Create, edit, or analyze Excel spreadsheets with formulas, formatting, and data analysis. Apply this skill for spreadsheet processing using openpyxl and pandas. Recalculate formulas and ensure zero errors for publication-quality outputs.
Visual Enhancement with Scientific Schematics
When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.
If your document does not already contain schematics or diagrams:
Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
Simply describe your desired diagram in natural language
Nano Banana Pro will automatically generate, review, and refine the schematic
For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.
Create publication-quality images with proper formatting
Review and refine through multiple iterations
Ensure accessibility (colorblind-friendly, high contrast)
Save outputs in the figures/ directory
When to add schematics:
Spreadsheet workflow diagrams
Data processing pipeline illustrations
Formula calculation flow diagrams
Financial model structure diagrams
Data analysis flowcharts
Any complex concept that benefits from visualization
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
Important Requirements
LibreOffice Required for Formula Recalculation : You can assume LibreOffice is installed for recalculating formula values using the recalc.py script. The script automatically configures LibreOffice on first run
Reading and analyzing data
Data analysis with pandas
For data analysis, visualization, and basic operations, use pandas which provides powerful data manipulation capabilities:
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)
Excel File Workflows
CRITICAL: Use Formulas, Not Hardcoded Values
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
# 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
# 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.
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) : Use the recalc.py script
python recalc.py output.xlsx
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
# 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
# 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')
Recalculating formulas
Excel files created or modified by openpyxl contain formulas as strings but not calculated values. Use the provided recalc.py script to recalculate formulas:
python recalc.py <excel_file> [timeout_seconds]
Example:
python 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
Formula Verification Checklist
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)
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 recalc.py Output
The script returns JSON with error details:
{
"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"]
}
}
}
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 (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 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'])
