🎨 Plot Styling: Style Sheets and Themes in Matplotlib

The Wardrobe Analogy

Imagine your plot is like a person getting ready for an event. Without any styling, they’re wearing plain white clothes — functional but boring! Style sheets are like complete outfits you can put on with one click. rcParams are like adjusting individual pieces — changing the hat, the shoes, or the belt.

🌟 Style Sheets Basics

What Are Style Sheets?

Style sheets are pre-made collections of settings that change how your plot looks — colors, fonts, line thickness, background, and more.

Think of it like this:

• You want to decorate a room
• Instead of picking each item one by one…
• You pick a “theme” and everything matches instantly!

How to Use a Style Sheet

import matplotlib.pyplot as plt

# Apply a style sheet
plt.style.use('ggplot')

# Now make your plot
plt.plot([1, 2, 3], [1, 4, 9])
plt.show()

That’s it! One line changes everything.

Temporary Styling (Just for One Plot)

What if you want a style only for ONE plot?

with plt.style.context('dark_background'):
    plt.plot([1, 2, 3], [1, 4, 9])
    plt.show()
# After this block, style goes back to normal!

It’s like borrowing a costume for one party, then returning it.

📚 Built-in Style Sheets

Matplotlib comes with many free outfits already in the closet!

See All Available Styles

import matplotlib.pyplot as plt
print(plt.style.available)

Popular Built-in Styles

Example: Comparing Styles

import matplotlib.pyplot as plt

styles = ['ggplot', 'seaborn', 'bmh']
data = [1, 4, 9, 16, 25]

for style in styles:
    with plt.style.context(style):
        plt.plot(data)
        plt.title(f'Style: {style}')
        plt.show()

Each style gives your data a completely different personality!

✨ Custom Style Sheets

Why Make Your Own?

Built-in styles are great, but what if you want:

• Your company’s colors?
• Your favorite font?
• A special look for your reports?

Create your own style sheet!

Step 1: Create a .mplstyle File

Make a text file called my_style.mplstyle:

# My Custom Style
axes.facecolor: #f0f0f0
axes.edgecolor: #333333
axes.labelcolor: #333333
axes.prop_cycle: cycler('color', ['#e41a1c', '#377eb8', '#4daf4a'])
lines.linewidth: 2
font.size: 12
figure.facecolor: white

Step 2: Use Your Custom Style

Option A: Put the file in Matplotlib’s style folder:

import matplotlib
print(matplotlib.get_configdir())
# Put your file in: [configdir]/stylelib/

Option B: Use the full file path:

plt.style.use('./my_style.mplstyle')

What Can You Customize?

Almost everything!

• Colors: Background, lines, text
• Fonts: Family, size, weight
• Lines: Width, markers, dashes
• Grid: Show/hide, color, style
• Figure: Size, resolution

⚙️ rcParams Configuration

What is rcParams?

rcParams = Runtime Configuration Parameters

It’s a giant dictionary that controls EVERY setting in Matplotlib. Style sheets are just organized collections of rcParams!

View Current Settings

import matplotlib as mpl

# See one setting
print(mpl.rcParams['lines.linewidth'])

# See all settings (there are hundreds!)
print(mpl.rcParams.keys())

Change Settings Directly

import matplotlib as mpl

# Change one setting
mpl.rcParams['lines.linewidth'] = 3
mpl.rcParams['font.size'] = 14

# Now all plots use these settings!

Common rcParams to Know

# Line appearance
mpl.rcParams['lines.linewidth'] = 2
mpl.rcParams['lines.linestyle'] = '-'
mpl.rcParams['lines.marker'] = 'o'

# Font settings
mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.size'] = 12

# Axes settings
mpl.rcParams['axes.grid'] = True
mpl.rcParams['axes.facecolor'] = '#f5f5f5'

# Figure settings
mpl.rcParams['figure.figsize'] = [8, 6]
mpl.rcParams['figure.dpi'] = 100

Reset to Defaults

Made a mess? Start fresh!

import matplotlib as mpl
mpl.rcParams.update(mpl.rcParamsDefault)

rcParams vs Style Sheets

graph TD
    A[Want to style your plot?] --> B{How many settings?}
    B -->|Just 1-2 settings| C[Use rcParams directly]
    B -->|Many settings| D{Reuse often?}
    D -->|Yes| E[Create custom .mplstyle]
    D -->|No| F[Use with block + rcParams]

🎯 Quick Reference

🚀 Pro Tips

1. Combine styles: plt.style.use(['seaborn', 'my_custom']) — later styles override earlier ones.

2. Check before changing:

   print(mpl.rcParams['lines.linewidth'])
   

3. Use context for experiments:

   with plt.style.context('bmh'):
       # Try something new here
       pass
   # Original style is back!
   

4. Save your favorites: Create a .mplstyle file for styles you use often.

💡 Remember

• Style sheets = Complete outfits (one line, many changes)
• Built-in styles = Free wardrobe options
• Custom styles = Design your own look
• rcParams = Fine-tune individual settings

You now have the power to make your plots look exactly how you want them! 🎨