Excel2Insights Pro transforms structured data files into interactive, brandable HTML dashboards with a single command. It combines automated statistical analysis, data quality checks, and Plotly interactive visualizations into a polished, self-contained report.
| Feature | Free Version | Pro Version |
|---|---|---|
| Charts | Static PNG (matplotlib) | Interactive Plotly (zoom, hover, pan) |
| Output | Markdown report | Branded HTML dashboard |
| Customization | None | Brand colors, logo, fonts |
| Data Quality | Basic stats | Visual quality panel |
| Insights | Raw numbers | Tagged insights with scores |
| Pipeline | 5 separate commands | One-command auto-pipeline |
# One-command: load → analyze → chart → dashboard
python3 scripts/auto-pipeline.py --file data.csv
# Opens: output/<dataset>_dashboard.html
# 1. Generate brand template
python3 scripts/dashboard-generator.py --init-brand brand.json
# 2. Edit brand.json (colors, logo, company name)
# 3. Run with branding
python3 scripts/auto-pipeline.py --file data.csv --brand-json brand.json
python3 scripts/auto-pipeline.py --file data.csv \
--charts histogram,bar,heatmap,scatter,pairplot \
--brand-json brand.json
| Script | Description | Usage |
|---|---|---|
scripts/excel-reader.py |
Load & inspect file metadata | --file DATA |
scripts/data-analyzer.py |
Full statistical analysis | --file DATA --output analysis.json |
scripts/chart-generator.py |
Interactive Plotly charts | --file DATA --charts TYPES --output DIR |
scripts/dashboard-generator.py |
HTML dashboard + brand | --analysis JSON --charts DIR --output DASHBOARD |
scripts/auto-pipeline.py |
One-command full pipeline | --file DATA [--brand-json BRAND] |
excel-reader.py
--file PATH — Path to Excel/CSV/TSV file--sheet NAME — Sheet name (XLSX only)--encoding ENC — File encoding (default: utf-8)data-analyzer.py
--file PATH — Path to data file--output PATH — Output JSON path--correlation — Include correlation matrix--outliers — Enable outlier detectionchart-generator.py (Pro: Plotly interactive)
--file PATH — Path to data file--charts TYPES — Comma-separated: histogram,boxplot,bar,line,pie,heatmap,scatter,pairplot--columns COLS — Specific columns to visualize--output DIR — Output directoryGenerated charts are interactive HTML files with:
dashboard-generator.py (Pro exclusive)
--analysis JSON — Analysis results JSON (required)--charts DIR — Charts directory--output PATH — Output dashboard HTML path--brand-json PATH — Brand configuration file--init-brand PATH — Generate brand template--file DATA — Original data file (for stats)auto-pipeline.py (Pro exclusive)
--file PATH — Data file (required)--charts TYPES — Chart types (default: histogram,bar,heatmap,scatter)--output DIR — Output directory--format FORMAT — html / markdown / both--brand-json PATH — Brand configurationCreate a brand.json file to customize your dashboard:
{ "company_name": "Your Company", "logo_url": "", "primary_color": "#1a73e8", "secondary_color": "#34a853", "accent_color": "#ea4335", "font_family": "'Inter', sans-serif", "show_footer": true, "footer_text": "Generated by Excel2Insights Pro | Your Company" }更多技能请访问小葱技能站7w4.net。
Generate a template: python3 scripts/dashboard-generator.py --init-brand brand.json
python3 scripts/auto-pipeline.py --file sales_data.csv
# Output: output/sales_data_dashboard.html
# → Interactive dashboard with histogram, bar, and correlation charts
python3 scripts/auto-pipeline.py --file survey_2024.xlsx \
--charts histogram,bar,boxplot,heatmap,scatter \
--brand-json brand.json
# Output: output/survey_2024_dashboard.html
# → Branded dashboard with 5 chart types
# Step 1: Analyze
python3 scripts/data-analyzer.py --file data.csv \
--output analysis.json --correlation --outliers
# Step 2: Generate charts
python3 scripts/chart-generator.py --file data.csv \
--charts histogram,bar,heatmap,scatter --output charts/
# Step 3: Build dashboard
python3 scripts/dashboard-generator.py \
--file data.csv --analysis analysis.json \
--charts charts/ --output dashboard.html \
--brand-json brand.json
The dashboard is a self-contained HTML file that includes:
<!-- Summary Cards → overview stats -->
<!-- Data Quality → missing values, column profile -->
<!-- Interactive Charts → Plotly with zoom/hover/pan -->
<!-- Key Insights → tagged findings with severity -->
Open it directly in any browser — no server required.
This tool provides data analysis and visualization. It does not make business decisions. Users are responsible for interpreting results and making informed decisions.
MIT License. See LICENSE file for details.
| Script | Purpose | Input | Output | Network | Filesystem |
|---|---|---|---|---|---|
excel-reader.py |
Load & preview file | File path | stdout | No | Read only |
data-analyzer.py |
Statistical analysis | File path / JSON | stdout / JSON | No | Read + write output |
chart-generator.py |
Interactive Plotly charts | File path / columns | HTML files | No | Write charts |
dashboard-generator.py |
HTML dashboard + brand | JSON + charts | HTML file | No | Write dashboard |
auto-pipeline.py |
Full pipeline orchestrator | File path | Dashboard | No | Write all outputs |
这个工具能把 Excel 和 CSV 文件自动变成带交互图表的可视化报告,操作简单、功能丰富、图表美观。品牌定制功能让报告看起来很专业,数据分析也很全面。但存在两个问题:完整流程有个脚本缺失,会导致一键生成失败;部分示例文档描述与实际输出不符,可能让人摸不着头脑。总体不错,但稳定性和文档准确性还需改进。