matplotlib-viz

Matplotlib Visualization

Scientific visualization and publication-quality figures using Matplotlib and NumPy.

When to Use

• Static plots: line, scatter, bar, histogram, heatmap
• Publication-ready scientific figures
• Multi-panel (subplot) layouts
• Saving figures to PNG, SVG, or PDF
• Annotated or styled plots for presentations and papers

When NOT to Use

• Interactive dashboards (use Plotly or Dash)
• Web-based charts (use D3.js or Chart.js)
• Real-time streaming visualizations
• Geographic/map plots (use Cartopy or Folium)

Basic Setup

import matplotlib
matplotlib.use('Agg')  # non-interactive backend for saving files
import matplotlib.pyplot as plt
import numpy as np

Line and Scatter Plots

x = np.linspace(0, 10, 100)
fig, ax = plt.subplots(figsize=(8, 5))
ax.plot(x, np.sin(x), label='sin(x)', linewidth=2)
ax.plot(x, np.cos(x), label='cos(x)', linestyle='--')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Trigonometric Functions')
ax.legend()
fig.savefig('line_plot.png', dpi=150, bbox_inches='tight')
plt.close(fig)

# Scatter with colormap
fig, ax = plt.subplots()
sc = ax.scatter(x_data, y_data, c=color_values, cmap='viridis', s=50, alpha=0.7)
fig.colorbar(sc, ax=ax, label='Magnitude')
fig.savefig('scatter.png', dpi=150, bbox_inches='tight')
plt.close(fig)

Bar Charts and Histograms

categories = ['A', 'B', 'C', 'D']
values = [23, 45, 12, 67]
fig, ax = plt.subplots()
ax.bar(categories, values, color='steelblue', edgecolor='black')
ax.set_ylabel('Count')
fig.savefig('bar_chart.png', dpi=150, bbox_inches='tight')
plt.close(fig)

# Histogram with KDE overlay
fig, ax = plt.subplots()
ax.hist(data, bins=30, density=True, alpha=0.7, color='skyblue', edgecolor='black')
ax.set_xlabel('Value')
ax.set_ylabel('Density')
fig.savefig('histogram.png', dpi=150, bbox_inches='tight')
plt.close(fig)

Heatmaps

data_matrix = np.random.rand(10, 10)
fig, ax = plt.subplots(figsize=(8, 6))
im = ax.imshow(data_matrix, cmap='coolwarm', aspect='auto')
fig.colorbar(im, ax=ax)
ax.set_xticks(range(10))
ax.set_yticks(range(10))
fig.savefig('heatmap.png', dpi=150, bbox_inches='tight')
plt.close(fig)