xlsx-parsing

1# xlsx-parsing
2 
3Excel workbooks are the lingua franca of operational documents that nobody bothered to put in a database — playbooks, rate cards, deviation policies, finance models, SLAs. They show up in tasks with three properties that trip up naive readers:
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51. **Multiple sheets**, only one of which is the data you actually want.
62. **Sparse cells** — a row that uses a column may sit next to a row that doesn't, leaving `None` cells. Empty is meaningful (the rule does not apply), not an error.
73. **Composite cells** — a single cell that contains a comma-separated list, a JSON blob, or a sentence rather than an atomic value.
8 
9Treat the workbook as a *typed table* with declared columns, not a free-form spreadsheet. Read every sheet you need, normalise it to `list[dict[str, Any]]`, then operate on that.
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11## Reading with `openpyxl` (pure Python, no compiled dependencies)
12 
13```python
14import openpyxl
15 
16wb = openpyxl.load_workbook("workbook.xlsx", data_only=True, read_only=True)
17print(wb.sheetnames)            # e.g., ['Metadata', 'Definitions', 'Rules']
18 
19ws = wb["Rules"]
20rows = ws.iter_rows(values_only=True)
21header = [str(c).strip() if c else "" for c in next(rows)]
22records = [dict(zip(header, row)) for row in rows if any(cell is not None for cell in row)]
23```
24 
25Notes:
26- `data_only=True` returns the cached value of formula cells instead of the formula expression. Without this you may get strings like `"=A1+B2"`.
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