Seaborn is an amazing visualization library for statistical graphics plotting in Python. It provides beautiful default styles and color palettes to make statistical plots more attractive. It is built on the top of matplotlib library and also closely integrated into the data structures from pandas.
A violin plot plays a similar activity that is pursued through whisker or box plot do. As it shows several quantitative data across one or more categorical variables. It can be an effective and attractive way to show multiple data at several units. A “wide-form” Data Frame helps to maintain each numeric column which can be plotted on the graph. It is possible to use NumPy or Python objects, but pandas objects are preferable because the associated names will be used to annotate the axes.
Syntax: seaborn.violinplot(x=None, y=None, hue=None, data=None, order=None, hue_order=None, bw=’scott’, cut=2, scale=’area’, scale_hue=True, gridsize=100, width=0.8, inner=’box’, split=False, dodge=True, orient=None, linewidth=None, color=None, palette=None, saturation=0.75, ax=None, **kwargs)
x, y, hue: Inputs for plotting long-form data.
data: Dataset for plotting.
scale: The method used to scale the width of each violin.
Returns: This method returns the Axes object with the plot drawn onto it.
Example 1: Basic visualization of “fmri” dataset using violinplot()
Example 2: Grouping data points on the basis of category, here as region and event.
Example 3: Basic visualization of “tips” dataset using lineplot()
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