3D Visualizations

Why 3D Charts?

3D charts help when you have three variables:

X, Y, and Z coordinates
Height, width, and depth
Three related measurements

3D Scatter Plot

code.py

import plotly.express as px
import pandas as pd

df = pd.DataFrame({
    'X': [1, 2, 3, 4, 5],
    'Y': [2, 3, 4, 5, 6],
    'Z': [1, 4, 9, 16, 25]
})

fig = px.scatter_3d(df, x='X', y='Y', z='Z')
fig.show()

Click and drag to rotate!

Color in 3D

code.py

df['Category'] = ['A', 'B', 'A', 'B', 'A']

fig = px.scatter_3d(df, x='X', y='Y', z='Z', color='Category')
fig.show()

Size in 3D

code.py

df['Size'] = [10, 20, 30, 40, 50]

fig = px.scatter_3d(df, x='X', y='Y', z='Z', size='Size')
fig.show()

3D Line Plot

code.py

import numpy as np

t = np.linspace(0, 10, 100)
df = pd.DataFrame({
    'X': np.cos(t),
    'Y': np.sin(t),
    'Z': t
})

fig = px.line_3d(df, x='X', y='Y', z='Z')
fig.show()

Creates a spiral!

3D Surface Plot

code.py

import plotly.graph_objects as go
import numpy as np

# Create grid
x = np.linspace(-5, 5, 50)
y = np.linspace(-5, 5, 50)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

fig = go.Figure(data=[go.Surface(z=Z, x=X, y=Y)])
fig.show()

Creates a wave surface!

3D Bar Chart

code.py

import plotly.graph_objects as go

fig = go.Figure(data=[go.Mesh3d(
    x=[0, 1, 0, 1, 0, 1, 0, 1],
    y=[0, 0, 1, 1, 0, 0, 1, 1],
    z=[0, 0, 0, 0, 1, 1, 1, 1],
    color='blue',
    opacity=0.5
)])
fig.show()

Customize 3D View

code.py

fig = px.scatter_3d(df, x='X', y='Y', z='Z')

# Set initial camera angle
fig.update_layout(
    scene_camera=dict(
        eye=dict(x=1.5, y=1.5, z=1.5)
    )
)
fig.show()

Add Labels

code.py

fig = px.scatter_3d(df, x='X', y='Y', z='Z')

fig.update_layout(
    scene=dict(
        xaxis_title='X Axis',
        yaxis_title='Y Axis',
        zaxis_title='Z Axis'
    )
)
fig.show()

Complete Example

code.py

import plotly.express as px
import pandas as pd
import numpy as np

# Sample 3D data
np.random.seed(42)
n = 100
df = pd.DataFrame({
    'Height': np.random.normal(170, 10, n),
    'Weight': np.random.normal(70, 15, n),
    'Age': np.random.randint(20, 60, n),
    'Gender': np.random.choice(['Male', 'Female'], n)
})

fig = px.scatter_3d(df, x='Height', y='Weight', z='Age',
                    color='Gender',
                    title='Height, Weight, and Age',
                    labels={'Height': 'Height (cm)',
                            'Weight': 'Weight (kg)',
                            'Age': 'Age (years)'})
fig.show()

When to Use 3D?

Good for:

Three numeric variables
Spatial data
Scientific visualization

Avoid when:

2D would be clearer
Presenting to non-technical audience
Exact values matter (hard to read)

Key Points

scatter_3d() for 3D points
line_3d() for 3D lines
Surface() for 3D surfaces
Click and drag to rotate
Use color and size for 4th variable
3D looks cool but can be hard to read

What's Next?

Learn to create geographic maps with Plotly.