Python利用openpyxl与pandas处理Excel多工作表的实战对比

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

# 1. pandas快速汇总数据
sales_data = pd.concat([
    pd.read_excel(f'store_{i}.xlsx', sheet_name='销售') 
    for i in range(1, 6)
])
summary = sales_data.groupby('产品类别').agg({'销售额':'sum', '销量':'sum'})

# 2. openpyxl美化输出
wb = load_workbook('template.xlsx')
ws = wb['汇总表']

# 写入数据（跳过标题行）
for r_idx, row in enumerate(summary.itertuples(), start=2):
    for c_idx, value in enumerate(row[1:], start=1):
        ws.cell(row=r_idx, column=c_idx, value=value)

# 设置标题样式
title_font = Font(bold=True, color='FFFFFF')
title_fill = PatternFill(start_color='4F81BD', end_color='4F81BD', fill_type='solid')
for cell in ws[1]:
    cell.font = title_font
    cell.fill = title_fill

wb.save('sales_report.xlsx')

from openpyxl import Workbook
import pandas as pd

# 1. 创建基础模板结构
wb = Workbook()
wb.remove(wb.active)  # 删除默认Sheet

# 添加预算表（带格式）
budget_ws = wb.create_sheet('部门预算')
budget_ws.append(['部门', '项目', '预算金额', '申请日期'])

# 设置表头样式
for cell in budget_ws[1]:
    cell.font = Font(bold=True)
    cell.border = Border(left=Side(style='thin'), 
                         right=Side(style='thin'),
                         top=Side(style='thin'),
                         bottom=Side(style='thin'))

# 2. 填充数据（从数据库导出）
dept_data = pd.read_sql("SELECT * FROM budget_requests", con)
for row in dept_data.itertuples(index=False):
    budget_ws.append(list(row))

# 3. 添加数据验证（下拉列表）
from openpyxl.worksheet.datavalidation import DataValidation
dv = DataValidation(type="list", formula1='"行政部,技术部,市场部,财务部"', allow_blank=True)
budget_ws.add_data_validation(dv)
dv.add('A2:A1000')  # 应用到A列所有单元格

wb.save('budget_template.xlsx')

from openpyxl.utils.dataframe import dataframe_to_rows

# 从带样式的模板创建新文件
template = load_workbook('template.xlsx')
new_wb = Workbook()
new_ws = new_wb.active

# 复制模板样式（需手动实现样式复制逻辑）
for row in template['数据区'].iter_rows():
    new_row = [cell.value for cell in row]
    new_ws.append(new_row)
    # 这里需要补充样式复制代码

# 写入pandas处理后的数据
df = pd.DataFrame(...)  # 处理后的数据
for r_idx, row in enumerate(dataframe_to_rows(df, index=False, header=True), start=3):
    new_ws.append(row)