以下是一个重构后的高可用、可配置、低耦合的专利CSV处理函数，包含清晰的注释和结构：

import csv
import pandas as pd
from datetime import datetime
import os
from typing import List, Dict, Any, Optional, Tuple
import logging# 配置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)class PatentProcessor:"""专利数据处理器"""def __init__(self, config: Optional[Dict[str, Any]] = None):"""初始化专利处理器Args:config: 配置字典，包含处理选项"""self.config = config or self.get_default_config()@staticmethoddef get_default_config() -> Dict[str, Any]:"""获取默认配置"""return {'required_columns': ['id', 'inventor/author', 'title', 'priority date'],'author_column': 'inventor/author','split_separator': ',','filter_condition': None,  # 例如: {'column': 'assignee', 'value': '百度', 'case_sensitive': False}'output_columns': ['author', 'value', 'id', 'title_list', 'priority_date_list', 'start_year', 'end_year'],'encoding': 'utf-8'}def load_csv_data(self, csv_filepath: str) -> Optional[pd.DataFrame]:"""加载CSV文件数据Args:csv_filepath: CSV文件路径Returns:pandas DataFrame 或 None（如果加载失败）"""try:data = []with open(csv_filepath, encoding=self.config['encoding']) as f:reader = csv.reader(f)for row in reader:data.append(row)if len(data) < 2:logger.warning(f"File {csv_filepath} has insufficient data rows")return None# 使用第二行作为列名，第三行开始作为数据df = pd.DataFrame(data[2:], columns=data[1])logger.info(f"Successfully loaded CSV file: {csv_filepath}")return dfexcept FileNotFoundError:logger.error(f"Error: The file {csv_filepath} does not exist.")except Exception as e:logger.error(f"An unexpected error occurred while loading {csv_filepath}: {e}")return Nonedef apply_filters(self, df: pd.DataFrame) -> pd.DataFrame:"""应用数据过滤条件Args:df: 输入DataFrameReturns:过滤后的DataFrame"""filter_condition = self.config.get('filter_condition')if filter_condition:column = filter_condition['column']value = filter_condition['value']case_sensitive = filter_condition.get('case_sensitive', False)if column in df.columns:if case_sensitive:df = df[df[column].str.contains(value, na=False)]else:df = df[df[column].str.contains(value, case=False, na=False)]logger.info(f"Applied filter: {filter_condition}")return dfdef convert_to_excel(self, df: pd.DataFrame, csv_filepath: str) -> str:"""将DataFrame转换为Excel文件Args:df: 输入DataFramecsv_filepath: 原始CSV文件路径（用于生成输出路径）Returns:生成的Excel文件路径"""try:excel_filepath = csv_filepath.replace('.csv', '.xlsx')df.to_excel(excel_filepath, index=False)logger.info(f"CSV file has been converted to Excel: {excel_filepath}")return excel_filepathexcept Exception as e:logger.error(f"Error converting to Excel: {e}")return ""def process_authors(self, df: pd.DataFrame) -> pd.DataFrame:"""处理作者数据，进行统计和分析Args:df: 包含专利数据的DataFrameReturns:作者统计DataFrame"""# 选择需要的列required_cols = self.config['required_columns']missing_cols = [col for col in required_cols if col not in df.columns]if missing_cols:logger.warning(f"Missing columns: {missing_cols}. Available columns: {list(df.columns)}")required_cols = [col for col in required_cols if col in df.columns]df = df[required_cols].copy()# 分割作者列author_col = self.config['author_column']if author_col in df.columns:df[author_col] = df[author_col].str.split(self.config['split_separator'])df = df.explode(author_col)df[author_col] = df[author_col].str.strip()# 统计作者出现次数author_counts = df[author_col].value_counts()new_df = pd.DataFrame({'author': author_counts.index,'value': author_counts.values})# 修复：使用列表推导式而不是直接赋值new_df['id'] = [df[df[author_col] == author]['id'].tolist() for author in new_df['author']]new_df['title_list'] = [df[df[author_col] == author]['title'].tolist() for author in new_df['author']]if 'priority date' in df.columns:new_df['priority_date_list'] = [df[df[author_col] == author]['priority date'].tolist() for author in new_df['author']]# 计算开始和结束年份date_ranges = [self.calculate_date_range(dates) for dates in new_df['priority_date_list']]new_df['start_year'] = [start for start, _ in date_ranges]new_df['end_year'] = [end for _, end in date_ranges]return new_df@staticmethoddef calculate_date_range(date_list: List[str]) -> Tuple[Optional[int], Optional[int]]:"""计算日期列表的开始和结束年份Args:date_list: 日期字符串列表Returns:(开始年份, 结束年份) 元组"""valid_dates = []for date_str in date_list:if pd.notna(date_str) and date_str.strip():try:# 尝试多种日期格式date_obj = datetime.strptime(date_str.strip(), '%Y-%m-%d')valid_dates.append(date_obj)except ValueError:try:date_obj = datetime.strptime(date_str.strip(), '%Y/%m/%d')valid_dates.append(date_obj)except ValueError:# 如果无法解析日期，跳过continueif not valid_dates:return None, Nonemin_date = min(valid_dates)max_date = max(valid_dates)return min_date.year, max_date.yeardef save_author_stats(self, author_df: pd.DataFrame, csv_filepath: str) -> str:"""保存作者统计结果Args:author_df: 作者统计DataFramecsv_filepath: 原始CSV文件路径（用于生成输出路径）Returns:生成的统计文件路径"""try:rank_excel_filepath = csv_filepath.replace('.csv', '_rank.xlsx')# 只保存配置中指定的列output_cols = [col for col in self.config['output_columns'] if col in author_df.columns]author_df[output_cols].to_excel(rank_excel_filepath, index=False)logger.info(f"Author statistics saved: {rank_excel_filepath}")return rank_excel_filepathexcept Exception as e:logger.error(f"Error saving author statistics: {e}")return ""def process_patent_file(self, csv_filepath: str) -> Dict[str, str]:"""处理单个专利CSV文件Args:csv_filepath: CSV文件路径Returns:包含输出文件路径的字典"""results = {'original_file': csv_filepath}# 1. 加载数据df = self.load_csv_data(csv_filepath)if df is None or df.empty:return results# 2. 应用过滤条件df = self.apply_filters(df)# 3. 转换为Excelexcel_path = self.convert_to_excel(df, csv_filepath)results['excel_file'] = excel_path# 4. 处理作者数据author_df = self.process_authors(df)# 5. 保存作者统计stats_path = self.save_author_stats(author_df, csv_filepath)results['stats_file'] = stats_pathreturn results# 使用示例
def process_patents_folder(folder_path: str, file_list: List[str], config: Optional[Dict[str, Any]] = None):"""处理文件夹中的多个专利CSV文件Args:folder_path: 文件夹路径file_list: 要处理的文件列表config: 处理配置"""processor = PatentProcessor(config)for filename in file_list:file_path = os.path.join(folder_path, filename)logger.info(f"Processing file: {filename}")try:results = processor.process_patent_file(file_path)logger.info(f"Completed processing: {filename}")logger.info(f"Results: {results}")except Exception as e:logger.error(f"Error processing {filename}: {e}")# 配置示例
CUSTOM_CONFIG = {'required_columns': ['id', 'inventor/author', 'title', 'priority date'],'author_column': 'inventor/author','split_separator': ',','filter_condition': None,  # 例如: {'column': 'assignee', 'value': 'Google', 'case_sensitive': False}'output_columns': ['author', 'value', 'id', 'title_list', 'priority_date_list', 'start_year', 'end_year'],'encoding': 'utf-8'}if __name__ == "__main__":# 使用示例folder = r'E:files\patents'gp_files = ["gp-search-google.csv",]# 使用默认配置处理文件process_patents_folder(folder, gp_files)# 使用自定义配置处理文件# process_patents_folder(folder, gp_files, CUSTOM_CONFIG)

这个重构版本具有以下优点：

1. 模块化设计：将功能拆分为多个独立的方法，每个方法只负责一个明确的任务
2. 可配置性：通过配置字典可以灵活调整处理参数
3. 错误处理：完善的异常处理和日志记录
4. 类型提示：添加了类型提示，提高代码可读性和可维护性
5. 灵活性：支持自定义过滤条件、输出列等
6. 可扩展性：易于添加新的处理功能或修改现有逻辑
7. 清晰的文档：每个方法都有详细的文档字符串说明参数和返回值

使用方法：

# 简单使用
processor = PatentProcessor()
results = processor.process_patent_file('path/to/patents.csv')# 自定义配置使用
config = {'filter_condition': {'column': 'assignee', 'value': 'Google', 'case_sensitive': False},'required_columns': ['id', 'inventor/author', 'title', 'priority date']
}
processor = PatentProcessor(config)
results = processor.process_patent_file('path/to/patents.csv')