Python Data Analysis Bootcamp class 4 - 07 Seaborn BoxenPlot

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A boxenplot in Seaborn is a valuable choice when you want to explore and visualize the distribution of a continuous variable across different categories or groups, especially when you have a large dataset with complex distributions. It excels at providing a detailed view of data distribution, emphasizing the tails and outliers, making it suitable for identifying extreme values and comparing distribution shapes across categories. Boxenplots are particularly useful in data science when you need a robust visualization tool to uncover fine-grained distribution characteristics and outliers within your data.

Arguments of Interests:
scale (optional): Control how the width of the boxen plots is computed. Options include "linear" (default) and "exponential."

linewidth (optional): Set the linewidth of the boxen plot elements.

outlier_prop (optional): Control the proportion of data points shown as "outliers" on the boxen plot. It can be used to adjust the level of detail for outlier display.

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