exstruct 0.2.0

Excel to structured JSON (tables, shapes, charts) for LLM/RAG pipelines

ExStruct — Excel Structured Extraction Engine

ExStruct reads Excel workbooks and outputs structured data (tables, shapes, charts) as JSON by default, with optional YAML/TOON formats. It targets both COM/Excel environments (rich extraction) and non-COM environments (cells + table candidates), with tunable detection heuristics and multiple output modes to fit LLM/RAG pipelines.

Features

• Excel → Structured JSON: cells, shapes, charts, and table candidates per sheet.
• Output modes: light (cells + table candidates only), standard (texted shapes + arrows, charts), verbose (all shapes with width/height).
• Formats: JSON (compact by default, --pretty available), YAML, TOON (optional dependencies).
• Table detection tuning: adjust heuristics at runtime via API.
• CLI rendering (Excel required): optional PDF and per-sheet PNGs.
• Graceful fallback: if Excel COM is unavailable, extraction falls back to cells + table candidates without crashing.

Installation

pip install exstruct

Optional extras:

• YAML: pip install pyyaml
• TOON: pip install python-toon
• Rendering (PDF/PNG): Excel + pip install pypdfium2

Quick Start (CLI)

exstruct input.xlsx > output.json          # compact JSON to stdout (default)
exstruct input.xlsx -o out.json --pretty   # pretty JSON to a file
exstruct input.xlsx --format yaml          # YAML (needs pyyaml)
exstruct input.xlsx --format toon          # TOON (needs python-toon)
exstruct input.xlsx --sheets-dir sheets/   # split per sheet in chosen format
exstruct input.xlsx --mode light           # cells + table candidates only
exstruct input.xlsx --pdf --image          # PDF and PNGs (Excel required)

Quick Start (Python)

from pathlib import Path
from exstruct import extract, export, set_table_detection_params

# Tune table detection (optional)
set_table_detection_params(table_score_threshold=0.3, density_min=0.04)

# Extract with modes: "light", "standard", "verbose"
wb = extract("input.xlsx", mode="standard")
export(wb, Path("out.json"), pretty=False)  # compact JSON

# Model helpers: iterate, index, and serialize directly from the models
first_sheet = wb["Sheet1"]           # __getitem__ access
for name, sheet in wb:               # __iter__ yields (name, SheetData)
    print(name, len(sheet.rows))
wb.save("out.json", pretty=True)     # WorkbookData → file (by extension)
first_sheet.save("sheet.json")       # SheetData → file (by extension)
print(first_sheet.to_yaml())         # YAML text (requires pyyaml)

Table Detection Tuning

from exstruct import set_table_detection_params

set_table_detection_params(
    table_score_threshold=0.35,  # increase to be stricter
    density_min=0.05,
    coverage_min=0.2,
    min_nonempty_cells=3,
)

Use higher thresholds to reduce false positives; lower them if true tables are missed.

Output Modes

• light: cells + table candidates (no COM needed).
• standard: texted shapes + arrows, charts (COM if available), table candidates.
• verbose: all shapes (with width/height), charts, table candidates.

Error Handling / Fallbacks

• Excel COM unavailable → falls back to cells + table candidates; shapes/charts empty.
• Shape extraction failure → logs warning, still returns cells + table candidates.
• CLI prints errors to stdout/stderr and returns non-zero on failures.

Optional Rendering

Requires Excel and pypdfium2.

exstruct input.xlsx --pdf --image --dpi 144

Creates <output>.pdf and <output>_images/ PNGs per sheet.

Benchmark: Excel Structuring Demo

To show how well exstruct can structure Excel, we parse a workbook that combines three elements on one sheet and share an AI reasoning benchmark that uses the JSON output.

• Table (sales data)
• Line chart
• Flowchart built only with shapes

(Screenshot below is the actual sample Excel sheet)

1. Input: Excel Sheet Overview

This sample Excel contains:

① Table (Sales Data)

Month	Product A	Product B	Product C
Jan-25	120	80	60
Feb-25	135	90	64
Mar-25	150	100	70
Apr-25	170	110	72
May-25	160	120	75
Jun-25	180	130	80

② Chart (Line Chart)

• Title: Sales Data
• Series: Product A / Product B / Product C (six months)
• Y axis: 0–200

③ Flowchart built with shapes

The sheet includes this flow:

• Start / End
• Format check
• Loop (items remaining?)
• Error handling
• Yes/No decision for sending email

2. Output: Structured JSON produced by exstruct (excerpt)

Below is a shortened JSON output example from parsing this Excel workbook.

{
  "book_name": "sample.xlsx",
  "sheets": {
    "Sheet1": {
      "rows": [
        {
          "r": 3,
          "c": {
            "1": "月",
            "2": "製品A",
            "3": "製品B",
            "4": "製品C"
          }
        },
        ...
      ],
      "shapes": [
        {
          "text": "開始",
          "l": 148,
          "t": 220,
          "type": "AutoShape-FlowchartProcess"
        },
        {
          "text": "入力データ読み込み",
          "l": 132,
          "t": 282,
          "type": "AutoShape-FlowchartProcess"
        },
        {
          "l": 193,
          "t": 246,
          "type": "AutoShape-Mixed",
          "begin_arrow_style": 1,
          "end_arrow_style": 2,
          "direction": "N"
        },
        ...
      ],
      "charts": [
        {
          "name": "Chart 1",
          "chart_type": "Line",
          "title": "売上データ",
          "y_axis_range": [
            0.0,
            200.0
          ],
          "series": [
            {
              "name": "製品A",
              "name_range": "Sheet1!$C$3",
              "x_range": "Sheet1!$B$4:$B$9",
              "y_range": "Sheet1!$C$4:$C$9"
            },
            ...
          ],
          "l": 377,
          "t": 25
        }
      ],
      "table_candidates": [
        "B3:E9"
      ]
    }
  }
}

3. How AI (Copilot / LLM) interprets the JSON

Below is the Markdown reconstruction of the Excel workbook. The table, chart, and flowchart are all represented.

---

## 📊 Sales Data Table

| Month      | Product A | Product B | Product C |
| ---------- | --------- | --------- | --------- |
| 2025-01-01 | 120       | 80        | 60        |
| 2025-02-01 | 135       | 90        | 64        |
| 2025-03-01 | 150       | 100       | 70        |
| 2025-04-01 | 170       | 110       | 72        |
| 2025-05-01 | 160       | 120       | 75        |
| 2025-06-01 | 180       | 130       | 80        |

---

## 📈 Sales Data (Line Chart)

- Chart title: **売上データ (Sales Data)**
- Chart type: Line
- Y-axis range: 0 to 200
- Data series:
  - Product A: 120 → 135 → 150 → 170 → 160 → 180
  - Product B: 80 → 90 → 100 → 110 → 120 → 130
  - Product C: 60 → 64 → 70 → 72 → 75 → 80

---

## 🔄 Process Flow (Mermaid Flowchart)

```mermaid
flowchart TD
    A[Start]
    B[Load input data]
    C{Is format valid?}
    D[Show error]
    E[Process one item]
    F{Items remaining?}
    G[Generate output]
    H{Send email?}
    I[Send email]
    J[Finish]

    A --> B
    B --> C
    C -- no --> D
    C -- yes --> E
    E --> F
    F -- yes --> E
    F -- no --> G
    G --> H
    H -- yes --> I
    H -- no --> J
    I --> J
```

From this we can see:

exstruct's JSON is already in a format that AI can read and reason over directly.

4. Summary

This benchmark confirms exstruct can:

• Parse tables, charts, and shapes (flowcharts) simultaneously
• Convert the semantic structure of Excel into JSON
• Let AI/LLMs read that JSON directly and reconstruct the workbook contents

In short, exstruct = “an engine that converts Excel into a format AI can understand.”

Notes

• Default JSON is compact to reduce tokens; use --pretty or pretty=True when readability matters.
• Field table_candidates replaces tables; adjust downstream consumers accordingly.

License

BSD-3-Clause. See LICENSE for details.

Documentation

• API Reference (GitHub Pages): https://harumiweb.github.io/exstruct/