Topic: Python Matplotlib – From Easy to Top: Part 6 of 6: 3D Plotting, Animation, and Interactive Visuals

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### 1. Introduction

Matplotlib supports advanced visualizations including:

• 3D plots using mpl_toolkits.mplot3d
• Animations with FuncAnimation
• Interactive plots using widgets and event handling

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### 2. Creating 3D Plots

You need to import the 3D toolkit:

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np

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### 3. 3D Line Plot

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

z = np.linspace(0, 15, 100)
x = np.sin(z)
y = np.cos(z)

ax.plot3D(x, y, z, 'purple')
ax.set_title("3D Line Plot")
plt.show()

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### 4. 3D Surface Plot

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

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))

surf = ax.plot_surface(X, Y, Z, cmap='viridis')
fig.colorbar(surf)

ax.set_title("3D Surface Plot")
plt.show()

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### 5. 3D Scatter Plot

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

x = np.random.rand(100)
y = np.random.rand(100)
z = np.random.rand(100)

ax.scatter(x, y, z, c=z, cmap='plasma')
ax.set_title("3D Scatter Plot")
plt.show()

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### 6. Creating Animations

Use FuncAnimation for animated plots.

import matplotlib.animation as animation

fig, ax = plt.subplots()
x = np.linspace(0, 2*np.pi, 128)
line, = ax.plot(x, np.sin(x))

def update(frame):
    line.set_ydata(np.sin(x + frame / 10))
    return line,

ani = animation.FuncAnimation(fig, update, frames=100, interval=50)
plt.title("Sine Wave Animation")
plt.show()

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### 7. Save Animation as a File

ani.save("sine_wave.gif", writer='pillow')

Make sure to install pillow using:

pip install pillow

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### 8. Adding Interactivity with Widgets

import matplotlib.widgets as widgets

fig, ax = plt.subplots()
plt.subplots_adjust(left=0.1, bottom=0.25)

x = np.linspace(0, 2*np.pi, 100)
freq = 1
line, = ax.plot(x, np.sin(freq * x))

ax_slider = plt.axes([0.25, 0.1, 0.65, 0.03])
slider = widgets.Slider(ax_slider, 'Frequency', 0.1, 5.0, valinit=freq)

def update(val):
    line.set_ydata(np.sin(slider.val * x))
    fig.canvas.draw_idle()

slider.on_changed(update)
plt.title("Interactive Sine Wave")
plt.show()

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### 9. Mouse Interaction with Events

def onclick(event):
    print(f'You clicked at x={event.xdata:.2f}, y={event.ydata:.2f}')

fig, ax = plt.subplots()
ax.plot([1, 2, 3], [4, 5, 6])
fig.canvas.mpl_connect('button_press_event', onclick)
plt.title("Click to Print Coordinates")
plt.show()

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### 10. Summary

• 3D plots are ideal for visualizing spatial data and surfaces
• Animations help convey dynamic changes in data
• Widgets and events add interactivity for data exploration
• Mastering these tools enables the creation of interactive dashboards and visual storytelling

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### Exercise

• Plot a 3D surface of z = cos(sqrt(x² + y²)).
• Create a slider to change frequency of a sine wave in real-time.
• Animate a circle that rotates along time.
• Build a 3D scatter plot of 3 correlated variables.

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#Python #Matplotlib #3DPlots #Animations #InteractiveVisuals #DataVisualization

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