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3D Bubble Charts in Python

How to make 3D Bubble Charts in Python with Plotly. Three examples of 3D Bubble Charts.


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.

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()

Simple Bubble Chart

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='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-marker-sizeref
        size = planet_diameter,
        color = planet_colors,
        )
))

fig.update_layout(width=800, height=800, title = 'Planets!',
                  scene = dict(xaxis=dict(title='Distance from Sun', titlefont_color='white'),
                               yaxis=dict(title='Density', titlefont_color='white'),
                               zaxis=dict(title='Gravity', titlefont_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-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 = 'Planets!',
                  scene = dict(xaxis=dict(title='Distance from Sun', titlefont_color='white'),
                               yaxis=dict(title='Density', titlefont_color='white'),
                               zaxis=dict(title='Gravity', titlefont_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( ... )

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