Lære Exploring Data with Seaborn | Data Visualization for Makers

Seaborn is a powerful Python library designed to make data visualization simple and attractive, especially for creators and makers who want to quickly understand their projects. Built on top of matplotlib, Seaborn provides high-level functions for drawing informative and beautiful statistical graphics with just a few lines of code. Its built-in styles and color palettes help you present your data in a visually appealing way without needing to spend time on complex customization. Whether you are tracking sensor readings, comparing design prototypes, or analyzing results from creative experiments, Seaborn makes it easy to spot trends and patterns at a glance.


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import seaborn as sns
import matplotlib.pyplot as plt

# Sample project measurements: width and height of 8 prototypes
data = {
    "width": [12, 15, 14, 10, 13, 16, 15, 11],
    "height": [22, 25, 23, 19, 21, 28, 26, 20]
}

# Create a scatter plot of width vs height
sns.scatterplot(x="width", y="height", data=data)
plt.title("Prototype Measurements: Width vs Height")
plt.xlabel("Width (cm)")
plt.ylabel("Height (cm)")
plt.show()

With Seaborn, you can create clear and attractive charts using minimal code. In the scatter plot above, you simply pass your data and specify which columns to plot—Seaborn automatically applies a modern style and sensible defaults. This means you spend less time tweaking visuals and more time focusing on your creative projects. Seaborn's built-in themes and color palettes enhance your plots, making them easier to interpret and more visually engaging, which is especially helpful when sharing your results with others.


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import seaborn as sns
import matplotlib.pyplot as plt

# Sample results from three design iterations
data = {
    "iteration": ["A", "A", "A", "B", "B", "B", "C", "C", "C"],
    "score": [7.2, 6.8, 7.5, 8.0, 8.3, 7.8, 6.5, 6.9, 7.0]
}

# Create a boxplot to compare scores across iterations
sns.boxplot(x="iteration", y="score", data=data)
plt.title("Design Iteration Scores")
plt.xlabel("Iteration")
plt.ylabel("Performance Score")
plt.show()

1. What is a key benefit of using Seaborn over plain matplotlib?

2. Which type of plot is useful for visualizing the distribution of data?

3. Fill in the blank: To create a scatter plot in Seaborn, you use the ________ function.

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Suggested prompts:

What other types of plots can I create with Seaborn?

How can I customize the appearance of my Seaborn charts?

Can you explain how to interpret a boxplot?

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