数据可视化指南：图表选择、Python代码与设计原则

data-visualization by anthropics/knowledge-work-plugins

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安装命令

npx skills add https://github.com/anthropics/knowledge-work-plugins --skill data-visualization

开发数据分析

🇨🇳中文介绍

数据可视化技能

创建有效数据可视化的图表选择指南、Python 可视化代码模式、设计原则和无障碍访问注意事项。

图表选择指南

根据数据关系选择

要展示的内容	最佳图表	备选方案
随时间变化的趋势	折线图	面积图（如果显示累计值或构成）
跨类别比较	垂直条形图	水平条形图（类别多时）、棒棒糖图
排名	水平条形图	点图、斜率图（比较两个时期）
部分与整体构成	堆叠条形图	树状图（分层）、华夫饼图
随时间变化的构成	堆叠面积图	100% 堆叠条形图（侧重比例）
分布	直方图	箱线图（比较组别）、小提琴图、带状图

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何时不应使用某些图表

饼图：除非类别少于6个且精确比例不如粗略比较重要，否则应避免使用。人类不擅长比较角度。请改用条形图。
3D图表：永远不要使用。它们会扭曲感知且不增加任何信息。
双轴图表：谨慎使用。它们可能通过暗示相关性而产生误导。如果使用，请清晰标注两个轴。
堆叠条形图（类别多时）：难以比较中间部分。请改用小多图或分组条形图。
环形图：比饼图稍好，但存在相同的基本问题。最多用于显示单一KPI。

Python 可视化代码模式

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

# 专业样式设置
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,
})

# 色盲友好调色板
PALETTE_CATEGORICAL = ['#4C72B0', '#DD8452', '#55A868', '#C44E52', '#8172B3', '#937860']
PALETTE_SEQUENTIAL = 'YlOrRd'
PALETTE_DIVERGING = 'RdBu_r'

折线图（时间序列）

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)

# 格式化 x 轴日期
fig.autofmt_xdate()

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

条形图（比较）

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

# 按值排序以便阅读
df_sorted = df.sort_values('metric', ascending=True)

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

# 添加数值标签
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()
plt.savefig('bar_chart.png', dpi=150, bbox_inches='tight')

直方图（分布）

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

ax.hist(df['value'], bins=30, color=PALETTE_CATEGORICAL[0], edgecolor='white', alpha=0.8)

# 添加均值和中间值线
mean_val = df['value'].mean()
median_val = df['value'].median()
ax.axvline(mean_val, color='red', linestyle='--', linewidth=1.5, label=f'Mean: {mean_val:,.1f}')
ax.axvline(median_val, color='green', linestyle='--', linewidth=1.5, label=f'Median: {median_val:,.1f}')

ax.set_title('Distribution of Values', fontweight='bold')
ax.set_xlabel('Value')
ax.set_ylabel('Frequency')
ax.legend()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

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

fig, ax = plt.subplots(figsize=(10, 8))

# 透视数据以适应热力图格式
pivot = df.pivot_table(index='row_dim', columns='col_dim', values='metric', aggfunc='sum')

sns.heatmap(pivot, annot=True, fmt=',.0f', cmap='YlOrRd',
            linewidths=0.5, ax=ax, cbar_kws={'label': 'Metric Value'})

ax.set_title('Metric by Row Dimension and Column Dimension', fontweight='bold')
ax.set_xlabel('Column Dimension')
ax.set_ylabel('Row Dimension')

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

categories = df['category'].unique()
n_cats = len(categories)
n_cols = min(3, n_cats)
n_rows = (n_cats + n_cols - 1) // n_cols

fig, axes = plt.subplots(n_rows, n_cols, figsize=(5*n_cols, 4*n_rows), sharex=True, sharey=True)
axes = axes.flatten() if n_cats > 1 else [axes]

for i, cat in enumerate(categories):
    ax = axes[i]
    subset = df[df['category'] == cat]
    ax.plot(subset['date'], subset['value'], color=PALETTE_CATEGORICAL[i % len(PALETTE_CATEGORICAL)])
    ax.set_title(cat, fontsize=12)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

# 隐藏空的子图
for j in range(i+1, len(axes)):
    axes[j].set_visible(False)

fig.suptitle('Trends by Category', fontsize=14, fontweight='bold', y=1.02)
plt.tight_layout()
plt.savefig('small_multiples.png', dpi=150, bbox_inches='tight')

数字格式化辅助函数

def format_number(val, format_type='number'):
    """Format numbers for chart labels."""
    if format_type == 'currency':
        if abs(val) >= 1e9:
            return f'${val/1e9:.1f}B'
        elif abs(val) >= 1e6:
            return f'${val/1e6:.1f}M'
        elif abs(val) >= 1e3:
            return f'${val/1e3:.1f}K'
        else:
            return f'${val:,.0f}'
    elif format_type == 'percent':
        return f'{val:.1f}%'
    elif format_type == 'number':
        if abs(val) >= 1e9:
            return f'{val/1e9:.1f}B'
        elif abs(val) >= 1e6:
            return f'{val/1e6:.1f}M'
        elif abs(val) >= 1e3:
            return f'{val/1e3:.1f}K'
        else:
            return f'{val:,.0f}'
    return str(val)

# 与轴格式化器一起使用
ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, p: format_number(x, 'currency')))

使用 Plotly 的交互式图表

import plotly.express as px
import plotly.graph_objects as go

# 简单的交互式折线图
fig = px.line(df, x='date', y='value', color='category',
              title='Interactive Metric Trend',
              labels={'value': 'Metric Value', 'date': 'Date'})
fig.update_layout(hovermode='x unified')
fig.write_html('interactive_chart.html')
fig.show()

# 带有悬停数据的交互式散点图
fig = px.scatter(df, x='metric_a', y='metric_b', color='category',
                 size='size_metric', hover_data=['name', 'detail_field'],
                 title='Correlation Analysis')
fig.show()

有目的地使用颜色：颜色应用于编码数据，而非装饰
突出故事：对关键洞察使用明亮的强调色；其他部分用灰色
顺序数据：对有序值使用单色调渐变（从浅到深）
发散数据：对具有有意义中心点的数据，使用带有中性中点的双色调渐变
分类数据：使用不同的色调，最多6-8种，否则会变得混乱
避免仅使用红/绿：8%的男性是红绿色盲。使用蓝/橙作为主要配对色

标题陈述洞察："收入同比增长23%"优于"月度收入"
副标题添加上下文：日期范围、应用的筛选器、数据源
坐标轴标签可读：如果可以避免，切勿旋转90度。应缩短或换行
数据标签增加精度：在关键点使用，而非每个条形
标注高亮：使用文本标注标出特定点

减少图表垃圾：移除不承载信息的网格线、边框、背景
有意义地排序：除非有自然顺序（月份、阶段），否则按值（而非字母顺序）对类别排序
合适的宽高比：时间序列图宽大于高（3:1 到 2:1）；比较图可以更方正
留白是好的：不要将图表挤在一起。给每个可视化留出呼吸空间

条形图从零开始：始终如此。从95到100的条形会夸大5%的差异
折线图可以有非零基线：当变化范围有意义时
跨面板统一刻度：比较多个图表时，使用相同的坐标轴范围
显示不确定性：当数据不确定时，使用误差线、置信区间或范围
标注坐标轴：永远不要让读者猜测数字的含义

无障碍访问注意事项

切勿仅依赖颜色来区分数据系列
添加图案填充、不同的线条样式（实线、虚线、点线）或直接标签
使用色盲模拟器进行测试（例如 Coblis、Sim Daltonism）
使用色盲友好调色板：sns.color_palette("colorblind")

包含描述图表关键发现的替代文本
在可视化旁边提供数据表格替代方案
使用语义化的标题和标签

数据元素与背景之间有足够的对比度
标签文字大小至少10磅，标题至少12磅
避免仅通过空间位置传递信息（添加标签）
考虑打印：图表在黑白模式下是否有效？

无障碍检查清单

分享可视化之前：

图表在不依赖颜色的情况下有效（图案、标签或线条样式区分系列）
在标准缩放级别下文本可读
标题描述洞察，而不仅仅是数据
坐标轴标注了单位
图例清晰且位置不遮挡数据
注明了数据源和日期范围

🇺🇸English

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

Histogram (Distribution)

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

ax.hist(df['value'], bins=30, color=PALETTE_CATEGORICAL[0], edgecolor='white', alpha=0.8)

# Add mean and median lines
mean_val = df['value'].mean()
median_val = df['value'].median()
ax.axvline(mean_val, color='red', linestyle='--', linewidth=1.5, label=f'Mean: {mean_val:,.1f}')
ax.axvline(median_val, color='green', linestyle='--', linewidth=1.5, label=f'Median: {median_val:,.1f}')

ax.set_title('Distribution of Values', fontweight='bold')
ax.set_xlabel('Value')
ax.set_ylabel('Frequency')
ax.legend()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

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

Heatmap

fig, ax = plt.subplots(figsize=(10, 8))

# Pivot data for heatmap format
pivot = df.pivot_table(index='row_dim', columns='col_dim', values='metric', aggfunc='sum')

sns.heatmap(pivot, annot=True, fmt=',.0f', cmap='YlOrRd',
            linewidths=0.5, ax=ax, cbar_kws={'label': 'Metric Value'})

ax.set_title('Metric by Row Dimension and Column Dimension', fontweight='bold')
ax.set_xlabel('Column Dimension')
ax.set_ylabel('Row Dimension')

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

Small Multiples

categories = df['category'].unique()
n_cats = len(categories)
n_cols = min(3, n_cats)
n_rows = (n_cats + n_cols - 1) // n_cols

fig, axes = plt.subplots(n_rows, n_cols, figsize=(5*n_cols, 4*n_rows), sharex=True, sharey=True)
axes = axes.flatten() if n_cats > 1 else [axes]

for i, cat in enumerate(categories):
    ax = axes[i]
    subset = df[df['category'] == cat]
    ax.plot(subset['date'], subset['value'], color=PALETTE_CATEGORICAL[i % len(PALETTE_CATEGORICAL)])
    ax.set_title(cat, fontsize=12)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

# Hide empty subplots
for j in range(i+1, len(axes)):
    axes[j].set_visible(False)

fig.suptitle('Trends by Category', fontsize=14, fontweight='bold', y=1.02)
plt.tight_layout()
plt.savefig('small_multiples.png', dpi=150, bbox_inches='tight')

Number Formatting Helpers

def format_number(val, format_type='number'):
    """Format numbers for chart labels."""
    if format_type == 'currency':
        if abs(val) >= 1e9:
            return f'${val/1e9:.1f}B'
        elif abs(val) >= 1e6:
            return f'${val/1e6:.1f}M'
        elif abs(val) >= 1e3:
            return f'${val/1e3:.1f}K'
        else:
            return f'${val:,.0f}'
    elif format_type == 'percent':
        return f'{val:.1f}%'
    elif format_type == 'number':
        if abs(val) >= 1e9:
            return f'{val/1e9:.1f}B'
        elif abs(val) >= 1e6:
            return f'{val/1e6:.1f}M'
        elif abs(val) >= 1e3:
            return f'{val/1e3:.1f}K'
        else:
            return f'{val:,.0f}'
    return str(val)

# Usage with axis formatter
ax.yaxis.set_major_formatter(mticker.FuncFormatter(lambda x, p: format_number(x, 'currency')))

Interactive Charts with Plotly

import plotly.express as px
import plotly.graph_objects as go

# Simple interactive line chart
fig = px.line(df, x='date', y='value', color='category',
              title='Interactive Metric Trend',
              labels={'value': 'Metric Value', 'date': 'Date'})
fig.update_layout(hovermode='x unified')
fig.write_html('interactive_chart.html')
fig.show()

# Interactive scatter with hover data
fig = px.scatter(df, x='metric_a', y='metric_b', color='category',
                 size='size_metric', hover_data=['name', 'detail_field'],
                 title='Correlation Analysis')
fig.show()

Design Principles

Color

Use color purposefully : Color should encode data, not decorate
Highlight the story : Use a bright accent color for the key insight; grey everything else
Sequential data : Use a single-hue gradient (light to dark) for ordered values
Diverging data : Use a two-hue gradient with neutral midpoint for data with a meaningful center
Categorical data : Use distinct hues, maximum 6-8 before it gets confusing
Avoid red/green only : 8% of men are red-green colorblind. Use blue/orange as primary pair

Typography

Title states the insight : "Revenue grew 23% YoY" beats "Revenue by Month"
Subtitle adds context : Date range, filters applied, data source
Axis labels are readable : Never rotated 90 degrees if avoidable. Shorten or wrap instead
Data labels add precision : Use on key points, not every single bar
Annotation highlights : Call out specific points with text annotations

Layout

Reduce chart junk : Remove gridlines, borders, backgrounds that don't carry information
Sort meaningfully : Categories sorted by value (not alphabetically) unless there's a natural order (months, stages)
Appropriate aspect ratio : Time series wider than tall (3:1 to 2:1); comparisons can be squarer
White space is good : Don't cram charts together. Give each visualization room to breathe

Accuracy

Bar charts start at zero : Always. A bar from 95 to 100 exaggerates a 5% difference
Line charts can have non-zero baselines : When the range of variation is meaningful
Consistent scales across panels : When comparing multiple charts, use the same axis range
Show uncertainty : Error bars, confidence intervals, or ranges when data is uncertain
Label your axes : Never make the reader guess what the numbers mean

Accessibility Considerations

Color Blindness

Never rely on color alone to distinguish data series
Add pattern fills, different line styles (solid, dashed, dotted), or direct labels
Test with a colorblind simulator (e.g., Coblis, Sim Daltonism)
Use the colorblind-friendly palette: sns.color_palette("colorblind")

Screen Readers

Include alt text describing the chart's key finding
Provide a data table alternative alongside the visualization
Use semantic titles and labels

General Accessibility

Sufficient contrast between data elements and background
Text size minimum 10pt for labels, 12pt for titles
Avoid conveying information only through spatial position (add labels)
Consider printing: does the chart work in black and white?

Accessibility Checklist

Before sharing a visualization:

Chart works without color (patterns, labels, or line styles differentiate series)
Text is readable at standard zoom level
Title describes the insight, not just the data
Axes are labeled with units
Legend is clear and positioned without obscuring data
Data source and date range are noted

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