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Matplotlib is a customizable 2D plotting library in Python, while Seaborn, built on Matplotlib, offers more visually appealing and easier-to-use statistical graphics. Seaborn is preferred when visual aesthetics and the simplicity of creating complex visualizations are important, particularly for statistical data. The FacetGrid feature in Seaborn enables the creation of a grid of plots based on categorical data, making it ideal for comparing trends across different subsets.

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17 views1 page

Answer

Matplotlib is a customizable 2D plotting library in Python, while Seaborn, built on Matplotlib, offers more visually appealing and easier-to-use statistical graphics. Seaborn is preferred when visual aesthetics and the simplicity of creating complex visualizations are important, particularly for statistical data. The FacetGrid feature in Seaborn enables the creation of a grid of plots based on categorical data, making it ideal for comparing trends across different subsets.

Uploaded by

anuj rawat
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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Q1: Can you differentiate between Matplotlib and Seaborn?

Under what circums


tances would you prefer to use Seaborn?
Answer: Matplotlib is the foundational tool for 2D plotting in Python, highly custo
mizable but sometimes requiring detailed manual tweaking. Seaborn, built on Mat
plotlib, provides more attractive, statistical graphics with better defaults and simpl
er, high-
level functions. When to use Seaborn: When visual appeal and ease of creating co
mplex visualizations matter, especially for statistical data.
Q2: Explain the concept of a Seaborn 'FacetGrid'. How would you utilize it in a co
mplex data visualization task?
Answer:FacetGrid in Seaborn allows creating a grid of plots based on categorical d
ata, perfect for visualizing data distribution across multiple subsets. For instance, i
t’s useful for comparing trends in sales data for different products over time, maki
ng patterns and outliers easier to spot.

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