3D Plotting

🎢 Welcome to the 3D Dimension!

Your Journey into Matplotlib’s 3D World

Imagine you’re an architect building a model of a roller coaster. You can’t show the twists, loops, and heights on a flat piece of paper—you need three dimensions to bring it to life!

That’s exactly what 3D plotting does. It lets your data pop out of the screen like a video game world.

🧱 The Universal Analogy: Building with LEGOs

Think of 3D plotting like building with LEGOs:

• The X-axis is how far LEFT or RIGHT you place your brick
• The Y-axis is how far FORWARD or BACKWARD
• The Z-axis is how HIGH you stack

Every point in 3D space needs all three coordinates—just like placing a LEGO brick exactly where you want it!

🎬 Chapter 1: Setting Up Your 3D Stage

The 3D Axes Setup

Before you can build anything in 3D, you need a special canvas that understands three dimensions.

Think of it like this: A regular piece of paper is flat (2D). But a cardboard box? That’s 3D! We need to tell Python: “Hey, give me a box to draw in, not just paper!”

The Magic Words

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

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

What’s happening here?

1. plt.figure() → Creates an empty frame (like a picture frame)
2. projection='3d' → The magic spell! This turns flat paper into a 3D box
3. ax → Your 3D drawing tool is ready!

graph TD
    A["Import Libraries"] --> B["Create Figure"]
    B --> C["Add 3D Projection"]
    C --> D["🎨 Ready to Draw!"]

📈 Chapter 2: Drawing Lines in 3D

3D Line Plot

Remember drawing a line on paper? In 3D, your line can fly through space like a paper airplane!

Real-world example: The path of a bee flying from flower to flower goes up, down, left, right, forward, and back!

import numpy as np

# Create time points
t = np.linspace(0, 4*np.pi, 100)

# The bee's path
x = np.sin(t)      # Wiggle left-right
y = np.cos(t)      # Wiggle forward-back
z = t              # Go higher and higher

ax.plot(x, y, z, label='Bee Path')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()

What you’ll see: A beautiful spiral rising up—like a spring or a spiral staircase! 🌀

Key Function: ax.plot(x, y, z)

⚫ Chapter 3: Scatter Points in Space

3D Scatter Plot

Instead of connecting dots with lines, what if we just sprinkle dots in 3D space? Like stars in the night sky!

Real-world example: Imagine plotting where every balloon is floating at a birthday party—each one has a position and height!

# Random balloon positions
n = 50
x = np.random.rand(n) * 10
y = np.random.rand(n) * 10
z = np.random.rand(n) * 10
colors = np.random.rand(n)

ax.scatter(x, y, z, c=colors, s=50)
ax.set_title('Balloons at the Party!')
plt.show()

Making It Pop! 🎈

# Size can change too!
sizes = np.random.rand(n) * 200

ax.scatter(x, y, z,
           c=colors,      # Color by value
           s=sizes,       # Size varies
           alpha=0.6)     # Slightly see-through

Pro tip: Use c for color and s for size to show MORE information in the same plot!

📊 Chapter 4: Bars That Stand Tall

3D Bar Plot

Regular bar charts lie flat. But 3D bars? They stand up like buildings in a city skyline!

Real-world example: Imagine a city where each building’s height shows how many people live there, and its position shows which neighborhood it’s in.

# Data setup
x = [1, 2, 3, 4]
y = [1, 2, 3, 4]
z = [0, 0, 0, 0]  # Bars start at ground
dx = dy = 0.5     # Bar width
dz = [5, 10, 7, 3]  # Bar heights!

ax.bar3d(x, y, z, dx, dy, dz,
         color='skyblue',
         edgecolor='navy')
ax.set_title('City Skyline')
plt.show()

Understanding bar3d Parameters

graph TD
    A["bar3d Parameters"] --> B["x, y, z = Start Position"]
    A --> C["dx, dy = Width & Depth"]
    A --> D["dz = HEIGHT ⬆️"]

🏔️ Chapter 5: Mountains and Valleys

3D Surface Plot

This is where things get really cool! A surface plot creates a smooth, continuous sheet—like a bedsheet draped over lumpy pillows, or real mountain terrain!

Real-world example: Weather maps showing temperature across a region, where “hills” are hot spots and “valleys” are cool areas.

# Create a grid
x = np.linspace(-5, 5, 50)
y = np.linspace(-5, 5, 50)
X, Y = np.meshgrid(x, y)

# The mountain formula!
Z = np.sin(np.sqrt(X**2 + Y**2))

# Draw the surface
surf = ax.plot_surface(X, Y, Z,
                       cmap='viridis')
plt.colorbar(surf)
plt.show()

What’s meshgrid? 🤔

Think of it like drawing a checkerboard, then asking “what’s the height at each square?”

Before meshgrid:     After meshgrid:
x = [1, 2, 3]        Every combo!
y = [a, b, c]        (1,a) (2,a) (3,a)
                     (1,b) (2,b) (3,b)
                     (1,c) (2,c) (3,c)

Make It Beautiful! 🎨

ax.plot_surface(X, Y, Z,
                cmap='coolwarm',  # Blue=low, Red=high
                alpha=0.8)        # Slightly transparent

Popular colormaps: viridis, plasma, coolwarm, terrain

🕸️ Chapter 6: See-Through Structures

3D Wireframe Plot

What if you could see through the mountain? A wireframe is like the skeleton of a surface—just the lines, no filling!

Real-world example: Like a 3D model made of wire coat hangers, or the frame of a tent before you put the fabric on.

ax.plot_wireframe(X, Y, Z,
                  color='purple',
                  linewidth=0.5)
ax.set_title('Mountain Skeleton')
plt.show()

When to Use Wireframe vs Surface?

Customize Your Wireframe

ax.plot_wireframe(X, Y, Z,
                  rstride=5,    # Row step
                  cstride=5,    # Column step
                  color='teal')

Stride controls density: Higher stride = fewer lines = cleaner look!

🎯 Quick Reference: All 3D Plot Types

graph TD
    A["3D Plotting"] --> B["Line Plot"]
    A --> C["Scatter Plot"]
    A --> D["Bar Plot"]
    A --> E["Surface Plot"]
    A --> F["Wireframe Plot"]

    B --> G["Paths & Trajectories"]
    C --> H["Points in Space"]
    D --> I["Comparing Categories"]
    E --> J["Continuous Data"]
    F --> K["Transparent Structures"]

💡 Golden Tips for 3D Success

1. Always Label Your Axes!

ax.set_xlabel('Time')
ax.set_ylabel('Distance')
ax.set_zlabel('Height')

2. Rotate Your View

ax.view_init(elev=30, azim=45)
# elev = looking from above/below
# azim = spinning around

3. Add a Title

ax.set_title('My Amazing 3D Plot!')

🚀 Your 3D Toolkit Summary

🎉 You Did It!

You’ve just unlocked the power to visualize data in THREE DIMENSIONS!

Remember:

• 🧱 Every 3D plot needs projection='3d'
• 📍 Every point needs X, Y, and Z
• 🎨 Use colors and sizes to show extra information
• 🔄 Rotate your view to find the best angle

Now go create some amazing 3D visualizations! Your data is about to jump off the screen! 🚀