seaborn
使用 Seaborn 创建统计可视化
Também disponível em: K-Dense-AI
Seaborn 用最少的代码提供复杂的统计可视化。此技能帮助您为探索性数据分析和演示创建可用于发表的图表。
Baixar o ZIP da skill
Upload no Claude
Vá em Configurações → Capacidades → Skills → Upload skill
Ative e comece a usar
Testar
A utilizar "seaborn". Create a box plot showing distribution of test scores across three different classes
Resultado esperado:
Seaborn boxplot code with data parameter, x-axis for class categories, y-axis for scores, including whiskers for quartiles and outlier detection
A utilizar "seaborn". Generate a pair plot for my housing dataset to visualize all numeric feature relationships
Resultado esperado:
Seaborn pairplot code with corner optimization, KDE diagonal plots, and hue mapping for property type differentiation
A utilizar "seaborn". Show me how to create a regression plot with confidence interval shading
Resultado esperado:
Seaborn regplot or lmplot code with scatter points, regression line, and automatic confidence interval calculation and visualization
Auditoria de Segurança
SeguroThis skill contains documentation and code examples for the Seaborn Python visualization library. All static scanner findings are false positives: the detected patterns are Python code examples within markdown code blocks (triple backticks), not executable commands. The skill does not execute any code, access networks, or perform file operations. It is safe for publication.
Fatores de risco
⚙️ Comandos externos (1011)
🌐 Acesso à rede (1)
Pontuação de qualidade
O Que Você Pode Construir
探索性数据分析
数据科学家使用此技能在初始数据探索期间快速可视化数据集分布、相关性以及变量之间的关系。
发表用图表
研究人员生成可用于发表的统计图表,包含适当的误差线、置信区间和格式化的美观样式,用于学术论文。
商业分析仪表板
分析师创建引人注目的业务指标、趋势和类别比较可视化,用于利益相关者演示。
Tente Estes Prompts
Create a seaborn scatter plot showing the relationship between price and quantity, colored by product category
Generate a violin plot comparing sales distributions across different regions, with individual data points overlaid
Build a correlation heatmap for all numeric columns in my dataset, with annotations showing correlation coefficients
Create a faceted line plot showing revenue trends over time for each product category, with 95% confidence intervals
Melhores Práticas
- 始终使用 pandas DataFrame 指定 data 参数,以实现清晰的变量映射
- 使用 relplot 或 catplot 等图形级函数进行自动分面和子图管理
- 根据数据类型应用适当的调色板:有序数据使用连续色,无中点意义的数据使用发散色,离散类别使用分类色
Evitar
- 避免在同一图表上混合使用轴级和图形级函数而不了解 matplotlib 图形结构
- 不要对连续变量使用默认分类颜色,或对无序类别使用连续颜色
- 永远不要依赖隐式数据排序,而不显式排序或为分类轴指定 order 参数
Perguntas Frequentes
轴级函数和图形级函数之间有什么区别?
如何在我的项目中选择 seaborn 和 matplotlib?
我可以自定义 seaborn 图表超过默认样式吗?
seaborn 期望什么数据格式?
如何保存高分辨率 seaborn 图表?
此技能执行代码还是仅提供示例?
Detalhes do Desenvolvedor
Autor
davila7Licença
MIT
Repositório
https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/scientific/seabornReferência
main
Estrutura de arquivos