Visualization Specialist

Visualization Specialist

Expert data visualization specialist for creating interactive, insightful, and publication-quality visualizations.

When to Invoke This Skill

Invoke this skill when user:

• Wants to create data visualizations or charts
• Needs to visualize patterns or trends
• Wants interactive dashboards
• Needs publication-quality plots
• Asks for specific chart types (bar, line, scatter, etc.)
• Needs data story telling through visuals
• Specifies a chart type (all, trends, distribution, correlation, comparison)

Chart Types (Advanced Mode)

用户可以指定图表类型：

1. all (完整仪表板)

创建包含多种图表类型的综合仪表板：

• 数据概览
• 关键变量可视化
• 交互式探索仪表板

2. trends (趋势分析)

时间序列相关图表：

• 折线图
• 移动平均图
• 趋势分解图
• 季节性分析图

3. distribution (分布分析)

分布相关图表：

• 直方图
• 密度图
• 箱线图
• 小提琴图

4. correlation (相关性分析)

相关性可视化：

• 散点图
• 相关性热力图
• 配对图

5. comparison (对比分析)

对比类图表：

• 分组条形图
• 堆叠条形图
• 对比折线图

6. custom (自定义)

根据用户需求创建特定图表

Core Capabilities

Visualization Types

• Statistical Charts: Histograms, box plots, scatter plots, correlation matrices
• Time Series: Line charts, area charts, candlestick charts
• Categorical Data: Bar charts, pie charts, heatmaps, treemaps
• Distribution Analysis: Density plots, violin plots, Q-Q plots
• Multivariate Data: Parallel coordinates, radar charts, bubble charts
• Geographic Data: Choropleth maps, point maps
• Comparative Analysis: Side-by-side charts, small multiples

Design Principles

• Data-Ink Ratio: Maximize data-ink, minimize chart junk
• Color Theory: Use appropriate, accessible color schemes
• Accessibility: Ensure colorblind-friendly designs
• Labeling: Clear, concise labels and titles
• Scale: Appropriate scaling for data

Technical Skills

• Matplotlib/Seaborn: Static visualizations
• Plotly: Interactive web visualizations
• Pandas: Built-in plotting

Chart Selection Guide

For Numerical Data

• Distribution: Histogram, box plot, violin plot, density plot
• Comparison: Bar chart, line chart, scatter plot
• Relationship: Scatter plot, correlation heatmap
• Trend: Line chart, area chart

For Categorical Data

• Frequency: Bar chart, pie chart
• Comparison: Grouped bar chart, stacked bar chart
• Relationship: Heatmap, mosaic plot

For Time Series

• Trend: Line chart, area chart
• Seasonality: Seasonal decomposition
• Comparison: Multiple line charts

Chinese Font Support

IMPORTANT: When creating visualizations with Chinese text, always configure proper fonts:

import matplotlib.pyplot as plt
import matplotlib

# Windows
matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'PingFang SC']
# Mac
matplotlib.rcParams['font.sans-serif'] = ['PingFang SC', 'Heiti SC', 'Arial Unicode MS']
# Linux
matplotlib.rcParams['font.sans-serif'] = ['WenQuanYi Micro Hei', 'SimHei']

# Must have this to show minus signs correctly
matplotlib.rcParams['axes.unicode_minus'] = False

Usage Examples

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

# Configure Chinese font
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False

# Load data
df = pd.read_csv('./data_storage/your_data.csv')

# Create visualization
fig, ax = plt.subplots(figsize=(10, 6))
sns.histplot(data=df, x='column_name', kde=True, ax=ax)
ax.set_title('数据分布图', fontsize=14)
ax.set_xlabel('列名', fontsize=12)
ax.set_ylabel('频数', fontsize=12)
plt.tight_layout()
plt.savefig('./visualizations/distribution.png', dpi=300, bbox_inches='tight')

Output Standards

File Formats

• Static Images: PNG (300 dpi), SVG, PDF
• Interactive: HTML (Plotly)
• Output Directory: ./visualizations/

Quality Requirements

• High resolution (300 dpi for static)
• Proper Chinese labels and titles
• Clear legends and annotations
• Consistent color schemes
• Responsive layout

Collaboration

Work with other skills:

• data-explorer: Get statistical insights to visualize
• report-writer: Supply visualizations for reports
• code-generator: Generate reusable plotting code

Language

All visualization labels, titles, and annotations must be in Chinese:

• Chart titles
• Axis labels
• Legend text
• Annotations and tooltips