Analyzing Spreadsheets

Excel Spreadsheet (.xlsx) Guide

This guide covers creating, editing, and analyzing Excel files with a focus on formula-based calculations and professional formatting.

Critical Requirements

Zero Formula Errors

All deliverables must have ZERO formula errors:

• #REF! - Invalid cell references
• #DIV/0! - Division by zero
• #VALUE! - Wrong data type
• #N/A - Value not available
• #NAME? - Unrecognized formula name

Use Formulas, Not Hardcoded Values

Always use Excel formulas instead of calculating values in Python.

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

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

This ensures spreadsheets remain dynamic and updateable.

Library Selection

Common Workflow

1. Choose tool: pandas for data, openpyxl for formulas
2. Create/Load: Create new or load existing workbook
3. Modify: Add data, formulas, and formatting
4. Save: Write to file
5. Recalculate (if using formulas): python recalc.py output.xlsx
6. Verify: Check for errors and fix

Creating Excel Files

With openpyxl (Formulas & Formatting)

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

wb = Workbook()
sheet = wb.active
sheet.title = "Sales Data"

# Add headers
headers = ['Product', 'Q1', 'Q2', 'Q3', 'Q4', 'Total']
for col, header in enumerate(headers, 1):
    cell = sheet.cell(row=1, column=col, value=header)
    cell.font = Font(bold=True)
    cell.fill = PatternFill('solid', start_color='CCCCCC')

# Add data
data = [
    ['Product A', 1000, 1200, 1100, 1300],
    ['Product B', 800, 900, 950, 1000],
    ['Product C', 1500, 1600, 1550, 1700],
]

for row_idx, row_data in enumerate(data, 2):
    for col_idx, value in enumerate(row_data, 1):
        sheet.cell(row=row_idx, column=col_idx, value=value)
    # Add formula for row total
    sheet.cell(row=row_idx, column=6, value=f'=SUM(B{row_idx}:E{row_idx})')

# Add column totals
for col in range(2, 7):
    col_letter = chr(64 + col)
    sheet.cell(row=5, column=col, value=f'=SUM({col_letter}2:{col_letter}4)')

wb.save('sales.xlsx')

With pandas (Data Analysis)

import pandas as pd

# Create DataFrame
df = pd.DataFrame({
    'Product': ['A', 'B', 'C'],
    'Sales': [1000, 800, 1500],
    'Region': ['North', 'South', 'East']
})

# Export to Excel
df.to_excel('output.xlsx', index=False, sheet_name='Data')

# Multiple sheets
with pd.ExcelWriter('multi_sheet.xlsx') as writer:
    df.to_excel(writer, sheet_name='Sales', index=False)
    summary = df.groupby('Region').sum()
    summary.to_excel(writer, sheet_name='Summary')

Editing Existing Files

from openpyxl import load_workbook

# Load preserving formulas
wb = load_workbook('existing.xlsx')
sheet = wb.active

# Modify cells
sheet['A1'] = 'New Value'

# Add new rows
sheet.insert_rows(2)
sheet['A2'] = 'Inserted row'

# Add new sheet
new_sheet = wb.create_sheet('Analysis')
new_sheet['A1'] = '=Sheet1!A1'  # Cross-sheet reference

wb.save('modified.xlsx')

Warning: Opening with data_only=True replaces formulas with values permanently.

Financial Model Standards

Color Coding Convention

Number Formatting

from openpyxl.styles import numbers

# Currency
sheet['B2'].number_format = '$#,##0'

# Percentages
sheet['C2'].number_format = '0.0%'

# Years as text
sheet['A2'].number_format = '@'  # Text format
sheet['A2'] = '2024'

# Negative in parentheses
sheet['D2'].number_format = '$#,##0_);($#,##0)'

# Zeros as dash
sheet['E2'].number_format = '$#,##0;-$#,##0;"-"'

Assumptions Section

All growth rates and multiples should:

• Be in separate cells (not embedded in formulas)
• Have proper documentation
• Include source, date, and URL where applicable

# Good: Reference assumption cells
sheet['B10'] = '=B9*(1+$G$2)'  # G2 contains growth rate

# Bad: Hardcoded in formula
sheet['B10'] = '=B9*1.05'  # Where does 5% come from?

Formula Recalculation

Excel files created by openpyxl contain formulas as strings but not calculated values. Use the recalc script:

python recalc.py output.xlsx [timeout_seconds]

Script Output

{
  "status": "success",
  "total_errors": 0,
  "total_formulas": 42
}

Or with errors:

{
  "status": "errors_found",
  "total_errors": 3,
  "error_summary": {
    "#REF!": {
      "count": 2,
      "locations": ["Sheet1!B5", "Sheet1!C10"]
    },
    "#DIV/0!": {
      "count": 1,
      "locations": ["Sheet1!D15"]
    }
  }
}

Formula Verification Checklist

Essential Verification

• Test 2-3 sample references before building full model
• Verify column mapping (column 64 = BL, not BK)
• Check row offset (DataFrame row 5 = Excel row 6)

Common Pitfalls

• Handle NaN values with pd.notna()
• Check far-right columns (FY data often in columns 50+)
• Search all occurrences, not just first
• Check denominators before division formulas

Testing Strategy

• Test formulas on 2-3 cells before applying broadly
• Verify all referenced cells exist
• Test edge cases (zero, negative, very large values)

Best Practices

Library Selection

• pandas: Data analysis, bulk operations, simple exports
• openpyxl: Formulas, formatting, Excel-specific features

openpyxl Notes

• Cell indices are 1-based (A1 = row=1, column=1)
• Use data_only=True to read calculated values (but formulas are lost)
• For large files: read_only=True or write_only=True

pandas Notes

• Specify dtypes: pd.read_excel('file.xlsx', dtype={'id': str})
• Read specific columns: usecols=['A', 'C', 'E']
• Handle dates: parse_dates=['date_column']

Code Style

Python code: Minimal, concise, no unnecessary comments

Excel files:

• Add cell comments for complex formulas
• Document data sources for hardcoded values
• Include notes for key calculations

Dependencies

pip install openpyxl pandas xlrd xlsxwriter

# For recalc.py
# Requires LibreOffice installed
brew install libreoffice  # macOS
apt-get install libreoffice  # Ubuntu