spreadsheet-processor

name spreadsheet-processor description Process and manipulate spreadsheet documents. Creates, edits, analyzes, and transforms Excel files with formula and formatting support. license Proprietary. LICENSE.txt has complete terms Requirements for Outputs All Excel files 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 Financial models 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 =B5*1.05 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 Overview 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 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) 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 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']) Handle dates properly: pd.read_excel('file.xlsx', parse_dates=['date_column']) Code Style Guidelines 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