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3D Axes in Python

How to format axes of 3d plots in Python with Plotly.


If you're using Dash Enterprise's Data Science Workspaces, you can copy/paste any of these cells into a Workspace Jupyter notebook.
Alternatively, download this entire tutorial as a Jupyter notebook and import it into your Workspace.
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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.

Range of axes

3D figures have an attribute in layout called scene, which contains attributes such as xaxis, yaxis and zaxis parameters, in order to set the range, title, ticks, color etc. of the axes.

For creating 3D charts, see this page.

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

N = 70

fig = go.Figure(data=[go.Mesh3d(x=(70*np.random.randn(N)),
                   y=(55*np.random.randn(N)),
                   z=(40*np.random.randn(N)),
                   opacity=0.5,
                   color='rgba(244,22,100,0.6)'
                  )])

fig.update_layout(
    scene = dict(
        xaxis = dict(nticks=4, range=[-100,100],),
                     yaxis = dict(nticks=4, range=[-50,100],),
                     zaxis = dict(nticks=4, range=[-100,100],),),
    width=700,
    margin=dict(r=20, l=10, b=10, t=10))

fig.show()

Fixed Ratio Axes

In [2]:
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np

N = 50

fig = make_subplots(rows=2, cols=2,
                    specs=[[{'is_3d': True}, {'is_3d': True}],
                           [{'is_3d': True}, {'is_3d': True}]],
                    print_grid=False)
for i in [1,2]:
    for j in [1,2]:
        fig.append_trace(
            go.Mesh3d(
                x=(60*np.random.randn(N)),
                y=(25*np.random.randn(N)),
                z=(40*np.random.randn(N)),
                opacity=0.5,
              ),
            row=i, col=j)

fig.update_layout(width=700, margin=dict(r=10, l=10, b=10, t=10))
# fix the ratio in the top left subplot to be a cube
fig.update_layout(scene_aspectmode='cube')
# manually force the z-axis to appear twice as big as the other two
fig.update_layout(scene2_aspectmode='manual',
                  scene2_aspectratio=dict(x=1, y=1, z=2))
# draw axes in proportion to the proportion of their ranges
fig.update_layout(scene3_aspectmode='data')
# automatically produce something that is well proportioned using 'data' as the default
fig.update_layout(scene4_aspectmode='auto')
fig.show()

Set Axes Title

In [3]:
import plotly.graph_objects as go
import numpy as np

# Define random surface
N = 50
fig = go.Figure()
fig.add_trace(go.Mesh3d(x=(60*np.random.randn(N)),
                   y=(25*np.random.randn(N)),
                   z=(40*np.random.randn(N)),
                   opacity=0.5,
                   color='yellow'
                  ))
fig.add_trace(go.Mesh3d(x=(70*np.random.randn(N)),
                   y=(55*np.random.randn(N)),
                   z=(30*np.random.randn(N)),
                   opacity=0.5,
                   color='pink'
                  ))

fig.update_layout(scene = dict(
                    xaxis_title='X AXIS TITLE',
                    yaxis_title='Y AXIS TITLE',
                    zaxis_title='Z AXIS TITLE'),
                    width=700,
                    margin=dict(r=20, b=10, l=10, t=10))

fig.show()

Ticks Formatting

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

# Define random surface
N = 50
fig = go.Figure(data=[go.Mesh3d(x=(60*np.random.randn(N)),
                   y=(25*np.random.randn(N)),
                   z=(40*np.random.randn(N)),
                   opacity=0.5,
                   color='rgba(100,22,200,0.5)'
                  )])

# Different types of customized ticks
fig.update_layout(scene = dict(
                    xaxis = dict(
                        ticktext= ['TICKS','MESH','PLOTLY','PYTHON'],
                        tickvals= [0,50,75,-50]),
                    yaxis = dict(
                        nticks=5, tickfont=dict(
                            color='green',
                            size=12,
                            family='Old Standard TT, serif',),
                        ticksuffix='#'),
                    zaxis = dict(
                        nticks=4, ticks='outside',
                        tick0=0, tickwidth=4),),
                    width=700,
                    margin=dict(r=10, l=10, b=10, t=10)
                  )

fig.show()

Background and Grid Color

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

N = 50
fig = go.Figure(data=[go.Mesh3d(x=(30*np.random.randn(N)),
                   y=(25*np.random.randn(N)),
                   z=(30*np.random.randn(N)),
                   opacity=0.5,)])


# xaxis.backgroundcolor is used to set background color
fig.update_layout(scene = dict(
                    xaxis = dict(
                         backgroundcolor="rgb(200, 200, 230)",
                         gridcolor="white",
                         showbackground=True,
                         zerolinecolor="white",),
                    yaxis = dict(
                        backgroundcolor="rgb(230, 200,230)",
                        gridcolor="white",
                        showbackground=True,
                        zerolinecolor="white"),
                    zaxis = dict(
                        backgroundcolor="rgb(230, 230,200)",
                        gridcolor="white",
                        showbackground=True,
                        zerolinecolor="white",),),
                    width=700,
                    margin=dict(
                    r=10, l=10,
                    b=10, t=10)
                  )
fig.show()

Disabling tooltip spikes

By default, guidelines originating from the tooltip point are drawn. It is possible to disable this behaviour with the showspikes parameter. In this example we only keep the z spikes (projection of the tooltip on the x-y plane). Hover on the data to show this behaviour.

In [6]:
import plotly.graph_objects as go
import numpy as np

N = 50
fig = go.Figure(data=[go.Mesh3d(x=(30*np.random.randn(N)),
                   y=(25*np.random.randn(N)),
                   z=(30*np.random.randn(N)),
                   opacity=0.5,)])
fig.update_layout(scene=dict(xaxis_showspikes=False,
                             yaxis_showspikes=False))
fig.show()
In [ ]:
 

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