Python for Data Visualization with Matplotlib

Python for Data Visualization with Matplotlib

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Who is this for?

• Beginners to Computer Science: If you're new to Python and want to learn how to visualize data effectively
• Aspiring Data Scientists: If you're looking to build foundational skills in data analysis and visualization
• Software Developers: If you need to present data insights in a clear and engaging way.
• Researchers & Analysts: If you want to use Python to turn raw data into meaningful charts.

No prior experience with Matplotlib is required! All that is needed is basic Python knowledge.

Next

Next

Getting Started

1. Download Python following instructions here:
2. Open your terminal (Command Prompt, PowerShell, or macOS Terminal)
3. Run: pip install matplotlib
4. Download and open a Jupyter Notebook :
5. In the first line verify installation by running: import matplotlib.pyplot as plt

When you are finished, you should have something that looks like this:

Numpy Installation

Customization

Challenge #1: Creating a Line Chart

Create a line chart titled "Company Sales Over Time" in your Jupyter Notebook demonstrating the following data:years = [2018, 2019, 2020, 2021, 2022] sales = [10, 20, 15, 25, 30] In this case sales are measured in increments of $1000 Be creative and add personalized touches to your chart.

Possible Solution

Customization

Challenge #2: Creating a Bar Chart

Create a bar chart titled "Product Sales Performance" in your Jupyter Notebook to visualize sales data for five different products. Your chart should include:

• Product Categories: ["Product A", "Product B", "Product C", "Product D", "Product E"]
• Sales Data (in units sold): [250, 400, 320, 150, 500]
• Title and labels

Possible Solution

Customization

Challenge #3: Creating a Scatter Plot

Create a scatter plot titled "Advertising Spend vs. Revenue" in your Jupyter Notebook to visualize the relationship between advertising budget and revenue. Your scatter plot should include:

• Advertising Spend (Independent Variable, x-axis, in $1000s): [5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75]
• Revenue (Dependent Variable, y-axis, in $1000s): [20, 25, 30, 45, 50, 55, 70, 72, 80, 90, 95, 110, 120, 130, 140]
• Title & Labels + Customization

Possible Solution

Congratulations!