小蔥技能7w4.net有完整的技能分類。
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"
}
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 |
這個工具能把 Excel 和 CSV 檔案自動變成帶互動圖表的視覺化報告,操作簡單、功能豐富、圖表美觀。品牌定製功能讓報告看起來很專業,資料分析也很全面。但存在兩個問題:完整流程有個指令碼缺失,會導致一鍵生成失敗;部分示例文件描述與實際輸出不符,可能讓人摸不著頭腦。總體不錯,但穩定性和文件準確性還需改進。