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name: xlsx-parsing description: Read Microsoft Excel (.xlsx) files robustly with openpyxl (or pandas). Covers multi-sheet workbooks, header rows, empty cells, merged cells, comma-separated list cells, and converting a sheet to a list-of-dicts the rest of your code can consume. Use when a task input or reference document is an .xlsx file rather than JSON/CSV.

xlsx-parsing

Excel 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:

1. Multiple sheets, only one of which is the data you actually want.
2. 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.
3. Composite cells — a single cell that contains a comma-separated list, a JSON blob, or a sentence rather than an atomic value.

Treat 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.

Reading with openpyxl (pure Python, no compiled dependencies)

import openpyxl

wb = openpyxl.load_workbook("workbook.xlsx", data_only=True, read_only=True)
print(wb.sheetnames)            # e.g., ['Metadata', 'Definitions', 'Rules']

ws = wb["Rules"]
rows = ws.iter_rows(values_only=True)
header = [str(c).strip() if c else "" for c in next(rows)]
records = [dict(zip(header, row)) for row in rows if any(cell is not None for cell in row)]

Notes:

• data_only=True returns the cached value of formula cells instead of the formula expression. Without this you may get strings like "=A1+B2".
• read_only=True is faster on big workbooks and avoids loading styles you don't need.
• The any(cell is not None ...) filter drops entirely-blank rows that Excel preserves at the bottom of a sheet.
• dict(zip(header, row)) handles trailing empty columns gracefully when a row is shorter than the header.

Reading with pandas (if it's installed)

import pandas as pd

# Multi-sheet read returns a dict of DataFrames
sheets = pd.read_excel("workbook.xlsx", sheet_name=None, dtype=object)
rules_df = sheets["Rules"]

# Drop fully-empty rows; keep partial rows
rules_df = rules_df.dropna(how="all")

# Iterate as dicts; NaN becomes None
records = rules_df.where(rules_df.notna(), None).to_dict(orient="records")

pandas is heavier but useful when you want grouping, joins, or numeric aggregation. Either library is fine; do not mix them in the same module.

Empty cells: None is the answer, not an error

A row that does not specify a numeric cap leaves that cell blank. The blank is part of the rule's shape — it means "no cap applies" or "this constraint is not engaged for this rule." Code defensively:

def get(rec, key, default=None):
    val = rec.get(key)
    return default if val is None or (isinstance(val, str) and not val.strip()) else val

Compare against is None or call .strip() rather than truthiness — 0 and False are valid values that fail truthy tests.

Composite cells

Authors often put list-valued data into a single cell. A cell containing "Delaware, New York, California" is one string, not three rows.

def split_list_cell(value):
    if value is None:
        return []
    return [item.strip() for item in str(value).split(",") if item.strip()]

If you see a cell with curly-brace text, it is probably an embedded JSON document; parse with json.loads. Try the simple split first.

Merged cells

Merged cells appear once in the underlying data; only the top-left cell holds the value, and the rest are None. If a column is intentionally merged for a "section header" effect, fill the value down to recover row-wise records:

last = None
for row in records:
    if row["section"] is None:
        row["section"] = last
    else:
        last = row["section"]

If you need to know whether a cell is in a merged range, ws.merged_cells.ranges gives you the list.

Multiple sheets

Use the metadata sheet (often named Metadata, Info, or README) for workbook-level fields, and the data sheet(s) for per-record rows. Read all sheets you need before processing — do not assume the schema of one sheet is described inside another sheet you have not opened.

Putting it together for a configuration-style workbook

import openpyxl

def load_sheet_as_records(wb, sheet_name):
    ws = wb[sheet_name]
    rows = ws.iter_rows(values_only=True)
    header = [str(c).strip() if c else "" for c in next(rows)]
    return [
        dict(zip(header, row))
        for row in rows
        if any(cell is not None for cell in row)
    ]

wb = openpyxl.load_workbook("workbook.xlsx", data_only=True, read_only=True)
metadata = {row[0]: row[1] for row in wb["Metadata"].iter_rows(min_row=2, values_only=True)}
defs  = load_sheet_as_records(wb, "Definitions")
rules = load_sheet_as_records(wb, "Rules")

After this, rules[0]["key"], rules[0]["rule_type"], etc. are plain Python values you can branch on. The rest of your code does not need to know the input was Excel.

When not to use this skill

• The file is .csv — use csv.DictReader directly.
• The file is .json or .jsonl — use json.loads.
• The file is .xls (legacy binary) — openpyxl will refuse; use xlrd<2 or convert to .xlsx first.