''' multi-line comment ''', write a few sentences about what your visualization tells you about the data.
    • What trends, patterns, or insights can you gather from your chart?

Acknowledgement

Content on this page is adapted from How to Think Like a Data Scientist on Runestone Academy - Brad Miller, Jacqueline Boggs, and Jan Pearce.