Matplotlib Styling Guide

Matplotlib Styling Guide

Comprehensive guide for styling and customizing matplotlib visualizations.

Colormaps

Colormap Categories

1. Perceptually Uniform Sequential Best for ordered data that progresses from low to high values.

• viridis (default, colorblind-friendly)
• plasma
• inferno
• magma
• cividis (optimized for colorblind viewers)

Usage:

im = ax.imshow(data, cmap='viridis')
scatter = ax.scatter(x, y, c=values, cmap='plasma')

2. Sequential Traditional colormaps for ordered data.

• Blues, Greens, Reds, Oranges, Purples
• YlOrBr, YlOrRd, OrRd, PuRd
• BuPu, GnBu, PuBu, YlGnBu

3. Diverging Best for data with a meaningful center point (e.g., zero, mean).

• coolwarm (blue to red)
• RdBu (red-blue)
• RdYlBu (red-yellow-blue)
• RdYlGn (red-yellow-green)
• PiYG, PRGn, BrBG, PuOr, RdGy

Usage:

# Center colormap at zero
im = ax.imshow(data, cmap='coolwarm', vmin=-1, vmax=1)

4. Qualitative Best for categorical/nominal data without inherent ordering.

• tab10 (10 distinct colors)
• tab20 (20 distinct colors)
• Set1, Set2, Set3
• Pastel1, Pastel2
• Dark2, Accent, Paired

Usage:

colors = plt.cm.tab10(np.linspace(0, 1, n_categories))
for i, category in enumerate(categories):
    ax.plot(x, y[i], color=colors[i], label=category)

5. Cyclic Best for cyclic data (e.g., phase, angle).

• twilight
• twilight_shifted
• hsv

Colormap Best Practices

1. Avoid jet colormap - Not perceptually uniform, misleading
2. Use perceptually uniform colormaps - viridis, plasma, cividis
3. Consider colorblind users - Use viridis, cividis, or test with colorblind simulators
4. Match colormap to data type:
   • Sequential: increasing/decreasing data
   • Diverging: data with meaningful center
   • Qualitative: categories
5. Reverse colormaps - Add _r suffix: viridis_r, coolwarm_r

Creating Custom Colormaps

from matplotlib.colors import LinearSegmentedColormap

# From color list
colors = ['blue', 'white', 'red']
n_bins = 100
cmap = LinearSegmentedColormap.from_list('custom', colors, N=n_bins)

# From RGB values
colors = [(0, 0, 1), (1, 1, 1), (1, 0, 0)]  # RGB tuples
cmap = LinearSegmentedColormap.from_list('custom', colors)

# Use the custom colormap
ax.imshow(data, cmap=cmap)

Discrete Colormaps

import matplotlib.colors as mcolors

# Create discrete colormap from continuous
cmap = plt.cm.viridis
bounds = np.linspace(0, 10, 11)
norm = mcolors.BoundaryNorm(bounds, cmap.N)
im = ax.imshow(data, cmap=cmap, norm=norm)

Style Sheets

Using Built-in Styles

# List available styles
print(plt.style.available)

# Apply a style
plt.style.use('seaborn-v0_8-darkgrid')

# Apply multiple styles (later styles override earlier ones)
plt.style.use(['seaborn-v0_8-whitegrid', 'seaborn-v0_8-poster'])

# Temporarily use a style
with plt.style.context('ggplot'):
    fig, ax = plt.subplots()
    ax.plot(x, y)

Popular Built-in Styles

• default - Matplotlib's default style
• classic - Classic matplotlib look (pre-2.0)
• seaborn-v0_8-* - Seaborn-inspired styles
  • seaborn-v0_8-darkgrid, seaborn-v0_8-whitegrid
  • seaborn-v0_8-dark, seaborn-v0_8-white
  • seaborn-v0_8-ticks, seaborn-v0_8-poster, seaborn-v0_8-talk
• ggplot - ggplot2-inspired style
• bmh - Bayesian Methods for Hackers style
• fivethirtyeight - FiveThirtyEight style
• grayscale - Grayscale style

Creating Custom Style Sheets

Create a file named custom_style.mplstyle:

# custom_style.mplstyle

# Figure
figure.figsize: 10, 6
figure.dpi: 100
figure.facecolor: white

# Font
font.family: sans-serif
font.sans-serif: Arial, Helvetica
font.size: 12

# Axes
axes.labelsize: 14
axes.titlesize: 16
axes.facecolor: white
axes.edgecolor: black
axes.linewidth: 1.5
axes.grid: True
axes.axisbelow: True

# Grid
grid.color: gray
grid.linestyle: --
grid.linewidth: 0.5
grid.alpha: 0.3

# Lines
lines.linewidth: 2
lines.markersize: 8

# Ticks
xtick.labelsize: 10
ytick.labelsize: 10
xtick.direction: in
ytick.direction: in
xtick.major.size: 6
ytick.major.size: 6
xtick.minor.size: 3
ytick.minor.size: 3

# Legend
legend.fontsize: 12
legend.frameon: True
legend.framealpha: 0.8
legend.fancybox: True

# Savefig
savefig.dpi: 300
savefig.bbox: tight
savefig.facecolor: white

Load and use:

plt.style.use('path/to/custom_style.mplstyle')

rcParams Configuration

Global Configuration

import matplotlib.pyplot as plt

# Configure globally
plt.rcParams['figure.figsize'] = (10, 6)
plt.rcParams['font.size'] = 12
plt.rcParams['axes.labelsize'] = 14

# Or update multiple at once
plt.rcParams.update({
    'figure.figsize': (10, 6),
    'font.size': 12,
    'axes.labelsize': 14,
    'axes.titlesize': 16,
    'lines.linewidth': 2
})

Temporary Configuration

# Context manager for temporary changes
with plt.rc_context({'font.size': 14, 'lines.linewidth': 2.5}):
    fig, ax = plt.subplots()
    ax.plot(x, y)

Common rcParams

Figure settings:

plt.rcParams['figure.figsize'] = (10, 6)
plt.rcParams['figure.dpi'] = 100
plt.rcParams['figure.facecolor'] = 'white'
plt.rcParams['figure.edgecolor'] = 'white'
plt.rcParams['figure.autolayout'] = False
plt.rcParams['figure.constrained_layout.use'] = True

Font settings:

plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Arial', 'Helvetica', 'DejaVu Sans']
plt.rcParams['font.size'] = 12
plt.rcParams['font.weight'] = 'normal'

Axes settings:

plt.rcParams['axes.facecolor'] = 'white'
plt.rcParams['axes.edgecolor'] = 'black'
plt.rcParams['axes.linewidth'] = 1.5
plt.rcParams['axes.grid'] = True
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['axes.titlesize'] = 16
plt.rcParams['axes.labelweight'] = 'normal'
plt.rcParams['axes.spines.top'] = True
plt.rcParams['axes.spines.right'] = True

Line settings:

plt.rcParams['lines.linewidth'] = 2
plt.rcParams['lines.linestyle'] = '-'
plt.rcParams['lines.marker'] = 'None'
plt.rcParams['lines.markersize'] = 6

Save settings:

plt.rcParams['savefig.dpi'] = 300
plt.rcParams['savefig.format'] = 'png'
plt.rcParams['savefig.bbox'] = 'tight'
plt.rcParams['savefig.pad_inches'] = 0.1
plt.rcParams['savefig.transparent'] = False

Color Palettes

Named Color Sets

# Tableau colors
tableau_colors = plt.cm.tab10.colors

# CSS4 colors (subset)
css_colors = ['steelblue', 'coral', 'teal', 'goldenrod', 'crimson']

# Manual definition
custom_colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd']

Color Cycles

# Set default color cycle
from cycler import cycler
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
plt.rcParams['axes.prop_cycle'] = cycler(color=colors)

# Or combine color and line style
plt.rcParams['axes.prop_cycle'] = cycler(color=colors) + cycler(linestyle=['-', '--', ':', '-.'])

Palette Generation

# Evenly spaced colors from colormap
n_colors = 5
colors = plt.cm.viridis(np.linspace(0, 1, n_colors))

# Use in plot
for i, (x, y) in enumerate(data):
    ax.plot(x, y, color=colors[i])

Typography

Font Configuration

# Set font family
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.serif'] = ['Times New Roman', 'DejaVu Serif']

# Or sans-serif
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif'] = ['Arial', 'Helvetica']

# Or monospace
plt.rcParams['font.family'] = 'monospace'
plt.rcParams['font.monospace'] = ['Courier New', 'DejaVu Sans Mono']

Font Properties in Text

from matplotlib import font_manager

# Specify font properties
ax.text(x, y, 'Text',
        fontsize=14,
        fontweight='bold',  # 'normal', 'bold', 'heavy', 'light'
        fontstyle='italic',  # 'normal', 'italic', 'oblique'
        fontfamily='serif')

# Use specific font file
prop = font_manager.FontProperties(fname='path/to/font.ttf')
ax.text(x, y, 'Text', fontproperties=prop)

Mathematical Text

# LaTeX-style math
ax.set_title(r'$\alpha > \beta$')
ax.set_xlabel(r'$\mu \pm \sigma$')
ax.text(x, y, r'$\int_0^\infty e^{-x} dx = 1$')

# Subscripts and superscripts
ax.set_ylabel(r'$y = x^2 + 2x + 1$')
ax.text(x, y, r'$x_1, x_2, \ldots, x_n$')

# Greek letters
ax.text(x, y, r'$\alpha, \beta, \gamma, \delta, \epsilon$')

Using Full LaTeX

# Enable full LaTeX rendering (requires LaTeX installation)
plt.rcParams['text.usetex'] = True
plt.rcParams['text.latex.preamble'] = r'\usepackage{amsmath}'

ax.set_title(r'\textbf{Bold Title}')
ax.set_xlabel(r'Time $t$ (s)')

Spines and Grids

Spine Customization

# Hide specific spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Move spine position
ax.spines['left'].set_position(('outward', 10))
ax.spines['bottom'].set_position(('data', 0))

# Change spine color and width
ax.spines['left'].set_color('red')
ax.spines['bottom'].set_linewidth(2)

Grid Customization

# Basic grid
ax.grid(True)

# Customized grid
ax.grid(True, which='major', linestyle='--', linewidth=0.8, alpha=0.3)
ax.grid(True, which='minor', linestyle=':', linewidth=0.5, alpha=0.2)

# Grid for specific axis
ax.grid(True, axis='x')  # Only vertical lines
ax.grid(True, axis='y')  # Only horizontal lines

# Grid behind or in front of data
ax.set_axisbelow(True)  # Grid behind data

Legend Customization

Legend Positioning

# Location strings
ax.legend(loc='best')  # Automatic best position
ax.legend(loc='upper right')
ax.legend(loc='upper left')
ax.legend(loc='lower right')
ax.legend(loc='lower left')
ax.legend(loc='center')
ax.legend(loc='upper center')
ax.legend(loc='lower center')
ax.legend(loc='center left')
ax.legend(loc='center right')

# Precise positioning (bbox_to_anchor)
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')  # Outside plot area
ax.legend(bbox_to_anchor=(0.5, -0.15), loc='upper center', ncol=3)  # Below plot

Legend Styling

ax.legend(
    fontsize=12,
    frameon=True,           # Show frame
    framealpha=0.9,         # Frame transparency
    fancybox=True,          # Rounded corners
    shadow=True,            # Shadow effect
    ncol=2,                 # Number of columns
    title='Legend Title',   # Legend title
    title_fontsize=14,      # Title font size
    edgecolor='black',      # Frame edge color
    facecolor='white'       # Frame background color
)

Custom Legend Entries

from matplotlib.lines import Line2D

# Create custom legend handles
custom_lines = [Line2D([0], [0], color='red', lw=2),
                Line2D([0], [0], color='blue', lw=2, linestyle='--'),
                Line2D([0], [0], marker='o', color='w', markerfacecolor='green', markersize=10)]

ax.legend(custom_lines, ['Label 1', 'Label 2', 'Label 3'])

Layout and Spacing

Constrained Layout

# Preferred method (automatic adjustment)
fig, axes = plt.subplots(2, 2, constrained_layout=True)

Tight Layout

# Alternative method
fig, axes = plt.subplots(2, 2)
plt.tight_layout(pad=1.5, h_pad=2.0, w_pad=2.0)

Manual Adjustment

# Fine-grained control
plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1,
                    hspace=0.3, wspace=0.4)

Professional Publication Style

Example configuration for publication-quality figures:

# Publication style configuration
plt.rcParams.update({
    # Figure
    'figure.figsize': (8, 6),
    'figure.dpi': 100,
    'savefig.dpi': 300,
    'savefig.bbox': 'tight',
    'savefig.pad_inches': 0.1,

    # Font
    'font.family': 'sans-serif',
    'font.sans-serif': ['Arial', 'Helvetica'],
    'font.size': 11,

    # Axes
    'axes.labelsize': 12,
    'axes.titlesize': 14,
    'axes.linewidth': 1.5,
    'axes.grid': False,
    'axes.spines.top': False,
    'axes.spines.right': False,

    # Lines
    'lines.linewidth': 2,
    'lines.markersize': 8,

    # Ticks
    'xtick.labelsize': 10,
    'ytick.labelsize': 10,
    'xtick.major.size': 6,
    'ytick.major.size': 6,
    'xtick.major.width': 1.5,
    'ytick.major.width': 1.5,
    'xtick.direction': 'in',
    'ytick.direction': 'in',

    # Legend
    'legend.fontsize': 10,
    'legend.frameon': True,
    'legend.framealpha': 1.0,
    'legend.edgecolor': 'black'
})

Dark Theme

# Dark background style
plt.style.use('dark_background')

# Or manual configuration
plt.rcParams.update({
    'figure.facecolor': '#1e1e1e',
    'axes.facecolor': '#1e1e1e',
    'axes.edgecolor': 'white',
    'axes.labelcolor': 'white',
    'text.color': 'white',
    'xtick.color': 'white',
    'ytick.color': 'white',
    'grid.color': 'gray',
    'legend.facecolor': '#1e1e1e',
    'legend.edgecolor': 'white'
})

Color Accessibility

Colorblind-Friendly Palettes

# Use colorblind-friendly colormaps
colorblind_friendly = ['viridis', 'plasma', 'cividis']

# Colorblind-friendly discrete colors
cb_colors = ['#0173B2', '#DE8F05', '#029E73', '#CC78BC',
             '#CA9161', '#949494', '#ECE133', '#56B4E9']

# Test with simulation tools or use these validated palettes

High Contrast

# Ensure sufficient contrast
plt.rcParams['axes.edgecolor'] = 'black'
plt.rcParams['axes.linewidth'] = 2
plt.rcParams['xtick.major.width'] = 2
plt.rcParams['ytick.major.width'] = 2