crossborder/hebei/gov_commodity_hebei_import_export.py

from pathlib import Path

import pandas as pd
import re

from utils import base_country_code, base_mysql
from utils.base_country_code import format_sql_value

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 process_folder(path):
    year, month = base_country_code.extract_year_month_from_path(path)

    name_index = 1 if year == 2025 and month >= 3 else 0
    value_index = 5 if year == 2025 and month >= 3 else 4
    res = df_data(path, name_index, value_index)
    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
    export_df, import_df = res

    merged_df = pd.merge(
        import_df.groupby('commodity')['import'].sum().reset_index() if not import_df.empty else pd.DataFrame(columns=['commodity', 'import']),
        export_df.groupby('commodity')['export'].sum().reset_index() if not export_df.empty else pd.DataFrame(columns=['commodity', 'export']),
        on='commodity',
        how='outer'
    ).infer_objects()

    save_to_database(merged_df, year, month)

def save_to_database(merged_df, year, month):
    processed_commodities = set()
    sql_arr = []
    sql_arr_copy = []
    year_month = f'{year}-{month:02d}'
    try:
        for _, row in merged_df.iterrows():
            commodity_name = str(row['commodity']).strip()
            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:
                continue

            monthly_import = round(row['import'], 4)
            monthly_export = round(row['export'], 4)
            monthly_total = round(monthly_import + monthly_export, 4)

            if year_month == '2023-02':
                monthly_import = round(monthly_import / 2, 4)
                monthly_export = round(monthly_export / 2, 4)
                monthly_total = round(monthly_import + monthly_export, 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"('2023', '2023-01', '130000', '河北省', '{commodity_code}', '{commodity_name_fix}', {format_sql_value(monthly_total)}, {format_sql_value(monthly_export)}, {format_sql_value(monthly_import)}, now());")
                sql_arr_copy.append(sql)

            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}', '130000', '河北省', '{commodity_code}', '{commodity_name_fix}', {format_sql_value(monthly_total)}, {format_sql_value(monthly_export)}, {format_sql_value(monthly_import)}, now());")
            sql_arr.append(sql)

            processed_commodities.add(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)
    if year_month == '2023-02':
        print(f"√ {year_month} prov_commodity_trade copy 成功生成 SQL 文件 size {len(sql_arr_copy)} ")
        base_mysql.bulk_insert(sql_arr_copy)
    print(f"√ {year_month} prov_commodity_trade SQL 存表完成!")


def df_data(path, name_index, value_index):
    file_paths = list(Path(path).glob('*'))
    if not file_paths:
        print("未找到任何文件")
        return None
    import_df = pd.DataFrame()
    export_df = pd.DataFrame()
    for file_path in file_paths:
        if '出口' in file_path.name:
            df = pd.read_excel(file_path, header=None).iloc[5:]
            temp_df = df[[name_index, value_index]].rename(columns={name_index: 'commodity', value_index: 'export'})
            # temp_df['commodity'] = temp_df['commodity'].str.replace(r'[*#]', '', regex=True)
            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')
            temp_df = temp_df.drop_duplicates(subset=['commodity'], keep='first')
            export_df = pd.concat([export_df, temp_df])

        if '进口' in file_path.name:
            df = pd.read_excel(file_path, header=None).iloc[5:]
            temp_df = df[[name_index, value_index]].rename(columns={name_index: 'commodity', value_index: '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')
            temp_df = temp_df.drop_duplicates(subset=['commodity'], keep='first')
            import_df = pd.concat([import_df, temp_df])
    return export_df, import_df

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'/'02'
    # process_folder(root)
    print(f"河北石家庄海关出入口商品所有文件处理完成！")