How to make violin plots in Python with Plotly.
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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()
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()
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()
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()
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()
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()
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()Choosing The Algorithm For Computing Quartiles¶
New in 5.10
By default, quartiles for violin plots are computed using the linear method (for more about linear interpolation, see #10 listed on http://jse.amstat.org/v14n3/langford.html and https://en.wikipedia.org/wiki/Quartile for more details).
However, you can also choose to use an exclusive or an inclusive algorithm to compute quartiles.
The exclusive algorithm uses the median to divide the ordered dataset into two halves. If the sample is odd, it does not include the median in either half. Q1 is then the median of the lower half and Q3 is the median of the upper half.
The inclusive algorithm also uses the median to divide the ordered dataset into two halves, but if the sample is odd, it includes the median in both halves. Q1 is then the median of the lower half and Q3 the median of the upper half.
In [12]:
import plotly.express as px df = px.data.tips() fig = px.violin(df, y="total_bill") fig.update_traces(quartilemethod="exclusive") # or "inclusive", or "linear" by default fig.show()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( ... )
from dash import Dash, dcc, html
app = Dash()
app.layout = html.Div([
dcc.Graph(figure=fig)
])
app.run(debug=True, use_reloader=False) # Turn off reloader if inside Jupyter
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