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3D Scatter Plots in Python

How to make 3D scatter plots in Python with Plotly.


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 scatter 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.

Like the 2D scatter plot px.scatter, the 3D function px.scatter_3d plots individual data in three-dimensional space.

In [1]:
import plotly.express as px
df = px.data.iris()
fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_width',
              color='species')
fig.show()

A 4th dimension of the data can be represented thanks to the color of the markers. Also, values from the species column are used below to assign symbols to markers.

In [2]:
import plotly.express as px
df = px.data.iris()
fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_width',
                    color='petal_length', symbol='species')
fig.show()

Style 3d scatter plot

It is possible to customize the style of the figure through the parameters of px.scatter_3d for some options, or by updating the traces or the layout of the figure through fig.update.

In [3]:
import plotly.express as px
df = px.data.iris()
fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_width',
              color='petal_length', size='petal_length', size_max=18,
              symbol='species', opacity=0.7)

# tight layout
fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))

3D Scatter Plot with go.Scatter3d

Basic 3D Scatter Plot

If Plotly Express does not provide a good starting point, it is also possible to use the more generic go.Scatter3D class from plotly.graph_objects. Like the 2D scatter plot go.Scatter, go.Scatter3d plots individual data in three-dimensional space.

In [4]:
import plotly.graph_objects as go
import numpy as np

# Helix equation
t = np.linspace(0, 10, 50)
x, y, z = np.cos(t), np.sin(t), t

fig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z,
                                   mode='markers')])
fig.show()

3D Scatter Plot with Colorscaling and Marker Styling

In [5]:
import plotly.graph_objects as go
import numpy as np

# Helix equation
t = np.linspace(0, 20, 100)
x, y, z = np.cos(t), np.sin(t), t

fig = go.Figure(data=[go.Scatter3d(
    x=x,
    y=y,
    z=z,
    mode='markers',
    marker=dict(
        size=12,
        color=z,                # set color to an array/list of desired values
        colorscale='Viridis',   # choose a colorscale
        opacity=0.8
    )
)])

# tight layout
fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))
fig.show()

Reference

See https://plotly.com/python/reference/scatter3d/ 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