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Violin Plots in Python

How to make violin plots in Python with Plotly.


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New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Violin Plot with Plotly Express

A violin plot is a statistical representation of numerical data. It is similar to a box plot, with the addition of a rotated kernel density plot on each side.

See also the list of other statistical charts.

Basic Violin Plot with Plotly Express

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.

In [1]:
import plotly.express as px

df = px.data.tips()
fig = px.violin(df, y="total_bill")
fig.show()

Violin plot with box and data points

In [2]:
import plotly.express as px

df = px.data.tips()
fig = px.violin(df, y="total_bill", box=True, # draw box plot inside the violin
                points='all', # can be 'outliers', or False
               )
fig.show()

Multiple Violin Plots

In [3]:
import plotly.express as px

df = px.data.tips()
fig = px.violin(df, y="tip", x="smoker", color="sex", box=True, points="all",
          hover_data=df.columns)
fig.show()
In [4]:
import plotly.express as px

df = px.data.tips()
fig = px.violin(df, y="tip", color="sex",
                violinmode='overlay', # draw violins on top of each other
                # default violinmode is 'group' as in example above
                hover_data=df.columns)
fig.show()

Violin Plot with go.Violin

If Plotly Express does not provide a good starting point, you can use the more generic go.Violin class from plotly.graph_objects. All the options of go.Violin are documented in the reference https://plotly.com/python/reference/violin/

Basic Violin Plot

In [5]:
import plotly.graph_objects as go

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/violin_data.csv")

fig = go.Figure(data=go.Violin(y=df['total_bill'], box_visible=True, line_color='black',
                               meanline_visible=True, fillcolor='lightseagreen', opacity=0.6,
                               x0='Total Bill'))

fig.update_layout(yaxis_zeroline=False)
fig.show()

Multiple Traces

In [6]:
import plotly.graph_objects as go

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/violin_data.csv")

fig = go.Figure()

days = ['Thur', 'Fri', 'Sat', 'Sun']

for day in days:
    fig.add_trace(go.Violin(x=df['day'][df['day'] == day],
                            y=df['total_bill'][df['day'] == day],
                            name=day,
                            box_visible=True,
                            meanline_visible=True))

fig.show()

Grouped Violin Plot

In [7]:
import plotly.graph_objects as go

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/violin_data.csv")

fig = go.Figure()

fig.add_trace(go.Violin(x=df['day'][ df['sex'] == 'Male' ],
                        y=df['total_bill'][ df['sex'] == 'Male' ],
                        legendgroup='M', scalegroup='M', name='M',
                        line_color='blue')
             )
fig.add_trace(go.Violin(x=df['day'][ df['sex'] == 'Female' ],
                        y=df['total_bill'][ df['sex'] == 'Female' ],
                        legendgroup='F', scalegroup='F', name='F',
                        line_color='orange')
             )

fig.update_traces(box_visible=True, meanline_visible=True)
fig.update_layout(violinmode='group')
fig.show()

Split Violin Plot

In [8]:
import plotly.graph_objects as go

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/violin_data.csv")

fig = go.Figure()

fig.add_trace(go.Violin(x=df['day'][ df['smoker'] == 'Yes' ],
                        y=df['total_bill'][ df['smoker'] == 'Yes' ],
                        legendgroup='Yes', scalegroup='Yes', name='Yes',
                        side='negative',
                        line_color='blue')
             )
fig.add_trace(go.Violin(x=df['day'][ df['smoker'] == 'No' ],
                        y=df['total_bill'][ df['smoker'] == 'No' ],
                        legendgroup='No', scalegroup='No', name='No',
                        side='positive',
                        line_color='orange')
             )
fig.update_traces(meanline_visible=True)
fig.update_layout(violingap=0, violinmode='overlay')
fig.show()

Advanced Violin Plot

In [9]:
import plotly.graph_objects as go

import pandas as pd

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/violin_data.csv")

pointpos_male = [-0.9,-1.1,-0.6,-0.3]
pointpos_female = [0.45,0.55,1,0.4]
show_legend = [True,False,False,False]

fig = go.Figure()

for i in range(0,len(pd.unique(df['day']))):
    fig.add_trace(go.Violin(x=df['day'][(df['sex'] == 'Male') &
                                        (df['day'] == pd.unique(df['day'])[i])],
                            y=df['total_bill'][(df['sex'] == 'Male')&
                                               (df['day'] == pd.unique(df['day'])[i])],
                            legendgroup='M', scalegroup='M', name='M',
                            side='negative',
                            pointpos=pointpos_male[i], # where to position points
                            line_color='lightseagreen',
                            showlegend=show_legend[i])
             )
    fig.add_trace(go.Violin(x=df['day'][(df['sex'] == 'Female') &
                                        (df['day'] == pd.unique(df['day'])[i])],
                            y=df['total_bill'][(df['sex'] == 'Female')&
                                               (df['day'] == pd.unique(df['day'])[i])],
                            legendgroup='F', scalegroup='F', name='F',
                            side='positive',
                            pointpos=pointpos_female[i],
                            line_color='mediumpurple',
                            showlegend=show_legend[i])
             )

# update characteristics shared by all traces
fig.update_traces(meanline_visible=True,
                  points='all', # show all points
                  jitter=0.05,  # add some jitter on points for better visibility
                  scalemode='count') #scale violin plot area with total count
fig.update_layout(
    title_text="Total bill distribution<br><i>scaled by number of bills per gender",
    violingap=0, violingroupgap=0, violinmode='overlay')
fig.show()

Ridgeline plot

A ridgeline plot (previously known as Joy Plot) shows the distribution of a numerical value for several groups. They can be used for visualizing changes in distributions over time or space.

In [10]:
import plotly.graph_objects as go
from plotly.colors import n_colors
import numpy as np
np.random.seed(1)

# 12 sets of normal distributed random data, with increasing mean and standard deviation
data = (np.linspace(1, 2, 12)[:, np.newaxis] * np.random.randn(12, 200) +
            (np.arange(12) + 2 * np.random.random(12))[:, np.newaxis])

colors = n_colors('rgb(5, 200, 200)', 'rgb(200, 10, 10)', 12, colortype='rgb')

fig = go.Figure()
for data_line, color in zip(data, colors):
    fig.add_trace(go.Violin(x=data_line, line_color=color))

fig.update_traces(orientation='h', side='positive', width=3, points=False)
fig.update_layout(xaxis_showgrid=False, xaxis_zeroline=False)
fig.show()

Violin Plot With Only Points

A strip chart is like a violin plot with points showing, and no violin:

In [11]:
import plotly.express as px
df = px.data.tips()
fig = px.strip(df, x='day', y='tip')
fig.show()

Reference

See function reference for px.violin() or https://plotly.com/python/reference/violin/ for more information and chart attribute options!

What About Dash?

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter