crossborder/shandong/shandong_parse_excel.py

import re
from pathlib import Path

import pandas as pd

from db_helper import DBHelper
from quanguo.CountryTrade import COUNTRY_CODE_MAPPING
from utils.constants import DOWNLOAD_DIR
from utils.parse_utils import clean_county_name, clean_commodity_name, convert_wan_to_yuan, \
    extract_year_month_from_path, get_previous_month_dir, find_unmatched_countries, traverse_and_process

# 常量配置
PROV_CODE = "370000"
PROV_NAME = "山东省"


SHANDONG_CITY = {
    "济南": "370100", "青岛": "370200", "淄博": "370300", "枣庄": "370400",
    "东营": "370500", "烟台": "370600", "潍坊": "370700", "济宁": "370800",
    "泰安": "370900", "威海": "371000", "日照": "371100", "临沂": "371300",
    "德州": "371400", "聊城": "371500", "滨州": "371600", "菏泽": "371700"
}
download_dir = DOWNLOAD_DIR / "shandong"

YEAR_PATTERN = re.compile(r"^\d{4}$")
MONTH_PATTERN = re.compile(r"^(0[1-9]|1[0-2])$")


def parse_excel(current_dir):
    """主解析入口（优化为单参数模式）
    Args:
        current_dir (str): 当前月份数据目录（格式：/年份/省份/月份）
    """
    current_path = Path(current_dir)
    year, month = extract_year_month_from_path(current_path)

    try:
        # 动态获取前月目录
        prev_dir = get_previous_month_dir(current_path) if month != 1 else None

        # 处理商品贸易数据
        process_combined_trade(current_path, year, month, prev_dir)

        # 处理地市贸易数据
        current_file_path = next(current_path.glob("*地市*"), None)
        prev_file_path = next(Path(prev_dir).glob("*地市*"), None) if prev_dir else None
        if current_file_path:
            process_region_trade(current_file_path, prev_file_path, year, month)

        # 处理国别贸易数据（保持原有逻辑结构）
        country_file = next(current_path.glob("*国别*"), None)
        prev_country_file = next(Path(prev_dir).glob("*国别*"), None) if prev_dir else None
        if country_file:
            process_country_trade(country_file, prev_country_file, year, month)

        print(f"{current_dir}数据已全部成功处理")
    except Exception as e:
        print(f"处理失败：{current_dir}，错误：{str(e)}")
        raise


def process_combined_trade(current_dir, year, month, previous_dir=None):
    """处理合并商品贸易数据（增强1月逻辑）"""
    import_file = next(current_dir.glob("*进口20位主要商品总值*"), None)
    export_file = next(current_dir.glob("*出口20位主要商品总值*"), None)
    if not (import_file and export_file):
        raise FileNotFoundError("缺少进口或出口文件")

    # 读取当前月数据（保持原有逻辑）
    current_data = read_trade_pair(import_file, export_file)

    # 处理历史数据
    prev_data = pd.DataFrame()
    if previous_dir and month != 1:
        prev_import = next(Path(previous_dir).glob("*进口20位主要商品总值*"), None)
        prev_export = next(Path(previous_dir).glob("*出口20位主要商品总值*"), None)
        if prev_import and prev_export:
            prev_data = read_trade_pair(prev_import, prev_export)

    # 计算逻辑优化
    merged_data = current_data if month == 1 else calculate_monthly_values(current_data, prev_data)

    # 保留原有数据库交互逻辑
    db = DBHelper()
    merged_data['commodity_code'] = merged_data['commodity_name'].apply(db.get_commodity_id)
    valid_data = merged_data[merged_data['commodity_code'].notnull()].copy()

    # 构建入库数据（保持原有字段结构）
    valid_data['crossborder_year'] = year
    valid_data['crossborder_year_month'] = f"{year}-{month:02d}"
    valid_data['prov_code'] = PROV_CODE
    valid_data['prov_name'] = PROV_NAME
    valid_data['monthly_total'] = valid_data['monthly_import'] + valid_data['monthly_export']

    # 入库逻辑保持不变
    target_cols = [
        'crossborder_year', 'crossborder_year_month', 'prov_code', 'prov_name',
        'commodity_code', 'commodity_name', 'monthly_total', 'monthly_import', 'monthly_export'
    ]
    db.bulk_insert(
        valid_data[target_cols],
        't_yujin_crossborder_prov_commodity_trade',
        conflict_columns=['crossborder_year_month', 'prov_code', 'commodity_code'],
        update_columns=['monthly_total', 'monthly_import', 'monthly_export']
    )


def process_region_trade(current_file_path, prev_file_path, year, month):
    """处理地市贸易数据（增强1月逻辑）"""
    # 读取当前数据
    current_df = pd.read_excel(
        current_file_path,
        skipfooter=1,
        header=4,
        names=['city_name', 'monthly_total', 'yoy_import_export',
               'monthly_export', 'yoy_export', 'monthly_import', 'yoy_import']
    )

    # 1月特殊处理
    if month == 1:
        df = current_df[['city_name', 'monthly_total',
                         'monthly_export', 'monthly_import']].copy()
        df['yoy_import_export'] = current_df['yoy_import_export']
        df['yoy_export'] = current_df['yoy_export']
        df['yoy_import'] = current_df['yoy_import']
    else:
        prev_df = pd.read_excel(
            prev_file_path,
            skipfooter=1,
            header=4,
            names=['city_name', 'monthly_total', 'yoy_import_export',
                   'monthly_export', 'yoy_export', 'monthly_import', 'yoy_import']
        ) if prev_file_path else pd.DataFrame()

        merged_df = pd.merge(
            current_df,
            prev_df,
            on='city_name',
            suffixes=('_current', '_prev')
        )
        df = pd.DataFrame({
            'city_name': merged_df['city_name'],
            'monthly_total': merged_df['monthly_total_current'] - merged_df['monthly_total_prev'],
            'yoy_import_export': merged_df['yoy_import_export_current'],
            'monthly_export': merged_df['monthly_export_current'] - merged_df['monthly_export_prev'],
            'yoy_export': merged_df['yoy_export_current'],
            'monthly_import': merged_df['monthly_import_current'] - merged_df['monthly_import_prev'],
            'yoy_import': merged_df['yoy_import_current']
        })

    # 保留原有处理逻辑
    df['city_code'] = df['city_name'].map(SHANDONG_CITY)
    df['crossborder_year'] = year
    df['crossborder_year_month'] = f"{year}-{month:02d}"
    df['prov_code'] = PROV_CODE
    df['prov_name'] = PROV_NAME

    # 单位转换
    for col in ['monthly_total', 'monthly_import', 'monthly_export']:
        df[col] = df[col].apply(convert_wan_to_yuan)

    db = DBHelper()
    db.bulk_insert(
        df,
        't_yujin_crossborder_prov_region_trade',
        conflict_columns=['crossborder_year_month', 'city_code'],
        update_columns=['monthly_total', 'monthly_import', 'monthly_export',
                        'yoy_import_export', 'yoy_import', 'yoy_export']
    )


def process_country_trade(current_file_path, prev_file_path, year, month):
    """处理国别贸易数据（增强1月逻辑）"""
    # 读取当前数据
    current_df = read_with_header4(current_file_path)

    current_df = current_df[~current_df['country_name'].str.contains('注：', na=False)]
    current_df = current_df.dropna(subset=['country_name'])
    current_df = current_df[current_df['country_name'].str.strip() != '']

    # 1月特殊处理
    if month == 1:
        final_df = current_df.copy()
        final_df[['monthly_total', 'monthly_export', 'monthly_import']] = \
            current_df[['monthly_total', 'monthly_export', 'monthly_import']]
    else:
        prev_df = read_with_header4(prev_file_path)

        prev_df = prev_df[~prev_df['country_name'].str.contains('注：', na=False)]
        prev_df = prev_df.dropna(subset=['country_name'])
        prev_df = prev_df[prev_df['country_name'].str.strip() != '']

        merged_df = pd.merge(
            current_df,
            prev_df,
            on='country_name',
            suffixes=('_current', '_prev'),
            how='inner'
        )

        merged_df['monthly_total'] = merged_df['monthly_total_current'] - merged_df['monthly_total_prev']
        merged_df['monthly_export'] = merged_df['monthly_export_current'] - merged_df['monthly_export_prev']
        merged_df['monthly_import'] = merged_df['monthly_import_current'] - merged_df['monthly_import_prev']
        merged_df['yoy_import_export'] = merged_df['yoy_import_export_current']
        merged_df['yoy_export'] = merged_df['yoy_export_current']
        merged_df['yoy_import'] = merged_df['yoy_import_current']
        final_df = merged_df[[
            'country_name','monthly_total', 'monthly_import', 'monthly_export',
            'yoy_import_export', 'yoy_import', 'yoy_export'
        ]]

    # 排除特殊国家（新增过滤逻辑）
    final_df = final_df[
        ~final_df['country_name'].str.contains('东盟|欧盟', na=False, regex=True)
    ]
    final_df['country_code'] = final_df['country_name'].map(COUNTRY_CODE_MAPPING)

    find_unmatched_countries(final_df)

    final_df['crossborder_year'] = year
    final_df['crossborder_year_month'] = f"{year}-{month:02d}"
    final_df['prov_code'] = PROV_CODE
    final_df['prov_name'] = PROV_NAME

    # 单位转换
    for col in ['monthly_total', 'monthly_import', 'monthly_export']:
        final_df[col] = final_df[col].apply(convert_wan_to_yuan)

    db = DBHelper()
    db.bulk_insert(
        final_df,
        't_yujin_crossborder_prov_country_trade',
        conflict_columns=['crossborder_year_month', 'prov_code', 'country_code'],
        update_columns=['monthly_total', 'monthly_import', 'monthly_export',
                        'yoy_import_export', 'yoy_import', 'yoy_export']
    )


def read_with_header4(file_path):
    # 第一阶段：读取原始数据（固定列范围）
    raw_df = pd.read_excel(
        file_path,
        usecols="A:G",  # 强制读取前7列
        header=None,  # 禁用自动表头识别
        skipfooter=1
    )

    # 第二阶段：计算列偏移量
    if raw_df.iloc[:, 0:2].isnull().all().all():  # 前两列全为空
        col_offset = 2  # 从第三列开始（A3起始）
    else:
        col_offset = 0  # 默认从第一列开始（A1起始）

    # 第三阶段：应用header=4逻辑
    header_row = 4  # 保持原有header行位置
    data_start_row = header_row + 1  # 数据起始行

    # 重新读取有效数据
    final_df = pd.read_excel(
        file_path,
        usecols=raw_df.columns[col_offset:col_offset + 7],  # 动态列范围
        header=header_row,
        skipfooter=1
    )

    # 第四阶段：强制列名对齐
    final_df.columns = [
        'country_name', 'monthly_total', 'yoy_import_export',
        'monthly_export', 'yoy_export', 'monthly_import', 'yoy_import'
    ]

    # 清洗国家名称
    final_df['country_name'] = final_df['country_name'].apply(clean_county_name)

    return final_df


def read_trade_pair(import_path, export_path):
    """进/出口表格合并"""
    df_import = pd.read_excel(import_path, skiprows=3, skipfooter=1,
                              usecols=[0, 1], names=["commodity_name", "monthly_import"]).pipe(lambda df: df.assign(
        commodity_name=df["commodity_name"].apply(clean_commodity_name)
    ))

    df_export = pd.read_excel(export_path, skiprows=3, skipfooter=1,
                              usecols=[0, 1], names=["commodity_name", "monthly_export"]).pipe(lambda df: df.assign(
        commodity_name=df["commodity_name"].apply(clean_commodity_name)
    ))
    merged = pd.merge(df_import, df_export, on="commodity_name", how="outer").fillna(0)
    merged["monthly_import"] = merged["monthly_import"].apply(convert_wan_to_yuan)
    merged["monthly_export"] = merged["monthly_export"].apply(convert_wan_to_yuan)
    return merged


def calculate_monthly_values(current_data, prev_data):
    """根据上个月进出口数据计算当月数据"""
    merged = pd.merge(current_data, prev_data, on="commodity_name",
                      how="left", suffixes=("_current", "_prev")).fillna(0)
    merged["monthly_import"] = merged["monthly_import_current"] - merged["monthly_import_prev"]
    merged["monthly_export"] = merged["monthly_export_current"] - merged["monthly_export_prev"]
    return merged[["commodity_name", "monthly_import", "monthly_export"]]



if __name__ == "__main__":
    # traverse_and_process(download_dir, parse_excel, province_name="shandong")
    print("\n山东省地级市数据同比更新中...")
    db_helper = DBHelper()
    db_helper.update_shandong_yoy()