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Practice

Seaborn vs. Matplotlib


Seaborn and Matplotlib are closely related — in fact, Seaborn is built on top of Matplotlib.
Both can create a wide variety of visualizations, but their approach, defaults, and ease of use differ.


Matplotlib: The Foundation​

  • Low-Level Control – Allows fine-grained adjustments to every plot element.
  • Flexible but Verbose – Requires more lines of code for styling and complex layouts.
  • General Purpose – Suitable for all types of plots, even non-statistical ones.
  • Base for Other Libraries – Many libraries, including Seaborn, depend on Matplotlib’s plotting engine.
Matplotlib Example
import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [10, 15, 8, 12]

plt.plot(x, y)
plt.title("Matplotlib Line Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Seaborn: The High-Level Wrapper​

  • Beautiful Defaults – Plots look polished right out of the box.
  • Less Code – Many complex visualizations require just one function call.
  • Statistics-Friendly – Includes built-in support for statistical analysis and specialized plot types.
  • Works with Pandas Directly – Easily accepts DataFrames without manual unpacking.
Seaborn Example
import seaborn as sns

tips = sns.load_dataset("tips")
sns.lineplot(data=tips, x="size", y="total_bill")

When to Use Each​

  • Use Matplotlib when you need full customization or non-statistical plots.
  • Use Seaborn when you want quick, stylish, and statistical visualizations with less code.

What’s Next?​

Next, we’ll start creating categorical plots in Seaborn, such as barplot() and countplot(), to visualize category-based data.

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