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.

• 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.

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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()

• 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.

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import seaborn as sns

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

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

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