xlsx

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

Color Coding Standards

Unless otherwise stated by the user or existing template

#### 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 or cells that need to be updated

Number Formatting Standards

#### Required Format Rules

• 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

#### Assumptions Placement

• Place ALL assumptions (growth rates, margins, multiples, etc.) in separate assumption cells
• Use cell references instead of hardcoded values in formulas
• Example: Use =B5(1+$B$6) instead of =B51.05

#### Formula Error Prevention

• Verify all cell references are correct
• Check for off-by-one errors in ranges
• Ensure consistent formulas across all projection periods
• Test with edge cases (zero values, negative numbers)
• Verify no unintended circular references

#### Documentation Requirements for Hardcodes

• Comment or in cells beside (if end of table). Format: "Source: [System/Document], [Date], [Specific Reference], [URL if applicable]"
• Examples:

- "Source: Company 10-K, FY2024, Page 45, Revenue Note, [SEC EDGAR URL]"

- "Source: Company 10-Q, Q2 2025, Exhibit 99.1, [SEC EDGAR URL]"

- "Source: Bloomberg Terminal, 8/15/2025, AAPL US Equity"

- "Source: FactSet, 8/20/2025, Consensus Estimates Screen"

# XLSX creation, editing, and analysis

A user may ask you to create, edit, or analyze the contents of an .xlsx file. You have different tools and workflows available for different tasks.

LibreOffice Required for Formula Recalculation: You can assume LibreOffice is installed for recalculating formula values using the scripts/recalc.py script. The script automatically configures LibreOffice on first run, including in sandboxed environments where Unix sockets are restricted (handled by scripts/office/soffice.py)

Data analysis with pandas

For data analysis, visualization, and basic operations, use pandas which provides powerful data manipulation capabilities:

```python

import pandas as pd

# Read Excel

df = pd.read_excel('file.xlsx') # Default: first sheet

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

# Analyze

df.head() # Preview data

df.info() # Column info

df.describe() # Statistics

# Write Excel

df.to_excel('output.xlsx', index=False)

```

Always use Excel formulas instead of calculating values in Python and hardcoding them. This ensures the spreadsheet remains dynamic and updateable.

❌ WRONG - Hardcoding Calculated Values

```python

# Bad: Calculating in Python and hardcoding result

total = df['Sales'].sum()

sheet['B10'] = total # Hardcodes 5000

# Bad: Computing growth rate in Python

growth = (df.iloc[-1]['Revenue'] - df.iloc[0]['Revenue']) / df.iloc[0]['Revenue']

sheet['C5'] = growth # Hardcodes 0.15

# Bad: Python calculation for average

avg = sum(values) / len(values)

sheet['D20'] = avg # Hardcodes 42.5

```

✅ CORRECT - Using Excel Formulas

```python

# Good: Let Excel calculate the sum

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

# Good: Growth rate as Excel formula

sheet['C5'] = '=(C4-C2)/C2'

# Good: Average using Excel function

sheet['D20'] = '=AVERAGE(D2:D19)'

```

This applies to ALL calculations - totals, percentages, ratios, differences, etc. The spreadsheet should be able to recalculate when source data changes.

1. Choose tool: pandas for data, openpyxl for formulas/formatting
2. Create/Load: Create new workbook or load existing file
3. Modify: Add/edit data, formulas, and formatting
4. Save: Write to file
5. Recalculate formulas (MANDATORY IF USING FORMULAS): Use the scripts/recalc.py script

```bash

python scripts/recalc.py output.xlsx

```

1. Verify and fix any errors:

- The script returns JSON with error details

- If status is errors_found, check error_summary for specific error types and locations

- Fix the identified errors and recalculate again

- Common errors to fix:

- #REF!: Invalid cell references

- #DIV/0!: Division by zero

- #VALUE!: Wrong data type in formula

- #NAME?: Unrecognized formula name

Creating new Excel files

```python

# Using openpyxl for formulas and formatting

from openpyxl import Workbook

from openpyxl.styles import Font, PatternFill, Alignment

wb = Workbook()

sheet = wb.active

# Add data

sheet['A1'] = 'Hello'

sheet['B1'] = 'World'

sheet.append(['Row', 'of', 'data'])

# Add formula

sheet['B2'] = '=SUM(A1:A10)'

# Formatting

sheet['A1'].font = Font(bold=True, color='FF0000')

sheet['A1'].fill = PatternFill('solid', start_color='FFFF00')

sheet['A1'].alignment = Alignment(horizontal='center')

# Column width

sheet.column_dimensions['A'].width = 20

wb.save('output.xlsx')

```

Editing existing Excel files

```python

# Using openpyxl to preserve formulas and formatting

from openpyxl import load_workbook

# Load existing file

wb = load_workbook('existing.xlsx')

sheet = wb.active # or wb['SheetName'] for specific sheet

# Working with multiple sheets

for sheet_name in wb.sheetnames:

sheet = wb[sheet_name]

print(f"Sheet: {sheet_name}")

# Modify cells

sheet['A1'] = 'New Value'

sheet.insert_rows(2) # Insert row at position 2

sheet.delete_cols(3) # Delete column 3

# Add new sheet

new_sheet = wb.create_sheet('NewSheet')

new_sheet['A1'] = 'Data'

wb.save('modified.xlsx')

```

Excel files created or modified by openpyxl contain formulas as strings but not calculated values. Use the provided scripts/recalc.py script to recalculate formulas:

```bash

python scripts/recalc.py [timeout_seconds]

```

Example:

```bash

python scripts/recalc.py output.xlsx 30

```

The script:

• Automatically sets up LibreOffice macro on first run
• Recalculates all formulas in all sheets
• Scans ALL cells for Excel errors (#REF!, #DIV/0!, etc.)
• Returns JSON with detailed error locations and counts
• Works on both Linux and macOS

Quick checks to ensure formulas work correctly:

Essential Verification

• [ ] Test 2-3 sample references: Verify they pull correct values before building full model
• [ ] Column mapping: Confirm Excel columns match (e.g., column 64 = BL, not BK)
• [ ] Row offset: Remember Excel rows are 1-indexed (DataFrame row 5 = Excel row 6)

Common Pitfalls

• [ ] NaN handling: Check for null values with pd.notna()
• [ ] Far-right columns: FY data often in columns 50+
• [ ] Multiple matches: Search all occurrences, not just first
• [ ] Division by zero: Check denominators before using / in formulas (#DIV/0!)
• [ ] Wrong references: Verify all cell references point to intended cells (#REF!)
• [ ] Cross-sheet references: Use correct format (Sheet1!A1) for linking sheets

Formula Testing Strategy

• [ ] Start small: Test formulas on 2-3 cells before applying broadly
• [ ] Verify dependencies: Check all cells referenced in formulas exist
• [ ] Test edge cases: Include zero, negative, and very large values

Interpreting scripts/recalc.py Output

The script returns JSON with error details:

```json

{

"status": "success", // or "errors_found"

"total_errors": 0, // Total error count

"total_formulas": 42, // Number of formulas in file

"error_summary": { // Only present if errors found

"#REF!": {

"count": 2,

"locations": ["Sheet1!B5", "Sheet1!C10"]

}

}

}

```

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 (row=1, column=1 refers to cell A1)
• Use data_only=True to read calculated values: load_workbook('file.xlsx', data_only=True)
• Warning: If opened with data_only=True and saved, formulas are replaced with values and permanently lost
• For large files: Use read_only=True for reading or write_only=True for writing
• Formulas are preserved but not evaluated - use scripts/recalc.py to update values

Working with pandas

• Specify data types to avoid inference issues: pd.read_excel('file.xlsx', dtype={'id': str})
• For large files, read specific columns: pd.read_excel('file.xlsx', usecols=['A', 'C', 'E'])
• Handle dates properly: pd.read_excel('file.xlsx', parse_dates=['date_column'])

IMPORTANT: When generating Python code for Excel operations:

• Write minimal, concise Python code without unnecessary comments
• Avoid verbose variable names and redundant operations
• Avoid unnecessary print statements

For Excel files themselves:

• Add comments to cells with complex formulas or important assumptions
• Document data sources for hardcoded values
• Include notes for key calculations and model sections