Matplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive plots in Python. This cheat sheet provides a production-grade reference for matplotlib ≥3.8 with emphasis on publication-quality figures, OOP interface, and performance optimization.

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#Getting Started

#Import Convention

import matplotlib.pyplot as plt
import numpy as np

# For high-quality figures
plt.rcParams['figure.dpi'] = 120
plt.rcParams['savefig.dpi'] = 300
plt.rcParams['font.size'] = 10
plt.rcParams['axes.labelsize'] = 12
plt.rcParams['axes.titlesize'] = 14
plt.rcParams['legend.fontsize'] = 10

#Two Interfaces

Description Code
Pyplot (procedural) - quick plt.plot(x, y); plt.show()
OOP (explicit) - production fig, ax = plt.subplots(); ax.plot(x, y); fig.savefig('fig.pdf')
Create figure and axes fig, ax = plt.subplots(figsize=(8, 6))
Create figure with grid fig, axes = plt.subplots(2, 2, figsize=(10, 8))
Get current figure/axes fig = plt.gcf(); ax = plt.gca()

#Basic Plot

x = np.linspace(0, 10, 100)
y = np.sin(x)
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title("Sine Wave")
ax.set_xlabel("x")
ax.set_ylabel("sin(x)")
plt.show()

#Plot Types

#Line Plot

Description Code
Simple line plot ax.plot(x, y)
With custom style ax.plot(x, y, 'r--o', linewidth=2, markersize=4)
Multiple series ax.plot(x, y1, label='sin'); ax.plot(x, y2, label='cos'); ax.legend()
Step plot ax.step(x, y, where='mid')
Stacked lines ax.stackplot(x, y1, y2, labels=['A','B'])
Fill between ax.fill_between(x, y1, y2, alpha=0.3)

#Scatter Plot

Description Code
Basic scatter ax.scatter(x, y)
Scatter with sizes ax.scatter(x, y, s=sizes, c=colors, alpha=0.6, cmap='viridis')
Scatter with categorical colors ax.scatter(x, y, c=labels, cmap='Set1', edgecolors='black')
3D scatter ax.scatter3D(x, y, z, c=z, cmap='viridis')

#Bar Charts

Description Code
Vertical bar ax.bar(categories, values, width=0.8, color='steelblue')
Horizontal bar ax.barh(categories, values, height=0.8)
Grouped bar ax.bar(x - width/2, y1, width, label='A'); ax.bar(x + width/2, y2, width, label='B')
Stacked bar ax.bar(x, y1, label='A'); ax.bar(x, y2, bottom=y1, label='B')
Error bars ax.bar(x, y, yerr=errors, capsize=5)

#Histograms

Description Code
Basic histogram ax.hist(data, bins=30, edgecolor='black')
Multiple histograms ax.hist([data1, data2], bins=30, label=['A','B'], alpha=0.6)
Density (normalized) ax.hist(data, bins=30, density=True, histtype='step')
Cumulative ax.hist(data, bins=30, cumulative=True, histtype='step')
2D histogram ax.hist2d(x, y, bins=30, cmap='viridis')

#Pie Charts

Description Code
Basic pie ax.pie(sizes, labels=labels, autopct='%1.1f%%')
Exploded pie ax.pie(sizes, labels=labels, explode=(0, 0.1, 0, 0), shadow=True)
Donut chart ax.pie(sizes, labels=labels, wedgeprops=dict(width=0.3))
Custom colors ax.pie(sizes, labels=labels, colors=plt.cm.Set3(range(len(sizes))))

#Box Plots

Description Code
Basic box plot ax.boxplot(data)
Grouped box plots ax.boxplot([data1, data2, data3], labels=['A','B','C'])
With notches ax.boxplot(data, notch=True, patch_artist=True)
Violin plot ax.violinplot(data, showmeans=True, showmedians=True)

#Customization

#Figure Elements

Description Code
Title ax.set_title("Title", fontsize=14, fontweight='bold')
Axis labels ax.set_xlabel("X Label", fontsize=12); ax.set_ylabel("Y Label")
Axis limits ax.set_xlim(0, 10); ax.set_ylim(-1.5, 1.5)
Grid ax.grid(True, linestyle='--', alpha=0.7)
Legend ax.legend(loc='best', frameon=True, fancybox=True, shadow=True)
Legend outside ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

#Line & Marker Styles

Description Code
Linestyles '-', '--', '-.', ':'
Markers '.', 'o', 'v', '^', 's', '*', '+', 'x'
Color specification 'r', '#FF5733', (0.2, 0.4, 0.6), 'red'
Line width linewidth=2 or lw=2
Marker size markersize=6 or ms=6
Transparency alpha=0.5

#Text & Annotations

Description Code
Add text ax.text(x, y, "Text", fontsize=10, ha='center', va='bottom')
Annotate with arrow ax.annotate("Peak", xy=(7, 1), xytext=(6, 1.5), arrowprops=dict(arrowstyle='->', color='red'))
Math expressions ax.text(5, 0.5, r'$E = mc^2$', fontsize=14)
Formula in title ax.set_title(r'$\sin(x) + \cos(x)$')

#Ticks & Scales

Description Code
Custom ticks ax.set_xticks([0, 2, 4, 6, 8, 10])
Custom tick labels ax.set_xticklabels(['zero', 'two', 'four', 'six', 'eight', 'ten'])
Rotate ticks plt.xticks(rotation=45)
Log scale ax.set_xscale('log'); ax.set_yscale('log')
Symlog ax.set_xscale('symlog', linthresh=0.01)
Twin axes ax2 = ax.twinx(); ax2.plot(x, y2, 'r-')

#Colormaps

Description Code
Sequential 'viridis', 'plasma', 'inferno', 'magma', 'cividis'
Diverging 'RdBu', 'RdBu_r', 'coolwarm', 'seismic'
Qualitative 'Set1', 'Set2', 'Set3', 'tab10', 'tab20'
Reversed 'viridis_r'
Use colormap plt.cm.viridis(np.linspace(0, 1, 10))

#Stylesheets

Description Code
Default plt.style.use('default')
Seaborn plt.style.use('seaborn-v0_8')
FiveThirtyEight plt.style.use('fivethirtyeight')
GGPlot plt.style.use('ggplot')
Dark background plt.style.use('dark_background')
Publication plt.style.use('bmh')
Available styles print(plt.style.available)

#Subplots & Layouts

#Creating Subplots

Description Code
2x2 grid fig, axes = plt.subplots(2, 2, figsize=(10, 8))
Access subplot axes[0, 0].plot(x, y)
Flatten axes array axes_flat = axes.flatten()
Share x-axis fig, axes = plt.subplots(2, 1, sharex=True)
Share y-axis fig, axes = plt.subplots(2, 1, sharey=True)
Add subplot manually ax = fig.add_subplot(2, 2, 1)
GridSpec gs = fig.add_gridspec(2, 2, width_ratios=[1, 2], height_ratios=[1, 2])
Span multiple cells ax = fig.add_subplot(gs[0, :])

#Layout Adjustments

Description Code
Automatic spacing plt.tight_layout(pad=1.5)
Fine-tune spacing plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1, wspace=0.3, hspace=0.3)
Constrained layout fig, axes = plt.subplots(constrained_layout=True)
Colorbar fig.colorbar(im, ax=axes, orientation='vertical', fraction=0.02, pad=0.02)

#Figure Size & DPI

Description Code
Set figure size fig = plt.figure(figsize=(8, 6))
Set DPI fig.set_dpi(150)
Auto-figure size fig.set_size_inches(8, 6)
Save with DPI fig.savefig('fig.png', dpi=300, bbox_inches='tight')

#Advanced Visualizations

#Image & Heatmap

Description Code
Display image ax.imshow(image, cmap='gray', interpolation='bilinear')
Heatmap im = ax.imshow(matrix, cmap='hot', aspect='auto')
Colorbar fig.colorbar(im, ax=ax, label='Intensity')
Hide axes ax.axis('off')
Aspect ratio ax.set_aspect('equal')

#Contour & Surface

Description Code
Contour lines contour = ax.contour(X, Y, Z, levels=20, cmap='viridis')
Filled contour ax.contourf(X, Y, Z, levels=20, cmap='viridis')
Contour labels ax.clabel(contour, inline=True, fontsize=8)
3D surface ax.plot_surface(X, Y, Z, cmap='viridis', alpha=0.8)
3D wireframe ax.plot_wireframe(X, Y, Z, color='black', alpha=0.3)

#3D Plots

Description Code
Setup 3D axes from mpl_toolkits.mplot3d import Axes3D; fig = plt.figure(); ax = fig.add_subplot(111, projection='3d')
3D line ax.plot3D(x, y, z)
3D scatter ax.scatter3D(x, y, z, c=z, cmap='viridis')
3D bar ax.bar3d(x, y, z, dx, dy, dz)
Set 3D view ax.view_init(elev=30, azim=45)

#Specialized Plots

Description Code
Error bars ax.errorbar(x, y, yerr=errors, xerr=x_errors, fmt='o', capsize=5)
Stem plot ax.stem(x, y)
Event plot ax.eventplot(data, lineoffsets=1, linelengths=0.5)
Stream plot ax.streamplot(X, Y, U, V, density=1.5)
Quiver (vector field) ax.quiver(x, y, u, v, scale=0.5)
Polar plot ax = fig.add_subplot(111, projection='polar'); ax.plot(theta, r)

#Working with Images

Description Code
Load image import matplotlib.image as mpimg; img = mpimg.imread('image.jpg')
Display image ax.imshow(img)
Save image mpimg.imsave('output.jpg', img, dpi=300)
Resize with interpolation ax.imshow(img, interpolation='bicubic')
RGB channels red = img[:,:,0]; green = img[:,:,1]; blue = img[:,:,2]

#For Machine Learning

#Training Curves

Description Code
Loss vs epochs ax.plot(epochs, train_loss, label='Train'); ax.plot(epochs, val_loss, label='Validation')
Learning rate schedule ax.semilogy(epochs, learning_rates)
Accuracy ax.plot(epochs, train_acc, 'b-', label='Train'); ax.plot(epochs, val_acc, 'r-', label='Val')
Multiple metrics ax1 = ax.twinx(); ax1.plot(epochs, loss, 'r-')

#Confusion Matrix

Description Code
Confusion matrix from sklearn.metrics import confusion_matrix; cm = confusion_matrix(y_true, y_pred)
Heatmap im = ax.imshow(cm, cmap='Blues', interpolation='nearest')
Annotate values for i in range(cm.shape[0]): for j in range(cm.shape[1]): ax.text(j, i, cm[i, j], ha='center', va='center')
Normalized cm_norm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

#ROC & Precision-Recall

Description Code
ROC curve from sklearn.metrics import roc_curve, auc; fpr, tpr, _ = roc_curve(y_true, y_scores); auc_val = auc(fpr, tpr)
ROC plot ax.plot(fpr, tpr, label=f'AUC = {auc_val:.2f}'); ax.plot([0,1], [0,1], 'k--')
Precision-Recall from sklearn.metrics import precision_recall_curve; precision, recall, _ = precision_recall_curve(y_true, y_scores)
PR plot ax.plot(recall, precision, label='PR Curve')

#Feature Analysis

Description Code
Feature importance ax.barh(feature_names, importances, color='steelblue')
Correlation matrix im = ax.imshow(corr_matrix, cmap='RdBu_r', vmin=-1, vmax=1)
Pairwise scatter import seaborn as sns; sns.pairplot(df)
Parallel coordinates from pandas.plotting import parallel_coordinates; parallel_coordinates(df, 'class')

#Animations

Description Code
Basic animation from matplotlib.animation import FuncAnimation; anim = FuncAnimation(fig, update, frames=100, interval=50)
Save animation anim.save('animation.gif', writer='pillow', fps=20)
Update function def update(frame): ax.clear(); ax.plot(x[:frame], y[:frame]); return ax,
Interactive animation plt.ion(); fig, ax = plt.subplots(); ax.plot(x, y); plt.pause(0.01); plt.ioff()

#Saving & Exporting

#File Formats

Description Code
PNG (high DPI) fig.savefig('figure.png', dpi=300, bbox_inches='tight')
PDF (vector) fig.savefig('figure.pdf', bbox_inches='tight')
SVG (vector) fig.savefig('figure.svg', bbox_inches='tight')
EPS (vector) fig.savefig('figure.eps', bbox_inches='tight')
Transparent background fig.savefig('figure.png', transparent=True)
With metadata fig.savefig('figure.pdf', metadata={'Author': 'Name', 'Title': 'Plot'})

#Publication Quality

Description Code
High-res for paper plt.rcParams['figure.dpi'] = 300
Fonts for publication plt.rcParams['font.family'] = 'serif'; plt.rcParams['font.serif'] = ['Times New Roman']
Multiple figures fig.savefig('figure.pdf', bbox_inches='tight', pad_inches=0.05)
Size constraints fig = plt.figure(figsize=(3.4, 2.4)) (single column)

#Show & Clear

Description Code
Display plot plt.show()
Clear current figure plt.clf()
Clear current axes plt.cla()
Close all figures plt.close('all')
Close specific figure plt.close(fig)
Suppress display plt.ioff()

#Performance & Memory

Description Code
Fast plotting (AGG backend) import matplotlib; matplotlib.use('Agg')
Interactive backend import matplotlib; matplotlib.use('TkAgg')
Reduce memory ax.plot(x[::10], y[::10]) (downsample)
Efficient scatter ax.scatter(x, y, s=1) (small markers)
Disable antialiasing ax.plot(x, y, antialiased=False)
Batch plotting fig, axes = plt.subplots(2, 2); for ax in axes.flat: ax.plot(x, y)

#Common Pitfalls & Best Practices

Pitfall Solution
Mixing pyplot and OOP Stick to one interface; prefer OOP for production
Overlapping legends Place legend outside: ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
Small fonts in publication Set plt.rcParams['font.size'] = 12 globally
Slow rendering Downsample data or use agg backend
Figures cut off Use bbox_inches='tight' in savefig
Memory leaks in loops Use plt.clf() or plt.close() after each iteration
Inconsistent colormaps Use same colormap for comparable visualizations
Missing axis labels Always label for reproducibility
3D plot overlapping Adjust view with ax.view_init()