xlsx

Requirements for Outputs

All Excel files

Professional Font

Use a consistent, professional font (e.g., Arial, Times New Roman) for all deliverables unless otherwise instructed by the user

Zero Formula Errors

Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)

Preserve Existing Templates (when updating templates)

Study and EXACTLY match existing format, style, and conventions when modifying files
Never impose standardized formatting on files with established patterns
Existing template conventions ALWAYS override these guidelines

Financial models

Color Coding Standards

Unless otherwise stated by the user or existing template

Blue text (RGB: 0,0,255): Hardcoded inputs, and numbers users will change for scenarios
Black text (RGB: 0,0,0): ALL formulas and calculations
Green text (RGB: 0,128,0): Links pulling from other worksheets within same workbook
Red text (RGB: 255,0,0): External links to other files
Yellow background (RGB: 255,255,0): Key assumptions needing attention or cells that need to be updated

Number Formatting Standards

Years: Format as text strings (e.g., "2024" not "2,024")
Currency: Use $#,##0 format; ALWAYS specify units in headers ("Revenue ($mm)")
Zeros: Use number formatting to make all zeros "-", including percentages (e.g., "$#,##0;($#,##0);-")
Percentages: Default to 0.0% format (one decimal)
Multiples: Format as 0.0x for valuation multiples (EV/EBITDA, P/E)
Negative numbers: Use parentheses (123) not minus -123

Formula Construction Rules

Place ALL assumptions in separate assumption cells
Use cell references instead of hardcoded values in formulas
Example: Use =B5*(1+$B$6) instead of =B5*1.05

XLSX creation, editing, and analysis

Overview

A user may ask you to create, edit, or analyze the contents of an .xlsx file.

Reading and analyzing data

import pandas as pd

df = pd.read_excel("file.xlsx")
all_sheets = pd.read_excel("file.xlsx", sheet_name=None)

CRITICAL: Use Formulas, Not Hardcoded Values

Always use Excel formulas instead of calculating values in Python and hardcoding them.

❌ WRONG

total = df["Sales"].sum()
sheet["B10"] = total  # Hardcodes 5000

✅ CORRECT

sheet["B10"] = "=SUM(B2:B9)"

Common Workflow

Choose tool: pandas for data, openpyxl for formulas/formatting
Create/Load: Create new workbook or load existing file
Modify: Add/edit data, formulas, and formatting
Save: Write to file
Recalculate formulas (MANDATORY IF USING FORMULAS):
```
python scripts/recalc.py output.xlsx
```
Verify and fix any errors

Creating new Excel files

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

wb = Workbook()
sheet = wb.active
sheet["A1"] = "Hello"
sheet["B1"] = "World"
sheet["B2"] = "=SUM(A1:A10)"
sheet["A1"].font = Font(bold=True, color="FF0000")
wb.save("output.xlsx")

Editing existing Excel files

from openpyxl import load_workbook

wb = load_workbook("existing.xlsx")
sheet = wb.active
sheet["A1"] = "New Value"
wb.save("modified.xlsx")

Recalculating formulas

python scripts/recalc.py <excel_file> [timeout_seconds]

The script:

Automatically sets up LibreOffice macro on first run
Recalculates all formulas in all sheets
Scans ALL cells for Excel errors
Returns JSON with detailed error locations and counts

Formula Verification Checklist

Test 2-3 sample references: Verify they pull correct values
Column mapping: Confirm Excel columns match (e.g., column 64 = BL)
Row offset: Excel rows are 1-indexed (DataFrame row 5 = Excel row 6)
NaN handling: Check for null values with pd.notna()
Division by zero: Check denominators before using /

Best Practices

Library Selection

pandas: Best for data analysis, bulk operations, and simple data export
openpyxl: Best for complex formatting, formulas, and Excel-specific features

Working with openpyxl

Cell indices are 1-based
Use data_only=True to read calculated values
Warning: If opened with data_only=True and saved, formulas are permanently lost
Formulas are preserved but not evaluated — use scripts/recalc.py to update values

Code Style Guidelines

Write minimal, concise Python code without unnecessary comments
Avoid verbose variable names and redundant operations
For Excel files: add comments to cells with complex formulas, document data sources for hardcoded values

Dependencies

pandas: Data analysis and manipulation
openpyxl: Excel file creation and editing with formulas/formatting
LibreOffice: Formula recalculation (via scripts/soffice.py)