Table Creator
by lijinke666
用于透视表的创建,当用于需要基于明细数据做出分析或者展示时调用
Skill Details
Repository Files
2 files in this skill directory
name: table-creator description: 用于透视表的创建,当用于需要基于明细数据做出分析或者展示时调用
Table Creator
用于透视表的创建,当用于需要基于明细数据做出分析或者展示时调用
创建步骤
-
基于 https://github.com/antvis/S2 这个库生成透视表
- 如果用户的代码环境是 .js : 使用
@antv/s2 - 如果用户的代码环境是 React: 使用
@antv/s2-react - 如果用户的代码环境是 Vue 的使用
@antv/s2-vue - 其他场景兜底使用
@antv/s2
- 如果用户的代码环境是 .js : 使用
-
使用用户本地已安装的包管理器安装依赖,比如:
npm install @antv/s2 --save
yarn add @antv/s2
pnpm add @antv/s2
切记不需要额外安装包管理器
代码示例
import { PivotSheet } from '@antv/s2';
async function bootstrap() {
const container = document.getElementById('container');
const s2DataConfig = await fetch('https://assets.antv.antgroup.com/s2/en-data-config.json')
.then(r => r.json())
const s2 = new PivotSheet(container, s2DataConfig, {
width: 600,
height: 400,
});
await s2.render();
}
bootstrap()
API 文档
Related Skills
Attack Tree Construction
Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.
Grafana Dashboards
Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.
Matplotlib
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
Scientific Visualization
Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.
Seaborn
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
Shap
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Query Writing
For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations
Pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Scientific Visualization
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
