Seaborn vs. Matplotlib

Seaborn and Matplotlib are tightly connected — Seaborn is actually built on top of Matplotlib.

Both can produce a wide range of visualizations, but they differ in ease of use, default styling, and intended purpose.

• Low-level control – Allows full customization of every plot element.
• Flexible but verbose – Requires more code to produce refined visuals.
• General-purpose – Suitable for all types of plots, including non-statistical ones.
• Foundation for others – Many libraries, including Seaborn, use Matplotlib as their core 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 – Charts look polished and modern without manual styling.
• Concise syntax – Complex visualizations often need only one line of code.
• Statistical focus – Includes tools for distributions, regressions, and comparisons.
• Pandas integration – Works directly with DataFrames, no manual unpacking required.

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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 deep customization or are working on non-statistical plots.
• Use Seaborn when you want quick, visually appealing, and statistically focused charts with minimal code.