Matplotlib

# Matplotlib

## Overview

Matplotlib is Python's foundational visualization library for creating static, animated, and interactive plots. This skill provides guidance on using matplotlib effectively, covering both the pyplot interface (MATLAB-style) and the object-oriented API (Figure/Axes), along with best practices for creating publication-quality visualizations.

## When to Use This Skill

This skill should be used when:
- Creating any type of plot or chart (line, scatter, bar, histogram, heatmap, contour, etc.)
- Generating scientific or statistical visualizations
- Customizing plot appearance (colors, styles, labels, legends)
- Creating multi-panel figures with subplots
- Exporting visualizations to various formats (PNG, PDF, SVG, etc.)
- Building interactive plots or animations
- Working with 3D visualizations
- Integrating plots into Jupyter notebooks or GUI applications

## Core Concepts

### The Matplotlib Hierarchy

Matplotlib uses a hierarchical structure of objects:

1. **Figure** - The top-level container for all plot elements
2. **Axes** - The actual plotting area where data is displayed (one Figure can contain multiple Axes)
3. **Artist** - Everything visible on the figure (lines, text, ticks, etc.)
4. **Axis** - The number line objects (x-axis, y-axis) that handle ticks and labels

### Two Interfaces

**1. pyplot Interface (Implicit, MATLAB-style)**
```python
import matplotlib.pyplot as plt

plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()
```
- Convenient for quick, simple plots
- Maintains state automatically
- Good for interactive work and simple scripts

**2. Object-Oriented Interface (Explicit)**
```python
import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4])
ax.set_ylabel('some numbers')
plt.show()
```
- **Recommended for most use cases**
- More explicit control over figure and axes
- Better for complex figures with multiple subplots
- Easier to maintain and debug

## Common Workflows

### 1. Basic Plot Creation

**Single plot workflow:**
```python
import matplotlib.pyplot as plt
import numpy as np

# Create figure and axes (OO interface - RECOMMENDED)
fig, ax = plt.subplots(figsize=(10, 6))

# Generate and plot data
x = np.linspace(0, 2*np.pi, 100)
ax.plot(x, np.sin(x), label='sin(x)')
ax.plot(x, np.cos(x), label='cos(x)')

# Customize
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('Trigonometric Functions')
ax.legend()
ax.grid(True, alpha=0.3)

# Save and/or display
plt.savefig('plot.png', dpi=300, bbox_inches='tight')
plt.show()
```

### 2. Multiple Subplots

**Creating subplot layouts:**
```python
# Method 1: Regular grid
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
axes[0, 0].plot(x, y1)
axes[0, 1].scatter(x, y2)
axes[1, 0].bar(categories, values)
axes[1, 1].hist(data, bins=30)

# Method 2: Mosaic layout (more flexible)
fig, axes = plt.subplot_mosaic([['left', 'right_top'],
                                 ['left', 'right_bottom']],
                                figsize=(10, 8))
axes['left'].plot(x, y)
axes['right_top'].scatter(x, y)
axes['right_bottom'].hist(data)

# Method 3: GridSpec (maximum control)
from matplotlib.gridspec import GridSpec
fig = plt.figure(figsize=(12, 8))
gs = GridSpec(3, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, :])  # Top row, all columns
ax2 = fig.add_subplot(gs[1:, 0])  # Bottom two rows, first column
ax3 = fig.add_subplot(gs[1:, 1:])  # Bottom two rows, last two columns
```

### 3. Plo