xlsx-parsing

name	xlsx-parsing
version	1.1.1
description	Ingests Microsoft Excel .xlsx workbooks with openpyxl (or pandas) into list-of-dict rows, handling multi-sheet books, sparse rows, merged cells, and composite list or JSON cells. Use when a task input is .xlsx rather than a database. Not for .csv, .json, or legacy .xls; never mix openpyxl and pandas objects in one pipeline or load untrusted files with read_only=False.
risk	safe
source	openrouter-deepsearch
date_added	2026-06-14T00:00:00.000Z

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.

When to Use

• You need to ingest data from a Microsoft Excel .xlsx file.
• The workbook contains multiple sheets, sparse rows, merged cells, or cells with embedded lists/JSON.
• You prefer a pure-Python solution (openpyxl >= 3.1) or already have pandas >= 2.2 available for richer data manipulation.

Do NOT use this skill when:

• 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.
• Do not mix openpyxl and pandas objects in the same processing pipeline; this can cause subtle type-conversion bugs.
• Avoid loading workbooks with read_only=False on large files; it can exhaust memory and expose you to DoS-style attacks if untrusted files are processed.

Prerequisites

Install the required library. On a Windows PowerShell host:

pip install "openpyxl>=3.1"
# OR, if you prefer pandas:
pip install "pandas>=2.2" "openpyxl>=3.1"

Verify installation:

python -c "import openpyxl; print(openpyxl.__version__)"

Procedure

Step 1 — Load the workbook with safe flags

Always use data_only=True, read_only=True, and keep_links=False. These three flags are mandatory for security and performance:

from pathlib import Path
import openpyxl
from typing import Any, List, Dict

def load_sheet_as_records(path: Path, sheet_name: str) -> List[Dict[str, Any]]:
    wb = openpyxl.load_workbook(path, data_only=True, read_only=True, keep_links=False)
    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)
    ]

# Example usage
records = load_sheet_as_records(Path("workbook.xlsx"), "Rules")

Why these flags:

• data_only=True returns the cached value of formula cells instead of the formula expression.
• read_only=True is faster on big workbooks and avoids loading styles you don't need.
• keep_links=False drops external link metadata, mitigating a class of XML-entity attacks.
• 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.

Step 2 — (Alternative) Load with pandas

If pandas is installed and you need grouping, joins, or numeric aggregation:

import pandas as pd
from pathlib import Path
from typing import Any, List, Dict

def load_excel_sheets(path: Path) -> Dict[str, List[Dict[str, Any]]]:
    sheets = pd.read_excel(
        path,
        sheet_name=None,
        dtype=object,
        engine="openpyxl",          # explicit engine for security
        keep_default_na=False,      # treat empty strings as empty, not NaN
    )
    result: Dict[str, List[Dict[str, Any]]] = {}
    for name, df in sheets.items():
        df = df.dropna(how="all")
        records: List[Dict[str, Any]] = (
            df.where(df.notna(), None)
            .to_dict(orient="records")
        )
        result[name] = records
    return result

# Example usage
all_records = load_excel_sheets(Path("workbook.xlsx"))
rules = all_records["Rules"]

HARD RULE: Do not mix openpyxl and pandas objects in the same module. Pick one library per processing pipeline.

Step 3 — Handle empty cells safely

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

def get(rec: dict, key: str, default: Any = None) -> Any:
    """Return a safe value for possibly-empty cells."""
    val = rec.get(key)
    return default if val is None or (isinstance(val, str) and not val.strip()) else val

Step 4 — Parse composite cells

A single cell may contain a comma-separated list, a JSON blob, or a single token. Try the simple split first, then fall back to JSON:

import json
from typing import List

def split_list_cell(value: Any) -> List[str]:
    """Split a comma-separated list cell, falling back to JSON if appropriate."""
    if value is None:
        return []
    text = str(value).strip()
    if "," in text:
        return [item.strip() for item in text.split(",") if item.strip()]
    try:
        parsed = json.loads(text)
        if isinstance(parsed, list):
            return [str(item).strip() for item in parsed]
    except json.JSONDecodeError:
        pass
    return [text]  # single token

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

Step 5 — Propagate merged cells

Merged cells in Excel only store the value in the top-left cell; the rest are None. Fill down:

def propagate_merged_column(records: List[dict], column: str) -> None:
    """Fill down values for a column that was merged in Excel."""
    last = None
    for row in records:
        if row.get(column) is None:
            row[column] = last
        else:
            last = row[column]

To check whether a cell is in a merged range, inspect ws.merged_cells.ranges.

Step 6 — Read all needed sheets before processing

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.

Step 7 — Full example: configuration-style workbook

from pathlib import Path
import openpyxl
from typing import Any, Dict, List

def load_sheet_as_records(wb: openpyxl.Workbook, sheet_name: str) -> List[Dict[str, Any]]:
    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)
    ]

path = Path("workbook.xlsx")
wb = openpyxl.load_workbook(path, data_only=True, read_only=True, keep_links=False)

metadata = {
    row[0]: row[1]
    for row in wb["Metadata"].iter_rows(min_row=2, values_only=True)
    if row[0] is not None
}
definitions = load_sheet_as_records(wb, )
rules = load_sheet_as_records(wb, )
propagate_merged_column(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.

Examples

# Print each rule's key and type
for rule in rules:
    print(f"{rule['key']}: {rule['rule_type']}")

# Split a composite cell containing a state list
states = split_list_cell(rules[0]["states"])
print(states)  # ['Delaware', 'New York', 'California']

# Safe access to a possibly-empty cell
threshold = get(rules[0], "threshold", default=0)

Pitfalls

1. Truthiness on 0 or False — These are valid cell values. Always compare with is None or .strip(), never bare if cell:.
2. Mixing openpyxl and pandas in one pipeline — Causes subtle type-conversion bugs. Pick one per module.
3. Forgetting keep_links=False — Exposes you to XML-entity attacks from untrusted workbooks.
4. Using read_only=False on large files — Exhausts memory. Always use read_only=True unless you need to write.
5. Assuming sheet order — Sheet indices can change. Always reference sheets by name, not position.
6. Merged cells producing None — Only the top-left cell of a merge range holds the value. Call propagate_merged_column before processing.
7. Formula cells without data_only=True — You get the formula string (e.g., =A1+B1) instead of the computed value.
8. .xls files — openpyxl will refuse them. Use xlrd<2 or convert to .xlsx first.
9. Trailing blank rows — Excel preserves them. The any(cell is not None ...) filter removes them, but only if you include it.
10. Header row not on row 1 — If headers start on a different row, adjust min_row in iter_rows or use pandas with header=<row_index>.

Verification

Run these checks after implementing the parser:

# 1. Verify openpyxl is installed and importable
python -c "import openpyxl; print(f'openpyxl {openpyxl.__version__}')"

# 2. Run a quick parse test on a sample workbook
python -c "from pathlib import Path; import openpyxl; wb = openpyxl.load_workbook(Path('workbook.xlsx'), data_only=True, read_only=True, keep_links=False); print(wb.sheetnames)"

Checklist:

• Records are correctly parsed from a sample workbook — sheet names print as expected.
• Empty cells become None (not NaN, not empty string unless explicitly kept).
• Merged cells are propagated correctly — no None values in columns that were merged.
• Composite list cells are split as expected — split_list_cell returns a list, not a string.
• 0 and False values are preserved — not dropped by truthiness checks.
• No openpyxl/pandas mixing in the same module.

Related skills

• csv-parsing — for handling comma-separated value files.
• json-parsing — for reading JSON or JSONL inputs.
• xls-parsing — for legacy Excel .xls files (requires xlrd).