Style Plots using Matpotlib

Using Built-in Styles in Matplotlib

import matplotlib.pyplot as plt

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

# Apply a specific style
plt.style.use('ggplot')

# Sample plot
x = [1, 2, 3, 4, 5]
y = [2, 5, 3, 8, 6]
plt.plot(x, y)
plt.title("Styled Line Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.show()

Customizing Line Style and Width

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y, linestyle='--', linewidth=2, color='purple')
plt.title("Dashed Sine Wave")
plt.xlabel("Time")
plt.ylabel("Amplitude")
plt.grid(True)
plt.show()

Temporarily Applying a Style

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2 * np.pi, 100)
y = np.cos(x)

with plt.style.context('seaborn-darkgrid'):
    plt.plot(x, y, marker='o', color='green')
    plt.title("Cosine Wave with Temporary Style")
    plt.xlabel("Angle")
    plt.ylabel("Cosine Value")
    plt.show()

Conclusion

Styling plots using Matplotlib enhances the visual clarity and professionalism of your charts. Whether you're using built-in themes, customizing line properties, or applying temporary styles, Matplotlib offers powerful options to present your data in the most effective way.