---
name: large-excel-analysis-and-formatting-28d0d70c6655
description: "用于处理多Sheet大型Excel文件，支持大文件Parquet格式转换提速，并使用openpyxl生成带条件高亮和自定义样式的格式化Excel报告及下载链接。"
---

## Skill Steps

Step1 读取Excel文件，统计所有Sheet的总行数。若数据量过大（如≥1万行），则转换为Parquet格式以显著提升后续读取和分析效率。
```python
import pandas as pd

file_path = "input.xlsx"
xls = pd.ExcelFile(file_path)
total_rows = 0

# 统计所有 sheet 的总行数
for name in xls.sheet_names:
    df_temp = pd.read_excel(file_path, sheet_name=name, header=None)
    total_rows += len(df_temp)

print(f"总行数: {total_rows}")

# 大文件处理：超过阈值转换为 Parquet 提升效率
if total_rows >= 10000:
    parquet_path = "/mnt/data/temp.parquet"
    # 此处以读取第一个sheet为例，实际可根据需求合并多个sheet
    df = pd.read_excel(file_path, sheet_name=0)
    df.to_parquet(engine='pyarrow', path=parquet_path)
    df = pd.read_parquet(parquet_path)
else:
    df = pd.read_excel(file_path, sheet_name=0)
```

Step2 提取目标数据进行分组汇总分析，并识别出最大值及其对应的分类项。
```python
# 占位示例：根据实际数据集替换列名
group_col = '分类列名'  # 如 '控股类型'
target_col = '目标数值列'  # 如 '建筑业总产值'

# 假设 df 已清洗并包含所需列，进行汇总分析
summary = df.groupby(group_col)[target_col].sum().reset_index()

# 识别最大值及其对应的分类
max_idx = summary[target_col].idxmax()
max_type = summary.loc[max_idx, group_col]
print(f"最高产值类型: {max_type}")
```

Step3 使用 openpyxl 将分析结果写入新的Excel文件，配置表头样式、边框、列宽，并对满足特定条件（如最大值）的行进行绿色高亮标注，最后生成下载链接。
```python
from openpyxl import Workbook
from openpyxl.styles import PatternFill, Font, Alignment, Border, Side

wb = Workbook()
ws = wb.active
ws.title = "分析报告"

# 样式定义
header_fill = PatternFill(start_color="4472C4", end_color="4472C4", fill_type="solid")
header_font = Font(name="微软雅黑", bold=True, color="FFFFFF", size=12)
highlight_fill = PatternFill(start_color="00B050", end_color="00B050", fill_type="solid")
highlight_font = Font(name="微软雅黑", bold=True, color="FFFFFF", size=12)
normal_font = Font(name="微软雅黑", size=11)
center_align = Alignment(horizontal="center", vertical="center")
thin_border = Border(
    left=Side(style="thin"), right=Side(style="thin"),
    top=Side(style="thin"), bottom=Side(style="thin")
)

# 写入表头并应用样式
headers = [group_col, target_col]
for col, header in enumerate(headers, 1):
    cell = ws.cell(row=1, column=col, value=header)
    cell.fill = header_fill
    cell.font = header_font
    cell.alignment = center_align
    cell.border = thin_border

# 写入数据并进行条件高亮
for row_idx, row_data in enumerate(summary.itertuples(index=False), 2):
    type_name, value = row_data[0], row_data[1]
    
    cell_type = ws.cell(row=row_idx, column=1, value=type_name)
    cell_value = ws.cell(row=row_idx, column=2, value=value)
    
    # 基础样式
    for cell in [cell_type, cell_value]:
        cell.alignment = center_align
        cell.border = thin_border
        cell.font = normal_font
    
    # 命中最大值条件时高亮整行
    if type_name == max_type:
        cell_type.fill = highlight_fill
        cell_type.font = highlight_font
        cell_value.fill = highlight_fill
        cell_value.font = highlight_font

# 调整列宽
ws.column_dimensions['A'].width = 18
ws.column_dimensions['B'].width = 25

# 保存文件
output_path = "/mnt/data/formatted_analysis_report.xlsx"
wb.save(output_path)
print(f"文件已保存至: {output_path}")

# 提供下载链接
download_link = f"sandbox:{output_path}"
print(f"下载链接: {download_link}")
```


---
Source attribution: OpenSenseNova
Original project: https://github.com/OpenSenseNova/SenseNova-Skills
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: OpenSenseNova
Original project: https://github.com/OpenSenseNova/SenseNova-Skills
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Marketing
Roles: Data Analyst, Growth Marketer, Marketing Analyst
Specialties: API, Database, Excel
Tools: Google Sheets, Microsoft Excel, Parquet
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 27e3061fc133e5c798d8c61a0134c100cf9e283c50a81847bd26da094d22bccd