from pathlib import Path import re import pandas as pd from com.zf.crawl import base_country_code from com.zf.crawl import base_mysql CUSTOM_COMMODITY_REPLACEMENTS = { '稻谷及大米': '稻谷、大米及大米粉', '有机发光二极管平板显示模组': '有机发光二极管（OLED）平板显示模组', } PRESERVE_PARENTHESES_KEYWORDS = { '汽车（包括底盘）', } def clean_commodity_name(name, preserve_keywords=None): """ 自定义清洗商品名称逻辑，支持条件保留中文括号内容 :param name: 商品名称字符串 :param preserve_keywords: 需要保留括号的关键词集合 :return: 清洗后的商品名称 """ name = str(name).strip().replace('(', '（').replace(')', '）') # 去除非必要符号 name = re.sub(r'[#*?]', '', name) name = re.sub(r'_x000D_', '', name) # 判断是否需要保留括号内容 if preserve_keywords: for keyword in preserve_keywords: if keyword == name: # 匹配到关键词时，不移除括号内容 return name # 默认移除中文括号及内容 name = re.sub(r'（[^）]*）', '', name) return name.strip() def get_df_import_export(path, year_month): file_paths = list(Path(path).glob('*')) if not file_paths: print("未找到任何文件") return None file_path = file_paths[0] print(f"处理文件: {file_path.name}") xls = pd.ExcelFile(file_path) import_df = pd.DataFrame() export_df = pd.DataFrame() flag = True sheet_name = base_country_code.find_sheet_by_keyword(file_path, "主出商品") if not sheet_name: print(f"{file_path} 单文件未找到包含 主出商品 sheet") # 23年1-11月数据要在多文件里找 for file_path in file_paths: if '主要出口商品' in file_path.name: file_path = file_path flag = False break if not sheet_name and flag: print(f"{path} 中未找到 主出商品 sheet或文件") return None if flag: df = pd.read_excel(xls, sheet_name=sheet_name, header=None).iloc[2:] else: df = pd.read_excel(file_path, header=None).iloc[1:] temp_df = df[[0, 1]].rename(columns={0: 'commodity', 1: 'export'}) temp_df['commodity'] = ( temp_df['commodity'] .astype(str) .apply(lambda x: clean_commodity_name(x, preserve_keywords=PRESERVE_PARENTHESES_KEYWORDS)) .replace(CUSTOM_COMMODITY_REPLACEMENTS, regex=False) ) temp_df['export'] = pd.to_numeric(temp_df['export'].replace('--', 0), errors='coerce').astype(float) if year_month and year_month == '2024-07': temp_df['export'] = temp_df['export'] / 10000 export_df = pd.concat([export_df, temp_df]) flag_2 = True sheet_name = base_country_code.find_sheet_by_keyword(file_path, "主进商品") if not sheet_name: print(f"{file_path} 单文件未找到包含 主进商品 sheet") # 23年1-11月数据要在多文件里找 for file_path in file_paths: if '主要进口商品' in file_path.name: file_path = file_path flag_2 = False break if not sheet_name and flag_2: print(f"{path} 中未找到 主进商品 sheet或文件") return None if flag_2: df = pd.read_excel(xls, sheet_name=sheet_name, header=None).iloc[2:] else: df = pd.read_excel(file_path, header=None).iloc[1:] temp_df = df[[0, 1]].rename(columns={0: 'commodity', 1: 'import'}) temp_df['commodity'] = ( temp_df['commodity'] .astype(str) .apply(lambda x: clean_commodity_name(x, preserve_keywords=PRESERVE_PARENTHESES_KEYWORDS)) .replace(CUSTOM_COMMODITY_REPLACEMENTS, regex=False) ) temp_df['import'] = pd.to_numeric(temp_df['import'].replace('--', 0), errors='coerce').astype(float) if year_month and year_month == '2024-07': temp_df['import'] = temp_df['import'] / 10000 import_df = pd.concat([import_df, temp_df]) return import_df, export_df def process_folder(path): res = get_df_import_export(path, None) if not res: print(f"{path} 目录里文件未找到包含 主出、主进商品 sheet") return import_df, export_df = res year, month = base_country_code.extract_year_month_from_path(path) year_month = f'{year}-{month:02d}' # 当月数据分组清洗 curr_import = import_df.groupby('commodity')['import'].sum().reset_index() curr_export = export_df.groupby('commodity')['export'].sum().reset_index() if not month == 1: previous_month_dir = base_country_code.get_previous_month_dir(path) res = get_df_import_export(previous_month_dir, year_month) if not res: print(f"{path} 上月目录里文件未找到包含 主出、主进商品 sheet") return prev_import_df, prev_export_df = res # 上月数据分组 prev_import = prev_import_df.groupby('commodity')['import'].sum().reset_index() prev_export = prev_export_df.groupby('commodity')['export'].sum().reset_index() # 差值计算 curr_import = pd.merge(curr_import, prev_import, on='commodity', how='left') curr_import['import'] = round(curr_import['import_x'] - curr_import['import_y'], 4) curr_export = pd.merge(curr_export, prev_export, on='commodity', how='left') curr_export['export'] = round(curr_export['export_x'] - curr_export['export_y'], 4) print(f"合并文件: {path}*********{previous_month_dir}") # 合并进出口数据 merged_df = pd.merge(curr_import, curr_export, on='commodity', how='outer') save_to_database(merged_df, year, month) def save_to_database(merged_df, year, month): year_month = f'{year}-{month:02d}' processed_commodities = set() sql_arr = [] try: for _, row in merged_df.iterrows(): commodity_name = str(row['commodity']).strip() if commodity_name == '消费品' or commodity_name == '劳动密集型产品': print(f'{commodity_name} 商品不存在，跳过') continue commodity_code, commodity_name_fix = base_mysql.get_commodity_id(commodity_name) if not commodity_code: print(f"未找到商品名称 '{commodity_name}' 对应的 ID") continue if not commodity_name_fix or commodity_name_fix in processed_commodities: print(f"已处理过 '{commodity_name_fix}'，传入name：{commodity_name}") continue if year == 2025 or (year == 2024 and month in [7, 8, 9, 10, 11, 12]): monthly_import = round(row['import'], 4) monthly_export = round(row['export'], 4) monthly_total = round(monthly_import + monthly_export, 4) else: monthly_import = round(row['import'] / 10000, 4) monthly_export = round(row['export'] / 10000, 4) monthly_total = round((monthly_import + monthly_export) / 10000, 4) sql = (f"INSERT INTO t_yujin_crossborder_prov_commodity_trade " f"(crossborder_year, crossborder_year_month, prov_code, prov_name, commodity_code, commodity_name, monthly_total, monthly_export, monthly_import, create_time) VALUES " f"('{year}', '{year_month}', '330000', '浙江省', '{commodity_code}', '{commodity_name_fix}', {monthly_total}, {monthly_export}, {monthly_import}, now());") sql_arr.append(sql) processed_commodities.add(commodity_name_fix) # print(f'{commodity_name} -> {commodity_name_fix}') except Exception as e: print(f"{year_month} prov_commodity_trade 生成 SQL 文件时发生异常: {str(e)}") print(f"√ {year_month} prov_commodity_trade 成功生成 SQL 文件 size {len(sql_arr)} ") # 解析完后生成sql文件批量入库 base_mysql.bulk_insert(sql_arr) print(f"√ {year_month} prov_commodity_trade SQL 存表完成!\n") def hierarchical_traversal(root_path): """分层遍历：省份->年份->月目录""" root = Path(root_path) # 获取所有年份目录 year_dirs = [ item for item in root.iterdir() if item.is_dir() and base_country_code.YEAR_PATTERN.match(item.name) ] # 按年倒序 for year_dir in sorted(year_dirs, key=lambda x: x.name, reverse=True): print(f"\n年份：{year_dir.name} | 省份：jiangsu") # 提取月份目录 month_dirs = [] for item in year_dir.iterdir(): if item.is_dir() and base_country_code.MONTH_PATTERN.match(item.name): month_dirs.append({ "path": item, "month": int(item.name) }) # 按月倒序输出 if month_dirs: for md in sorted(month_dirs, key=lambda x: x["month"], reverse=True): print(f" 月份：{md['month']:02d} | 路径：{md['path']}") process_folder(md['path']) if __name__ == '__main__': hierarchical_traversal(base_country_code.download_dir) # root = Path(base_country_code.download_dir)/'2023'/'01' # process_folder(root) print("浙江杭州海关类章所有文件处理完成！")