Matplotlib Cheat Sheet

Matplotlib is a popular Python library for creating static, interactive, and animated visualizations in a variety of formats. Below is a Matplotlib cheat sheet covering common plotting techniques and customization options:

Basic Plotting

Line Plot:

import matplotlib.pyplot as plt

plt.plot(x, y, label='label')
plt.xlabel('x-axis label')
plt.ylabel('y-axis label')
plt.title('Title')
plt.legend()
plt.show()

Scatter Plot:

plt.scatter(x, y, label='label', marker='o')

Bar Plot:

plt.bar(x, height, width=0.8, align='center', label='label')

Histogram:

plt.hist(data, bins=10, edgecolor='black')

Customization

Colors and Markers:

plt.plot(x, y, color='blue', linestyle='--', marker='o', markersize=8, label='label')

Title and Labels:

plt.title('Title', fontsize=16)
plt.xlabel('x-axis label', fontsize=12)
plt.ylabel('y-axis label', fontsize=12)

Legend:

plt.legend(loc='upper right', fontsize=10)

Grid:

plt.grid(True, linestyle='--', alpha=0.5)

Axis Limits:

plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)

Subplots

Subplots:

plt.subplot(rows, cols, index)

Multiple Plots:

plt.subplot(2, 1, 1)
plt.plot(x1, y1)

plt.subplot(2, 1, 2)
plt.plot(x2, y2)

Annotations and Text

Text Annotation:

plt.annotate('text', xy=(x, y), xytext=(x_text, y_text), arrowprops=dict(facecolor='black', shrink=0.05))

Text:

plt.text(x, y, 'text', fontsize=12, ha='center', va='center')

Save and Show

Save Figure:

plt.savefig('figure.png', dpi=300, bbox_inches='tight')

Show Plot:

plt.show()

Advanced Plots

Heatmap:

import seaborn as sns

sns.heatmap(data, cmap='viridis', annot=True)

3D Plot:

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z, c='r', marker='o')

Pie Chart:

labels = ['Label1', 'Label2', 'Label3']
sizes = [25, 40, 35]

plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90, colors=['red', 'green', 'blue'])

This cheat sheet provides a quick reference for common Matplotlib plotting techniques. For more detailed information and customization options, refer to the official Matplotlib documentation. Additionally, you can explore the Seaborn library for more advanced visualizations built on top of Matplotlib.