Excel / XLSX

When to Use

Use when the main artifact is a Microsoft Excel workbook or spreadsheet file, especially when formulas, dates, formatting, merged cells, workbook structure, or cross-platform behavior matter.

Core Rules

1. Choose the workflow by job, not by habit

• Use pandas for analysis, reshaping, and CSV-like tasks.

• Use openpyxl when formulas, styles, sheets, comments, merged cells, or workbook preservation matter.

• Treat CSV as plain data exchange, not as an Excel feature-complete format.

• Reading values, preserving a live workbook, and building a model from scratch are different spreadsheet jobs.

2. Dates are serial numbers with legacy quirks

• Excel stores dates as serial numbers, not real date objects.

• The 1900 date system includes the false leap-day bug, and some workbooks use the 1904 system.

• Time is fractional day data, so formatting and conversion both matter.

• Date correctness is not enough if the number format still displays the wrong thing to the user.

3. Keep calculations in Excel when the workbook should stay live

• Write formulas into cells instead of hardcoding derived results from Python.

• Use references to assumption cells instead of magic numbers inside formulas.

• Cached formula values can be stale, so do not trust them blindly after edits.

• Check copied formulas for wrong ranges, wrong sheets, and silent off-by-one drift before delivery.

• Absolute and relative references are part of the logic, so copied formulas can be wrong even when they still "work".

• Test new formulas on a few representative cells before filling them across a whole block.

• Verify denominators, named ranges, and precedent cells before shipping formulas that depend on them.

• A workbook should ship with zero formula errors, not with known #REF!, #DIV/0!, #VALUE!, #NAME?, or circular-reference fallout left for the user to fix.

• For model-style work, document non-obvious hardcodes, assumptions, or source inputs in comments or nearby notes.

4. Protect data types before Excel mangles them

• Long identifiers, phone numbers, ZIP codes, and leading-zero values should usually be stored as text.

• Excel silently truncates numeric precision past 15 digits.

• Mixed text-number columns need explicit handling on read and on write.

• Scientific notation, auto-parsed dates, and stripped leading zeros are common corruption, not cosmetic issues.

5. Preserve workbook structure before changing content

• Existing templates override generic styling advice.

• Only the top-left cell of a merged range stores the value.

• Hidden rows, hidden columns, named ranges, and external references can still affect formulas and outputs.

• Shared strings, defined names, and sheet-level conventions can matter even when the visible cells look simple.

• Match styles for newly filled cells instead of quietly introducing a new visual system.

• If the workbook is a template, preserve sheet order, widths, freezes, filters, print settings, validations, and visual conventions unless the task explicitly changes them.

• Conditional formatting, filters, print areas, and data validation often carry business meaning even when users only mention the numbers.

• If there is no existing style guide and the file is a model, keep editable inputs visually distinguishable from formulas, but never override an established template to force a generic house style.

6. Recalculate and review before delivery

• Formula strings alone are not enough if the recipient needs current values.

• openpyxl preserves formulas but does not calculate them.

• Verify no #REF!, #DIV/0!, #VALUE!, #NAME?, or circular-reference fallout remains.

• If layout matters, render or visually review the workbook before calling it finished.

• Be careful with read modes: opening a workbook for values only and then saving can flatten formulas into static values.

• If assumptions or hardcoded overrides must stay, make them obvious enough that the next editor can audit the workbook.

7. Scale the workflow to the file size

• Large workbooks can fail for boring reasons: memory spikes, padded empty rows, and slow full-sheet reads.

• Use streaming or chunked reads when the file is big enough that loading everything at once becomes fragile.

• Large-file workflows also need narrower reads, explicit dtypes, and sheet targeting to avoid accidental damage.

Common Traps

• Type inference on read can leave numbers as text or convert IDs into damaged numeric values.

• Column indexing varies across tools, so off-by-one mistakes are common in generated formulas.

• Newlines in cells need wrapping to display correctly.

• External references break easily when source files move.

• Password protection in old Excel workflows is not serious security.

• .xlsm can contain macros, and .xls remains a tighter legacy format.

• Large files may need streaming reads or more careful memory handling.

• Google Sheets and LibreOffice can reinterpret dates, formulas, or styling differently from Excel.

• Dynamic array or newer Excel functions like FILTER, XLOOKUP, SORT, or SEQUENCE may fail or degrade in older viewers.

• A workbook can look fine while still carrying stale cached values from a prior recalculation.

• Saving the wrong workbook view can replace formulas with cached values and quietly destroy a live model.

• Copying formulas without checking relative references can push one bad range across an entire block.

• Hidden sheets, named ranges, validations, and merged areas often keep business logic that is invisible in a quick skim.

• A workbook can appear numerically correct while still failing because filters, conditional formats, print settings, or data validation were stripped.

• A workbook can be numerically correct and still fail visually because wrapped text, clipped labels, or narrow columns were never reviewed.

Related Skills

Install with clawhub install <slug> if user confirms:

• csv — Plain-text tabular import and export workflows.

• data — General data handling patterns before spreadsheet output.

• data-analysis — Higher-level analysis that can feed workbook deliverables.

Feedback

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