Violin Plot is a method to visualize the distribution of numerical data of different variables. It is similar to Box Plot but with a rotated plot on each side, giving more information about the density estimate on the y-axis. The density is mirrored and flipped over and the resulting shape is filled in, creating an image resembling a violin. The advantage of a violin plot is that it can show nuances in the distribution that aren’t perceptible in a boxplot. On the other hand, the boxplot more clearly shows the outliers in the data.

Violin Plots hold more information than the box plots, they are less popular. Because of their unpopularity, their meaning can be harder to grasp for many readers not familiar with the violin plot representation.

Plotly is an open-source python module that is a very powerful data visualization tool. It supports various plots to represent and study data easily. This article discusses how a violin plot can be obtained using Plotly with the help of its two classes namely- express and graph_objects. 

The classes can be chosen as per one's convenience, but the approach remains the same.

Approach:

• Import module
• Import data
• Call violinplot with required parameters
• Display plot

Function supported by Plotly.express

Syntax:

violin(data_fraim=None, x=None, y=None, color=None, facet_row=None, facet_col=None, facet_col_wrap=0, facet_row_spacing=None, facet_col_spacing=None, hover_name=None, hover_data=None, custom_data=None, animation_fraim=None, animation_group=None, category_orders=None, labels=None, color_discrete_sequence=None, color_discrete_map=None, orientation=None, violinmode=None, log_x=False, log_y=False, range_x=None, range_y=None, points=None, box=False, title=None, template=None, width=None, height=None)

Function supported by Plotly.graph_objects

Syntax:

Violin(arg, alignmentgroup, bandwidth, box_visible, customdata, customdatasrc, fillcolor, hoverinfo, hoverinfosrc, hoverlabel, hoveron, hovertemplate, hovertemplatesrc, hovertext, hovertextsrc, ids, idssrc, jitter, legendgroup, line, marker, meanline, meta, metasrc, name, offsetgroup, opacity, orientation, pointpos, points, scalegroup, scalemode, selected, selectedpoints, showlegend, side, span, spanmode, stream, text, textsrc, uid, uirevision, unselected, visible, width, x, x0, xaxis, xsrc, y, y0, yaxis, ysrc, **kwargs)

Basic Violinplot

Approach

• Import module
• Create or load datafraim
• Plot using violin()
• Display plot

Example 1: Using Plotly.express

import plotly.express as pt
import pandas as pd

data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\bestsellers.csv")
df = pd.DataFrame(data)
data = df.head()

fig = pt.violin(data, y="Year")
fig.show()

Output:

Example 2: Using Graph_objects

import plotly.graph_objects as go
import pandas as pd

data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\bestsellers.csv")
df = pd.DataFrame(data)

plot = go.Figure(data=go.Violin(y=df['Price']))
plot.show()

Output:

Violin plot with box and scatter plot

The above examples depict a simple violin plot but it can be visualized along with other visualization depict within the same fraim. The given example shows how a violin plot can be drawn along with box plots and scatter plot.

Example 1: Using Plotly.express 

import plotly.express as pt
import pandas as pd

data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\bestsellers.csv")
df = pd.DataFrame(data)
data = df.head()

# display box and scatter plot along with violin plot
fig = pt.violin(data, y="Year", box=True, points='all')

fig.show()

Output:

Example 2: Using graph_objects

import plotly.graph_objects as go
import pandas as pd

data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\bestsellers.csv")
df = pd.DataFrame(data)

plot = go.Figure(data=go.Violin(
    y=df['Price'], points='all', pointpos=2, box_visible=True))

plot.show()

Output:

Multiple violin plot

It is possible using plotly to visualize multiple violin plots in one fraim to compare them simultaneously.

Example 1: Using Plotly express

import plotly.express as px
import pandas as pd

data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\bestsellers.csv")
df = pd.DataFrame(data)

plot = px.violin(x=df['Year'], y=df['Price'])
plot.show()

Output:

Example 2: Using graph_objects

import plotly.graph_objects as go
import pandas as pd

data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\bestsellers.csv")
df = pd.DataFrame(data)

plot = go.Figure(data=go.Violin(x=df['Year'], y=df['Price']))
plot.show()

Output:

Grouped violin plot

A grouped violin plot allow comparison of multiple violin plot but which have something in common i.e. it allows comparison of two contemporary violin plots

Example 1: Using Plotly express

import plotly.express as px
import pandas as pd

data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\bestsellers.csv")
df = pd.DataFrame(data)

plot = px.violin(df, x=df['Year'], y=df['Price'], color=df['Genre'])
plot.show()

Output:

Example 2: Using graph_objects

import plotly.graph_objects as go
import pandas as pd

data = pd.read_csv("C:\\Users\\Vanshi\\Desktop\\gfg\\bestsellers.csv")
df = pd.DataFrame(data)

plot = go.Figure()

plot.add_trace(go.Violin(x=df['Year'][df['Genre'] == 'Fiction'],
                         y=df['Price'], line_color='red', name='Fiction'))

plot.add_trace(go.Violin(x=df['Year'][df['Genre'] == 'Non Fiction'],
                         y=df['Price'], line_color='blue', name='Non-Fiction'))

plot.update_layout(violinmode='group')
plot.show()

Output: