How to make 3D Bubble Charts in Python with Plotly. Three examples of 3D Bubble Charts.
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3d Bubble chart with Plotly Express¶In [1]:
import plotly.express as px
import numpy as np
df = px.data.gapminder()
fig = px.scatter_3d(df, x='year', y='continent', z='pop', size='gdpPercap', color='lifeExp',
hover_data=['country'])
fig.update_layout(scene_zaxis_type="log")
fig.show()
In [2]:
import plotly.graph_objects as go
import pandas as pd
# Get Data: this ex will only use part of it (i.e. rows 750-1500)
df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv')
start, end = 750, 1500
fig = go.Figure(data=go.Scatter3d(
x=df['year'][start:end],
y=df['continent'][start:end],
z=df['pop'][start:end],
text=df['country'][start:end],
mode='markers',
marker=dict(
sizemode='diameter',
sizeref=750,
size=df['gdpPercap'][start:end],
color = df['lifeExp'][start:end],
colorscale = 'Viridis',
colorbar_title = 'Life<br>Expectancy',
line_color='rgb(140, 140, 170)'
)
))
fig.update_layout(height=800, width=800,
title=dict(text='Examining Population and Life Expectancy Over Time'))
fig.show()
Bubble Chart Sized by a Variable¶
Plot planets' distance from sun, density, and gravity with bubble size based on planet size
In [3]:
import plotly.graph_objects as go
planets = ['Mercury', 'Venus', 'Earth', 'Mars', 'Jupiter', 'Saturn', 'Uranus', 'Neptune', 'Pluto']
planet_colors = ['rgb(135, 135, 125)', 'rgb(210, 50, 0)', 'rgb(50, 90, 255)',
'rgb(178, 0, 0)', 'rgb(235, 235, 210)', 'rgb(235, 205, 130)',
'rgb(55, 255, 217)', 'rgb(38, 0, 171)', 'rgb(255, 255, 255)']
distance_from_sun = [57.9, 108.2, 149.6, 227.9, 778.6, 1433.5, 2872.5, 4495.1, 5906.4]
density = [5427, 5243, 5514, 3933, 1326, 687, 1271, 1638, 2095]
gravity = [3.7, 8.9, 9.8, 3.7, 23.1, 9.0, 8.7, 11.0, 0.7]
planet_diameter = [4879, 12104, 12756, 6792, 142984, 120536, 51118, 49528, 2370]
# Create trace, sizing bubbles by planet diameter
fig = go.Figure(data=go.Scatter3d(
x = distance_from_sun,
y = density,
z = gravity,
text = planets,
mode = 'markers',
marker = dict(
sizemode = 'diameter',
sizeref = 750, # info on sizeref: https://plotly.com/python/reference/scatter/#scatter-marker-sizeref
size = planet_diameter,
color = planet_colors,
)
))
fig.update_layout(
width=800,
height=800,
title=dict(text="Planets!"),
scene=dict(
xaxis=dict(
title=dict(
text="Distance from Sun",
font=dict(
color="white"
)
)
),
yaxis=dict(
title=dict(
text="Density",
font=dict(
color="white"
)
)
),
zaxis=dict(
title=dict(
text="Gravity",
font=dict(
color="white"
)
)
),
bgcolor="rgb(20, 24, 54)"
)
)
fig.show()
Edit the Colorbar¶
Plot planets' distance from sun, density, and gravity with bubble size based on planet size
In [4]:
import plotly.graph_objects as go
planets = ['Mercury', 'Venus', 'Earth', 'Mars', 'Jupiter', 'Saturn', 'Uranus', 'Neptune', 'Pluto']
temperatures = [167, 464, 15, -20, -65, -110, -140, -195, -200, -225]
distance_from_sun = [57.9, 108.2, 149.6, 227.9, 778.6, 1433.5, 2872.5, 4495.1, 5906.4]
density = [5427, 5243, 5514, 3933, 1326, 687, 1271, 1638, 2095]
gravity = [3.7, 8.9, 9.8, 3.7, 23.1, 9.0, 8.7, 11.0, 0.7]
planet_diameter = [4879, 12104, 12756, 6792, 142984, 120536, 51118, 49528, 2370]
# Create trace, sizing bubbles by planet diameter
fig = go.Figure(go.Scatter3d(
x = distance_from_sun,
y = density,
z = gravity,
text = planets,
mode = 'markers',
marker = dict(
sizemode = 'diameter',
sizeref = 750, # info on sizeref: https://plotly.com/python/reference/scatter/#scatter-marker-sizeref
size = planet_diameter,
color = temperatures,
colorbar_title = 'Mean<br>Temperature',
colorscale=[[0, 'rgb(5, 10, 172)'], [.3, 'rgb(255, 255, 255)'], [1, 'rgb(178, 10, 28)']]
)
))
fig.update_layout(
width=800,
height=800,
title=dict(text="Planets!"),
scene=dict(
xaxis=dict(
title=dict(
text="Distance from Sun",
font=dict(
color="white"
)
)
),
yaxis=dict(
title=dict(
text="Density",
font=dict(
color="white"
)
)
),
zaxis=dict(
title=dict(
text="Gravity",
font=dict(
color="white"
)
)
),
bgcolor="rgb(20, 24, 54)"
)
)
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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