Data Visualization

数据可视化

Overview

概述

This skill creates effective data visualizations that communicate insights clearly across multiple domains: analytics dashboards, infrastructure monitoring, and ML model performance tracking. It combines data analysis expertise, dashboard architecture patterns, UX design principles, and accessibility considerations.

该技能可创建高效的数据可视化内容，在多个领域清晰传达洞察：分析仪表盘、基础设施监控以及ML模型性能追踪。它融合了数据分析专业知识、仪表盘架构模式、UX设计原则和无障碍设计考量。

Instructions

操作指南

1. Understand the Data and Context

1. 理解数据与上下文

Analyze data structure, types, and granularity
Identify key metrics, dimensions, and relationships
Determine the analytical question or story to tell
Consider the target audience and their technical level
Assess update frequency and real-time requirements
Identify domain-specific needs (analytics, monitoring, ML)

分析数据结构、类型和粒度
识别关键指标、维度及关联关系
确定要解答的分析问题或要讲述的数据故事
考虑目标受众及其技术水平
评估更新频率和实时性要求
明确特定领域需求（分析、监控、ML）

2. Select Visualization Domain

2. 选择可视化领域

Analytics Dashboards: Business metrics, KPIs, trends, comparisons

Tools: Tableau, Superset, Metabase, Plotly Dash, Streamlit
Focus: Executive summaries, drill-down exploration, report generation

Infrastructure Monitoring: System health, resource usage, alerts

Tools: Grafana, Prometheus, Datadog, CloudWatch
Focus: Real-time metrics, alerting thresholds, SLA tracking

ML Model Performance: Training metrics, predictions, model diagnostics

Tools: TensorBoard, Weights & Biases, MLflow, matplotlib
Focus: Loss curves, confusion matrices, feature importance

分析仪表盘：业务指标、KPI、趋势、对比

工具：Tableau、Superset、Metabase、Plotly Dash、Streamlit
核心：高管摘要、下钻探索、报告生成

基础设施监控：系统健康、资源使用、告警

工具：Grafana、Prometheus、Datadog、CloudWatch
核心：实时指标、告警阈值、SLA追踪

ML模型性能：训练指标、预测结果、模型诊断

工具：TensorBoard、Weights & Biases、MLflow、matplotlib
核心：损失曲线、混淆矩阵、特征重要性

3. Choose Chart Type and Layout

3. 选择图表类型与布局

Match chart type to data relationship (see Chart Selection Guide)
Consider data volume and complexity
Plan for interactivity needs (tooltips, filters, zoom)
Design information hierarchy (KPIs first, details below)
Account for accessibility (color, contrast, screen readers)

根据数据关联匹配图表类型（参考图表选择指南）
考虑数据量和复杂度
规划交互需求（工具提示、筛选、缩放）
设计信息层级（优先展示KPI，细节置于下方）
兼顾无障碍设计（颜色、对比度、屏幕阅读器适配）

4. Design for Clarity and Usability

4. 设计清晰易用的可视化

Visual Design:

Choose accessible color schemes (colorblind-safe palettes)
Label axes and data clearly with units
Remove chart junk and unnecessary decorations
Highlight key insights with annotations
Use consistent styling across dashboard

UX Considerations:

Provide context (baselines, targets, historical comparisons)
Enable progressive disclosure (summary → details)
Add clear legends and tooltips
Include data freshness indicators
Design for both desktop and mobile views

Accessibility:

WCAG AA color contrast ratios (4.5:1 text, 3:1 graphics)
Use patterns/textures in addition to color
Provide alt text for static visualizations
Support keyboard navigation for interactive charts
Include data tables as fallback

视觉设计:

选择无障碍配色方案（色弱友好调色板）
清晰标注坐标轴和数据单位
移除冗余元素和不必要装饰
用注释突出关键洞察
保持仪表盘样式一致性

UX考量:

提供上下文信息（基准值、目标值、历史对比）
支持渐进式信息披露（摘要 → 详情）
添加清晰图例和工具提示
包含数据新鲜度标识
适配桌面和移动端视图

无障碍设计:

遵循WCAG AA级颜色对比度标准（文本4.5:1，图形3:1）
除颜色外，使用图案/纹理区分数据
为静态可视化提供替代文本
支持交互式图表的键盘导航
提供数据表格作为备选展示方式

5. Implement and Validate

5. 实现与验证

Build visualization with chosen tool
Test with real data at production scale
Validate calculations and aggregations
Verify performance (load time < 3s for dashboards)
Gather feedback from end users
Iterate based on usage patterns and analytics

使用选定工具构建可视化
用生产规模的真实数据测试
验证计算和聚合逻辑
检查性能（仪表盘加载时间<3秒）
收集终端用户反馈
根据使用模式和分析结果迭代优化

Best Practices

最佳实践

General Principles

通用原则

Right Chart for Data: Match visualization to data relationship and question
Less is More: Remove unnecessary elements, maximize data-ink ratio
Consistent Styling: Use coherent color schemes and typography
Accessible Design: Support colorblind users, screen readers, keyboard navigation
Clear Labels: Descriptive titles, axis labels with units, data sources
Context Matters: Include baselines, targets, historical comparisons
Interactive When Helpful: Add tooltips, filters, zoom for exploration
Performance First: Optimize for < 3s dashboard load times
Mobile-Responsive: Design for both desktop and mobile viewports

图表适配数据：根据数据关联和问题匹配可视化类型
极简原则：移除不必要元素，最大化数据墨水比
样式一致：使用连贯配色方案和排版
无障碍设计：支持色弱用户、屏幕阅读器和键盘导航
清晰标注：描述性标题、带单位的坐标轴标签、数据源说明
上下文优先：包含基准值、目标值、历史对比
按需交互：添加工具提示、筛选、缩放功能辅助探索
性能优先：优化仪表盘加载时间至<3秒
响应式设计：适配桌面和移动端视口

Domain-Specific Guidelines

特定领域指南

Analytics Dashboards:

Start with executive summary (4-6 KPIs with trends)
Support drill-down from summary to detail
Include date range selectors and common filters
Provide export options (PDF, CSV, PNG)
Cache expensive queries, refresh on schedule

Infrastructure Monitoring:

Use time-series line charts for metrics over time
Set alert thresholds with colored bands
Show current status prominently (healthy/degraded/down)
Include percentile metrics (p50, p95, p99) not just averages
Auto-refresh dashboards every 30-60 seconds

ML Model Performance:

Plot training/validation curves together
Show confusion matrices with normalized values
Visualize feature importance with horizontal bars
Include sample predictions for interpretability
Track metrics across experiments for comparison

分析仪表盘:

以高管摘要开篇（4-6个带趋势的KPI）
支持从摘要到详情的下钻操作
包含日期范围选择器和常用筛选器
提供导出选项（PDF、CSV、PNG）
缓存复杂查询，按计划刷新

基础设施监控:

使用时序折线图展示指标随时间变化
设置带颜色标识的告警阈值
突出显示当前状态（健康/降级/故障）
展示百分位指标（p50、p95、p99）而非仅平均值
每30-60秒自动刷新仪表盘

ML模型性能:

同时绘制训练/验证曲线
展示带归一化值的混淆矩阵
用水平条形图可视化特征重要性
包含样本预测结果提升可解释性
跨实验追踪指标用于对比

Accessibility Checklist

无障碍设计检查清单

Use colorblind-safe palettes (avoid red/green only)
Ensure 4.5:1 contrast ratio for text
Provide alt text for static images
Support keyboard navigation (tab, arrow keys)
Include data tables as alternative representation
Test with screen readers (NVDA, JAWS)
Use patterns/textures in addition to color
Avoid flashing or rapidly changing content

使用色弱友好调色板（避免仅用红/绿区分）
确保文本对比度达4.5:1
为静态图片提供替代文本
支持键盘导航（Tab、箭头键）
提供数据表格作为替代展示方式
用屏幕阅读器测试（NVDA、JAWS）
除颜色外使用图案/纹理区分
避免闪烁或快速变化的内容

Examples

示例

Example 1: Python with Matplotlib/Seaborn

示例1：Python结合Matplotlib/Seaborn

python

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

python

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

Set style for professional look

plt.style.use('seaborn-v0_8-whitegrid') sns.set_palette("husl")

Create figure with subplots

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

Example 1: Line chart for time series

df_sales = pd.DataFrame({ 'date': pd.date_range('2024-01-01', periods=12, freq='M'), 'revenue': [100, 120, 115, 140, 155, 170, 165, 180, 195, 210, 225, 250], 'target': [110, 115, 120, 130, 145, 160, 175, 185, 200, 215, 230, 245] })

ax1 = axes[0, 0] ax1.plot(df_sales['date'], df_sales['revenue'], marker='o', linewidth=2, label='Actual') ax1.plot(df_sales['date'], df_sales['target'], linestyle='--', linewidth=2, label='Target') ax1.fill_between(df_sales['date'], df_sales['revenue'], df_sales['target'], alpha=0.3, where=(df_sales['revenue'] >= df_sales['target']), color='green') ax1.fill_between(df_sales['date'], df_sales['revenue'], df_sales['target'], alpha=0.3, where=(df_sales['revenue'] < df_sales['target']), color='red') ax1.set_title('Monthly Revenue vs Target', fontsize=14, fontweight='bold') ax1.set_xlabel('Month') ax1.set_ylabel('Revenue ($K)') ax1.legend() ax1.tick_params(axis='x', rotation=45)

df_sales = pd.DataFrame({ 'date': pd.date_range('2024-01-01', periods=12, freq='M'), 'revenue': [100, 120, 115, 140, 155, 170, 165, 180, 195, 210, 225, 250], 'target': [110, 115, 120, 130, 145, 160, 175, 185, 200, 215, 230, 245] })

ax1 = axes[0, 0] ax1.plot(df_sales['date'], df_sales['revenue'], marker='o', linewidth=2, label='Actual') ax1.plot(df_sales['date'], df_sales['target'], linestyle='--', linewidth=2, label='Target') ax1.fill_between(df_sales['date'], df_sales['revenue'], df_sales['target'], alpha=0.3, where=(df_sales['revenue'] >= df_sales['target']), color='green') ax1.fill_between(df_sales['date'], df_sales['revenue'], df_sales['target'], alpha=0.3, where=(df_sales['revenue'] < df_sales['target']), color='red') ax1.set_title('Monthly Revenue vs Target', fontsize=14, fontweight='bold') ax1.set_xlabel('Month') ax1.set_ylabel('Revenue ($K)') ax1.legend() ax1.tick_params(axis='x', rotation=45)

Example 2: Bar chart for comparison

df_products = pd.DataFrame({ 'product': ['Product A', 'Product B', 'Product C', 'Product D', 'Product E'], 'sales': [45, 32, 28, 22, 18] })

ax2 = axes[0, 1] colors = sns.color_palette("Blues_r", len(df_products)) bars = ax2.barh(df_products['product'], df_products['sales'], color=colors) ax2.bar_label(bars, padding=3, fmt='$%.0fK') ax2.set_title('Sales by Product', fontsize=14, fontweight='bold') ax2.set_xlabel('Sales ($K)') ax2.invert_yaxis()

df_products = pd.DataFrame({ 'product': ['Product A', 'Product B', 'Product C', 'Product D', 'Product E'], 'sales': [45, 32, 28, 22, 18] })

ax2 = axes[0, 1] colors = sns.color_palette("Blues_r", len(df_products)) bars = ax2.barh(df_products['product'], df_products['sales'], color=colors) ax2.bar_label(bars, padding=3, fmt='$%.0fK') ax2.set_title('Sales by Product', fontsize=14, fontweight='bold') ax2.set_xlabel('Sales ($K)') ax2.invert_yaxis()

Example 3: Scatter plot with regression

np.random.seed(42) df_scatter = pd.DataFrame({ 'ad_spend': np.random.uniform(10, 100, 50), 'conversions': lambda x: x['ad_spend'] * 2.5 + np.random.normal(0, 15, 50) }.class.call(pd.DataFrame({'ad_spend': np.random.uniform(10, 100, 50)}))) df_scatter['conversions'] = df_scatter['ad_spend'] * 2.5 + np.random.normal(0, 15, 50)

ax3 = axes[1, 0] sns.regplot(data=df_scatter, x='ad_spend', y='conversions', ax=ax3, scatter_kws={'alpha': 0.6}, line_kws={'color': 'red'}) ax3.set_title('Ad Spend vs Conversions', fontsize=14, fontweight='bold') ax3.set_xlabel('Ad Spend ($K)') ax3.set_ylabel('Conversions')

np.random.seed(42) df_scatter = pd.DataFrame({ 'ad_spend': np.random.uniform(10, 100, 50), 'conversions': lambda x: x['ad_spend'] * 2.5 + np.random.normal(0, 15, 50) }.class.call(pd.DataFrame({'ad_spend': np.random.uniform(10, 100, 50)}))) df_scatter['conversions'] = df_scatter['ad_spend'] * 2.5 + np.random.normal(0, 15, 50)

ax3 = axes[1, 0] sns.regplot(data=df_scatter, x='ad_spend', y='conversions', ax=ax3, scatter_kws={'alpha': 0.6}, line_kws={'color': 'red'}) ax3.set_title('Ad Spend vs Conversions', fontsize=14, fontweight='bold') ax3.set_xlabel('Ad Spend ($K)') ax3.set_ylabel('Conversions')

Example 4: Pie/Donut chart for composition

df_channels = pd.DataFrame({ 'channel': ['Organic', 'Paid Search', 'Social', 'Email', 'Direct'], 'traffic': [35, 25, 20, 12, 8] })

ax4 = axes[1, 1] wedges, texts, autotexts = ax4.pie( df_channels['traffic'], labels=df_channels['channel'], autopct='%1.1f%%', pctdistance=0.75, wedgeprops=dict(width=0.5) ) ax4.set_title('Traffic by Channel', fontsize=14, fontweight='bold')

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

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df_channels = pd.DataFrame({ 'channel': ['Organic', 'Paid Search', 'Social', 'Email', 'Direct'], 'traffic': [35, 25, 20, 12, 8] })

ax4 = axes[1, 1] wedges, texts, autotexts = ax4.pie( df_channels['traffic'], labels=df_channels['channel'], autopct='%1.1f%%', pctdistance=0.75, wedgeprops=dict(width=0.5) ) ax4.set_title('Traffic by Channel', fontsize=14, fontweight='bold')

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

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Example 2: Interactive Visualization with Plotly

示例2：使用Plotly创建交互式可视化

python

import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd

python

import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd

Create interactive time series

df = pd.DataFrame({ 'date': pd.date_range('2024-01-01', periods=365, freq='D'), 'value': (pd.Series(range(365)) * 0.1 + np.sin(pd.Series(range(365)) * 0.1) * 20 + np.random.normal(0, 5, 365)).cumsum() })

fig = go.Figure()

fig.add_trace(go.Scatter( x=df['date'], y=df['value'], mode='lines', name='Daily Value', line=dict(color='#1f77b4', width=1.5), hovertemplate='%{x|%B %d, %Y}<br>Value: %{y:.2f}<extra></extra>' ))

df = pd.DataFrame({ 'date': pd.date_range('2024-01-01', periods=365, freq='D'), 'value': (pd.Series(range(365)) * 0.1 + np.sin(pd.Series(range(365)) * 0.1) * 20 + np.random.normal(0, 5, 365)).cumsum() })

fig = go.Figure()

fig.add_trace(go.Scatter( x=df['date'], y=df['value'], mode='lines', name='Daily Value', line=dict(color='#1f77b4', width=1.5), hovertemplate='%{x|%B %d, %Y}<br>Value: %{y:.2f}<extra></extra>' ))

Add moving average

df['ma_7'] = df['value'].rolling(7).mean() fig.add_trace(go.Scatter( x=df['date'], y=df['ma_7'], mode='lines', name='7-day MA', line=dict(color='#ff7f0e', width=2, dash='dash') ))

fig.update_layout( title='Daily Performance with Moving Average', xaxis_title='Date', yaxis_title='Value', hovermode='x unified', template='plotly_white', xaxis=dict( rangeselector=dict( buttons=list([ dict(count=7, label="1w", step="day", stepmode="backward"), dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=3, label="3m", step="month", stepmode="backward"), dict(step="all") ]) ), rangeslider=dict(visible=True) ) )

fig.write_html('interactive_chart.html') fig.show()

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df['ma_7'] = df['value'].rolling(7).mean() fig.add_trace(go.Scatter( x=df['date'], y=df['ma_7'], mode='lines', name='7-day MA', line=dict(color='#ff7f0e', width=2, dash='dash') ))

fig.update_layout( title='Daily Performance with Moving Average', xaxis_title='Date', yaxis_title='Value', hovermode='x unified', template='plotly_white', xaxis=dict( rangeselector=dict( buttons=list([ dict(count=7, label="1w", step="day", stepmode="backward"), dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=3, label="3m", step="month", stepmode="backward"), dict(step="all") ]) ), rangeslider=dict(visible=True) ) )

fig.write_html('interactive_chart.html') fig.show()

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Example 3: Chart Type Selection Guide

示例3：图表类型选择指南

markdown

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markdown

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Chart Selection by Data Type

Comparison

Bar Chart: Compare values across categories
Grouped Bar: Compare multiple series across categories
Bullet Chart: Show performance against target

Bar Chart: Compare values across categories
Grouped Bar: Compare multiple series across categories
Bullet Chart: Show performance against target

Distribution

Histogram: Show frequency distribution
Box Plot: Show distribution summary statistics
Violin Plot: Show distribution shape

Histogram: Show frequency distribution
Box Plot: Show distribution summary statistics
Violin Plot: Show distribution shape

Composition

Pie/Donut Chart: Show parts of a whole (< 6 categories)
Stacked Bar: Show composition across categories
Treemap: Show hierarchical composition

Pie/Donut Chart: Show parts of a whole (< 6 categories)
Stacked Bar: Show composition across categories
Treemap: Show hierarchical composition

Relationship

Scatter Plot: Show correlation between two variables
Bubble Chart: Add third dimension via size
Heatmap: Show correlation matrix

Scatter Plot: Show correlation between two variables
Bubble Chart: Add third dimension via size
Heatmap: Show correlation matrix

Time Series

Line Chart: Show trends over time
Area Chart: Show cumulative trends
Candlestick: Show OHLC financial data

Line Chart: Show trends over time
Area Chart: Show cumulative trends
Candlestick: Show OHLC financial data

Geographic

Choropleth: Show values by region
Point Map: Show locations with values
Flow Map: Show movement between locations

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Choropleth: Show values by region
Point Map: Show locations with values
Flow Map: Show movement between locations

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Example 4: Analytics Dashboard with Streamlit

示例4：使用Streamlit构建分析仪表盘

python

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python

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Streamlit Dashboard Example

import streamlit as st import pandas as pd import plotly.express as px

st.set_page_config(page_title="Sales Dashboard", layout="wide")

import streamlit as st import pandas as pd import plotly.express as px

st.set_page_config(page_title="Sales Dashboard", layout="wide")

Header

st.title("Sales Performance Dashboard") st.markdown("---")

KPI Row

col1, col2, col3, col4 = st.columns(4) with col1: st.metric("Total Revenue", "$1.2M", "+12%") with col2: st.metric("Orders", "8,543", "+8%") with col3: st.metric("Avg Order Value", "$140", "+3%") with col4: st.metric("Conversion Rate", "3.2%", "-0.5%")

st.markdown("---")

col1, col2, col3, col4 = st.columns(4) with col1: st.metric("Total Revenue", "$1.2M", "+12%") with col2: st.metric("Orders", "8,543", "+8%") with col3: st.metric("Avg Order Value", "$140", "+3%") with col4: st.metric("Conversion Rate", "3.2%", "-0.5%")

st.markdown("---")

Filters

with st.sidebar: st.header("Filters") date_range = st.date_input("Date Range", []) region = st.multiselect("Region", ["North", "South", "East", "West"]) category = st.selectbox("Category", ["All", "Electronics", "Clothing", "Home"])

Main Charts

left_col, right_col = st.columns([2, 1])

with left_col: st.subheader("Revenue Trend") # Line chart here

with right_col: st.subheader("Sales by Region") # Pie chart here

left_col, right_col = st.columns([2, 1])

with left_col: st.subheader("Revenue Trend") # Line chart here

with right_col: st.subheader("Sales by Region") # Pie chart here

Detail Table

st.subheader("Recent Orders")

Data table here

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Example 5: Grafana Dashboard for Infrastructure Monitoring

示例5：用于基础设施监控的Grafana仪表盘

yaml

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yaml

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Grafana dashboard JSON structure

Save as dashboard.json and import via Grafana UI

apiVersion: 1 providers:

name: "default" folder: "Infrastructure" type: file options: path: /etc/grafana/provisioning/dashboards

apiVersion: 1 providers:

name: "default" folder: "Infrastructure" type: file options: path: /etc/grafana/provisioning/dashboards

Dashboard panels configuration

{ "dashboard": { "title": "Service Health Dashboard", "tags": ["infrastructure", "monitoring"], "timezone": "browser", "panels": [ { "id": 1, "title": "Request Rate", "type": "graph", "targets": [ { "expr": "rate(http_requests_total[5m])", "legendFormat": "{{service}}", }, ], "yaxes": [{ "format": "reqps", "label": "Requests/sec" }], "alert": { "conditions": [ { "evaluator": { "params": [100], "type": "gt" }, "query": { "params": ["A", "5m", "now"] }, }, ], }, }, { "id": 2, "title": "Error Rate", "type": "graph", "targets": [ { "expr": "rate(http_errors_total[5m]) / rate(http_requests_total[5m])", "legendFormat": "Error %", }, ], "fieldConfig": { "defaults": { "thresholds": { "steps": [ { "value": 0, "color": "green" }, { "value": 0.01, "color": "yellow" }, { "value": 0.05, "color": "red" }, ], }, }, }, }, { "id": 3, "title": "Response Time (p95)", "type": "graph", "targets": [ { "expr": "histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))", "legendFormat": "p95", }, ], }, ], "refresh": "30s", "time": { "from": "now-1h", "to": "now" }, }, }

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{ "dashboard": { "title": "Service Health Dashboard", "tags": ["infrastructure", "monitoring"], "timezone": "browser", "panels": [ { "id": 1, "title": "Request Rate", "type": "graph", "targets": [ { "expr": "rate(http_requests_total[5m])", "legendFormat": "{{service}}", }, ], "yaxes": [{ "format": "reqps", "label": "Requests/sec" }], "alert": { "conditions": [ { "evaluator": { "params": [100], "type": "gt" }, "query": { "params": ["A", "5m", "now"] }, }, ], }, }, { "id": 2, "title": "Error Rate", "type": "graph", "targets": [ { "expr": "rate(http_errors_total[5m]) / rate(http_requests_total[5m])", "legendFormat": "Error %", }, ], "fieldConfig": { "defaults": { "thresholds": { "steps": [ { "value": 0, "color": "green" }, { "value": 0.01, "color": "yellow" }, { "value": 0.05, "color": "red" }, ], }, }, }, }, { "id": 3, "title": "Response Time (p95)", "type": "graph", "targets": [ { "expr": "histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))", "legendFormat": "p95", }, ], }, ], "refresh": "30s", "time": { "from": "now-1h", "to": "now" }, }, }

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Example 6: ML Model Performance Visualization

示例6：ML模型性能可视化

python

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python

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TensorBoard-style training visualization

import matplotlib.pyplot as plt import numpy as np from sklearn.metrics import confusion_matrix, classification_report import seaborn as sns

Training history visualization

fig, axes = plt.subplots(2, 2, figsize=(14, 10))

Loss curves

ax1 = axes[0, 0] epochs = range(1, 51) train_loss = np.exp(-np.array(epochs) / 10) + np.random.normal(0, 0.05, 50) val_loss = np.exp(-np.array(epochs) / 10) + np.random.normal(0, 0.08, 50) + 0.1

ax1.plot(epochs, train_loss, label='Training Loss', linewidth=2) ax1.plot(epochs, val_loss, label='Validation Loss', linewidth=2) ax1.axvline(x=30, color='red', linestyle='--', alpha=0.5, label='Best Model') ax1.set_xlabel('Epoch') ax1.set_ylabel('Loss') ax1.set_title('Training and Validation Loss', fontsize=14, fontweight='bold') ax1.legend() ax1.grid(alpha=0.3)

ax1 = axes[0, 0] epochs = range(1, 51) train_loss = np.exp(-np.array(epochs) / 10) + np.random.normal(0, 0.05, 50) val_loss = np.exp(-np.array(epochs) / 10) + np.random.normal(0, 0.08, 50) + 0.1

ax1.plot(epochs, train_loss, label='Training Loss', linewidth=2) ax1.plot(epochs, val_loss, label='Validation Loss', linewidth=2) ax1.axvline(x=30, color='red', linestyle='--', alpha=0.5, label='Best Model') ax1.set_xlabel('Epoch') ax1.set_ylabel('Loss') ax1.set_title('Training and Validation Loss', fontsize=14, fontweight='bold') ax1.legend() ax1.grid(alpha=0.3)

Accuracy curves

ax2 = axes[0, 1] train_acc = 1 - train_loss val_acc = 1 - val_loss

ax2.plot(epochs, train_acc, label='Training Accuracy', linewidth=2) ax2.plot(epochs, val_acc, label='Validation Accuracy', linewidth=2) ax2.axvline(x=30, color='red', linestyle='--', alpha=0.5) ax2.set_xlabel('Epoch') ax2.set_ylabel('Accuracy') ax2.set_title('Training and Validation Accuracy', fontsize=14, fontweight='bold') ax2.legend() ax2.grid(alpha=0.3)

ax2 = axes[0, 1] train_acc = 1 - train_loss val_acc = 1 - val_loss

ax2.plot(epochs, train_acc, label='Training Accuracy', linewidth=2) ax2.plot(epochs, val_acc, label='Validation Accuracy', linewidth=2) ax2.axvline(x=30, color='red', linestyle='--', alpha=0.5) ax2.set_xlabel('Epoch') ax2.set_ylabel('Accuracy') ax2.set_title('Training and Validation Accuracy', fontsize=14, fontweight='bold') ax2.legend() ax2.grid(alpha=0.3)

Confusion Matrix

ax3 = axes[1, 0] y_true = np.random.randint(0, 3, 500) y_pred = y_true.copy() y_pred[np.random.random(500) < 0.15] = np.random.randint(0, 3, (y_pred.shape[0]))

cm = confusion_matrix(y_true, y_pred) sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=ax3, xticklabels=['Class A', 'Class B', 'Class C'], yticklabels=['Class A', 'Class B', 'Class C']) ax3.set_xlabel('Predicted') ax3.set_ylabel('Actual') ax3.set_title('Confusion Matrix', fontsize=14, fontweight='bold')

ax3 = axes[1, 0] y_true = np.random.randint(0, 3, 500) y_pred = y_true.copy() y_pred[np.random.random(500) < 0.15] = np.random.randint(0, 3, (y_pred.shape[0]))

cm = confusion_matrix(y_true, y_pred) sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=ax3, xticklabels=['Class A', 'Class B', 'Class C'], yticklabels=['Class A', 'Class B', 'Class C']) ax3.set_xlabel('Predicted') ax3.set_ylabel('Actual') ax3.set_title('Confusion Matrix', fontsize=14, fontweight='bold')

Feature Importance

ax4 = axes[1, 1] features = ['Feature A', 'Feature B', 'Feature C', 'Feature D', 'Feature E'] importance = np.array([0.35, 0.28, 0.18, 0.12, 0.07])

colors = plt.cm.viridis(importance / importance.max()) bars = ax4.barh(features, importance, color=colors) ax4.set_xlabel('Importance') ax4.set_title('Feature Importance', fontsize=14, fontweight='bold') ax4.invert_yaxis()

for i, (bar, val) in enumerate(zip(bars, importance)): ax4.text(val + 0.01, i, f'{val:.2f}', va='center')

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

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ax4 = axes[1, 1] features = ['Feature A', 'Feature B', 'Feature C', 'Feature D', 'Feature E'] importance = np.array([0.35, 0.28, 0.18, 0.12, 0.07])

colors = plt.cm.viridis(importance / importance.max()) bars = ax4.barh(features, importance, color=colors) ax4.set_xlabel('Importance') ax4.set_title('Feature Importance', fontsize=14, fontweight='bold') ax4.invert_yaxis()

for i, (bar, val) in enumerate(zip(bars, importance)): ax4.text(val + 0.01, i, f'{val:.2f}', va='center')

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

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Example 7: Accessible Color Palettes

示例7：无障碍配色方案

python

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python

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Colorblind-safe palettes for data visualization

import matplotlib.pyplot as plt import seaborn as sns

Define accessible color schemes

palettes = { 'colorblind_safe': ['#0173B2', '#DE8F05', '#029E73', '#CC78BC', '#CA9161'], 'high_contrast': ['#000000', '#E69F00', '#56B4E9', '#009E73', '#F0E442'], 'okabe_ito': ['#E69F00', '#56B4E9', '#009E73', '#F0E442', '#0072B2', '#D55E00', '#CC79A7'] }

Test visualization with accessible palette

data = [25, 20, 18, 15, 12, 10] labels = ['A', 'B', 'C', 'D', 'E', 'F']

fig, axes = plt.subplots(1, 3, figsize=(15, 4))

for ax, (name, colors) in zip(axes, palettes.items()): ax.bar(labels, data, color=colors[:len(data)]) ax.set_title(f'{name.replace("_", " ").title()}', fontsize=12, fontweight='bold') ax.set_ylabel('Value')

# Add value labels for accessibility
for i, (label, value) in enumerate(zip(labels, data)):
    ax.text(i, value + 0.5, str(value), ha='center', va='bottom')

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

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data = [25, 20, 18, 15, 12, 10] labels = ['A', 'B', 'C', 'D', 'E', 'F']

fig, axes = plt.subplots(1, 3, figsize=(15, 4))

for ax, (name, colors) in zip(axes, palettes.items()): ax.bar(labels, data, color=colors[:len(data)]) ax.set_title(f'{name.replace("_", " ").title()}', fontsize=12, fontweight='bold') ax.set_ylabel('Value')

# Add value labels for accessibility
for i, (label, value) in enumerate(zip(labels, data)):
    ax.text(i, value + 0.5, str(value), ha='center', va='bottom')

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

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loom-data-visualization

Original

Translation

Data Visualization

数据可视化

Overview

概述

Instructions

操作指南

1. Understand the Data and Context

1. 理解数据与上下文

2. Select Visualization Domain

2. 选择可视化领域

3. Choose Chart Type and Layout

3. 选择图表类型与布局

4. Design for Clarity and Usability

4. 设计清晰易用的可视化

5. Implement and Validate

5. 实现与验证

Best Practices

最佳实践

General Principles

通用原则

Domain-Specific Guidelines

特定领域指南

Accessibility Checklist

无障碍设计检查清单

Examples

示例

Example 1: Python with Matplotlib/Seaborn

示例1：Python结合Matplotlib/Seaborn

Set style for professional look

Set style for professional look

Create figure with subplots

Create figure with subplots

Example 1: Line chart for time series

Example 1: Line chart for time series

Example 2: Bar chart for comparison

Example 2: Bar chart for comparison

Example 3: Scatter plot with regression

Example 3: Scatter plot with regression

Example 4: Pie/Donut chart for composition

Example 4: Pie/Donut chart for composition

Example 2: Interactive Visualization with Plotly

示例2：使用Plotly创建交互式可视化

Create interactive time series

Create interactive time series

Add moving average

Add moving average

Example 3: Chart Type Selection Guide

示例3：图表类型选择指南

Chart Selection by Data Type

Chart Selection by Data Type

Comparison

Comparison

Distribution

Distribution

Composition

Composition

Relationship

Relationship

Time Series

Time Series

Geographic

Geographic

Example 4: Analytics Dashboard with Streamlit

示例4：使用Streamlit构建分析仪表盘

Streamlit Dashboard Example

Streamlit Dashboard Example

Header

Header

KPI Row

KPI Row

Filters

Filters

Main Charts

Main Charts

Detail Table

Detail Table

Data table here