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
- If useful:
clawhub star excel-xlsx
- Stay updated:
clawhub sync
Code Implementation
Python实现: excel_parser.py
from excel_parser import ExcelParser, read_excel, write_excel, excel_to_csv
# 读取
parser = ExcelParser('data.xlsx')
df = parser.to_dataframe()
parser.close()
# 或使用便捷函数
df = read_excel('data.xlsx')
# 写入
write_excel(df, 'output.xlsx')
# XLSX转CSV
excel_to_csv('data.xlsx', 'data.csv')
