xlsx git:20260701.d034030A

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---
name: xlsx
description: "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"
---

# 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 is required for formula recalculation**: the `recalc.py` script drives LibreOffice (`soffice`) headlessly and configures it automatically on first run by writing a small recalculation macro into the LibreOffice user profile. Install with `brew install --cask libreoffice` or `apt-get install libreoffice`.

**Optional, macOS only — coreutils** (`brew install coreutils`): provides `gtimeout` so recalculation can be time-limited; without it, `recalc.py` warns on stderr and runs without a timeout.

**Python packages** (openpyxl, pandas, matplotlib) resolve automatically via `uv run` — declare them in each script's PEP 723 header.

## Reading and analyzing data

### Data analysis with pandas
For data analysis, visualization, and basic operations, use **pandas** which provides powerful data manipulation capabilities:

```python
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
```python
# 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
```python
# 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
1. **Choose tool**: pandas for data, openpyxl for formulas/formatting
2. **Create/Load**: Create new workbook or load existing file
3. **Modify**: Add/edit data, formulas, and formatting
4. **Save**: Write to file
5. **Recalculate formulas (MANDATORY IF USING FORMULAS)**: Use the recalc.py script
   ```bash
   uv run recalc.py output.xlsx
   ```
6. **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

```python
# 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

```python
# 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:

```bash
uv run recalc.py <excel_file> [timeout_seconds]
```

Example:
```bash
uv run 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:
```json
{
  "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

## Data Analysis Patterns

### Reading Multiple Sheets

Process all sheets efficiently with ExcelFile:

```python
import pandas as pd

excel_file = pd.ExcelFile("workbook.xlsx")

for sheet_name in excel_file.sheet_names:
    df = pd.read_excel(excel_file, sheet_name=sheet_name)
    print(f"{sheet_name}: {len(df)} rows")
```

### Pivot Tables

```python
import pandas as pd

df = pd.read_excel("sales_data.xlsx")

pivot = pd.pivot_table(
    df,
    values="sales",
    index="region",
    columns="product",
    aggfunc="sum",
    fill_value=0
)

pivot.to_excel("pivot_report.xlsx")
```

### Group By and Aggregate

```python
df = pd.read_excel("sales.xlsx")

# Group and sum
sales_by_region = df.groupby("region")["sales"].sum()

# Multiple aggregations
summary = df.groupby("region").agg({
    "sales": "sum",
    "quantity": "mean",
    "profit": ["min", "max"]
})
```

### Filtering

```python
# Simple filter
high_sales = df[df["sales"] > 10000]

# Multiple conditions
filtered = df[(df["region"] == "West") & (df["sales"] > 5000)]

# Calculate new columns
df["profit_margin"] = (df["revenue"] - df["cost"]) / df["revenue"]

# Sort
df_sorted = df.sort_values("sales", ascending=False)
```

## Data Cleaning

```python
import pandas as pd

df = pd.read_excel("messy_data.xlsx")

# Remove duplicates
df = df.drop_duplicates()

# Handle missing values
df = df.fillna(0)           # Fill with value
df = df.dropna()            # Drop rows with missing values
df = df.dropna(subset=["important_col"])  # Drop only if specific column is null

# Remove whitespace from strings
df["name"] = df["name"].str.strip()

# Convert data types
df["date"] = pd.to_datetime(df["date"])
df["amount"] = pd.to_numeric(df["amount"], errors="coerce")

# Save cleaned data
df.to_excel("cleaned_data.xlsx", index=False)
```

## Merging and Joining

```python
import pandas as pd

# Concatenate files vertically (stack rows)
df1 = pd.read_excel("sales_q1.xlsx")
df2 = pd.read_excel("sales_q2.xlsx")
combined = pd.concat([df1, df2], ignore_index=True)

# Merge on common column (like SQL JOIN)
customers = pd.read_excel("customers.xlsx")
sales = pd.read_excel("sales.xlsx")

merged = pd.merge(sales, customers, on="customer_id", how="left")

merged.to_excel("merged_data.xlsx", index=False)
```

## Charts and Visualization

Generate charts from Excel data using matplotlib:

```python
import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_excel("data.xlsx")

# Bar chart
df.plot(x="category", y="value", kind="bar")
plt.title("Sales by Category")
plt.xlabel("Category")
plt.ylabel("Sales")
plt.tight_layout()
plt.savefig("bar_chart.png")
plt.close()

# Pie chart
df.set_index("category")["value"].plot(kind="pie", autopct="%1.1f%%")
plt.title("Market Share")
plt.ylabel("")
plt.savefig("pie_chart.png")
plt.close()

# Line chart
df.plot(x="date", y="revenue", kind="line")
plt.savefig("trend.png")
plt.close()
```

## Conditional Formatting

Apply formatting programmatically based on cell values:

```python
import pandas as pd
from openpyxl import load_workbook
from openpyxl.styles import PatternFill, Font

df = pd.DataFrame({
    "Product": ["A", "B", "C"],
    "Sales": [100, 200, 150]
})

df.to_excel("formatted.xlsx", index=False)

wb = load_workbook("formatted.xlsx")
ws = wb.active

# Define fills
red_fill = PatternFill(start_color="FF0000", end_color="FF0000", fill_type="solid")
green_fill = PatternFill(start_color="00FF00", end_color="00FF00", fill_type="solid")

# Apply conditional formatting
for row in range(2, len(df) + 2):
    cell = ws[f"B{row}"]
    if cell.value < 150:
        cell.fill = red_fill
    else:
        cell.fill = green_fill

# Bold headers
for cell in ws[1]:
    cell.font = Font(bold=True)

wb.save("formatted.xlsx")
```

## Performance Tips

For large Excel files:

```python
import pandas as pd

# Read only specific columns
df = pd.read_excel("large.xlsx", usecols=["A", "C", "E"])

# Read in chunks for very large files
for chunk in pd.read_excel("huge.xlsx", chunksize=10000):
    # Process each chunk
    process(chunk)

# Specify dtypes to avoid inference overhead
df = pd.read_excel("data.xlsx", dtype={"id": str, "amount": float})

# For openpyxl with large files
from openpyxl import load_workbook
wb = load_workbook("large.xlsx", read_only=True)  # Read-only mode
```

## Utilities

### Auto-Adjust Column Widths

```python
import pandas as pd

df = pd.DataFrame({"Product": ["Widget A", "Widget B"], "Sales": [100, 200]})

writer = pd.ExcelWriter("output.xlsx", engine="openpyxl")
df.to_excel(writer, sheet_name="Sales", index=False)

worksheet = writer.sheets["Sales"]

for column in worksheet.columns:
    max_length = 0
    column_letter = column[0].column_letter
    for cell in column:
        try:
            if len(str(cell.value)) > max_length:
                max_length = len(str(cell.value))
        except:
            pass
    worksheet.column_dimensions[column_letter].width = max_length + 2

writer.close()
```