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Answer:: Q1: What Advantages Does Seaborn Have Over Other Plotting Libraries When It Co Mes To Statistical Visualization?

Seaborn offers advantages in statistical visualization through its simplicity, aesthetic appeal, and high-level interface, making it easy to create attractive plots. It excels in visualizing complex data types and integrates well with Pandas for DataFrame handling. To manage outliers, Seaborn provides methods such as visualization, filtering, transformations, and capping to mitigate their effects.

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13 views1 page

Answer:: Q1: What Advantages Does Seaborn Have Over Other Plotting Libraries When It Co Mes To Statistical Visualization?

Seaborn offers advantages in statistical visualization through its simplicity, aesthetic appeal, and high-level interface, making it easy to create attractive plots. It excels in visualizing complex data types and integrates well with Pandas for DataFrame handling. To manage outliers, Seaborn provides methods such as visualization, filtering, transformations, and capping to mitigate their effects.

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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Q1: What advantages does Seaborn have over other plotting libraries when it co

mes to statistical visualization?


Answer: Seaborn stands out for its simplicity and aesthetic appeal, making it easie
r to create attractive statistical plots without needing extensive customization. It’s
particularly good for complex visualizations like heatmaps, time series, and distrib
ution plots, thanks to its high-
level interface and better default themes. Plus, Seaborn integrates seamlessly with
Pandas, making it easier to handle DataFrames directly.
Q2: How do you deal with outliers in Seaborn? Please elaborate on some metho
ds.
Answer: Dealing with outliers in Seaborn can be approached in several ways:
• Visualization: First, identify outliers using plots like boxplots or scatter plots.
Seaborn's boxplot and scatterplot functions are handy here.
• Filtering: Remove outliers based on statistical methods (e.g., IQR rule) befor
e plotting. This can be done using Pandas to filter the DataFrame.
• Transformations: Apply transformations (like log or square root) to reduce t
he impact of outliers.
• Capping : Set limits to cap extreme values to a maximum threshold.

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