Data Visualization (Universal)

通用数据可视化

Overview

概述

This skill enables you to create professional scientific visualizations including scatter plots, line charts, heatmaps, violin plots, and more. Unlike cloud-hosted solutions, this skill uses the matplotlib and seaborn Python libraries and executes locally in your environment, making it compatible with ALL LLM providers including GPT, Gemini, Claude, DeepSeek, and Qwen.

此技能可帮助你创建专业的科学可视化图表，包括散点图、折线图、热图、小提琴图等。与云端托管解决方案不同，本技能使用matplotlib和seaborn Python库，并在你的本地环境中运行，因此兼容所有LLM提供商，包括GPT、Gemini、Claude、DeepSeek和Qwen。

When to Use This Skill

适用场景

Create publication-quality figures for papers and presentations
Generate exploratory data analysis (EDA) plots
Visualize gene expression, QC metrics, or clustering results
Create multi-panel figures combining different plot types
Export high-resolution images for reports
Customize plot aesthetics (colors, fonts, styles)

为论文和演示文稿创建出版级别的图表
生成探索性数据分析（EDA）图表
可视化基因表达、QC指标或聚类结果
创建组合多种图表类型的多面板图
导出高分辨率图片用于报告
自定义图表美学（颜色、字体、样式）

How to Use

使用方法

Step 1: Import Required Libraries

步骤1：导入所需库

python

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from matplotlib import gridspec
import matplotlib.patches as mpatches

python

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from matplotlib import gridspec
import matplotlib.patches as mpatches

Set style for publication-quality plots

sns.set_style("whitegrid") plt.rcParams['figure.dpi'] = 150 plt.rcParams['savefig.dpi'] = 300 plt.rcParams['font.size'] = 10

undefined

sns.set_style("whitegrid") plt.rcParams['figure.dpi'] = 150 plt.rcParams['savefig.dpi'] = 300 plt.rcParams['font.size'] = 10

undefined

Step 2: Basic Scatter Plot

步骤2：基础散点图

python

undefined

python

undefined

Create figure and axis

fig, ax = plt.subplots(figsize=(6, 5))

Scatter plot

ax.scatter(x_data, y_data, s=20, alpha=0.6, c='steelblue', edgecolors='k', linewidths=0.5)

Labels and title

ax.set_xlabel('Gene Expression (log2)', fontsize=12) ax.set_ylabel('Cell Count', fontsize=12) ax.set_title('Expression vs. Cell Count', fontsize=14, fontweight='bold')

Grid and styling

ax.grid(alpha=0.3) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False)

Save figure

plt.tight_layout() plt.savefig('scatter_plot.png', dpi=300, bbox_inches='tight') plt.show() print("✅ Scatter plot saved to: scatter_plot.png")

undefined

plt.tight_layout() plt.savefig('scatter_plot.png', dpi=300, bbox_inches='tight') plt.show() print("✅ Scatter plot saved to: scatter_plot.png")

undefined

Step 3: Line Plot with Multiple Series

步骤3：多系列折线图

python

fig, ax = plt.subplots(figsize=(8, 5))

python

fig, ax = plt.subplots(figsize=(8, 5))

Plot multiple lines

ax.plot(time_points, group1_values, marker='o', label='Group 1', color='#E74C3C', linewidth=2) ax.plot(time_points, group2_values, marker='s', label='Group 2', color='#3498DB', linewidth=2) ax.plot(time_points, group3_values, marker='^', label='Group 3', color='#2ECC71', linewidth=2)

Styling

ax.set_xlabel('Time Point', fontsize=12) ax.set_ylabel('Expression Level', fontsize=12) ax.set_title('Gene Expression Over Time', fontsize=14, fontweight='bold') ax.legend(frameon=True, loc='best', fontsize=10) ax.grid(alpha=0.3, linestyle='--')

plt.tight_layout() plt.savefig('line_plot.png', dpi=300, bbox_inches='tight') plt.show()

undefined

ax.set_xlabel('Time Point', fontsize=12) ax.set_ylabel('Expression Level', fontsize=12) ax.set_title('Gene Expression Over Time', fontsize=14, fontweight='bold') ax.legend(frameon=True, loc='best', fontsize=10) ax.grid(alpha=0.3, linestyle='--')

plt.tight_layout() plt.savefig('line_plot.png', dpi=300, bbox_inches='tight') plt.show()

undefined

Step 4: Box Plot and Violin Plot

步骤4：箱线图与小提琴图

python

undefined

python

undefined

Prepare data (long-form DataFrame)

df should have columns: 'cluster', 'expression', 'gene', etc.

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

Box plot

sns.boxplot(data=df, x='cluster', y='expression', palette='Set2', ax=ax1) ax1.set_title('Box Plot: Expression by Cluster', fontsize=12, fontweight='bold') ax1.set_xlabel('Cluster', fontsize=11) ax1.set_ylabel('Expression Level', fontsize=11) ax1.tick_params(axis='x', rotation=45)

Violin plot

sns.violinplot(data=df, x='cluster', y='expression', palette='muted', ax=ax2, inner='quartile') ax2.set_title('Violin Plot: Expression Distribution', fontsize=12, fontweight='bold') ax2.set_xlabel('Cluster', fontsize=11) ax2.set_ylabel('Expression Level', fontsize=11) ax2.tick_params(axis='x', rotation=45)

plt.tight_layout() plt.savefig('box_violin_plot.png', dpi=300, bbox_inches='tight') plt.show()

undefined

sns.violinplot(data=df, x='cluster', y='expression', palette='muted', ax=ax2, inner='quartile') ax2.set_title('Violin Plot: Expression Distribution', fontsize=12, fontweight='bold') ax2.set_xlabel('Cluster', fontsize=11) ax2.set_ylabel('Expression Level', fontsize=11) ax2.tick_params(axis='x', rotation=45)

plt.tight_layout() plt.savefig('box_violin_plot.png', dpi=300, bbox_inches='tight') plt.show()

undefined

Step 5: Heatmap

步骤5：热图

python

undefined

python

undefined

Prepare data matrix (rows=genes, columns=samples or clusters)

gene_expression_matrix: pandas DataFrame or numpy array

fig, ax = plt.subplots(figsize=(8, 6))

Create heatmap

sns.heatmap( gene_expression_matrix, cmap='viridis', cbar_kws={'label': 'Expression'}, xticklabels=True, yticklabels=True, linewidths=0.5, linecolor='gray', ax=ax )

ax.set_title('Gene Expression Heatmap', fontsize=14, fontweight='bold') ax.set_xlabel('Samples', fontsize=12) ax.set_ylabel('Genes', fontsize=12)

plt.tight_layout() plt.savefig('heatmap.png', dpi=300, bbox_inches='tight') plt.show()

undefined

sns.heatmap( gene_expression_matrix, cmap='viridis', cbar_kws={'label': 'Expression'}, xticklabels=True, yticklabels=True, linewidths=0.5, linecolor='gray', ax=ax )

ax.set_title('Gene Expression Heatmap', fontsize=14, fontweight='bold') ax.set_xlabel('Samples', fontsize=12) ax.set_ylabel('Genes', fontsize=12)

plt.tight_layout() plt.savefig('heatmap.png', dpi=300, bbox_inches='tight') plt.show()

undefined

Step 6: Bar Plot with Error Bars

步骤6：带误差棒的柱状图

python

fig, ax = plt.subplots(figsize=(7, 5))

python

fig, ax = plt.subplots(figsize=(7, 5))

Data

categories = ['Cluster 0', 'Cluster 1', 'Cluster 2', 'Cluster 3'] means = [120, 85, 200, 150] errors = [15, 10, 25, 20]

Bar plot

bars = ax.bar(categories, means, yerr=errors, capsize=5, color=['#E74C3C', '#3498DB', '#2ECC71', '#F39C12'], edgecolor='black', linewidth=1.2, alpha=0.8)

Labels

ax.set_ylabel('Cell Count', fontsize=12) ax.set_title('Cell Counts by Cluster', fontsize=14, fontweight='bold') ax.set_ylim(0, max(means) * 1.3)

Add value labels on bars

for bar, mean in zip(bars, means): height = bar.get_height() ax.text(bar.get_x() + bar.get_width()/2., height + 5, f'{mean}', ha='center', va='bottom', fontsize=10)

plt.tight_layout() plt.savefig('bar_plot.png', dpi=300, bbox_inches='tight') plt.show()

undefined

for bar, mean in zip(bars, means): height = bar.get_height() ax.text(bar.get_x() + bar.get_width()/2., height + 5, f'{mean}', ha='center', va='bottom', fontsize=10)

plt.tight_layout() plt.savefig('bar_plot.png', dpi=300, bbox_inches='tight') plt.show()

undefined

Advanced Features

高级功能

Multi-Panel Figure

多面板复合图

python

undefined

python

undefined

Create complex layout

fig = plt.figure(figsize=(12, 8)) gs = gridspec.GridSpec(2, 3, figure=fig, hspace=0.3, wspace=0.3)

Panel A: Scatter

ax1 = fig.add_subplot(gs[0, :2]) ax1.scatter(x_data, y_data, c=cluster_labels, cmap='tab10', s=10, alpha=0.6) ax1.set_title('A. UMAP Projection', fontsize=12, fontweight='bold', loc='left') ax1.set_xlabel('UMAP1') ax1.set_ylabel('UMAP2')

Panel B: Violin

ax2 = fig.add_subplot(gs[0, 2]) sns.violinplot(data=df, y='expression', palette='Set2', ax=ax2) ax2.set_title('B. Expression', fontsize=12, fontweight='bold', loc='left')

Panel C: Heatmap

ax3 = fig.add_subplot(gs[1, :]) sns.heatmap(matrix, cmap='coolwarm', center=0, ax=ax3, cbar_kws={'label': 'Z-score'}) ax3.set_title('C. Gene Expression Heatmap', fontsize=12, fontweight='bold', loc='left')

plt.savefig('multi_panel_figure.png', dpi=300, bbox_inches='tight') plt.show()

undefined

ax3 = fig.add_subplot(gs[1, :]) sns.heatmap(matrix, cmap='coolwarm', center=0, ax=ax3, cbar_kws={'label': 'Z-score'}) ax3.set_title('C. Gene Expression Heatmap', fontsize=12, fontweight='bold', loc='left')

plt.savefig('multi_panel_figure.png', dpi=300, bbox_inches='tight') plt.show()

undefined

Custom Color Palette

自定义配色方案

python

undefined

python

undefined

Define custom colors

custom_palette = ['#E74C3C', '#3498DB', '#2ECC71', '#F39C12', '#9B59B6']

Use in seaborn

sns.set_palette(custom_palette)

Or create color dict for specific mapping

color_dict = { 'T cells': '#E74C3C', 'B cells': '#3498DB', 'Monocytes': '#2ECC71', 'NK cells': '#F39C12' }

Use in scatter plot

for cell_type, color in color_dict.items(): mask = df['celltype'] == cell_type ax.scatter(df.loc[mask, 'x'], df.loc[mask, 'y'], c=color, label=cell_type, s=20, alpha=0.7) ax.legend()

undefined

for cell_type, color in color_dict.items(): mask = df['celltype'] == cell_type ax.scatter(df.loc[mask, 'x'], df.loc[mask, 'y'], c=color, label=cell_type, s=20, alpha=0.7) ax.legend()

undefined

Density Plot

密度散点图

python

from scipy.stats import gaussian_kde

fig, ax = plt.subplots(figsize=(8, 6))

python

from scipy.stats import gaussian_kde

fig, ax = plt.subplots(figsize=(8, 6))

Calculate density

xy = np.vstack([x_data, y_data]) z = gaussian_kde(xy)(xy)

Sort points by density for better visualization

idx = z.argsort() x, y, z = x_data[idx], y_data[idx], z[idx]

Scatter with density colors

scatter = ax.scatter(x, y, c=z, s=20, cmap='viridis', alpha=0.6, edgecolors='none') plt.colorbar(scatter, ax=ax, label='Density')

ax.set_xlabel('UMAP1', fontsize=12) ax.set_ylabel('UMAP2', fontsize=12) ax.set_title('Density Scatter Plot', fontsize=14, fontweight='bold')

plt.tight_layout() plt.savefig('density_plot.png', dpi=300, bbox_inches='tight') plt.show()

undefined

scatter = ax.scatter(x, y, c=z, s=20, cmap='viridis', alpha=0.6, edgecolors='none') plt.colorbar(scatter, ax=ax, label='Density')

ax.set_xlabel('UMAP1', fontsize=12) ax.set_ylabel('UMAP2', fontsize=12) ax.set_title('Density Scatter Plot', fontsize=14, fontweight='bold')

plt.tight_layout() plt.savefig('density_plot.png', dpi=300, bbox_inches='tight') plt.show()

undefined

Common Use Cases

常见应用场景

QC Metrics Visualization

QC指标可视化

python

undefined

python

undefined

Assuming adata.obs has QC columns: n_genes, n_counts, percent_mito

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

Plot 1: Histogram of genes per cell

axes[0].hist(adata.obs['n_genes'], bins=50, color='steelblue', edgecolor='black', alpha=0.7) axes[0].axvline(adata.obs['n_genes'].median(), color='red', linestyle='--', label='Median') axes[0].set_xlabel('Genes per Cell', fontsize=11) axes[0].set_ylabel('Frequency', fontsize=11) axes[0].set_title('Genes per Cell Distribution', fontsize=12, fontweight='bold') axes[0].legend()

Plot 2: Scatter UMI vs Genes

axes[1].scatter(adata.obs['n_counts'], adata.obs['n_genes'], s=5, alpha=0.5, c='coral') axes[1].set_xlabel('UMI Counts', fontsize=11) axes[1].set_ylabel('Genes Detected', fontsize=11) axes[1].set_title('UMIs vs Genes', fontsize=12, fontweight='bold')

Plot 3: Violin plot of mitochondrial percentage

sns.violinplot(y=adata.obs['percent_mito'], ax=axes[2], color='lightgreen') axes[2].axhline(y=20, color='red', linestyle='--', label='20% threshold') axes[2].set_ylabel('Mitochondrial %', fontsize=11) axes[2].set_title('Mitochondrial Content', fontsize=12, fontweight='bold') axes[2].legend()

plt.tight_layout() plt.savefig('qc_metrics.png', dpi=300, bbox_inches='tight') plt.show()

undefined

sns.violinplot(y=adata.obs['percent_mito'], ax=axes[2], color='lightgreen') axes[2].axhline(y=20, color='red', linestyle='--', label='20% threshold') axes[2].set_ylabel('Mitochondrial %', fontsize=11) axes[2].set_title('Mitochondrial Content', fontsize=12, fontweight='bold') axes[2].legend()

plt.tight_layout() plt.savefig('qc_metrics.png', dpi=300, bbox_inches='tight') plt.show()

undefined

UMAP/tSNE Visualization

UMAP/tSNE可视化

python

undefined

python

undefined

Assuming adata.obsm['X_umap'] exists and adata.obs['clusters'] exists

fig, ax = plt.subplots(figsize=(8, 7))

Get unique clusters

clusters = adata.obs['clusters'].unique() n_clusters = len(clusters)

Generate colors

colors = plt.cm.tab20(np.linspace(0, 1, n_clusters))

Plot each cluster

for i, cluster in enumerate(clusters): mask = adata.obs['clusters'] == cluster ax.scatter( adata.obsm['X_umap'][mask, 0], adata.obsm['X_umap'][mask, 1], c=[colors[i]], label=f'Cluster {cluster}', s=10, alpha=0.7, edgecolors='none' )

ax.set_xlabel('UMAP1', fontsize=12) ax.set_ylabel('UMAP2', fontsize=12) ax.set_title('UMAP Projection by Cluster', fontsize=14, fontweight='bold') ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', frameon=True, fontsize=9)

plt.tight_layout() plt.savefig('umap_clusters.png', dpi=300, bbox_inches='tight') plt.show()

undefined

for i, cluster in enumerate(clusters): mask = adata.obs['clusters'] == cluster ax.scatter( adata.obsm['X_umap'][mask, 0], adata.obsm['X_umap'][mask, 1], c=[colors[i]], label=f'Cluster {cluster}', s=10, alpha=0.7, edgecolors='none' )

ax.set_xlabel('UMAP1', fontsize=12) ax.set_ylabel('UMAP2', fontsize=12) ax.set_title('UMAP Projection by Cluster', fontsize=14, fontweight='bold') ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', frameon=True, fontsize=9)

plt.tight_layout() plt.savefig('umap_clusters.png', dpi=300, bbox_inches='tight') plt.show()

undefined

Gene Expression Dot Plot

基因表达点图

python

undefined

python

undefined

genes: list of gene names

clusters: list of cluster IDs

Create matrix: rows=genes, columns=clusters with mean expression and % expressing

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

Prepare data

from matplotlib.colors import Normalize

dot_size_matrix: % cells expressing (0-100)

color_matrix: mean expression level

for i, gene in enumerate(genes): for j, cluster in enumerate(clusters): # Size proportional to % expressing size = dot_size_matrix[i, j] * 5 # Scale factor # Color by expression level color_val = color_matrix[i, j]

    ax.scatter(j, i, s=size, c=[color_val], cmap='Reds',
               vmin=0, vmax=color_matrix.max(),
               edgecolors='black', linewidths=0.5)

for i, gene in enumerate(genes): for j, cluster in enumerate(clusters): # Size proportional to % expressing size = dot_size_matrix[i, j] * 5 # Scale factor # Color by expression level color_val = color_matrix[i, j]

    ax.scatter(j, i, s=size, c=[color_val], cmap='Reds',
               vmin=0, vmax=color_matrix.max(),
               edgecolors='black', linewidths=0.5)

Labels

ax.set_xticks(range(len(clusters))) ax.set_xticklabels(clusters, rotation=45, ha='right') ax.set_yticks(range(len(genes))) ax.set_yticklabels(genes) ax.set_xlabel('Cluster', fontsize=12) ax.set_ylabel('Gene', fontsize=12) ax.set_title('Marker Gene Expression', fontsize=14, fontweight='bold')

Colorbar

norm = Normalize(vmin=0, vmax=color_matrix.max()) sm = plt.cm.ScalarMappable(cmap='Reds', norm=norm) sm.set_array([]) cbar = plt.colorbar(sm, ax=ax, pad=0.02) cbar.set_label('Mean Expression', rotation=270, labelpad=15)

plt.tight_layout() plt.savefig('gene_dotplot.png', dpi=300, bbox_inches='tight') plt.show()

undefined

norm = Normalize(vmin=0, vmax=color_matrix.max()) sm = plt.cm.ScalarMappable(cmap='Reds', norm=norm) sm.set_array([]) cbar = plt.colorbar(sm, ax=ax, pad=0.02) cbar.set_label('Mean Expression', rotation=270, labelpad=15)

plt.tight_layout() plt.savefig('gene_dotplot.png', dpi=300, bbox_inches='tight') plt.show()

undefined

Volcano Plot (DEG Analysis)

火山图（差异基因分析）

python

undefined

python

undefined

Assuming deg_df has columns: gene, log2FC, pvalue

fig, ax = plt.subplots(figsize=(8, 7))

Calculate -log10(pvalue)

deg_df['-log10_pvalue'] = -np.log10(deg_df['pvalue'])

Classify genes

deg_df['significant'] = 'Not Significant' deg_df.loc[(deg_df['log2FC'] > 1) & (deg_df['pvalue'] < 0.05), 'significant'] = 'Up-regulated' deg_df.loc[(deg_df['log2FC'] < -1) & (deg_df['pvalue'] < 0.05), 'significant'] = 'Down-regulated'

Plot

for category, color in zip(['Not Significant', 'Up-regulated', 'Down-regulated'], ['gray', 'red', 'blue']): mask = deg_df['significant'] == category ax.scatter(deg_df.loc[mask, 'log2FC'], deg_df.loc[mask, '-log10_pvalue'], c=color, label=category, s=20, alpha=0.6, edgecolors='none')

Threshold lines

ax.axvline(x=1, color='black', linestyle='--', linewidth=1, alpha=0.5) ax.axvline(x=-1, color='black', linestyle='--', linewidth=1, alpha=0.5) ax.axhline(y=-np.log10(0.05), color='black', linestyle='--', linewidth=1, alpha=0.5)

Labels

ax.set_xlabel('log2 Fold Change', fontsize=12) ax.set_ylabel('-log10(p-value)', fontsize=12) ax.set_title('Volcano Plot: Differential Expression', fontsize=14, fontweight='bold') ax.legend(frameon=True, loc='upper right')

plt.tight_layout() plt.savefig('volcano_plot.png', dpi=300, bbox_inches='tight') plt.show()

undefined

ax.set_xlabel('log2 Fold Change', fontsize=12) ax.set_ylabel('-log10(p-value)', fontsize=12) ax.set_title('Volcano Plot: Differential Expression', fontsize=14, fontweight='bold') ax.legend(frameon=True, loc='upper right')

plt.tight_layout() plt.savefig('volcano_plot.png', dpi=300, bbox_inches='tight') plt.show()

undefined

Best Practices

最佳实践

Figure Size: Use appropriate dimensions for target medium (papers: 6-8 inches wide, posters: larger)
DPI: Save at 300 DPI for publications, 150 DPI for presentations
Colors: Use colorblind-friendly palettes (e.g.,
```
viridis
```
,
```
Set2
```
,
```
tab10
```
)
Fonts: Keep font sizes readable (titles: 12-14pt, labels: 10-12pt, ticks: 8-10pt)
Transparency: Use alpha for overlapping points to show density
Layout: Always call
```
plt.tight_layout()
```
before saving to prevent label clipping
File Format: PNG for general use, SVG for vector graphics (editable in Illustrator)
Close Figures: Call
```
plt.close()
```
after saving to free memory when generating many plots

图表尺寸：根据目标载体选择合适的尺寸（论文：6-8英寸宽，海报：更大尺寸）
分辨率：出版用图保存为300 DPI，演示用图保存为150 DPI
配色：选用色弱友好的配色方案（如
```
viridis
```
、
```
Set2
```
、
```
tab10
```
）
字体：保持字体大小易读（标题：12-14pt，标签：10-12pt，刻度：8-10pt）
透明度：对重叠点使用透明度以展示密度
布局：保存前务必调用
```
plt.tight_layout()
```
避免标签被截断
文件格式：通用场景用PNG，矢量图用SVG（可在Illustrator中编辑）
关闭图表：生成大量图表时，保存后调用
```
plt.close()
```
释放内存

Troubleshooting

常见问题排查

Issue: "Figure too cluttered with many points"

问题：图表包含大量点，显得过于拥挤

Solution: Use transparency and smaller point sizes

python

ax.scatter(x, y, s=5, alpha=0.3, edgecolors='none')

解决方案：使用透明度和更小的点尺寸

python

ax.scatter(x, y, s=5, alpha=0.3, edgecolors='none')

Issue: "Legend overlaps with data"

问题：图例与图表数据重叠

Solution: Place legend outside the plot area

python

ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

解决方案：将图例放置在图表区域外

python

ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')

Issue: "Labels are cut off in saved figure"

问题：保存的图表中标签被截断

Solution: Use

bbox_inches='tight'

python

plt.savefig('plot.png', dpi=300, bbox_inches='tight')

解决方案：使用

bbox_inches='tight'

参数

python

plt.savefig('plot.png', dpi=300, bbox_inches='tight')

Issue: "Colors don't match between plots"

问题：不同图表间颜色不匹配

Solution: Define color palette once and reuse

python

PALETTE = {'Group A': '#E74C3C', 'Group B': '#3498DB'}

解决方案：定义一次配色方案并重复使用

python

PALETTE = {'Group A': '#E74C3C', 'Group B': '#3498DB'}

Use PALETTE in all plots

在所有图表中使用PALETTE

undefined

undefined

Issue: "Heatmap text too small"

问题：热图中的文字过小

Solution: Adjust figure size or font size

python

fig, ax = plt.subplots(figsize=(12, 10))
sns.heatmap(data, ax=ax, annot_kws={'fontsize': 8})

解决方案：调整图表尺寸或字体大小

python

fig, ax = plt.subplots(figsize=(12, 10))
sns.heatmap(data, ax=ax, annot_kws={'fontsize': 8})

Technical Notes

技术说明

Libraries: Uses
```
matplotlib
```
and
```
seaborn
```
(widely supported, stable)
Execution: Runs locally in the agent's sandbox
Compatibility: Works with ALL LLM providers (GPT, Gemini, Claude, DeepSeek, Qwen, etc.)
File Formats: Supports PNG, PDF, SVG, JPEG
Performance: Typical plot generation takes <1 second for standard plots, 2-5 seconds for complex multi-panel figures
Memory: Keep figure count reasonable; close figures after saving if generating many plots

依赖库：使用
```
matplotlib
```
和
```
seaborn
```
（支持广泛、稳定可靠）
运行方式：在Agent的沙箱环境中本地运行
兼容性：兼容所有LLM提供商（GPT、Gemini、Claude、DeepSeek、Qwen等）
支持格式：支持PNG、PDF、SVG、JPEG等文件格式
性能：标准图表生成时间通常<1秒，复杂多面板图表耗时2-5秒
内存：合理控制图表数量；生成大量图表时，保存后关闭图表

References

参考资料

Matplotlib documentation: https://matplotlib.org/stable/contents.html
Seaborn documentation: https://seaborn.pydata.org/
Matplotlib gallery: https://matplotlib.org/stable/gallery/index.html
Seaborn gallery: https://seaborn.pydata.org/examples/index.html

Matplotlib官方文档：https://matplotlib.org/stable/contents.html
Seaborn官方文档：https://seaborn.pydata.org/
Matplotlib示例图库：https://matplotlib.org/stable/gallery/index.html
Seaborn示例图库：https://seaborn.pydata.org/examples/index.html

data-viz-plots

Original

Translation

Data Visualization (Universal)

通用数据可视化

Overview

概述

When to Use This Skill

适用场景

How to Use

使用方法

Step 1: Import Required Libraries

步骤1：导入所需库

Set style for publication-quality plots

Set style for publication-quality plots

Step 2: Basic Scatter Plot

步骤2：基础散点图

Create figure and axis

Create figure and axis

Scatter plot

Scatter plot

Labels and title

Labels and title

Grid and styling

Grid and styling

Save figure

Save figure

Step 3: Line Plot with Multiple Series

步骤3：多系列折线图

Plot multiple lines

Plot multiple lines

Styling

Styling

Step 4: Box Plot and Violin Plot

步骤4：箱线图与小提琴图

Prepare data (long-form DataFrame)

Prepare data (long-form DataFrame)

df should have columns: 'cluster', 'expression', 'gene', etc.

df should have columns: 'cluster', 'expression', 'gene', etc.

Box plot

Box plot

Violin plot

Violin plot

Step 5: Heatmap

步骤5：热图

Prepare data matrix (rows=genes, columns=samples or clusters)

Prepare data matrix (rows=genes, columns=samples or clusters)

gene_expression_matrix: pandas DataFrame or numpy array

gene_expression_matrix: pandas DataFrame or numpy array

Create heatmap

Create heatmap

Step 6: Bar Plot with Error Bars

步骤6：带误差棒的柱状图

Data

Data

Bar plot

Bar plot

Labels

Labels

Add value labels on bars

Add value labels on bars

Advanced Features

高级功能

Multi-Panel Figure

多面板复合图

Create complex layout

Create complex layout

Panel A: Scatter

Panel A: Scatter

Panel B: Violin

Panel B: Violin

Panel C: Heatmap

Panel C: Heatmap

Custom Color Palette

自定义配色方案

Define custom colors

Define custom colors

Use in seaborn

Use in seaborn

Or create color dict for specific mapping