The Ultimate Matplotlib Cheatsheet

import matplotlib.pyplot as plt
import numpy as np

# Simple line plot
plt.plot([1, 2, 3, 4], [1, 4, 2, 3])
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('My First Plot')
plt.show()

# Jupyter magic — renders plots inside notebooks
%matplotlib inline        # static images
%matplotlib widget        # interactive widgets
%matplotlib qt            # separate Qt window

# For scripts / non-interactive use
import matplotlib
matplotlib.use('Agg')     # non-interactive backend
plt.savefig('plot.png')

# Explicit figure & axes (recommended)
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x, y, label='data')
ax.set_xlabel('Time (s)')
ax.set_ylabel('Amplitude')
ax.legend()
fig.tight_layout()
plt.show()

# Grid of subplots
fig, axes = plt.subplots(2, 2, figsize=(12, 8))

axes[0, 0].plot(x, y1)           # top-left
axes[0, 1].bar(cats, vals)       # top-right
axes[1, 0].scatter(x, y2)        # bottom-left
axes[1, 1].hist(data, bins=30)   # bottom-right

fig.suptitle('Dashboard')
plt.tight_layout()

# Mosaic layout (matplotlib ≥ 3.3)
fig = plt.figure(figsize=(12, 8))
ax_dict = fig.subplot_mosaic('''
    AB
    CD
    EE
''')
ax_dict['A'].plot(x, y)
ax_dict['B'].bar(c, v)
ax_dict['E'].pie(vals, labels=c)  # spans full width

import matplotlib.gridspec as gridspec

fig = plt.figure(figsize=(14, 8))
gs = gridspec.GridSpec(3, 3, hspace=0.4, wspace=0.3)

ax1 = fig.add_subplot(gs[0, :])    # top row, all cols
ax2 = fig.add_subplot(gs[1, :2])   # middle-left 2 cols
ax3 = fig.add_subplot(gs[1:, 2])   # right column, rows 1-2
ax4 = fig.add_subplot(gs[2, :2])   # bottom-left 2 cols

# Inset axes
ax_inset = ax1.inset_axes([0.6, 0.5, 0.35, 0.35])
ax_inset.plot(x, y_zoom)

import numpy as np
x = np.linspace(0, 2 * np.pi, 200)
y = np.sin(x)

# Line with style
plt.plot(x, y, color='#2196F3', linewidth=2,
         linestyle='--', marker='o', markersize=4,
         label='sin(x)')

# Scatter — variable size & color
rng = np.random.default_rng(42)
colors = rng.random(100)
sizes = rng.uniform(10, 200, 100)
plt.scatter(rng.normal(0, 1, 100),
            rng.normal(0, 1, 100),
            c=colors, s=sizes, alpha=0.6, cmap='viridis')
plt.colorbar(label='Value')

categories = ['A', 'B', 'C', 'D']
values = [23, 45, 56, 78]
errors = [3, 5, 2, 7]

# Bar with error bars
plt.bar(categories, values, yerr=errors,
        capsize=5, color=['#4CAF50','#2196F3','#FF9800','#F44336'],
        edgecolor='white', linewidth=1.5)

# Stacked bar
plt.bar(cats, v1, label='Group 1', color='steelblue')
plt.bar(cats, v2, bottom=v1, label='Group 2', color='coral')

# Histogram
data = rng.standard_normal(5000)
plt.hist(data, bins=50, density=True, alpha=0.7,
         color='teal', edgecolor='white')
plt.hist(data, bins=50, density=True, alpha=0.4,
         color='orange', edgecolor='white',
         histtype='stepfilled', cumulative=True)

# Pie chart
labels = ['Python', 'R', 'Julia', 'Scala']
sizes = [40, 30, 20, 10]
explode = (0.05, 0, 0, 0)
plt.pie(sizes, labels=labels, autopct='%1.1f%%',
        explode=explode, shadow=True, startangle=140,
        wedgeprops=dict(edgecolor='white', linewidth=2))

# Box plot
plt.boxplot([rng.normal(m, s, 200)
             for m, s in [(0,1),(2,1.5),(4,0.5)]],
            labels=['Group A','Group B','Group C'],
            patch_artist=True, notch=True,
            boxprops=dict(facecolor='lightblue', alpha=0.7))

# Violin plot
plt.violinplot([g1, g2, g3], showmeans=True,
               showmedians=True)

x = np.linspace(-3, 3, 200)
y = np.linspace(-3, 3, 200)
X, Y = np.meshgrid(x, y)
Z = np.sin(X) * np.cos(Y)

# Contour lines
plt.contour(X, Y, Z, levels=15, cmap='RdBu')
# Filled contour
plt.contourf(X, Y, Z, levels=15, cmap='RdBu')
plt.colorbar(label='f(x, y)')

# Color formats
plt.plot(x, y, color='#FF5722')         # hex
plt.plot(x, y, color='dodgerblue')       # named
plt.plot(x, y, color=(0.2, 0.6, 0.8))   # RGB tuple 0-1
plt.plot(x, y, color='C3')               # property cycle

# Colormaps
cmap = plt.cm.get_cmap('viridis', 10)   # discrete: 10 colors
colors = cmap(np.linspace(0, 1, 5))

# Linestyles
for ls in ['-', '--', '-.', ':', (0, (5, 1)), (0, (3, 1, 1, 1))]:
    plt.plot(x, y, linestyle=ls, linewidth=2)

ax.set_xlabel('Time (s)', fontsize=12, color='gray')
ax.set_ylabel('Value', fontsize=12, labelpad=10)
ax.set_title('My Plot', fontsize=16, fontweight='bold', loc='left')

# Ticks & limits
ax.set_xlim(0, 100)
ax.set_ylim(-1, 1)
ax.set_xticks([0, 25, 50, 75, 100])
ax.set_xticklabels(['Start', 'Q1', 'Mid', 'Q3', 'End'],
                    rotation=45, ha='right')
ax.tick_params(axis='both', which='major', labelsize=10)

# Log / symlog scale
ax.set_xscale('log')
ax.set_yscale('symlog', linthresh=0.01)

# Legend placement
ax.legend(loc='upper right', frameon=True, shadow=True,
          fancybox=True, framealpha=0.9, fontsize=9)
ax.legend(loc='lower center', ncol=3, bbox_to_anchor=(0.5, -0.15))

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

# Spines
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color('gray')

# Built-in styles
plt.style.use('ggplot')          # R-style
plt.style.use('seaborn-v0_8')    # seaborn-inspired
plt.style.use('dark_background')
plt.style.use('fivethirtyeight')

# Context manager — temporary style
with plt.style.context('bmh'):
    plt.plot(x, y)
    plt.show()

# Custom rcParams
plt.rcParams.update({
    'font.size': 12,
    'axes.titlesize': 14,
    'figure.facecolor': '#1a1a2e',
    'axes.facecolor': '#16213e',
    'text.color': 'white',
    'axes.labelcolor': '#aaa',
    'xtick.color': '#aaa',
    'ytick.color': '#aaa',
})

ax.annotate('Peak value',
    xy=(3, 0.95),                   # arrow tip
    xytext=(5, 0.7),                # text position
    arrowprops=dict(
        arrowstyle='->',            # '->', '-|>', '<->', 'fancy'
        color='red',
        connectionstyle='arc3,rad=0.3',
        lw=1.5,
    ),
    fontsize=11, fontweight='bold',
    bbox=dict(boxstyle='round,pad=0.3',
              facecolor='lightyellow', alpha=0.9))

# Text on axes
ax.text(0.5, 0.5, 'Centered',
        transform=ax.transAxes,   # relative coords 0-1
        ha='center', va='center',
        fontsize=20, alpha=0.3,
        fontweight='bold', color='gray')

# Title & subtitle
ax.set_title('Main Title\nSubtitle line',
             fontsize=16, linespacing=1.5)

# Figure-level text
fig.text(0.5, 0.01, 'Data source: WHO 2024',
         ha='center', fontsize=9, color='gray')

# Arrows
ax.annotate('', xy=(x2, y2), xytext=(x1, y1),
            arrowprops=dict(arrowstyle='->', color='black', lw=2))

# Spanning highlights
ax.axvspan(xmin=2, xmax=4, alpha=0.1, color='red', label='Anomaly')
ax.axhspan(ymin=0.8, ymax=1.0, alpha=0.1, color='green')
ax.axvline(x=5, color='gray', linestyle='--', linewidth=0.8)
ax.axhline(y=0, color='gray', linewidth=0.8)

# Mathtext (no LaTeX install needed)
ax.set_xlabel(r'$\sigma = \frac{1}{\sqrt{2\pi}}$')
ax.set_title(r'$\int_0^\infty e^{-x^2} dx = \frac{\sqrt{\pi}}{2}$')

# Full LaTeX (requires LaTeX installation)
plt.rcParams.update({'text.usetex': True})
ax.set_xlabel(r'$\sigma = \frac{1}{\sqrt{2\pi}}$')

from mpl_toolkits.mplot3d import Axes3D
import numpy as np

fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111, projection='3d')

# Surface plot
x = np.linspace(-5, 5, 100)
y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2))

ax.plot_surface(X, Y, Z, cmap='plasma',
                alpha=0.8, edgecolor='none',
                antialiased=True, rstride=2, cstride=2)
ax.set_zlabel('Z')
ax.view_init(elev=30, azim=45)

# 3D scatter
ax.scatter(xs, ys, zs, c=zs, cmap='viridis',
           s=40, alpha=0.7, edgecolors='k', linewidth=0.5)

# Wireframe
ax.plot_wireframe(X, Y, Z, color='steelblue',
                  linewidth=0.5, alpha=0.5,
                  rstride=5, cstride=5)

# 3D line
ax.plot(xs, ys, zs, color='red', linewidth=2, label='trajectory')
ax.legend()

# 3D contour (projection on floor)
ax.contour(X, Y, Z, zdir='z', offset=-1, cmap='coolwarm')

# 2D contour projection
ax.contourf(X, Y, Z, zdir='x', offset=-5,
            cmap='coolwarm', alpha=0.5)

# Set pane colors (dark theme)
ax.xaxis.pane.fill = False
ax.yaxis.pane.fill = False
ax.zaxis.pane.fill = False
ax.xaxis.pane.set_edgecolor('gray')
ax.grid(True, alpha=0.3)

# Basic save
fig.savefig('plot.png', dpi=300, bbox_inches='tight')

# Vector formats — publication quality
fig.savefig('plot.pdf')            # PDF
fig.savefig('plot.svg')            # SVG
fig.savefig('plot.eps')            # EPS

# High-res raster
fig.savefig('plot.tiff', dpi=600,
            bbox_inches='tight',
            facecolor=fig.get_facecolor(),
            transparent=False)

fig.savefig(
    'output.png',
    dpi=300,                      # resolution (dots per inch)
    bbox_inches='tight',          # trim whitespace
    pad_inches=0.1,              # padding around plot
    facecolor='white',            # figure background
    transparent=False,            # transparent background
    metadata={'Author': 'You', 'Date': '2024'},
)