Python自动化Excel办公：从入门到实战

1. Python自动化办公Excel实用指南从入门到精通作为一名长期与Excel和Python打交道的开发者我深知重复性表格处理工作有多折磨人。曾经为了合并12个分公司的月度报表我不得不熬夜到凌晨3点手动复制粘贴。直到掌握了Python自动化处理Excel的技巧同样工作现在只需3分钟就能完成。这篇指南将分享我多年积累的实战经验带你用Python彻底解放双手。Python在Excel自动化领域的优势非常明显它能处理百万行级数据而不会卡顿可以完美复现所有手动操作步骤还能实现Excel本身难以完成的复杂逻辑。无论是财务对账、销售分析还是人事管理掌握这些技能至少能提升10倍工作效率。下面我会从环境搭建开始逐步深入到实战案例最后分享几个提升稳定性的独家技巧。2. 环境准备与基础配置2.1 Python环境搭建推荐使用Python 3.8版本这个版本在第三方库兼容性和性能之间取得了很好的平衡。新手可以直接安装Anaconda发行版它预装了数据分析所需的绝大多数工具包conda create -n excel_auto python3.8 conda activate excel_auto对于有经验的开发者使用原生Python配合virtualenv会更轻量python -m venv excel_venv source excel_venv/bin/activate # Linux/Mac excel_venv\Scripts\activate.bat # Windows注意无论哪种方式请确保将Python添加到系统PATH环境变量。验证方法是在命令行执行python --version能正确显示版本号。2.2 核心库安装处理Excel主要依赖以下几个库pip install openpyxl pandas xlrd xlwt xlsxwriter各库的作用如下openpyxl处理.xlsx格式文件Excel 2007pandas数据分析和处理的核心工具xlrd/xlwt读写.xls格式文件Excel 2003xlsxwriter生成带复杂格式的Excel文件我强烈建议同时安装pyxlsb以支持二进制.xlsb格式pip install pyxlsb2.3 开发工具选择VSCode是最适合Excel自动化的IDE之一需要安装以下扩展Python官方扩展Excel ViewerJupyter配置关键设置settings.json{ python.linting.enabled: true, python.formatting.provider: black, files.autoSave: afterDelay }3. Excel基础操作全解析3.1 文件读写操作使用openpyxl读取工作簿from openpyxl import load_workbook # 读取文件 wb load_workbook(财务报表.xlsx, data_onlyTrue) # data_only获取计算后的值 sheet wb[2023年度] # 按名称获取工作表 # 获取单元格值 cell_value sheet[B2].value # 获取B2单元格 row_data [cell.value for cell in sheet[2]] # 获取第2行所有数据 # 写入数据 sheet[C5] SUM(C2:C4) # 写入公式 sheet.cell(row8, column3, value42) # 行列号方式写入 # 保存文件 wb.save(财务报表_修改后.xlsx)避坑指南打开大文件时添加read_onlyTrue参数可显著提升性能但此时只能读取不能修改。另存时使用不同文件名可防止原始文件损坏。3.2 使用Pandas高效处理数据Pandas的DataFrame是处理表格数据的利器import pandas as pd # 读取Excel df pd.read_excel(销售数据.xlsx, sheet_nameQ1, header2) # 从第3行开始读 # 数据清洗 df.dropna(subset[客户ID], inplaceTrue) # 删除客户ID为空的行 df[销售额] df[单价] * df[数量] # 新增计算列 # 分组统计 summary df.groupby(产品类别)[销售额].agg([sum, mean, count]) # 写入新文件 with pd.ExcelWriter(销售报告.xlsx) as writer: df.to_excel(writer, sheet_name明细数据) summary.to_excel(writer, sheet_name汇总统计) # 添加格式 workbook writer.book fmt workbook.add_format({num_format: #,##0.00}) writer.sheets[汇总统计].set_column(B:D, 15, fmt)3.3 高级格式设置通过xlsxwriter设置专业级报表格式import xlsxwriter workbook xlsxwriter.Workbook(格式化报告.xlsx) worksheet workbook.add_worksheet() # 定义格式 title_fmt workbook.add_format({ bold: True, font_color: white, bg_color: #4F81BD, align: center, valign: vcenter }) currency_fmt workbook.add_format({num_format: $#,##0.00}) # 应用格式 worksheet.write(A1, 季度销售报告, title_fmt) worksheet.set_column(B:E, 12, currency_fmt) # 添加条件格式 worksheet.conditional_format(B2:B10, { type: data_bar, bar_color: #63C384 }) workbook.close()4. 实战案例精讲4.1 案例一多文件数据合并场景每月需要合并30个分店的销售报表from pathlib import Path import pandas as pd def merge_excel_files(folder_path, output_file): all_data [] # 遍历文件夹 for file in Path(folder_path).glob(*.xlsx): if file.name.startswith(~$): # 跳过临时文件 continue # 读取每个文件 df pd.read_excel(file, sheet_nameNone) # 读取所有sheet for sheet_name, data in df.items(): data[来源文件] file.name data[来源sheet] sheet_name all_data.append(data) # 合并保存 merged pd.concat(all_data, ignore_indexTrue) merged.to_excel(output_file, indexFalse) print(f成功合并{len(all_data)}个表格总行数{len(merged)}) # 使用示例 merge_excel_files(分店报表/, 合并报表.xlsx)经验技巧添加来源文件和来源sheet字段便于后续追踪数据来源。处理前先用glob检查文件格式可避免意外错误。4.2 案例二自动化数据校验自动检测常见数据问题def validate_excel(file_path): report { 空值检查: [], 格式问题: [], 逻辑错误: [] } wb load_workbook(file_path) for sheet in wb: for row in sheet.iter_rows(values_onlyTrue): # 检查空值 if any(cell is None for cell in row[:3]): # 前3列不能为空 report[空值检查].append(f{sheet.title}-行{row[0].row}) # 检查日期格式 if isinstance(row[1], str) and not row[1].count(/) 2: report[格式问题].append(f日期格式错误: {sheet.title}-{row[0]}) # 生成报告 with pd.ExcelWriter(校验报告.xlsx) as writer: for issue_type, details in report.items(): pd.DataFrame({issue_type: details}).to_excel( writer, sheet_nameissue_type[:31], indexFalse) return report4.3 案例三动态报表生成结合数据库生成动态报表import sqlite3 from datetime import datetime def generate_sales_report(start_date, end_date): # 连接数据库 conn sqlite3.connect(sales.db) # 执行SQL查询 query f SELECT 产品名称, SUM(数量) as 总销量, SUM(金额) as 总销售额 FROM 销售记录 WHERE 日期 BETWEEN {start_date} AND {end_date} GROUP BY 产品名称 ORDER BY 总销售额 DESC df pd.read_sql(query, conn) # 生成Excel with pd.ExcelWriter(f销售报告_{datetime.now().strftime(%Y%m%d)}.xlsx) as writer: df.to_excel(writer, sheet_name销售汇总, indexFalse) # 添加图表 workbook writer.book worksheet writer.sheets[销售汇总] chart workbook.add_chart({type: column}) chart.add_series({ name: 总销售额, categories: 销售汇总!$A$2:$A$20, values: 销售汇总!$C$2:$C$20 }) worksheet.insert_chart(E2, chart) print(f报告已生成时间段{start_date}至{end_date})5. 高级技巧与性能优化5.1 处理百万行数据当数据量超过50万行时需要特殊处理# 分块读取 chunk_size 100000 reader pd.read_excel(大数据文件.xlsx, chunksizechunk_size) for i, chunk in enumerate(reader): process(chunk) # 处理每个分块 print(f已处理{(i1)*chunk_size}行) # 使用dtype优化内存 dtype_map { 客户ID: str, 金额: float32, 数量: int16 } pd.read_excel(大数据文件.xlsx, dtypedtype_map)5.2 自动化任务调度结合Windows任务计划或Linux cron实现定时运行# 创建run.py import schedule import time def daily_report(): generate_sales_report(2023-01-01, 2023-12-31) schedule.every().day.at(08:00).do(daily_report) while True: schedule.run_pending() time.sleep(60)然后设置系统定时任务# Linux crontab 0 8 * * * /path/to/python /path/to/run.py5.3 异常处理与日志健壮的脚本需要完善的错误处理import logging from openpyxl.utils.exceptions import InvalidFileException logging.basicConfig( filenameexcel_auto.log, levellogging.INFO, format%(asctime)s - %(levelname)s - %(message)s ) def safe_process(file): try: wb load_workbook(file) # 处理逻辑... except InvalidFileException as e: logging.error(f文件格式错误: {file} - {str(e)}) except Exception as e: logging.critical(f处理{file}时发生未知错误, exc_infoTrue) else: logging.info(f成功处理{file})6. 常见问题解决方案6.1 文件被占用错误当遇到文件正在被其他程序使用错误时import os from time import sleep def safe_save(wb, filename, retries3): for i in range(retries): try: wb.save(filename) return True except PermissionError: sleep(2) # 等待2秒重试 return False替代方案是使用临时文件import tempfile def temp_save(wb, original_path): with tempfile.NamedTemporaryFile(deleteFalse) as tmp: temp_path tmp.name .xlsx wb.save(temp_path) os.replace(temp_path, original_path)6.2 公式计算问题确保公式计算结果更新# 方法1强制计算 wb load_workbook(带公式.xlsx, data_onlyFalse) sheet wb.active sheet[B2] SUM(A1:A10) wb.save(公式更新.xlsx) # 方法2使用win32com仅Windows import win32com.client excel win32com.client.Dispatch(Excel.Application) wb excel.Workbooks.Open(rC:\路径\文件.xlsx) wb.Save() excel.Quit()6.3 中文编码问题处理包含中文的文件名或内容# 设置系统默认编码 import sys import io sys.stdout io.TextIOWrapper(sys.stdout.buffer, encodingutf-8) # Pandas读取时指定编码 df pd.read_excel(中文文件.xlsx, engineopenpyxl, encodingutf-8-sig) # 写入CSV时处理中文 df.to_csv(输出.csv, encodingutf_8_sig, indexFalse)7. 扩展应用场景7.1 与邮件系统集成自动发送Excel报表邮件import smtplib from email.mime.multipart import MIMEMultipart from email.mime.base import MIMEBase from email import encoders def send_email_with_excel(to_email, subject, body, excel_path): msg MIMEMultipart() msg[From] auto_reportcompany.com msg[To] to_email msg[Subject] subject # 添加正文 msg.attach(MIMEText(body, plain)) # 添加Excel附件 with open(excel_path, rb) as f: part MIMEBase(application, octet-stream) part.set_payload(f.read()) encoders.encode_base64(part) part.add_header(Content-Disposition, fattachment; filename{os.path.basename(excel_path)}) msg.attach(part) # 发送邮件 with smtplib.SMTP(smtp.company.com, 587) as server: server.starttls() server.login(user, password) server.send_message(msg)7.2 Web数据抓取与Excel整合结合爬虫自动更新数据import requests from bs4 import BeautifulSoup def scrape_to_excel(url, output_file): response requests.get(url) soup BeautifulSoup(response.text, html.parser) data [] for row in soup.select(table.data tr): cols [col.get_text(stripTrue) for col in row.find_all(td)] if cols: data.append(cols) df pd.DataFrame(data, columns[日期, 产品, 销量, 金额]) df.to_excel(output_file, indexFalse)7.3 创建Excel插件用PyXLL将Python函数变成Excel公式# 在pyxll.cfg中添加 [PYTHON] pythonpath /path/to/your/scripts [FUNCTIONS] my_sum your_module.your_function # Python函数 xl_func def my_sum(x, y): 在Excel中使用的自定义求和函数 return float(x) float(y)8. 维护与最佳实践8.1 代码组织建议推荐的项目结构/excel_automation │── /config # 配置文件 │ └── settings.ini │── /data # 输入输出文件 │ ├── /input │ └── /output │── /lib # 公共函数 │ └── excel_utils.py │── /scripts # 主程序 │ ├── daily_report.py │ └── data_clean.py │── requirements.txt └── README.md8.2 版本控制策略处理Excel文件时的Git策略将原始Excel文件添加到.gitignore只提交处理脚本和生成的样例文件使用git-lfs管理大型二进制文件# .gitignore 示例 *.xlsx *.xls ~$*8.3 性能监控添加执行时间记录import time from functools import wraps def time_logger(func): wraps(func) def wrapper(*args, **kwargs): start time.perf_counter() result func(*args, **kwargs) end time.perf_counter() print(f{func.__name__} 执行时间: {end - start:.2f}秒) return result return wrapper time_logger def process_large_file(file_path): # 处理逻辑...9. 安全注意事项9.1 文件安全处理防止恶意文件攻击from openpyxl import Workbook from tempfile import mkstemp import shutil def safe_open(file_path): 验证文件安全性 if not file_path.endswith((.xlsx, .xls)): raise ValueError(仅支持Excel文件) # 创建临时副本 fd, temp_path mkstemp(suffix.xlsx) shutil.copy2(file_path, temp_path) try: wb load_workbook(temp_path) return wb finally: os.close(fd) os.unlink(temp_path)9.2 敏感数据处理加密包含敏感信息的Excelfrom io import BytesIO from cryptography.fernet import Fernet def encrypt_excel(df, password): 将DataFrame加密保存 buffer BytesIO() with pd.ExcelWriter(buffer) as writer: df.to_excel(writer) cipher_suite Fernet(password) encrypted cipher_suite.encrypt(buffer.getvalue()) with open(加密文件.xlsx, wb) as f: f.write(encrypted)9.3 宏病毒防护检查文件是否包含宏import zipfile def check_macros(file_path): 检查Excel是否包含宏 try: with zipfile.ZipFile(file_path) as z: return xl/vbaProject.bin in z.namelist() except zipfile.BadZipFile: return False10. 实战经验分享10.1 处理特殊格式的坑遇到合并单元格时的处理技巧def get_merged_cell_value(sheet, cell): 获取合并单元格的真实值 for range_ in sheet.merged_cells.ranges: if cell.coordinate in range_: return range_.start_cell.value return cell.value处理自定义数字格式def parse_custom_number(cell): 解析如1,000.00 USD这样的自定义格式 value cell.value if isinstance(value, str): return float(.join(c for c in value if c.isdigit() or c .)) return value10.2 跨平台兼容性问题处理Windows和Mac的路径差异from pathlib import Path def get_absolute_path(relative_path): 获取跨平台绝对路径 base_dir Path(__file__).parent return (base_dir / relative_path).resolve()处理不同系统的换行符def clean_text(text): 统一换行符 if text is None: return return text.replace(\r\n, \n).replace(\r, \n)10.3 性能优化实战加速openpyxl读取的秘诀def fast_read_excel(file_path): 快速读取大Excel文件 wb load_workbook(file_path, read_onlyTrue, data_onlyTrue) data [] for row in wb.active.iter_rows(values_onlyTrue): data.append(row) wb.close() return data减少Pandas内存占用的技巧def optimize_memory(df): 优化DataFrame内存使用 # 转换数值类型 for col in df.select_dtypes(include[int64]): df[col] pd.to_numeric(df[col], downcastinteger) # 转换字符串类型 for col in df.select_dtypes(include[object]): df[col] df[col].astype(category) return df11. 资源推荐与学习路径11.1 进阶学习资料推荐书籍《Python for Excel》by Felix Zumstein《Pandas Cookbook》by Theodore Petrou《Automate the Boring Stuff with Python》第12章在线课程Udemy: Python for Data Science and Machine LearningCoursera: Data Science at Scale with Python11.2 实用工具集开发辅助工具Tablib数据格式转换库PyInstaller打包成exetqdm进度条显示调试工具Excel Viewer快速预览文件内容File Watcher监控文件变化Memory Profiler内存使用分析11.3 社区支持遇到问题时可以求助Stack Overflow使用[python][excel]标签GitHubopenpyxl和pandas的issue区Reddit/r/learnpython和/r/excel中文社区知乎Python话题CSDN Python板块掘金数据分析专栏12. 持续集成与自动化部署12.1 自动化测试框架为Excel脚本添加单元测试import unittest from openpyxl import Workbook class TestExcelFunctions(unittest.TestCase): classmethod def setUpClass(cls): cls.wb Workbook() cls.sheet cls.wb.active cls.sheet[A1] 10 cls.sheet[A2] 20 def test_sum_formula(self): self.sheet[A3] SUM(A1:A2) self.assertEqual(self.sheet[A3].value, SUM(A1:A2)) self.assertEqual(self.sheet[A3].data_type, f) classmethod def tearDownClass(cls): cls.wb.close() if __name__ __main__: unittest.main()12.2 CI/CD集成GitHub Actions自动化示例name: Excel Automation CI on: [push, pull_request] jobs: test: runs-on: windows-latest steps: - uses: actions/checkoutv2 - name: Set up Python uses: actions/setup-pythonv2 with: python-version: 3.8 - name: Install dependencies run: | python -m pip install --upgrade pip pip install -r requirements.txt - name: Run tests run: | python -m unittest discover12.3 容器化部署Docker镜像构建FROM python:3.8-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY . . CMD [python, daily_report.py]构建命令docker build -t excel-automation . docker run -v $(pwd)/data:/app/data excel-automation13. 扩展思路与其他工具集成13.1 与数据库联动从SQL数据库导出到Excelimport pyodbc import pandas as pd def export_sql_to_excel(connection_str, query, output_file): conn pyodbc.connect(connection_str) df pd.read_sql(query, conn) # 添加Excel格式 writer pd.ExcelWriter(output_file, enginexlsxwriter) df.to_excel(writer, indexFalse) # 自动调整列宽 for column in df: col_idx df.columns.get_loc(column) writer.sheets[Sheet1].set_column(col_idx, col_idx, max(df[column].astype(str).map(len).max(), len(column)) 2) writer.save()13.2 与Power BI/Tableau集成生成供BI工具使用的数据模型def create_bi_ready_excel(data_frames, output_file): with pd.ExcelWriter(output_file) as writer: for name, df in data_frames.items(): df.to_excel(writer, sheet_namename[:31], indexFalse) # 添加为Excel表格对象(方便Power BI识别) worksheet writer.sheets[name[:31]] (max_row, max_col) df.shape worksheet.add_table(0, 0, max_row, max_col-1, { columns: [{header: col} for col in df.columns], style: Table Style Medium 2, name: name })13.3 与云存储结合自动上传到Google Drivefrom pydrive.auth import GoogleAuth from pydrive.drive import GoogleDrive def upload_to_drive(file_path, folder_id): gauth GoogleAuth() gauth.LocalWebserverAuth() # 首次使用需要授权 drive GoogleDrive(gauth) file drive.CreateFile({title: os.path.basename(file_path), parents: [{id: folder_id}]}) file.SetContentFile(file_path) file.Upload() return file[id]14. 可视化增强技巧14.1 条件格式高级应用创建热力图效果def add_heatmap(writer, sheet_name, range_, min_color, max_color): workbook writer.book worksheet writer.sheets[sheet_name] worksheet.conditional_format(range_, { type: 2_color_scale, min_color: min_color, max_color: max_color, min_type: percentile, min_value: 10, max_type: percentile, max_value: 90 })14.2 动态图表生成根据数据自动创建图表def create_dynamic_chart(writer, sheet_name, data_range, title): workbook writer.book worksheet writer.sheets[sheet_name] chart workbook.add_chart({type: line}) chart.add_series({ values: f{sheet_name}!{data_range}, categories: f{sheet_name}!$A$2:$A${len(data)1}, name: 实际值 }) chart.set_title({name: title}) chart.set_x_axis({name: 日期}) chart.set_y_axis({name: 数值}) worksheet.insert_chart(E2, chart)14.3 交互式控件添加下拉菜单和数据验证def add_data_validation(writer, sheet_name, cell, options): workbook writer.book worksheet writer.sheets[sheet_name] # 创建选项列表 option_str ,.join(options) # 添加数据验证 worksheet.data_validation(cell, { validate: list, source: option_str, input_title: 请选择:, error_title: 无效输入, error_message: 请从列表中选择 })15. 项目架构设计模式15.1 面向对象封装将常用功能封装成类class ExcelAutomator: def __init__(self, template_path): self.template template_path self.wb None def __enter__(self): self.wb load_workbook(self.template) return self def __exit__(self, exc_type, exc_val, exc_tb): if self.wb: self.wb.close() def fill_data(self, sheet_name, data_dict): 根据字典填充数据 sheet self.wb[sheet_name] for cell, value in data_dict.items(): sheet[cell] value def save_as(self, output_path): self.wb.save(output_path) # 使用示例 data {B2: 季度报告, C5: 42, D8: datetime.now()} with ExcelAutomator(模板.xlsx) as excel: excel.fill_data(Sheet1, data) excel.save_as(输出报告.xlsx)15.2 插件系统设计支持自定义处理插件class ExcelProcessor: def __init__(self): self.plugins [] def register_plugin(self, plugin): self.plugins.append(plugin) def process_file(self, file_path): wb load_workbook(file_path) for plugin in self.plugins: plugin.before_process(wb) for sheet in wb: for plugin in self.plugins: plugin.process_sheet(sheet) for plugin in self.plugins: plugin.after_process(wb) return wb # 示例插件 class FormatCleanerPlugin: def process_sheet(self, sheet): for row in sheet.iter_rows(): for cell in row: cell.style Normal15.3 配置驱动架构使用JSON/YAML配置定义处理流程# config/process.yaml steps: - name: 数据清洗 actions: - type: remove_empty_rows columns: [客户ID, 产品编号] - type: standardize_dates columns: [订单日期] format: %Y-%m-%d - name: 数据计算 actions: - type: add_formula cell: D2 formula: B2*C2 fill_range: D2:D100对应的处理器def process_with_config(wb, config_file): with open(config_file) as f: config yaml.safe_load(f) for step in config[steps]: print(f正在执行: {step[name]}) for action in step[actions]: execute_action(wb, action)16. 性能监控与调优16.1 内存分析使用memory_profiler检测内存泄漏profile def process_large_excel(file_path): df pd.read_excel(file_path) # 处理逻辑... return df.groupby(类别).sum() if __name__ __main__: process_large_excel(大数据.xlsx)运行分析python -m memory_profiler script.py16.2 执行时间优化使用cProfile分析性能瓶颈import cProfile def profile_excel_processing(): pr cProfile.Profile() pr.enable() # 要分析的代码 process_large_file(大数据.xlsx) pr.disable() pr.print_stats(sortcumtime) if __name__ __main__: profile_excel_processing()16.3 并行处理使用多进程加速from multiprocessing import Pool def process_sheet(sheet_name): df pd.read_excel(大数据.xlsx, sheet_namesheet_name) # 处理逻辑... return df if __name__ __main__: sheet_names [Q1, Q2, Q3, Q4] with Pool(4) as p: results p.map(process_sheet, sheet_names) final_df pd.concat(results)17. 错误处理与恢复机制17.1 事务处理模式实现原子性操作import shutil def safe_excel_operation(input_path, output_path, process_func): 确保操作失败时不影响原始文件 temp_path output_path .tmp try: # 执行处理 wb load_workbook(input_path) process_func(wb) wb.save(temp_path) # 验证文件 test_wb load_workbook(temp_path) test_wb.close() # 替换原文件 shutil.move(temp_path, output_path) except Exception as e: if os.path.exists(temp_path): os.remove(temp_path) raise e17.2 断点续处理记录处理进度import json class ProgressTracker: def __init__(self, state_fileprogress.json): self.state_file state_file self.state self._load_state() def _load_state(self): try: with open(self.state_file) as f: return json.load(f) except (FileNotFoundError, json.JSONDecodeError): return {last_processed: 0} def save_state(self, last_row): self.state[last_processed] last_row with open(self.state_file, w) as f: json.dump(self.state, f) def process_file(self, file_path): wb load_workbook(file_path) sheet wb.active start_row self.state[last_processed] 1 for row in sheet.iter_rows(min_rowstart_row, values_onlyTrue): try: process_row(row) self.save_state(row[0]) # 假设第一列是行ID except Exception as e: print(f处理行{row[0]}失败: {str(e)}) break17.3 自动重试机制处理网络或资源问题from tenacity import retry, stop_after_attempt, wait_exponential retry(stopstop_after_attempt(3), waitwait_exponential(multiplier1, min4, max10)) def download_excel(url): response requests.get(url, timeout10) response.raise_for_status() with open(download.xlsx, wb) as f: f.write(response.content)18. 文档与注释规范18.1 自动化文档生成使用pydoc生成API文档python -m pydoc -w your_module18.2 Excel元数据注释在Excel中添加文档说明def add_documentation_sheet(wb, content): if 文档说明 in wb.sheetnames: doc_sheet wb[文档说明] else: doc_sheet wb.create_sheet(文档说明, 0) doc_sheet[A1] 最后更新时间 doc_sheet[B1] datetime.now().strftime(%Y-%m-%d %H:%M) for i, line in enumerate(content.split(\n), start3): doc_sheet[fA{i}] line18.3 代码注释标准符合PEP