Matplotlib Python Library Cheatsheet

Matplotlib is a powerful and versatile plotting library for Python that is widely used in the data science and scientific computing communities. Whether you’re creating simple line charts or complex 3D plots, Matplotlib provides a flexible and comprehensive toolkit. To help you navigate through the vast capabilities of Matplotlib, here’s a handy cheatsheet that covers some of the most commonly used functions and features.

1. Importing Matplotlib

import matplotlib.pyplot as plt

This is the standard way to import Matplotlib. The plt alias is commonly used for brevity.

2. Basic Line Plot

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.plot(x, y)
plt.xlabel('X-axis Label')
plt.ylabel('Y-axis Label')
plt.title('Simple Line Plot')
plt.show()

3. Scatter Plot

plt.scatter(x, y, color='red', marker='o', label='Data Points')
plt.legend()
plt.xlabel('X-axis Label')
plt.ylabel('Y-axis Label')
plt.title('Scatter Plot')
plt.show()

4. Bar Chart

categories = ['Category A', 'Category B', 'Category C']
values = [25, 50, 75]

plt.bar(categories, values, color=['blue', 'green', 'orange'])
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Chart')
plt.show()

5. Histogram

data = [1, 1, 2, 3, 3, 3, 4, 4, 5, 5, 5, 5]

plt.hist(data, bins=5, color='skyblue', edgecolor='black')
plt.xlabel('Values')
plt.ylabel('Frequency')
plt.title('Histogram')
plt.show()

6. Pie Chart

labels = ['Label A', 'Label B', 'Label C']
sizes = [30, 45, 25]

plt.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90, colors=['gold', 'lightcoral', 'lightskyblue'])
plt.axis('equal')  # Equal aspect ratio ensures that the pie is drawn as a circle.
plt.title('Pie Chart')
plt.show()

7. Subplots

# Creating a 2x2 grid of subplots
plt.subplot(2, 2, 1)
plt.plot(x, y)
plt.title('Subplot 1')

plt.subplot(2, 2, 2)
plt.scatter(x, y, color='red')
plt.title('Subplot 2')

plt.subplot(2, 2, 3)
plt.bar(categories, values, color=['blue', 'green', 'orange'])
plt.title('Subplot 3')

plt.subplot(2, 2, 4)
plt.hist(data, bins=5, color='skyblue', edgecolor='black')
plt.title('Subplot 4')

plt.suptitle('Subplots')
plt.show()

8. Customizing Plots

# Example of customizing line plot
plt.plot(x, y, linestyle='--', marker='o', color='purple', label='Customized Line')
plt.legend()
plt.grid(True)
plt.title('Customized Line Plot')
plt.xlabel('X-axis Label')
plt.ylabel('Y-axis Label')
plt.show()

These examples cover only a fraction of Matplotlib’s capabilities. As you delve deeper into data visualization and exploration, you’ll find Matplotlib to be a powerful ally. Feel free to experiment with these examples and explore the extensive Matplotlib documentation for more advanced features and customization options.

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