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.

Zero Formula Errors

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

Preserve Existing Templates

Study and EXACTLY match existing format, style, and conventions when modifying files. Never impose standardized formatting on files with established patterns.

Financial models

Color Coding Standards

Industry-Standard Color Conventions

• 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

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 "-"
• Percentages: Default to 0.0% format (one decimal)
• Multiples: Format as 0.0x for valuation multiples
• Negative numbers: Use parentheses (123) not minus -123

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XLSX creation, editing, and analysis

CRITICAL: Use Formulas, Not Hardcoded Values

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

# ❌ WRONG - Hardcoding Calculated Values
total = df['Sales'].sum()
sheet['B10'] = total  # Hardcodes 5000

# ✅ CORRECT - Using Excel Formulas
sheet['B10'] = '=SUM(B2:B9)'
sheet['C5'] = '=(C4-C2)/C2'
sheet['D20'] = '=AVERAGE(D2:D19)'

Reading and analyzing data

import pandas as pd

# Read Excel
df = pd.read_excel('file.xlsx')
all_sheets = pd.read_excel('file.xlsx', sheet_name=None)  # All sheets as dict

# Analyze
df.head()
df.info()
df.describe()