xlsx

Excel / XLSX Spreadsheets

A .xlsx file is a ZIP archive of XML. Work with it programmatically using openpyxl (formulas + formatting) or pandas (bulk data). Choose the right tool for the job and apply it precisely.

Task	Tool
Create or edit with formulas and formatting	openpyxl
Bulk data in/out, pivot analysis	pandas (read_excel, to_excel)
Quick read of content	markitdown file.xlsx
Reading both formulas AND cached values	Two load_workbook passes — see gotchas

openpyxl, pandas, and markitdown are typically pre-installed. Import directly. Only run pip install if an import fails.

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Non-Negotiable Standards

Every spreadsheet delivered must meet these standards:

• Professional font throughout. Arial or Calibri for data, Times New Roman for formal reports. No default Calibri 11 unless that is the existing convention.
• Zero formula errors. Never ship while recalc shows errors. A #DIV/0!, #REF!, or #NAME? in a delivered file is a failure.
• Use formulas, not hardcoded results. Write =SUM(B2:B9), not the Python-computed value. The sheet must recalculate when inputs change.
• Follow the user's spec literally. Exact tab names, exact column headers, exact formula logic. Never redesign what was asked.
• Document every assumption. Hardcoded numbers need a cell comment or adjacent label with the source. "Source: Company 10-K, FY2024, Page 45" is correct. Unexplained magic numbers are not.
• Input cells need a legend. If you create a template for someone to fill in, add a legend naming which cells to edit and include one example row showing expected format.
• Editing existing files: match conventions exactly. Find the designated input cells (usually distinguished by font color or fill). Write only there. Leave all existing formulas untouched.

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Reading a Spreadsheet

import subprocess

# Quick content read (no cell coordinates — use for overview only)
result = subprocess.run(["markitdown", "file.xlsx"], capture_output=True, text=True)
print(result.stdout)

# Read into pandas (values only, no formulas)
import pandas as pd
df = pd.read_excel("file.xlsx", sheet_name="Sheet1")
print(df.head())
print(df.dtypes)

# Read formulas (openpyxl — formulas as strings, no cached values)
import openpyxl
wb = openpyxl.load_workbook("file.xlsx")
ws = wb["Sheet1"]
print(ws["B10"].value)  # prints the formula string e.g. "=SUM(B2:B9)"

# Read cached values (openpyxl data_only — values only, no formulas)
# WARNING: data_only=True is destructive if you save — loses all formulas
wb_vals = openpyxl.load_workbook("file.xlsx", data_only=True)
ws_vals = wb_vals["Sheet1"]
print(ws_vals["B10"].value)  # prints the last-saved calculated value