Area and Fill Plots

🎨 Matplotlib Area & Fill Plots: Painting Data Like a Coloring Book

🌊 The Big Picture: What Are Area Plots?

Imagine you’re coloring between lines in a coloring book. That’s exactly what area plots do with data! Instead of just drawing a line, you fill in the space beneath it with color.

Why does this matter?

• Lines show trends — but filled areas show volume and magnitude
• Your eyes instantly see “how much” instead of just “which direction”
• Perfect for showing things that accumulate — like money saved, rain fallen, or energy used

🖍️ 1. Area Plot — The Basic Fill

Think of an Area Plot like coloring below a mountain outline. You draw the mountain top (your data line), then fill everything below with color.

What It Does

Fills the space between your data line and the x-axis (the bottom).

Simple Example

import matplotlib.pyplot as plt

days = [1, 2, 3, 4, 5]
visitors = [10, 25, 40, 30, 50]

plt.fill(days, visitors)
plt.title("Website Visitors")
plt.show()

Real-Life Use

• 📈 Show website traffic over time
• 💰 Display savings growing in a bank account
• 🌡️ Temperature changes through the day

Pro Tip

Use alpha=0.5 to make the fill semi-transparent:

plt.fill(days, visitors, alpha=0.5)

🎭 2. Fill Between — Color the Gap

Fill Between is like coloring the space between two lines. Imagine two mountain ridges — you color only the valley between them!

What It Does

Fills the area between two curves (or between a curve and a value).

Simple Example

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.sin(x) + 0.5

plt.fill_between(x, y1, y2,
                 color='skyblue')
plt.title("Gap Between Waves")
plt.show()

Magic Trick: Conditional Coloring!

Color differently based on conditions:

plt.fill_between(x, y1, y2,
    where=(y1 < y2),
    color='green',
    alpha=0.5)
plt.fill_between(x, y1, y2,
    where=(y1 >= y2),
    color='red',
    alpha=0.5)

Real-Life Use

• 📊 Show profit vs loss (green above, red below)
• 🌡️ Comfortable temperature range
• 📉 Stock price confidence bands

📚 3. Stacked Area Plot — Layers Like a Cake

Remember stacking colored blocks? Stacked Area Plots show multiple data series piled on top of each other — like layers of a rainbow cake!

What It Does

Shows how different parts contribute to a whole over time. Each layer stacks on top of the previous one.

Simple Example

import matplotlib.pyplot as plt

months = ['Jan', 'Feb', 'Mar', 'Apr']
games = [10, 15, 12, 18]
videos = [5, 8, 10, 12]
music = [8, 6, 9, 7]

plt.stackplot(months,
              games, videos, music,
              labels=['Games',
                      'Videos',
                      'Music'])
plt.legend(loc='upper left')
plt.title("Screen Time Breakdown")
plt.show()

Key Insight

• The total height = sum of all categories
• You see both the whole and the parts!

Real-Life Use

• 💵 Budget breakdown over months
• ⚡ Energy sources powering a city
• 🌍 Population by age group over years

🎪 4. Hatching Patterns — Textures Instead of Colors

What if you’re printing in black and white? Or you need to help colorblind readers? Hatching adds patterns (lines, dots, crosses) instead of just colors!

What It Does

Adds texture patterns inside filled areas for accessibility and style.

Available Patterns

Simple Example

import matplotlib.pyplot as plt

x = [1, 2, 3, 4]
y = [2, 4, 3, 5]

plt.fill(x, y,
         facecolor='lightblue',
         hatch='///',
         edgecolor='black')
plt.title("Hatched Area")
plt.show()

Combining with Fill Between

plt.fill_between(x, y1, y2,
    facecolor='none',
    hatch='xxx',
    edgecolor='blue')

Real-Life Use

• 🖨️ Print-friendly graphs
• 👁️ Accessibility for colorblind readers
• 📊 Scientific papers requiring patterns

🗺️ How It All Connects

graph TD
    A["Area Plots"] --> B["fill"]
    A --> C["fill_between"]
    A --> D["stackplot"]
    A --> E["hatch"]

    B --> F["Color below<br>one line"]
    C --> G["Color between<br>two lines"]
    D --> H["Stack multiple<br>series"]
    E --> I["Add patterns<br>for texture"]

🔑 Quick Reference

💡 Remember This!

1. Area plots = line charts with coloring below
2. Fill between = color the gap between two lines
3. Stacked = layers showing parts of a whole
4. Hatching = patterns for accessibility

You’re now ready to paint beautiful data stories! 🎨