xlsx

Excel (.xlsx) workbooks

Work in the workspace dir (where uploads land) by writing short Python run via exec. Two libraries, both preinstalled — pick by task: After exec completes, use the Generated artifacts URL from the tool result in the final answer so the user can download the workbook.

• pandas — bulk tabular read/write/analysis. Use for "load this sheet, compute, dump a table". Drops all formatting and formulas.
• openpyxl — cells, formulas, styles, charts, merged cells, multi-sheet, number formats. Use whenever formatting, formulas, or fidelity matter.

THE critical gotcha: openpyxl writes formulas but never computes them

ws["B10"] = "=SUM(B2:B9)" stores the formula string. openpyxl has no formula engine — the cached value stays empty (or stale, on an edited file). So:

• A workbook you create/edit with openpyxl opens fine in Excel/LibreOffice (they recompute on open), but its cached values are wrong until then.
• Anything reading cached values first — data_only=True, another pandas/openpyxl pass, or a downstream tool — sees blanks/stale data.

Pick by what the deliverable needs:

1. Static numbers (most common). If the user just needs correct values and the sheet need not stay live, compute in Python and write the number, not a formula string: ws["B10"] = sum(c.value for c in ws["B2:B9"][0]). Correct immediately, no recalc needed.
2. Live model (formulas that recompute on the user's later edits). Write real formulas, and reference cells not literals (=B5*(1+$B$6), not =B5*1.05). openpyxl can't set the cached value too, so either recalc with LibreOffice if present (gate it — often absent):

   command -v soffice >/dev/null && \
     soffice --headless --convert-to xlsx --outdir /tmp out.xlsx \
       >/dev/null 2>&1 && cp /tmp/out.xlsx out.xlsx
   

   --convert-to xlsx reopens and recalculates, repopulating cached values. If soffice is missing, say so and warn the user the formulas populate when they open the file in Excel — never assume soffice exists.

Reading

import pandas as pd
df = pd.read_excel("in.xlsx")                          # first sheet
sheets = pd.read_excel("in.xlsx", sheet_name=None)     # dict of all sheets
df = pd.read_excel("in.xlsx", dtype={"id": str})       # stop id->float coercion

To read computed results of formulas (not the formula text), use openpyxl with data_only=True — returns the value Excel last cached:

from openpyxl import load_workbook
wb = load_workbook("in.xlsx", data_only=True)
val = wb["Sheet1"]["B10"].value     # None if Excel never opened/saved the file

Gotcha: never save() a workbook loaded with data_only=True — that discards every formula permanently (verified: the cell becomes None). Load twice if you need both formulas and values.

Large file: load_workbook(path, read_only=True) streams rows cheaply.

Creating

from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment

wb = Workbook(); ws = wb.active; ws.title = "Summary"
ws.append(["Region", "Sales"])              # header row
for r in [("West", 120), ("East", 95)]:
    ws.append(r)
ws["B4"] = "=SUM(B2:B3)"                     # see formula gotcha above

ws["A1"].font = Font(bold=True)
ws["A1"].fill = PatternFill("solid", fgColor="DDDDDD")
ws["A1"].alignment = Alignment(horizontal="center")
ws["B2"].number_format = "#,##0"            # thousands separator
ws.column_dimensions["A"].width = 18
ws.freeze_panes = "A2"                       # freeze header
wb.create_sheet("Detail")                    # second sheet
wb.save("out.xlsx")

Bulk data is faster via pandas, then style with openpyxl after:

df.to_excel("out.xlsx", index=False, sheet_name="Data")

Editing (preserve existing formatting)

load_workbook keeps styles, formulas, merged cells, charts intact — edit only what you touch. Do NOT round-trip through pandas to preserve formatting (pandas rewrites the whole sheet, losing styles).

from openpyxl import load_workbook
wb = load_workbook("in.xlsx")               # keep formulas (data_only=False)
ws = wb["Sheet1"]
ws["C2"] = "Updated"
wb.save("in.xlsx")

Match the file's existing conventions (font, number formats, colors) rather than imposing new ones — an established template wins over any default.

When inserting/deleting rows or columns (ws.insert_rows, ws.delete_cols), openpyxl does not rewrite formulas that reference shifted cells. Re-point affected formulas yourself, or avoid structural shifts in formula-heavy sheets.

Charts

from openpyxl.chart import BarChart, Reference
ch = BarChart(); ch.title = "Sales"
data = Reference(ws, min_col=2, min_row=1, max_row=3)   # include header for title
cats = Reference(ws, min_col=1, min_row=2, max_row=3)
ch.add_data(data, titles_from_data=True); ch.set_categories(cats)
ws.add_chart(ch, "E2")

LineChart / PieChart / ScatterChart follow the same shape.

Verifying you produced clean output

After writing, reload and scan for error strings — these mean broken formulas that recalc surfaced (#REF! bad reference, #DIV/0! zero denominator, #VALUE! type mismatch, #NAME? unknown function, #N/A):

from openpyxl import load_workbook
wb = load_workbook("out.xlsx", data_only=True)
errs = [f"{s}!{c.coordinate}={c.value}"
        for s in wb.sheetnames for row in wb[s].iter_rows() for c in row
        if isinstance(c.value, str) and c.value.startswith("#")]
print(errs or "clean")

This only catches errors in cached values. If you wrote formulas and couldn't recalc (no soffice), cached values are blank, so the check is meaningful only after a recalc or after Excel opens the file. Writing computed numbers (option 1) sidesteps this.

CSV / TSV

df = pd.read_csv("in.csv")                   # sep="\t" for TSV
df.to_csv("out.csv", index=False)

For messy input (junk rows, header not on row 1, ragged columns): inspect raw lines first, then pd.read_csv(..., skiprows=, header=, usecols=, on_bad_lines="skip").

Raw OOXML (rarely needed)

openpyxl covers essentially all xlsx features; reach for raw XML only for the narrow cases it can't express (e.g. preserving an exotic part it drops on re-save). An .xlsx is a ZIP: xl/workbook.xml, xl/worksheets/sheet1.xml, xl/sharedStrings.xml, plus [Content_Types].xml and _rels/. Unzip with stdlib zipfile, edit the part, re-zip — keep [Content_Types].xml and every .rels consistent, keep IDs unique, and don't pretty-print into value-bearing text nodes. Correctness check = it opens in Excel with no repair prompt.