matplotlib

Matplotlib - Data Visualization

The most widely used library for 2D (and basic 3D) plotting. It provides full control over every element of a figure, from line styles to axis spines.

When to Use

• Creating publication-quality 2D plots (Line, Scatter, Bar, Hist)
• Visualizing scientific data (Heatmaps, Contours, Vector fields)
• Generating complex multi-panel figures
• Fine-tuning plots for papers/reports (LaTeX support)
• Building custom visualization tools and dashboards
• Plotting data directly from NumPy arrays or Pandas DataFrames

Reference Documentation

Official docs: https://matplotlib.org/stable/index.html
Gallery: https://matplotlib.org/stable/gallery/index.html (Essential for finding examples)
Search patterns: plt.subplots, ax.set_title, ax.legend, plt.savefig, matplotlib.colors

Core Principles

Two Interfaces: Choose Wisely

Interface	Method	Use Case
Object-Oriented (OO)	fig, ax = plt.subplots()	Recommended. Best for complex, reproducible plots.
Pyplot (State-based)	plt.plot(x, y)	Quick interactive checks. Avoid for scripts/modules.

Use Matplotlib For

• High-level control over figure layout.
• Precise styling for publication.
• Embedding plots in GUI applications.

Do NOT Use For

• Interactive web dashboards (use Plotly or Bokeh).
• Rapid statistical exploration (use Seaborn — it's built on Matplotlib but simpler for stats).
• Very large datasets (>1M points) in real-time (use Datashader or VisPy).

Quick Reference

Installation

pip install matplotlib

Standard Imports

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib import gridspec

Basic Pattern - The OO Interface (The "Proper" Way)

import matplotlib.pyplot as plt
import numpy as np

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

# 1. Create Figure and Axis objects
fig, ax = plt.subplots(figsize=(8, 5))

# 2. Plot data
ax.plot(x, y, label='Sine Wave', color='tab:blue', linewidth=2)

# 3. Customize
ax.set_xlabel('Time (s)')
ax.set_ylabel('Amplitude')
ax.set_title('Oscillation Example')
ax.legend()
ax.grid(True, linestyle='--')

# 4. Show or Save
plt.show()
# fig.savefig('plot.pdf', dpi=300, bbox_inches='tight')