Unit 7.4: Interactive 3D Visualization

Combining Data, Widgets, and Plotly

Interactive visualizations let learners explore data by manipulating parameters in real time. In this notebook we load a dataset, create an ipywidgets slider, and use it to control the amplitude of a 3D surface rendered with Plotly. The same workflow works with matplotlib, Panel, and other Python visualization libraries.

1
2
3
4
5

import numpy as np import pandas as pd import plotly.graph_objects as go from ipywidgets import interact, FloatSlider from IPython.display import display

Use the Upload File button in the toolbar to add a CSV. The file is written to the in-browser filesystem and is immediately available to pandas.

1
2
3
4
5
6
7
8
9
10
11

# Read the uploaded CSV df = pd.read_csv("surface_data.csv") print(f"Loaded {len(df):,} records, {len(df.columns)} columns") # Reshape for surface plot x = df["x"].unique() y = df["y"].unique() X, Y = np.meshgrid(x, y) Z = df["z"].values.reshape(len(y), len(x)) df.head(8)

	x	y	z
0	-6.000000	-6.000000	0.921994
1	-5.586207	-6.000000	0.796034
2	-5.172414	-6.000000	0.531627
3	-4.758621	-6.000000	0.168203
4	-4.344828	-6.000000	-0.237841
5	-3.931035	-6.000000	-0.613529
6	-3.517241	-6.000000	-0.878465
7	-3.103448	-6.000000	-0.975218

The @interact decorator creates a widget control that re-runs the function whenever the slider moves. Drag the Amplitude slider to reshape the surface in real time.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

# Interactive 3D surface with widget control amplitude = FloatSlider( value=1.0, min=0.2, max=3.0, step=0.1, description='Amplitude:' ) @interact(amp=amplitude) def plot_surface(amp=1.0): fig = go.Figure(data=[go.Surface( x=X, y=Y, z=Z * amp, colorscale='Plasma', showscale=True )]) fig.update_layout( title=f'Surface (amplitude={amp:.1f})', scene=dict(xaxis_title='X', yaxis_title='Y', zaxis_title='Z') ) fig.show()

Surface (amplitude=1.0)

Drag to rotate · Scroll to zoom

1.0 -1.0