data-visualization

帮助你用 Python 制作清晰专业的数据可视化并选择合适图表。

来源

GitHub

更新于

2026-06-07

// 安全评估低风险

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凭证密钥
网络外发
代码执行
数据访问
来源供应链

// 安装

复制安装指令，让 AI 自动完成配置 · 推荐新手

请帮我安装 askskill 上的 "data-visualization" 技能：
1. 下载 https://raw.githubusercontent.com/anthropics/knowledge-work-plugins/main/data/skills/data-visualization/SKILL.md
2. 保存为 ~/.claude/skills/data-visualization/SKILL.md
3. 装好后重载技能，告诉我可以用了

// 下载

下载 SKILL.md机读安装清单 ↗

// 用法示例

为销售数据选择图表

输入

我有一份包含月份、地区、销售额和利润率的表格，请先判断每个字段适合展示的图表类型，再用 Python 分别用 matplotlib、seaborn 或 plotly 生成 3 个清晰的可视化方案，并说明为什么这样选。

预期产出

给出图表选型建议、对应 Python 代码，以及每种图表的使用理由。

生成论文级图表

输入

请把下面的实验结果画成适合论文发表的图表，要求字体统一、配色克制、坐标轴和图例规范，并输出可直接运行的 matplotlib 代码。如果有必要，请加入误差线和子图排版建议。

预期产出

返回高质量科研图表代码，并附带版式与标注优化建议。

优化可访问性配色

输入

我现有一张分类数据图表，但颜色区分不明显。请基于可访问性和色彩理论，重新设计一套适合色觉缺陷用户的配色方案，并用 seaborn 或 plotly 重写示例代码。

预期产出

提供更易读的配色方案、改进后的代码，以及可访问性设计说明。

// 文档

Data Visualization Skill

Chart selection guidance, Python visualization code patterns, design principles, and accessibility considerations for creating effective data visualizations.

Chart Selection Guide

Choose by Data Relationship

What You're Showing	Best Chart	Alternatives
Trend over time	Line chart	Area chart (if showing cumulative or composition)
Comparison across categories	Vertical bar chart	Horizontal bar (many categories), lollipop chart
Ranking	Horizontal bar chart	Dot plot, slope chart (comparing two periods)
Part-to-whole composition	Stacked bar chart	Treemap (hierarchical), waffle chart
Composition over time	Stacked area chart	100% stacked bar (for proportion focus)
Distribution	Histogram	Box plot (comparing groups), violin plot, strip plot
Correlation (2 variables)	Scatter plot	Bubble chart (add 3rd variable as size)
Correlation (many variables)	Heatmap (correlation matrix)	Pair plot
Geographic patterns	Choropleth map	Bubble map, hex map
Flow / process	Sankey diagram	Funnel chart (sequential stages)
Relationship network	Network graph	Chord diagram
Performance vs. target	Bullet chart	Gauge (single KPI only)
Multiple KPIs at once	Small multiples	Dashboard with separate charts

When NOT to Use Certain Charts

Pie charts: Avoid unless <6 categories and exact proportions matter less than rough comparison. Humans are bad at comparing angles. Use bar charts instead.
3D charts: Never. They distort perception and add no information.
Dual-axis charts: Use cautiously. They can mislead by implying correlation. Clearly label both axes if used.
Stacked bar (many categories): Hard to compare middle segments. Use small multiples or grouped bars instead.
Donut charts: Slightly better than pie charts but same fundamental issues. Use for single KPI display at most.

Python Visualization Code Patterns

Setup and Style

import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import seaborn as sns
import pandas as pd
import numpy as np

# Professional style setup
plt.style.use('seaborn-v0_8-whitegrid')
plt.rcParams.update({
    'figure.figsize': (10, 6),
    'figure.dpi': 150,
    'font.size': 11,
    'axes.titlesize': 14,
    'axes.titleweight': 'bold',
    'axes.labelsize': 11,
    'xtick.labelsize': 10,
    'ytick.labelsize': 10,
    'legend.fontsize': 10,
    'figure.titlesize': 16,
})

# Colorblind-friendly palettes
PALETTE_CATEGORICAL = ['#4C72B0', '#DD8452', '#55A868', '#C44E52', '#8172B3', '#937860']
PALETTE_SEQUENTIAL = 'YlOrRd'
PALETTE_DIVERGING = 'RdBu_r'

Line Chart (Time Series)

fig, ax = plt.subplots(figsize=(10, 6))

for label, group in df.groupby('category'):
    ax.plot(group['date'], group['value'], label=label, linewidth=2)

ax.set_title('Metric Trend by Category', fontweight='bold')
ax.set_xlabel('Date')
ax.set_ylabel('Value')
ax.legend(loc='upper left', frameon=True)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

# Format dates on x-axis
fig.autofmt_xdate()

plt.tight_layout()
plt.savefig('trend_chart.png', dpi=150, bbox_inches='tight')

Bar Chart (Comparison)

fig, ax = plt.subplots(figsize=(10, 6))

# Sort by value for easy reading
df_sorted = df.sort_values('metric', ascending=True)

bars = ax.barh(df_sorted['category'], df_sorted['metric'], color=PALETTE_CATEGORICAL[0])

# Add value labels
for bar in bars:
    width = bar.get_width()
    ax.text(width + 0.5, bar.get_y() + bar.get_height()/2,
            f'{width:,.0f}', ha='left', va='center', fontsize=10)

ax.set_title('Metric by Category (Ranked)', fontweight='bold')
ax.set_xlabel('Metric Value')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

plt.tight_layout()

…

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