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name: working-with-spreadsheets description: | Creates and edits Excel spreadsheets with formulas, formatting, and financial modeling standards. Use when working with .xlsx files, financial models, data analysis, or formula-heavy spreadsheets. Covers formula recalculation, color coding standards, and common pitfalls.

Working with Spreadsheets

Quick Start

from openpyxl import Workbook

wb = Workbook()
sheet = wb.active
sheet['A1'] = 'Revenue'
sheet['B1'] = 1000
sheet['B2'] = '=B1*1.1'  # Use formulas, not hardcoded values!
wb.save('output.xlsx')

Critical Rule: Use Formulas, Not Hardcoded Values

Always use Excel formulas instead of calculating in Python.

# WRONG - Hardcoding calculated values
total = df['Sales'].sum()
sheet['B10'] = total  # Hardcodes 5000

# CORRECT - Using Excel formulas
sheet['B10'] = '=SUM(B2:B9)'

Financial Model Color Coding Standards

Color	RGB	Usage
Blue text	0,0,255	Hardcoded inputs, scenario values
Black text	0,0,0	ALL formulas and calculations
Green text	0,128,0	Links from other worksheets
Red text	255,0,0	External links to other files
Yellow background	255,255,0	Key assumptions needing attention

from openpyxl.styles import Font

# Input cell (user changeable)
sheet['B5'].font = Font(color='0000FF')  # Blue

# Formula cell
sheet['C5'] = '=B5*1.1'
sheet['C5'].font = Font(color='000000')  # Black

# Cross-sheet link
sheet['D5'] = "=Sheet2!A1"
sheet['D5'].font = Font(color='008000')  # Green

Number Formatting Standards

# Currency with thousands separator
sheet['B5'].number_format = '$#,##0'

# Zeros display as dash
sheet['B5'].number_format = '$#,##0;($#,##0);-'

# Percentages with one decimal
sheet['C5'].number_format = '0.0%'

# Valuation multiples
sheet['D5'].number_format = '0.0x'

# Years as text (not 2,024)
sheet['A1'] = '2024'  # String, not number

Library Selection

Task	Library	Example
Data analysis	pandas	df = pd.read_excel('file.xlsx')
Formulas & formatting	openpyxl	sheet['A1'] = '=SUM(B:B)'
Large files (read)	openpyxl	load_workbook('file.xlsx', read_only=True)
Large files (write)	openpyxl	Workbook(write_only=True)

Reading Excel Files

import pandas as pd
from openpyxl import load_workbook

# pandas - data analysis
df = pd.read_excel('file.xlsx')
all_sheets = pd.read_excel('file.xlsx', sheet_name=None)  # Dict of DataFrames

# openpyxl - preserve formulas
wb = load_workbook('file.xlsx')
sheet = wb.active
print(sheet['A1'].value)  # Returns formula string

# openpyxl - get calculated values (WARNING: loses formulas on save!)
wb = load_workbook('file.xlsx', data_only=True)

Creating Excel Files

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

wb = Workbook()
sheet = wb.active
sheet.title = 'Model'

# Headers
sheet['A1'] = 'Metric'
sheet['B1'] = '2024'
sheet['A1'].font = Font(bold=True)

# Data with formulas
sheet['A2'] = 'Revenue'
sheet['B2'] = 1000000
sheet['B2'].font = Font(color='0000FF')  # Blue = input

sheet['A3'] = 'Growth'
sheet['B3'] = '=B2*0.1'
sheet['B3'].font = Font(color='000000')  # Black = formula

# Formatting
sheet['B2'].number_format = '$#,##0'
sheet.column_dimensions['A'].width = 20

wb.save('model.xlsx')

Editing Existing Files

from openpyxl import load_workbook

wb = load_workbook('existing.xlsx')
sheet = wb['Data']  # Or wb.active

# Modify cells
sheet['A1'] = 'Updated Value'
sheet.insert_rows(2)
sheet.delete_cols(3)

# Add new sheet
new_sheet = wb.create_sheet('Analysis')
new_sheet['A1'] = '=Data!B5'  # Cross-sheet reference

wb.save('modified.xlsx')

Formula Recalculation

openpyxl writes formulas but doesn't calculate values. Use LibreOffice to recalculate:

# Recalculate and check for errors
python recalc.py output.xlsx

The script returns JSON:

{
  "status": "success",  // or "errors_found"
  "total_errors": 0,
  "total_formulas": 42,
  "error_summary": {
    "#REF!": {"count": 2, "locations": ["Sheet1!B5", "Sheet1!C10"]}
  }
}

Formula Verification Checklist

Before Building

• Test 2-3 sample references first
• Confirm column mapping (column 64 = BL, not BK)
• Remember: DataFrame row 5 = Excel row 6 (1-indexed)

Common Pitfalls

• Check for NaN with pd.notna() before using values
• FY data often in columns 50+ (far right)
• Search ALL occurrences, not just first match
• Check denominators before division (#DIV/0!)
• Verify cross-sheet references use correct format (Sheet1!A1)

After Building

• Run recalc.py and fix any errors
• Verify #REF!, #DIV/0!, #VALUE!, #NAME? = 0

Common Errors

Error	Cause	Fix
#REF!	Invalid cell reference	Check deleted rows/columns
#DIV/0!	Division by zero	Add IF check: =IF(B5=0,0,A5/B5)
#VALUE!	Wrong data type	Check cell contains expected type
#NAME?	Unknown function	Check spelling, quotes around text

Verification

Run: python scripts/verify.py

Related Skills

• building-nextjs-apps - Frontend for spreadsheet uploads
• scaffolding-fastapi-dapr - API for spreadsheet processing