matplotlib

Matplotlib

Matplotlib is the grandfather of Python plotting. It is verbose but provides infinite control.

When to Use

• Publication-Quality Scientific Plotting: Generating exact, reproducible figures for academic papers, documentation, and reports.
• Fine-Grained Custom Visualizations: Complete control over every pixel, tick, axis spine, legend, and colorbar.
• Headless Server-Side Chart Rendering: Generating PNG, SVG, and PDF visualizations in backend microservices without GUI displays.
• Subplot Grid Layouts: Assembling multi-panel visual figure grids with GridSpec.

Quick Start

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

fig, ax = plt.subplots(figsize=(8, 4), dpi=100)
ax.plot(x, y1, label="sin(x)", color="#2563eb", linewidth=2)
ax.plot(x, y2, label="cos(x)", color="#dc2626", linestyle="--")

ax.set_title("Trigonometric Functions")
ax.set_xlabel("Time (s)")
ax.set_ylabel("Amplitude")
ax.legend()
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

Core Concepts

Object-Oriented Subplots & Modern Styling

Building clean, publication-ready multi-axis figures:

import matplotlib.pyplot as plt
import numpy as np

# Use non-interactive backend for server environments
import matplotlib
matplotlib.use('Agg')

# Set modern styling parameters
plt.style.use('seaborn-v0_8-whitegrid' if 'seaborn-v0_8-whitegrid' in plt.style.available else 'default')
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5), dpi=300)

x = np.linspace(0, 10, 200)
y1 = np.sin(x) * np.exp(-0.1 * x)
y2 = np.cos(x) * np.exp(-0.1 * x)

# Panel 1: Line plot with error bands
ax1.plot(x, y1, color='#2563eb', lw=2, label='Damped Sine')
ax1.fill_between(x, y1 - 0.2, y1 + 0.2, color='#2563eb', alpha=0.15)
ax1.set_title("Signal Decay", fontsize=12, fontweight='bold')
ax1.set_xlabel("Time (seconds)")
ax1.set_ylabel("Amplitude")
ax1.legend(loc='upper right')

# Panel 2: Distribution histogram
data = np.random.normal(loc=0, scale=1, size=1000)
ax2.hist(data, bins=30, color='#10b981', edgecolor='white', alpha=0.85)
ax2.set_title("Residual Distribution", fontsize=12, fontweight='bold')
ax2.set_xlabel("Value")
ax2.set_ylabel("Frequency")

plt.tight_layout()
fig.savefig("metrics_figure.png", dpi=300, bbox_inches='tight')
plt.close(fig)