xlsx
# XLSX Skill v2.0
## Overview
Complete Excel spreadsheet processing using `openpyxl` for creation/editing and `pandas` for data analysis. Supports formulas, formatting, charts, and complex data operations.
## Installation & Dependencies
### Required
```bash
pip install pandas openpyxl xlsxwriter
```
### Optional
```bash
# For chart support
pip install numpy
# For PDF export
brew install --cask libreoffice
```
## Quick Start
### Read Excel File
```python
import pandas as pd
# Read first sheet
df = pd.read_excel('file.xlsx')
# Read specific sheet
df = pd.read_excel('file.xlsx', sheet_name='Sheet1')
# Read all sheets
all_sheets = pd.read_excel('file.xlsx', sheet_name=None)
```
### Create Excel File
```python
from openpyxl import Workbook
wb = Workbook()
ws = wb.active
ws['A1'] = 'Hello'
ws['B1'] = 'World'
wb.save('output.xlsx')
```
### Add Formula
```python
from openpyxl import Workbook
wb = Workbook()
ws = wb.active
ws['A1'] = 10
ws['A2'] = 20
ws['A3'] = '=SUM(A1:A2)' # Excel formula
wb.save('formula.xlsx')
```
## Complete API Reference
### Reading Data with pandas
```python
import pandas as pd
# Basic read
df = pd.read_excel('data.xlsx')
# Read specific columns
df = pd.read_excel('data.xlsx', usecols=['A', 'B', 'C'])
# Read with header row
df = pd.read_excel('data.xlsx', header=0)
# Read without header
df = pd.read_excel('data.xlsx', header=None)
# Read specific rows
df = pd.read_excel('data.xlsx', skiprows=range(1, 10)) # Skip rows 1-9
# Read multiple sheets
sheets_dict = pd.read_excel('data.xlsx', sheet_name=None)
for sheet_name, df in sheets_dict.items():
print(f"Sheet: {sheet_name}, Rows: {len(df)}")
# Data exploration
df.head() # First 5 rows
df.tail() # Last 5 rows
df.info() # Column info
df.describe() # Statistics
df.shape # (rows, columns)
df.columns # Column names
df.dtypes # Data types
```
### Writing Data with pandas
```python
import pandas as pd
# Create DataFrame
df = pd.DataFrame({
'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35],
'City': ['NYC', 'LA', 'Chicago']
})
# Write to Excel
df.to_excel('output.xlsx', index=False)
# Write to specific sheet
df.to_excel('output.xlsx', sheet_name='Data', index=False)
# Write multiple sheets
with pd.ExcelWriter('multi-sheet.xlsx') as writer:
df1.to_excel(writer, sheet_name='Sheet1', index=False)
df2.to_excel(writer, sheet_name='Sheet2', index=False)
```
### Creating with openpyxl
```python
from openpyxl import Workbook
from openpyxl.styles import Font, Fill, PatternFill, Border, Side, Alignment, Color
from openpyxl.utils import get_column_letter
wb = Workbook()
ws = wb.active
ws.title = "Data"
# Write values
ws['A1'] = 'Header 1'
ws['B1'] = 'Header 2'
ws.append(['Row 1 Col 1', 'Row 1 Col 2'])
ws.append(['Row 2 Col 1', 'Row 2 Col 2'])
# Cell formatting
ws['A1'].font = Font(bold=True, size=14, color='FFFFFF')
ws['A1'].fill = PatternFill('solid', start_color='4472C4')
ws['A1'].alignment = Alignment(horizontal='center')
# Column width
ws.column_dimensions['A'].width = 20
ws.column_dimensions['B'].width = 15
# Row height
ws.row_dimensions[1].height = 25
# Merge cells
ws.merge_cells('A1:B1')
# Save
wb.save('formatted.xlsx')
```
### Formulas
```python
from openpyxl import Workbook
wb = Workbook()
ws = wb.active
# Basic formulas
ws['A1'] = 10
ws['A2'] = 20
ws['A3'] = '=SUM(A1:A2)' # Sum
ws['A4'] = '=AVERAGE(A1:A2)' # Average
ws['A5'] = '=MAX(A1:A2)' # Maximum
ws['A6'] = '=MIN(A1:A2)' # Minimum
ws['A7'] = '=COUNT(A1:A2)' # Count numbers
# Cross-sheet references
wb.create_sheet('Sheet2')
ws2 = wb['Sheet2']
ws2['A1'] = '=Data!A1' # Reference to Sheet1
# Save and recalculate
wb.save('formulas.xlsx')
```
### Charts
```python
from openpyxl import Workbook
from openpyxl.chart import BarChart, Reference, LineChart, PieChart
wb = Workbook()
ws = wb.active
# Add data
data = [
['Category', 'Value'],
['A', 10],
['B', 15],
['C', 20],
['D', 25]
]
for row in data:
ws.append(row)
# Create bar chart
chart = BarChart()
chart.type = "col"
chart.style = 10
chart.title = "Sales Chart"
chart.y_axis.title = 'Value'
chart.x_axis.title = 'Category'
# Define data range
data_ref = Reference(ws, min_col=2, min_row=1, max_row=5)
cats = Reference(ws, min_col=1, min_row=2, max_row=5)
chart.add_data(data_ref, titles_from_data=True)
chart.set_categories(cats)
chart.shape = 4
ws.add_chart(chart, "E1")
wb.save('chart.xlsx')
```
### Tables
```python
from openpyxl import Workbook
from openpyxl.worksheet.table import Table, TableStyleInfo
wb = Workbook()
ws = wb.active
# Add data
data = [
['Name', 'Age', 'City'],
['Alice', 25, 'NYC'],
['Bob', 30, 'LA'],
['Charlie', 35, 'Chicago']
]
for row in data:
ws.append(row)
# Create table
tab = Table(displayName="Table1", ref="A1:C4")
style = TableStyleInfo(name="TableStyleMedium9", showFirstColumn=False,
showLastColumn=False, showRowStripes=True,
showColumnStripes=False)
tab.tableStyleInfo = style
ws.add_table(tab)
wb.save('table.xlsx')
```
### Conditional Formatting
```python
from openpyxl import Workbook
from openpyxl.formatting.rule import CellIsRule, FormulaRule
from openpyxl.styles import PatternFill
wb = Workbook()
ws = wb.active
# Add data
for i in range(1, 11):
ws.cell(row=i, column=1, value=i * 10)
# Highlight cells greater than 50
red_fill = PatternFill(start_color='FF0000', end_color='FF0000', fill_type='solid')
ws.conditional_formatting.add(
'A1:A10',
CellIsRule(operator='greaterThan', formula=['50'], fill=red_fill)
)
# Highlight even numbers with formula
green_fill = PatternFill(start_color='00FF00', end_color='00FF00', fill_type='solid')
ws.conditional_formatting.add(
'A1:A10',
FormulaRule(formula=['IS EVEN(A1)'], fill=green_fill)
)
wb.save('conditional.xlsx')
```
### Data Validation
```python
from openpyxl import Workbook
from openpyxl.worksheet.datavalidation import DataValidation
wb = Workbook()
ws = wb.active
# Dropdown list
dv = DataValidation(type="list", formula1='"Yes,No,Maybe'", allow_blank=True)
dv.error = "Please select from the list"
dv.errorTitle = "Invalid Selection"
ws.add_data_validation(dv)
dv.add('A1:A10')
# Number range
dv2 = DataValidation(type="whole", operator="between", formula1=["1", "100"])
dv2.error = "Enter a number between 1 and 100"
ws.add_data_validation(dv2)
dv2.add('B1:B10')
wb.save('validation.xlsx')
```
## Complete Examples
### Example 1: Financial Report
```python
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
from openpyxl.utils import get_column_letter
wb = Workbook()
ws = wb.active
ws.title = "Financial Report"
# Styles
header_font = Font(bold=True, size=12, color='FFFFFF')
header_fill = PatternFill('solid', start_color='4472C4')
border = Border(
left=Side(style='thin'),
right=Side(style='thin'),
top=Side(style='thin'),
bottom=Side(style='thin')
)
center_align = Alignment(horizontal='center')
# Headers
headers = ['Category', 'Q1', 'Q2', 'Q3', 'Q4', 'Total']
ws.append(headers)
# Style header row
for cell in ws[1]:
cell.font = header_font
cell.fill = header_fill
cell.border = border
cell.alignment = center_align
# Data (blue for inputs, black for formulas)
data = [
['Revenue', 100000, 120000, 115000, 130000],
['COGS', 40000, 48000, 46000, 52000],
['Gross Profit', '=B2-B3', '=C2-C3', '=D2-D3', '=E2-E3'],
['Expenses', 30000, 32000, 31000, 33000],
['Net Income', '=B4-B5', '=C4-C5', '=D4-D5', '=E4-E5']
]
for row in data:
ws.append(row)
# Add Total formula for Q1 column
last_row = len(data) + 2
ws[f'B{last_row}'] = '=SUM(B2:B6)'
# Format as currency
for row in ws.iter_rows(min_row=2, max_row=last_row, min_col=2, max_col=6):
for cell in row:
cell.number_format = '$#,##0'
cell.border = border
# Column widths
ws.column_dimensions['A'].width = 15
for col in range(2, 7):
ws.column_dimensions[get_column_letter(col)].width = 12
wb.save('financial-report.xlsx')
print("✓ Financial report created!")
```
### Example 2: Sales Dashboard with pandas
```python
import pandas as pd
import numpy as np
# Create sample sales data
np.random.seed(42)
dates = pd.date_range('2024-01-01', periods=100, freq='D')
products = ['Product A', 'Product B', 'Product C']
regions = ['North', 'South', 'East', 'West']
data = {
'Date': np.random.choice(dates, 100),
'Product': np.random.choice(products, 100),
'Region': np.random.choice(regions, 100),
'Units': np.random.randint(1, 100, 100),
'Price': np.random.uniform(10, 100, 100)
}
df = pd.DataFrame(data)
df['Revenue'] = df['Units'] * df['Price']
# Analysis
summary = df.groupby('Product').agg({
'Units': 'sum',
'Revenue': 'sum'
}).round(2)
region_summary = df.groupby('Region').agg({
'Units': 'sum',
'Revenue': ['sum', 'mean']
}).round(2)
# Write to Excel with multiple sheets
with pd.ExcelWriter('sales-dashboard.xlsx', engine='openpyxl') as writer:
# Raw data
df.to_excel(writer, sheet_name='Raw Data', index=False)
# Product summary
summary.to_excel(writer, sheet_name='Product Summary')
# Region summary
region_summary.to_excel(writer, sheet_name='Region Summary')
# Pivot table
pivot = pd.pivot_table(df, values='Revenue', index='Product',
columns='Region', aggfunc='sum')
pivot.to_excel(writer, sheet_name='Pivot Table')
print("✓ Sales dashboard created!")
```
### Example 3: Invoice Generator
```python
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
from datetime import datetime
def create_invoice(invoice_num, client_name, items, output_file):
"""
items: list of dicts with 'description', 'quantity', 'unit_price'
"""
wb = Workbook()
ws = wb.active
ws.title = "Invoice"
# Styles
title_font = Font(bold=True, size=18)
header_font = Font(bold=True, size=11)
border = Border(
left=Side(style='thin'),
right=Side(style='thin'),
top=Side(style='thin'),
bottom=Side(style='thin')
)
# Header
ws['A1'] = "INVOICE"
ws['A1'].font = title_font
ws.merge_cells('A1:C1')
# Invoice details
ws['A3'] = f"Invoice #: {invoice_num}"
ws['A4'] = f"Date: {datetime.now().strftime('%Y-%m-%d')}"
ws['A6'] = f"Bill To: {client_name}"
# Table headers
headers = ['Description', 'Quantity', 'Unit Price', 'Amount']
start_row = 8
for col, header in enumerate(headers, 1):
cell = ws.cell(row=start_row, column=col, value=header)
cell.font = header_font
cell.border = border
# Items
row = start_row + 1
for item in items:
ws.cell(row=row, column=1, value=item['description'])
ws.cell(row=row, column=2, value=item['quantity'])
ws.cell(row=row, column=3, value=item['unit_price'])
amount = item['quantity'] * item['unit_price']
ws.cell(row=row, column=4, value=amount)
for col in range(1, 5):
ws.cell(row=row, column=col).border = border
row += 1
# Total row
total_row = row
ws.cell(row=total_row, column=3, value="Total:")
ws.cell(row=total_row, column=3).font = Font(bold=True)
total_formula = f"=SUM(D{start_row+1}:D{total_row-1})"
ws.cell(row=total_row, column=4, value=total_formula)
ws.cell(row=total_row, column=4).font = Font(bold=True)
ws.cell(row=total_row, column=4).border = border
# Column widths
ws.column_dimensions['A'].width = 40
ws.column_dimensions['B'].width = 12
ws.column_dimensions['C'].width = 15
ws.column_dimensions['D'].width = 15
wb.save(output_file)
print(f"✓ Invoice created: {output_file}")
# Usage
items = [
{'description': 'Web Development', 'quantity': 40, 'unit_price': 100},
{'description': 'Design', 'quantity': 20, 'unit_price': 80},
{'description': 'Consulting', 'quantity': 10, 'unit_price': 150}
]
create_invoice("INV-2024-001", "ABC Corporation", items, "invoice.xlsx")
```
### Example 4: Data Cleaning Pipeline
```python
import pandas as pd
from openpyxl import load_workbook
def clean_and_export(input_file, output_file):
"""Clean messy Excel data and export formatted version"""
# Read raw data
df = pd.read_excel(input_file)
# Cleaning operations
# 1. Remove duplicates
df = df.drop_duplicates()
# 2. Handle missing values
df = df.fillna('N/A')
# 3. Standardize text
text_cols = df.select_dtypes(include=['object']).columns
for col in text_cols:
df[col] = df[col].str.strip().str.title()
# 4. Remove invalid rows
df = df[df['Status'] != 'Cancelled'] # Example filter
# 5. Add calculated columns
if 'Quantity' in df.columns and 'Price' in df.columns:
df['Total'] = df['Quantity'] * df['Price']
# Export with formatting
with pd.ExcelWriter(output_file, engine='openpyxl') as writer:
df.to_excel(writer, sheet_name='Cleaned Data', index=False)
# Format the output
wb = writer.book
ws = writer.sheets['Cleaned Data']
# Header formatting
for cell in ws[1]:
cell.font = Font(bold=True)
cell.fill = PatternFill('solid', start_color='4472C4')
cell.font = Font(color='FFFFFF', bold=True)
# Auto-adjust column widths
for column in ws.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
adjusted_width = min(max_length + 2, 50)
ws.column_dimensions[column_letter].width = adjusted_width
print(f"✓ Cleaned data exported to {output_file}")
return df
# Usage
# clean_and_export('raw_data.xlsx', 'cleaned_data.xlsx')
```
## Financial Modeling Best Practices
### Color Coding Standards
| Color | RGB | Meaning |
|-------|-----|---------|
| Blue | 0, 0, 255 | Hardcoded inputs |
| Black | 0, 0, 0 | Formulas |
| Green | 0, 128, 0 | Internal links |
| Red | 255, 0, 0 | External links |
| Yellow BG | 255, 255, 0 | Key assumptions |
### Implementation
```python
from openpyxl.styles import Font
# Blue for inputs
input_font = Font(color='0000FF')
ws['B2'].font = input_font # Hardcoded value
# Black for formulas (default)
ws['B3'] = '=B2*1.1' # Formula stays black
# Green for internal references
# (When linking between sheets)
ws['Sheet2!B2'] = '=Sheet1!B2'
```
### Number Formatting
```python
# Currency
ws['A1'].number_format = '$#,##0'
# Currency with thousands separator
ws['A2'].number_format = '$#,##0;($#,##0);-'
# Percentage with one decimal
ws['A3'].number_format = '0.0%'
# Date
ws['A4'].number_format = 'YYYY-MM-DD'
# Thousands with K suffix
ws['A5'].number_format = '#,##0,"K"'
# Millions with M suffix
ws['A6'].number_format = '#,##0,,"M"'
```
## Error Handling
### Common Errors
#### Error: "Formula not calculating"
```python
# Solution: Recalculate after saving
wb.save('file.xlsx')
# Then use LibreOffice to recalculate
# python scripts/recalc.py file.xlsx
```
#### Error: "Chart not displaying"
```python
# Solution: Ensure data range is correct
data_ref = Reference(ws, min_col=2, min_row=1, max_row=5) # Check row numbers
```
#### Error: "File corrupted"
```python
# Solution: Use data_only=True when reading
wb = load_workbook('file.xlsx', data_only=True)
```
## Testing Your Setup
```python
# test-xlsx.py
import pandas as pd
from openpyxl import Workbook
print("Testing xlsx setup...")
# Test 1: pandas read/write
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df.to_excel('test-output.xlsx', index=False)
df_read = pd.read_excel('test-output.xlsx')
assert len(df_read) == 3
print("✓ pandas test passed")
# Test 2: openpyxl with formula
wb = Workbook()
ws = wb.active
ws['A1'] = 10
ws['A2'] = 20
ws['A3'] = '=SUM(A1:A2)'
wb.save('test-formula.xlsx')
print("✓ openpyxl test passed")
print("✓ All tests passed!")
```
Run test:
```bash
python test-xlsx.py
```
## License
MIT License - See LICENSE file for details.
标签
skill
ai
