A well-designed plot improves the readability and comprehension of the data presented. In this chapter, we will explore various ways to customize and adapt plots using Matplotlib.
Clear axis and plot titles are essential for understanding a plot.
import matplotlib.pyplot as plt
import numpy as np
t = np.linspace(0, 10, 100)
y = np.sin(t)
plt.plot(t, y, label='sin(t)', color='b')
plt.xlabel('Time (s)', fontsize=12)
plt.ylabel('Amplitude', fontsize=12)
plt.title('Line Plot with Labels', fontsize=14)
plt.legend()
plt.show()
The scaling of the axes should be chosen appropriately to best represent the data.
plt.plot(t, y, label='sin(t)', color='b')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.xlim(0, 10)
plt.ylim(-1.2, 1.2)
plt.grid(True, linestyle='--', alpha=0.7)
plt.title('Line Plot with Adjusted Axes')
plt.legend()
plt.show()
Colors and line styles help to highlight important information in the plot.
| Color | Code | Description |
|---|---|---|
| Blue | ‘b’ | blue |
| Green | ‘g’ | green |
| Red | ‘r’ | red |
| Cyan | ‘c’ | cyan |
| Magenta | ‘m’ | magenta |
| Yellow | ‘y’ | yellow |
| Black | ‘k’ | black |
| White | ‘w’ | white |
| Line Style | Code | Description |
|---|---|---|
| Solid | ‘-’ | default line |
| Dashed | ‘–’ | long dashes |
| Dotted | ‘:’ | only dots |
| Dash-dot | ‘-.’ | alternating dash-dot |
plt.plot(t, np.sin(t), linestyle='-', color='r', label='sin(t)')
plt.plot(t, np.cos(t), linestyle='--', color='g', label='cos(t)')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.title('Customization of Colors and Line Styles')
plt.legend()
plt.show()
Sometimes it’s useful to display multiple plots in a single figure.
fig, axs = plt.subplots(2, 1, figsize=(6, 6))
axs[0].plot(t, np.sin(t), color='b')
axs[0].set_title('Sine Function')
axs[1].plot(t, np.cos(t), color='r')
axs[1].set_title('Cosine Function')
plt.tight_layout()
plt.show()
Plots can be saved in various formats.
plt.plot(t, y, label='sin(t)', color='b')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude')
plt.title('Saving a Plot')
plt.legend()
plt.savefig('my_plot.png', dpi=300)
plt.show()
With careful customization, scientific plots can be significantly improved. In the next chapter, we will explore advanced techniques such as logarithmic scales and annotations.