Seaborn vs. Matplotlib

Seaborn and Matplotlib are closely related. In fact, Seaborn is built on top of Matplotlib.

Both libraries can create many types of visualizations, but they differ in ease of use, default styles, and main purpose.

• Low-level control: Lets you customize every part of the plot in detail.
• Flexible but verbose: Often requires more code to achieve a polished result.
• General purpose: Works well for all types of plots, including non-statistical ones.
• Base for other libraries: Many libraries, including Seaborn, rely 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: Charts look polished without extra styling.
• Less code: Complex visualizations often need just one function call.
• Built for statistics: Includes built-in support for statistical plots like distributions and regressions.
• Works directly with Pandas: 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 are working with non-statistical plots.
• Use Seaborn when you want quick, stylish, and statistical visualizations with less code.