🎨 Scatter Plots: Your Playground of Dots!

Imagine you have a big box of colorful stickers. You want to stick them on paper to show two things at once — like how tall your friends are AND how many candies they have. Scatter plots are exactly that! Each sticker (dot) tells a story about two pieces of information.

🌟 What is a Scatter Plot?

Think of a treasure map. Each “X marks the spot” shows WHERE something is. A scatter plot is like that — but instead of treasure, each dot shows two facts about something.

Real Life Example:

• 📍 Each dot = one person
• ➡️ Going right = they’re taller
• ⬆️ Going up = they have more candies

import matplotlib.pyplot as plt

# Heights and candies
heights = [4, 5, 5.5, 6]
candies = [10, 15, 20, 25]

plt.scatter(heights, candies)
plt.xlabel('Height (feet)')
plt.ylabel('Candies')
plt.title('My Friends')
plt.show()

The Magic: Now you can SEE if taller friends have more candies! 🍬

🎯 Scatter Plot Basics

The Two Axes

Every scatter plot has two rulers:

• X-axis (horizontal) → Goes left to right
• Y-axis (vertical) → Goes up and down

Each dot sits at the “meeting point” of two numbers!

graph TD
    A[Your Data] --> B[X value]
    A --> C[Y value]
    B --> D[Dot Position]
    C --> D
    D --> E[Scatter Plot!]

Simple Code Pattern

import matplotlib.pyplot as plt

# Your two lists of numbers
x = [1, 2, 3, 4, 5]
y = [2, 4, 1, 8, 7]

# Make the scatter plot
plt.scatter(x, y)

# Add labels (always do this!)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('My Scatter Plot')

plt.show()

🎈 Key Idea: plt.scatter(x, y) is all you need to start!

🌈 Color by Value

What if each dot could have a different color based on another piece of information? Like coloring stickers based on how happy each friend is!

The Magic Parameter: c

The letter c stands for color. You give it a list of numbers, and matplotlib picks colors automatically!

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 1, 8, 7]
happiness = [10, 30, 50, 70, 90]

plt.scatter(x, y, c=happiness)
plt.colorbar()  # Shows color legend
plt.title('Colored by Happiness!')
plt.show()

Using Color Maps

Colormaps are like paint palettes. Some popular ones:

plt.scatter(x, y, c=happiness, cmap='hot')
plt.colorbar()
plt.show()

🎨 Pro Tip: Always add plt.colorbar() so people know what colors mean!

📏 Size by Value

Now for another superpower! What if BIGGER dots meant BIGGER values? Like making stickers larger for friends who ate MORE pizza! 🍕

The Magic Parameter: s

The letter s stands for size. Give it numbers, and dots grow or shrink!

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 1, 8, 7]
pizza_slices = [1, 3, 2, 5, 4]

# Multiply by 50 to make sizes visible
sizes = [p * 50 for p in pizza_slices]

plt.scatter(x, y, s=sizes)
plt.title('Bigger = More Pizza!')
plt.show()

Combining Color AND Size

The REAL magic happens when you use BOTH!

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 1, 8, 7]
happiness = [10, 30, 50, 70, 90]
pizza = [100, 300, 200, 500, 400]

plt.scatter(x, y,
            c=happiness,  # Color = happiness
            s=pizza,      # Size = pizza eaten
            alpha=0.6)    # Slightly see-through

plt.colorbar(label='Happiness')
plt.title('Color=Happy, Size=Pizza!')
plt.show()

graph TD
    A[Each Dot Shows] --> B[X Position]
    A --> C[Y Position]
    A --> D[Color = 3rd info]
    A --> E[Size = 4th info]
    B --> F[4 FACTS in 1 dot!]
    C --> F
    D --> F
    E --> F

🚀 Amazing: One tiny dot can show FOUR pieces of information!

🐝 Hexbin Plot

The Problem with Too Many Dots

Imagine 10,000 stickers all piled on top of each other. You can’t see anything! It’s like a crowd of people — you can’t count them all.

The Solution: Hexagons!

A hexbin plot divides your space into honeycomb cells. Instead of showing individual dots, it COLORS the hexagons based on how many dots are hiding inside!

graph TD
    A[1000s of Dots] --> B[Too Crowded!]
    B --> C[Group into Hexagons]
    C --> D[Color by Count]
    D --> E[Clear Pattern!]

Basic Hexbin

import matplotlib.pyplot as plt
import numpy as np

# Make lots of random data
np.random.seed(42)
x = np.random.randn(1000)
y = np.random.randn(1000)

# Hexbin instead of scatter
plt.hexbin(x, y, gridsize=20)
plt.colorbar(label='Count')
plt.title('Hexbin: Dots Grouped!')
plt.show()

Key Parameter: gridsize

• Small gridsize (10) = Fewer, bigger hexagons
• Large gridsize (50) = More, smaller hexagons

# Try different gridsizes
plt.hexbin(x, y, gridsize=15)
plt.colorbar()
plt.show()

When to Use What?

🎮 Quick Reference

Scatter Plot Formula

plt.scatter(x, y,
            c=colors,    # Color by value
            s=sizes,     # Size by value
            cmap='viridis',
            alpha=0.7)
plt.colorbar()

Hexbin Formula

plt.hexbin(x, y,
           gridsize=20,
           cmap='viridis')
plt.colorbar()

🌟 Summary: Your New Superpowers!

1. Scatter Basics → Each dot = two facts (x and y position)
2. Color by Value → Use c= to add a third fact
3. Size by Value → Use s= to add a fourth fact
4. Hexbin → Group crowded dots into colored hexagons

You did it! Now you can tell stories with dots! Each scatter plot is like a picture book where every dot whispers its secrets. 🎉

Remember: The best scatter plots are like good stories — they help you SEE patterns you couldn’t see before!