# 3D Axes in Python

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

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### 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.

Set range on an axis to manually configure a range for that axis. If you don't set range, it's automatically calculated. In this example, we set a range on xaxis, yaxis, and zaxis.

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()


### Setting only a Lower or Upper Bound for Range¶

New in 5.17

You can also set just a lower or upper bound for range. In this case, autorange is used for the other bound. In this example, we apply autorange to the lower bound of the yaxis and the upper bound of zaxis by setting them to None.

In [2]:
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=[None, 100],),
zaxis = dict(nticks=4, range=[-100, None],),),
width=700,
margin=dict(r=20, l=10, b=10, t=10))

fig.show()


### Fixed Ratio Axes¶

In [3]:
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 [4]:
import plotly.graph_objects as go
import numpy as np

# Define random surface
N = 50
fig = go.Figure()
y=(25*np.random.randn(N)),
z=(40*np.random.randn(N)),
opacity=0.5,
color='yellow'
))
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 [5]:
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 [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,)])

# 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 [7]:
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()


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(...)