Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
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.
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 formattingfrom 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 formattingfrom openpyxl import load_workbook
# Load existing file
wb = load_workbook('existing.xlsx')
sheet = wb.active # or wb['SheetName'] for specific sheet# Working with multiple sheetsfor 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"]}}}
Template Integrity: Varsa mevcut Excel şablonunu (load_workbook) oku; renk kodlarına ve isimlendirme standartlarına (Financial Model Standards) uyum sağla.
Logic Blueprint: Python'da hesaplama yapmak yerine, Excel'e yazılacak formülleri (=SUM(A1:A10)) önceden tasarla.
Data Mapping: Row offset (1-indexed) ve sütun haritalama (A=1, BL=64) kontrollerini yap.
Aşama 2: Implementation & Formatting
Atomic Write: Verileri ve formülleri openpyxl veya pandas kullanarak ilgili hücrelere aktar.
Visual Hierarchy: Industry-Standard renkleri (Blue=Input, Black=Formula) uygula ve sayı formatlarını (Currency, Percentage) yönet.
Formula Evaluation: Dosyayı kaydettikten sonra python recalc.py <filename> ile tüm formülleri LibreOffice üzerinden hesaplat.
Aşama 3: Verification & Error Fixing
Recalc Audit: recalc.py çıktısındaki JSON'u incele; #REF!, #DIV/0! gibi hataları saptayıp gider.
Sample Testing: Kritik 2-3 hücrenin değerini kaynak veriyle çapraz kontrol et.
Artifact Finalization: Hatasız Excel dosyasını kullanıcıya sun veya bir sonraki işlem için sakla.
Kontrol Noktaları
Aşama
Doğrulama
1
Hardcoded değerler yerine dinamik formüller kullanıldı mı?
