Python 实现 Pandas 文件分组处理、地市拆分及邮件发送：附代码优化建议

```python import pandas as pd import smtplib from email.mime.multipart import MIMEMultipart from email.mime.base import MIMEBase from email import encoders def process_and_send_emails(file_path, mapping_table): data = pd.read_csv(file_path) group1 = data[data['column_name'].str.contains('一级')] group2 = data[~data['column_name'].str.contains('一级')] for city, data_subset in data.groupby('city_column_name'): file_name = f'{city}.csv' data_subset.to_csv(file_name, index=False) msg = MIMEMultipart() msg['From'] ='sender@example.com' msg['To'] ='recipient@example.com' msg['Subject'] = f'数据报告 - {city}' body = f'{city}，有“一级”{len(group1[data_subset["column_name"].str.contains("一级")])}，没有“一级”{len(group2[data_subset["column_name"].str.contains("一级")])}' msg.attach(MIMEText(body, 'plain')) attachment = open(file_name, 'rb') part = MIMEBase('application', 'octet-stream') part.set_payload((attachment).read()) encoders.encode_base64(part) part.add_header('Content-Disposition', "attachment; filename= %s" % file_name) msg.attach(part) s = smtplib.TP('smtp.example.com') s.send_message(msg) s.quit() # 以下是三个改进建议： # 1. 增加异常处理，例如在读取文件、发送邮件等操作时处理可能出现的错误。 # 2. 对邮件内容进行更详细和清晰的格式化，使其更易于阅读。 # 3. 可以考虑将数据处理和邮件发送的部分封装成独立的函数，提高代码的可维护性。 ``` ######[AI写代码神器 | 459点数解答 | 2024-12-25 01:17:06]