/matplotlib

name: matplotlib
description: "Pythonのmatplotlibでグラフ・チャートを作成するスキル。PNG/PDF/SVG出力対応。 「グラフを作って」「チャートを描いて」「可視化して」「matplotlib」等のリクエストで発動。"
triggers:
  - グラフを作って
  - チャートを描いて
  - データを可視化して
  - matplotlib
  - プロットを作成
  - 棒グラフを描いて
  - 散布図を作って
license: https://github.com/matplotlib/matplotlib/tree/main/LICENSE
metadata:
    skill-author: K-Dense Inc.
source: github.com/K-Dense-AI/claude-scientific-skills@main

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)**

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)**

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:**

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:**

# 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. Plot Types and Use Cases

**Line plots** - Time series, continuous data, trends

ax.plot(x, y, linewidth=2, linestyle='--', marker='o', color='blue')

**Scatter plots** - Relationships between variables, correlations

ax.scatter(x, y, s=sizes, c=colors, alpha=0.6, cmap='viridis')

**Bar charts** - Categorical comparisons

ax.bar(categories, values, color='steelblue', edgecolor='black')
# For horizontal bars:
ax.barh(categories, values)

**Histograms** - Distributions

ax.hist(data, bins=30, edgecolor='black', alpha=0.7)

**Heatmaps** - Matrix data, correlations

im = ax.imshow(matrix, cmap='coolwarm', aspect='auto')
plt.colorbar(im, ax=ax)

**Contour plots** - 3D data on 2D plane

contour = ax.contour(X, Y, Z, levels=10)
ax.clabel(contour, inline=True, fontsize=8)

**Box plots** - Statistical distributions

ax.boxplot([data1, data2, data3], labels=['A', 'B', 'C'])

**Violin plots** - Distribution densities

ax.violinplot([data1, data2, data3], positions=[1, 2, 3])

For comprehensive plot type examples and variations, refer to `references/plot_types.md`.

4. Styling and Customization

**Color specification methods:**

• Named colors: `'red'`, `'blue'`, `'steelblue'`
• Hex codes: `'#FF5733'`
• RGB tuples: `(0.1, 0.2, 0.3)`
• Colormaps: `cmap='viridis'`, `cmap='plasma'`, `cmap='coolwarm'`

**Using style sheets:**

plt.style.use('seaborn-v0_8-darkgrid')  # Apply predefined style
# Available styles: 'ggplot', 'bmh', 'fivethirtyeight', etc.
print(plt.style.available)  # List all available styles

**Customizing with rcParams:**

plt.rcParams['font.size'] = 12
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['axes.titlesize'] = 16
plt.rcParams['xtick.labelsize'] = 10
plt.rcParams['ytick.labelsize'] = 10
plt.rcParams['legend.fontsize'] = 12
plt.rcParams['figure.titlesize'] = 18

**Text and annotations:**

ax.text(x, y, 'annotation', fontsize=12, ha='center')
ax.annotate('important point', xy=(x, y), xytext=(x+1, y+1),
            arrowprops=dict(arrowstyle='->', color='re