{"id":63303,"date":"2026-01-21T20:16:46","date_gmt":"2026-01-21T12:16:46","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/63303.html"},"modified":"2026-01-21T20:16:46","modified_gmt":"2026-01-21T12:16:46","slug":"%e5%ad%98%e8%b4%a7%e6%94%b6%e5%8f%91%e5%ad%98%e6%a0%b8%e7%ae%97%ef%bc%88%e5%85%88%e8%bf%9b%e5%85%88%e5%87%ba%e6%b3%95%ef%bc%89-pandas-%e5%ae%9e%e7%8e%b0","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/63303.html","title":{"rendered":"\u5b58\u8d27\u6536\u53d1\u5b58\u6838\u7b97\uff08\u5148\u8fdb\u5148\u51fa\u6cd5\uff09- Pandas \u5b9e\u73b0"},"content":{"rendered":"

\u4e00\u3001\u751f\u6210\u6d4b\u8bd5 Excel \u6570\u636e（\u4e24\u5e74\u6570\u636e，\u6bcf\u6708\u7ea6 5 \u4e07\u884c）<\/h3>\n

\u6838\u5fc3\u601d\u8def<\/h4>\n
  • \u751f\u6210\u65f6\u95f4\u8303\u56f4：2 \u5e74（24 \u4e2a\u6708），\u6309\u81ea\u7136\u6708\u5212\u5206<\/li>\n
  • \u5b58\u8d27\u5c5e\u6027：\u6309\u6279\u6b21\u7ba1\u7406，\u5305\u542b\u7269\u6599\u7f16\u7801\u3001\u6279\u6b21\u53f7\u3001\u4ed3\u5e93\u7f16\u7801\u7b49\u6838\u5fc3\u5b57\u6bb5<\/li>\n
  • \u4e1a\u52a1\u7c7b\u578b：\u8986\u76d6\u91c7\u8d2d\u5165\u5e93\u3001\u9500\u552e\u51fa\u5e93\u3001\u54c1\u79cd\u8c03\u6574\u51fa\u5165\u5e93\u3001\u8c03\u62e8\u51fa\u5165\u5e93\u3001\u76d8\u4e8f\u76d8\u76c8<\/li>\n
  • \u6570\u636e\u91cf：\u6bcf\u6708\u7ea6 5 \u4e07\u884c，24 \u4e2a\u6708\u603b\u8ba1\u7ea6 120 \u4e07\u884c，\u5206\u6708\u751f\u6210 Excel \u6587\u4ef6（\u4fbf\u4e8e\u6708\u5ea6\u6eda\u52a8\u8ba1\u7b97）<\/li>\n

    def generate_inventory_excel(target_dir, years=2):
    \n """
    \n \u751f\u6210\u5b58\u8d27\u6536\u53d1\u5b58\u6d4b\u8bd5Excel\u6570\u636e
    \n :param target_dir: \u76ee\u6807\u4fdd\u5b58\u76ee\u5f55
    \n :param years: \u751f\u6210\u6570\u636e\u7684\u5e74\u6570
    \n """
    \n # 1. \u5b9a\u4e49\u57fa\u7840\u914d\u7f6e\u6570\u636e
    \n material_codes = [f'MAT{str(i).zfill(6)}' for i in range(1, 200)] # 200\u79cd\u7269\u6599
    \n warehouse_codes = [f'WH{str(i).zfill(3)}' for i in range(1, 50)] # 50\u4e2a\u4ed3\u5e93
    \n business_types = [
    \n '\u91c7\u8d2d\u5165\u5e93', '\u9500\u552e\u51fa\u5e93',
    \n '\u54c1\u79cd\u8c03\u6574\u5165\u5e93', '\u54c1\u79cd\u8c03\u6574\u51fa\u5e93',
    \n '\u8c03\u62e8\u5165\u5e93', '\u8c03\u62e8\u51fa\u5e93',
    \n '\u76d8\u76c8', '\u76d8\u4e8f'
    \n ]
    \n # \u4e1a\u52a1\u7c7b\u578b\u5bf9\u5e94\u6536\u53d1\u6807\u8bc6（\u5165\u5e93=1，\u51fa\u5e93=-1，\u7528\u4e8e\u540e\u7eed\u6570\u91cf\u8ba1\u7b97）
    \n biz_type_flag = {
    \n '\u91c7\u8d2d\u5165\u5e93': 1, '\u9500\u552e\u51fa\u5e93': -1,
    \n '\u54c1\u79cd\u8c03\u6574\u5165\u5e93': 1, '\u54c1\u79cd\u8c03\u6574\u51fa\u5e93': -1,
    \n '\u8c03\u62e8\u5165\u5e93': 1, '\u8c03\u62e8\u51fa\u5e93': -1,
    \n '\u76d8\u76c8': 1, '\u76d8\u4e8f': -1
    \n }<\/p>\n

    # 2. \u8ba1\u7b97\u65f6\u95f4\u8303\u56f4（\u4ece\u5f53\u524d\u65e5\u671f\u5f80\u524d\u63a82\u5e74，\u6309\u81ea\u7136\u6708\u62c6\u5206）
    \n end_date = datetime.now().replace(day=1, hour=0, minute=0, second=0, microsecond=0)
    \n start_date = (end_date – timedelta(days=365*years)).replace(day=1)<\/p>\n

    # 3. \u9010\u6708\u751f\u6210\u6570\u636e
    \n current_month = start_date
    \n while current_month < end_date:
    \n # \u83b7\u53d6\u5f53\u524d\u6708\u4efd\u7684\u8d77\u59cb\u548c\u7ed3\u675f\u65e5\u671f
    \n next_month = (current_month + timedelta(days=32)).replace(day=1)
    \n month_days = (next_month – timedelta(days=1)).day
    \n month_str = current_month.strftime('%Y%m')<\/p>\n

    # 4. \u751f\u6210\u5f53\u6708\u6570\u636e（\u7ea65\u4e07\u884c）
    \n row_count = 50000
    \n data = {
    \n '\u5355\u636e\u65e5\u671f': [current_month.replace(day=random.randint(1, month_days)) for _ in range(row_count)],
    \n '\u7269\u6599\u7f16\u7801': [random.choice(material_codes) for _ in range(row_count)],
    \n '\u4ed3\u5e93\u7f16\u7801': [random.choice(warehouse_codes) for _ in range(row_count)],
    \n '\u4e1a\u52a1\u7c7b\u578b': [random.choice(business_types) for _ in range(row_count)],
    \n '\u6570\u91cf': np.random.randint(1, 100, size=row_count), # \u57fa\u7840\u6570\u91cf（1-99）
    \n '\u5355\u4ef7': np.round(np.random.uniform(10, 1000, size=row_count), 2), # \u57fa\u7840\u5355\u4ef7（10-1000，\u4fdd\u75592\u4f4d\u5c0f\u6570）
    \n '\u6279\u6b21\u53f7': [f'BAT{current_month.strftime("%Y%m")}{str(i).zfill(6)}' for i in range(row_count)] # \u6309\u6708\u4efd\u751f\u6210\u6279\u6b21\u53f7
    \n }<\/p>\n

    # 5. \u5904\u7406\u51fa\u5165\u5e93\u6570\u91cf（\u51fa\u5e93\u8f6c\u4e3a\u8d1f\u6570）
    \n df = pd.DataFrame(data)
    \n df['\u6570\u91cf'] = df.apply(lambda x: x['\u6570\u91cf'] * biz_type_flag[x['\u4e1a\u52a1\u7c7b\u578b']], axis=1)<\/p>\n

    # 6. \u8ba1\u7b97\u91d1\u989d（\u6570\u91cf*\u5355\u4ef7，\u4fdd\u75592\u4f4d\u5c0f\u6570）
    \n df['\u91d1\u989d'] = np.round(df['\u6570\u91cf'] * df['\u5355\u4ef7'], 2)<\/p>\n

    # 7. \u6392\u5e8f（\u6309\u5355\u636e\u65e5\u671f\u3001\u7269\u6599\u7f16\u7801\u3001\u6279\u6b21\u53f7）
    \n df = df.sort_values(by=['\u5355\u636e\u65e5\u671f', '\u7269\u6599\u7f16\u7801', '\u6279\u6b21\u53f7']).reset_index(drop=True)<\/p>\n

    # 8. \u4fdd\u5b58\u4e3aExcel\u6587\u4ef6（\u6309\u6708\u4efd\u547d\u540d，\u4fbf\u4e8e\u6708\u5ea6\u6eda\u52a8）
    \n excel_path = os.path.join(target_dir, f'\u5b58\u8d27\u6536\u53d1\u5b58\u6d41\u6c34_{month_str}.xlsx')
    \n df.to_excel(excel_path, index=False, engine='openpyxl')
    \n print(f'\u5df2\u751f\u6210\u5f53\u6708\u6570\u636e：{excel_path}')<\/p>\n

    # 9. \u5207\u6362\u5230\u4e0b\u4e00\u4e2a\u6708
    \n current_month = next_month<\/p>\n

    # \u8c03\u7528\u51fd\u6570\u751f\u62102\u5e74\u6d4b\u8bd5\u6570\u636e
    \ngenerate_inventory_excel(target_dir, years=2)<\/p>\n

    \u4e8c\u3001\u5148\u8fdb\u5148\u51fa\u6cd5\u6838\u7b97\u5b9e\u73b0（\u652f\u6301\u6708\u5ea6\u6eda\u52a8\u8ba1\u7b97）<\/h3>\n

    \u5148\u8fdb\u5148\u51fa\u6cd5（FIFO）\u6838\u7b97<\/h4>\n

    \u5148\u8fdb\u5148\u51fa\u6cd5（FIFO）\u7684\u6838\u5fc3\u662f\u6309\u5165\u5e93\u65f6\u95f4\u5148\u540e\u987a\u5e8f\u5339\u914d\u51fa\u5e93，\u4f18\u5148\u6263\u51cf\u6700\u65e9\u5165\u5e93\u6279\u6b21\u7684\u5e93\u5b58，\u6eda\u52a8\u8ba1\u7b97\u7684\u6838\u5fc3\u662f\u300c\u4e0a\u6708\u671f\u672b\u6709\u6548\u7ed3\u5b58\u6279\u6b21（\u5e26\u6570\u91cf \/ \u91d1\u989d \/ \u5355\u4ef7）= \u672c\u6708\u671f\u521d\u53ef\u7528\u6279\u6b21\u300d，\u5173\u952e\u8981\u70b9：
    \n\u6838\u7b97\u7ef4\u5ea6：\u6309\u300c\u7269\u6599\u7f16\u7801 + \u4ed3\u5e93\u7f16\u7801\u300d\u5206\u7ec4，\u6bcf\u7ec4\u5185\u5355\u72ec\u6267\u884c FIFO \u903b\u8f91（\u4e0d\u540c\u7269\u6599 \/ \u4ed3\u5e93\u4e92\u4e0d\u5e72\u6270）\u3002
    \n\u5165\u5e93\u5904\u7406：\u76f4\u63a5\u8ffd\u52a0\u6279\u6b21\u4fe1\u606f（\u6570\u91cf \/ \u91d1\u989d \/ \u5355\u4ef7）\u5230\u5f53\u524d\u7ed3\u5b58\u5217\u8868，\u6309\u5165\u5e93\u65f6\u95f4\u6392\u5e8f\u3002
    \n\u51fa\u5e93\u5904\u7406：\u6309\u6279\u6b21\u5165\u5e93\u5148\u540e\u987a\u5e8f\u9010\u6279\u6263\u51cf，\u5148\u6263\u5b8c\u6700\u65e9\u6279\u6b21，\u518d\u6263\u6b21\u65e9\u6279\u6b21，\u76f4\u81f3\u6ee1\u8db3\u51fa\u5e93\u6570\u91cf\u3002
    \n\u671f\u672b\u7ed3\u5b58：\u5269\u4f59\u672a\u6263\u51cf\u7684\u6279\u6b21\u6784\u6210\u6708\u672b\u7ed3\u5b58，\u4f5c\u4e3a\u4e0b\u6708\u671f\u521d\u6570\u636e，\u4fdd\u6301\u6279\u6b21\u7684\u65f6\u95f4\u987a\u5e8f\u3002
    \n\u6838\u5fc3\u516c\u5f0f：
    \n\u6279\u6b21\u5355\u4ef7 = \u6279\u6b21\u91d1\u989d \/ \u6279\u6b21\u6570\u91cf
    \n\u53d1\u51fa\u91d1\u989d = \u6263\u51cf\u6570\u91cf \u00d7 \u5bf9\u5e94\u6279\u6b21\u5355\u4ef7
    \n\u671f\u672b\u7ed3\u5b58\u91d1\u989d = \u5404\u5269\u4f59\u6279\u6b21\u91d1\u989d\u4e4b\u548c
    \n\u671f\u672b\u7ed3\u5b58\u6570\u91cf = \u5404\u5269\u4f59\u6279\u6b21\u6570\u91cf\u4e4b\u548c<\/p>\n

    import os
    \nimport pandas as pd
    \nimport numpy as np
    \nfrom datetime import datetime, timedelta<\/p>\n

    # ===================== \u6838\u5fc3\u914d\u7f6e\u9879（\u4e0e\u4e4b\u524d\u6d41\u6c34\u6570\u636e\u4fdd\u6301\u4e00\u81f4） =====================
    \n# 1. \u6570\u636e\u76ee\u5f55（\u5fc5\u987b\u548c\u751f\u6210\u6d41\u6c34\u7684\u76ee\u5f55\u4e00\u81f4，\u5426\u5219\u65e0\u6cd5\u8bfb\u53d6\u6587\u4ef6）
    \ntarget_dir = r'E:\\\\BI\u6a21\u578b\\\\Python\u7ec3\u4e60\u6570\u636e\\\\\u6536\u53d1\u5b58\u7ec3\u4e60'
    \n# 2. \u6838\u7b97\u6708\u4efd\u8303\u56f4（\u683c\u5f0f：YYYYMM，\u5bf9\u5e94\u4e4b\u524d\u751f\u6210\u76842\u5e74\u6570\u636e，\u793a\u4f8b：202401-202512）
    \nstart_month = '202401'
    \nend_month = '202512'
    \n# 3. \u8d22\u52a1\u6570\u636e\u7cbe\u5ea6（\u4fdd\u75592\u4f4d\u5c0f\u6570，\u7b26\u5408\u4f1a\u8ba1\u6838\u7b97\u8981\u6c42）
    \ndecimal_places = 2
    \n# ==============================================================================<\/p>\n

    def ensure_directory_exists(dir_path):
    \n """\u786e\u4fdd\u76ee\u6807\u76ee\u5f55\u5b58\u5728，\u4e0d\u5b58\u5728\u5219\u81ea\u52a8\u521b\u5efa"""
    \n if not os.path.exists(dir_path):
    \n os.makedirs(dir_path, exist_ok=True)<\/p>\n

    def generate_month_list(start_month, end_month):
    \n """\u751f\u6210\u8d77\u59cb\u6708\u4efd\u5230\u7ed3\u675f\u6708\u4efd\u7684\u8fde\u7eed\u6708\u4efd\u5217\u8868，\u7528\u4e8e\u6279\u91cf\u6eda\u52a8\u6838\u7b97"""
    \n # \u8f6c\u6362\u4e3a\u65e5\u671f\u5bf9\u8c61
    \n start_date = datetime.strptime(start_month, '%Y%m')
    \n end_date = datetime.strptime(end_month, '%Y%m')
    \n month_list = []<\/p>\n

    current_date = start_date
    \n while current_date <= end_date:
    \n month_list.append(current_date.strftime('%Y%m'))
    \n # \u5207\u6362\u5230\u4e0b\u4e00\u4e2a\u6708（\u907f\u514d\u6708\u672b\u5929\u6570\u95ee\u9898，\u7528+32\u5929\u518d\u53d61\u53f7）
    \n current_date = (current_date + timedelta(days=32)).replace(day=1)<\/p>\n

    return month_list<\/p>\n

    def fifo_costing_single(month_str, data_dir):
    \n """
    \n \u5355\u6708\u5148\u8fdb\u5148\u51fa\u6cd5（FIFO）\u5355\u72ec\u6838\u7b97（\u6838\u5fc3\u51fd\u6570）
    \n :param month_str: \u5f85\u6838\u7b97\u6708\u4efd，\u683c\u5f0fYYYYMM
    \n :param data_dir: \u6d41\u6c34\u6570\u636e\u4e0e\u7ed3\u679c\u4fdd\u5b58\u76ee\u5f55
    \n :return: \u5f53\u6708FIFO\u6536\u53d1\u5b58\u6c47\u603b\u8868\u3001\u6708\u672b\u6709\u6548\u7ed3\u5b58\u8868（\u7528\u4e8e\u4e0b\u6708\u6eda\u52a8）
    \n """
    \n # 1. \u6821\u9a8c\u5f53\u6708\u6d41\u6c34\u6587\u4ef6\u662f\u5426\u5b58\u5728
    \n monthly_flow_file = os.path.join(data_dir, f'\u5b58\u8d27\u6536\u53d1\u5b58\u6d41\u6c34_{month_str}.xlsx')
    \n if not os.path.exists(monthly_flow_file):
    \n raise FileNotFoundError(f"\u5f53\u6708\u6536\u53d1\u5b58\u6d41\u6c34\u6587\u4ef6\u7f3a\u5931，\u65e0\u6cd5\u6838\u7b97：{monthly_flow_file}")<\/p>\n

    # 2. \u8bfb\u53d6\u5f53\u6708\u6d41\u6c34\u6570\u636e，\u6570\u636e\u6e05\u6d17\u4e0e\u9884\u5904\u7406
    \n df_flow = pd.read_excel(monthly_flow_file, engine='openpyxl')
    \n # \u6570\u636e\u7c7b\u578b\u8f6c\u6362\u4e0e\u7a7a\u503c\u586b\u5145（\u907f\u514d\u8ba1\u7b97\u5f02\u5e38）
    \n df_flow['\u5355\u636e\u65e5\u671f'] = pd.to_datetime(df_flow['\u5355\u636e\u65e5\u671f'], errors='coerce')
    \n df_flow['\u6570\u91cf'] = pd.to_numeric(df_flow['\u6570\u91cf'], errors='coerce').fillna(0)
    \n df_flow['\u91d1\u989d'] = pd.to_numeric(df_flow['\u91d1\u989d'], errors='coerce').fillna(0)
    \n # \u786e\u4fdd\u6838\u5fc3\u5173\u952e\u5b57\u6bb5\u975e\u7a7a，\u5220\u9664\u65e0\u6548\u8bb0\u5f55
    \n df_flow = df_flow.dropna(subset=['\u7269\u6599\u7f16\u7801', '\u4ed3\u5e93\u7f16\u7801', '\u6279\u6b21\u53f7', '\u5355\u636e\u65e5\u671f']).reset_index(drop=True)
    \n # \u6309\u7269\u6599\u3001\u4ed3\u5e93\u3001\u5355\u636e\u65e5\u671f\u6392\u5e8f（\u4fdd\u8bc1\u5165\u5e93\u987a\u5e8f\u6b63\u786e，FIFO\u6838\u5fc3\u524d\u63d0）
    \n df_flow = df_flow.sort_values(by=['\u7269\u6599\u7f16\u7801', '\u4ed3\u5e93\u7f16\u7801', '\u5355\u636e\u65e5\u671f', '\u6279\u6b21\u53f7']).reset_index(drop=True)<\/p>\n

    # 3. \u8bfb\u53d6\u4e0a\u6708FIFO\u7ed3\u5b58\u6570\u636e（\u5b9e\u73b0\u6708\u5ea6\u6eda\u52a8：\u4e0a\u6708\u671f\u672b = \u672c\u6708\u671f\u521d）
    \n last_month_date = datetime.strptime(month_str, '%Y%m') – timedelta(days=1)
    \n last_month_str = last_month_date.strftime('%Y%m')
    \n last_month_balance_file = os.path.join(data_dir, f'FIFO_\u5b58\u8d27\u7ed3\u5b58_{last_month_str}.xlsx')<\/p>\n

    if os.path.exists(last_month_balance_file):
    \n # \u4e0a\u6708\u6709\u7ed3\u5b58\u6570\u636e，\u8bfb\u53d6\u5e76\u4f5c\u4e3a\u672c\u6708\u671f\u521d\u6279\u6b21
    \n df_begin_balance = pd.read_excel(last_month_balance_file, engine='openpyxl')
    \n # \u6570\u636e\u7c7b\u578b\u6821\u9a8c
    \n df_begin_balance['\u7ed3\u5b58\u6570\u91cf'] = pd.to_numeric(df_begin_balance['\u7ed3\u5b58\u6570\u91cf'], errors='coerce').fillna(0)
    \n df_begin_balance['\u7ed3\u5b58\u91d1\u989d'] = pd.to_numeric(df_begin_balance['\u7ed3\u5b58\u91d1\u989d'], errors='coerce').fillna(0)
    \n df_begin_balance['\u7ed3\u5b58\u5355\u4ef7'] = pd.to_numeric(df_begin_balance['\u7ed3\u5b58\u5355\u4ef7'], errors='coerce').fillna(0)
    \n else:
    \n # \u9996\u6b21\u6838\u7b97（\u65e0\u4e0a\u6708\u7ed3\u5b58），\u521d\u59cb\u5316\u671f\u521d\u7ed3\u5b58\u4e3a\u7a7a\u8868
    \n df_begin_balance = pd.DataFrame(
    \n columns=['\u7269\u6599\u7f16\u7801', '\u4ed3\u5e93\u7f16\u7801', '\u6279\u6b21\u53f7', '\u7ed3\u5b58\u6570\u91cf', '\u7ed3\u5b58\u91d1\u989d', '\u7ed3\u5b58\u5355\u4ef7']
    \n )<\/p>\n

    # 4. \u6309\u300c\u7269\u6599\u7f16\u7801+\u4ed3\u5e93\u7f16\u7801\u300d\u5206\u7ec4，\u9010\u7ec4\u6267\u884cFIFO\u6838\u5fc3\u6838\u7b97
    \n summary_result_list = [] # \u5b58\u50a8\u5404\u7ec4\u6536\u53d1\u5b58\u6c47\u603b\u7ed3\u679c
    \n end_balance_list = [] # \u5b58\u50a8\u5404\u7ec4\u6708\u672b\u6709\u6548\u7ed3\u5b58<\/p>\n

    for (material, warehouse), group_df in df_flow.groupby(['\u7269\u6599\u7f16\u7801', '\u4ed3\u5e93\u7f16\u7801']):
    \n # 4.1 \u63d0\u53d6\u8be5\u7269\u6599-\u4ed3\u5e93\u7684\u671f\u521d\u7ed3\u5b58\u6279\u6b21，\u6309\u6279\u6b21\u53f7\u6392\u5e8f（\u4fdd\u6301\u5165\u5e93\u5148\u540e\u987a\u5e8f）
    \n group_begin_balance = df_begin_balance[
    \n (df_begin_balance['\u7269\u6599\u7f16\u7801'] == material) &
    \n (df_begin_balance['\u4ed3\u5e93\u7f16\u7801'] == warehouse)
    \n ].sort_values(by='\u6279\u6b21\u53f7').reset_index(drop=True)<\/p>\n

    # 4.2 \u521d\u59cb\u5316\u5f53\u524d\u7ed3\u5b58（\u671f\u521d\u6279\u6b21\u4f5c\u4e3a\u521d\u59cb\u53ef\u7528\u5e93\u5b58）
    \n current_balance = group_begin_balance.copy() if not group_begin_balance.empty else pd.DataFrame(
    \n columns=['\u6279\u6b21\u53f7', '\u7ed3\u5b58\u6570\u91cf', '\u7ed3\u5b58\u91d1\u989d', '\u7ed3\u5b58\u5355\u4ef7']
    \n )<\/p>\n

    # 4.3 \u62c6\u5206\u5f53\u6708\u6536\u53d1\u8bb0\u5f55（\u5165\u5e93=\u6b63\u6570，\u51fa\u5e93=\u8d1f\u6570）
    \n in_df = group_df[group_df['\u6570\u91cf'] > 0].reset_index(drop=True) # \u5165\u5e93\u8bb0\u5f55
    \n out_df = group_df[group_df['\u6570\u91cf'] < 0].reset_index(drop=True) # \u51fa\u5e93\u8bb0\u5f55<\/p>\n

    # 4.4 \u5904\u7406\u5165\u5e93：\u76f4\u63a5\u8ffd\u52a0\u6279\u6b21\u5230\u5f53\u524d\u7ed3\u5b58，\u8ba1\u7b97\u6279\u6b21\u5355\u4ef7
    \n for _, in_row in in_df.iterrows():
    \n batch_quantity = in_row['\u6570\u91cf']
    \n batch_amount = in_row['\u91d1\u989d']
    \n batch_price = np.round(batch_amount \/ batch_quantity, decimal_places) if batch_quantity > 0 else 0.0<\/p>\n

    # \u6784\u9020\u65b0\u6279\u6b21\u8bb0\u5f55
    \n new_batch = pd.DataFrame({
    \n '\u6279\u6b21\u53f7': [in_row['\u6279\u6b21\u53f7']],
    \n '\u7ed3\u5b58\u6570\u91cf': [batch_quantity],
    \n '\u7ed3\u5b58\u91d1\u989d': [batch_amount],
    \n '\u7ed3\u5b58\u5355\u4ef7': [batch_price]
    \n })<\/p>\n

    # \u8ffd\u52a0\u5230\u5f53\u524d\u7ed3\u5b58，\u4fdd\u6301\u987a\u5e8f
    \n current_balance = pd.concat([current_balance, new_batch], ignore_index=True)<\/p>\n

    # 4.5 \u5904\u7406\u51fa\u5e93：\u6309\u5148\u8fdb\u5148\u51fa\u987a\u5e8f\u9010\u6279\u6263\u51cf，\u6838\u5fc3FIFO\u903b\u8f91
    \n for _, out_row in out_df.iterrows():
    \n out_quantity_need = abs(out_row['\u6570\u91cf']) # \u9700\u51fa\u5e93\u7684\u603b\u6570\u91cf（\u8f6c\u4e3a\u6b63\u6570）
    \n remaining_out = out_quantity_need # \u5269\u4f59\u672a\u6263\u51cf\u7684\u51fa\u5e93\u6570\u91cf<\/p>\n

    # \u9010\u6279\u6263\u51cf，\u76f4\u81f3\u5269\u4f59\u51fa\u5e93\u6570\u91cf\u4e3a0\u6216\u7ed3\u5b58\u4e3a\u7a7a
    \n if not current_balance.empty:
    \n for idx, balance_row in current_balance.iterrows():
    \n if remaining_out <= 0:
    \n break<\/p>\n

    batch_available = balance_row['\u7ed3\u5b58\u6570\u91cf']
    \n batch_price = balance_row['\u7ed3\u5b58\u5355\u4ef7']<\/p>\n

    if batch_available > remaining_out:
    \n # \u60c5\u51b51：\u5f53\u524d\u6279\u6b21\u6570\u91cf\u8db3\u591f，\u6263\u51cf\u90e8\u5206\u6570\u91cf\u548c\u91d1\u989d
    \n current_balance.loc[idx, '\u7ed3\u5b58\u6570\u91cf'] = np.round(
    \n batch_available – remaining_out, decimal_places
    \n )
    \n current_balance.loc[idx, '\u7ed3\u5b58\u91d1\u989d'] = np.round(
    \n current_balance.loc[idx, '\u7ed3\u5b58\u6570\u91cf'] * batch_price, decimal_places
    \n )
    \n remaining_out = 0
    \n else:
    \n # \u60c5\u51b52：\u5f53\u524d\u6279\u6b21\u6570\u91cf\u4e0d\u8db3，\u5168\u989d\u6263\u51cf，\u5220\u9664\u8be5\u6279\u6b21
    \n remaining_out -= batch_available
    \n current_balance.drop(idx, inplace=True)<\/p>\n

    # \u91cd\u7f6e\u7d22\u5f15，\u907f\u514d\u540e\u7eed\u8fed\u4ee3\u5f02\u5e38
    \n current_balance = current_balance.reset_index(drop=True)<\/p>\n

    # 4.6 \u6c47\u603b\u8be5\u7269\u6599-\u4ed3\u5e93\u7684\u6536\u53d1\u5b58\u6570\u636e
    \n # \u671f\u521d\u6570\u636e\u6c47\u603b
    \n begin_quantity = np.round(group_begin_balance['\u7ed3\u5b58\u6570\u91cf'].sum(), decimal_places)
    \n begin_amount = np.round(group_begin_balance['\u7ed3\u5b58\u91d1\u989d'].sum(), decimal_places)
    \n # \u5f53\u6708\u6536\u53d1\u6570\u636e\u6c47\u603b
    \n total_in_quantity = np.round(in_df['\u6570\u91cf'].sum(), decimal_places) if not in_df.empty else 0.0
    \n total_in_amount = np.round(in_df['\u91d1\u989d'].sum(), decimal_places) if not in_df.empty else 0.0
    \n total_out_quantity = np.round(abs(out_df['\u6570\u91cf'].sum()), decimal_places) if not out_df.empty else 0.0
    \n # \u53d1\u51fa\u91d1\u989d：\u901a\u8fc7FIFO\u6263\u51cf\u8ba1\u7b97（\u671f\u521d+\u5165\u5e93-\u671f\u672b，\u907f\u514d\u76f4\u63a5\u6c42\u548c\u8bef\u5dee）
    \n end_quantity = np.round(current_balance['\u7ed3\u5b58\u6570\u91cf'].sum(), decimal_places)
    \n end_amount = np.round(current_balance['\u7ed3\u5b58\u91d1\u989d'].sum(), decimal_places)
    \n total_out_amount = np.round(begin_amount + total_in_amount – end_amount, decimal_places)<\/p>\n

    # 4.7 \u5b58\u5165\u6c47\u603b\u7ed3\u679c\u5217\u8868
    \n summary_result_list.append({
    \n '\u7269\u6599\u7f16\u7801': material,
    \n '\u4ed3\u5e93\u7f16\u7801': warehouse,
    \n '\u671f\u521d\u6570\u91cf': begin_quantity,
    \n '\u671f\u521d\u91d1\u989d': begin_amount,
    \n '\u672c\u6708\u6536\u5165\u6570\u91cf': total_in_quantity,
    \n '\u672c\u6708\u6536\u5165\u91d1\u989d': total_in_amount,
    \n '\u672c\u6708\u53d1\u51fa\u6570\u91cf': total_out_quantity,
    \n '\u672c\u6708\u53d1\u51fa\u91d1\u989d': total_out_amount,
    \n '\u671f\u672b\u6570\u91cf': end_quantity,
    \n '\u671f\u672b\u91d1\u989d': end_amount
    \n })<\/p>\n

    # 4.8 \u5b58\u5165\u6708\u672b\u7ed3\u5b58\u5217\u8868（\u7528\u4e8e\u4e0b\u6708\u6eda\u52a8，\u6dfb\u52a0\u7269\u6599\/\u4ed3\u5e93\u6807\u8bc6）
    \n if not current_balance.empty:
    \n current_balance['\u7269\u6599\u7f16\u7801'] = material
    \n current_balance['\u4ed3\u5e93\u7f16\u7801'] = warehouse
    \n end_balance_list.append(current_balance)<\/p>\n

    # 5. \u6574\u7406\u6700\u7ec8\u6838\u7b97\u7ed3\u679c
    \n # 5.1 \u5b8c\u6574\u6536\u53d1\u5b58\u6c47\u603b\u8868
    \n df_final_summary = pd.DataFrame(summary_result_list).reset_index(drop=True)
    \n # 5.2 \u6708\u672b\u6709\u6548\u7ed3\u5b58\u8868（\u4ec5\u4fdd\u7559\u6709\u7ed3\u5b58\u7684\u6279\u6b21，\u7528\u4e8e\u4e0b\u6708\u671f\u521d）
    \n if end_balance_list:
    \n df_end_balance_valid = pd.concat(end_balance_list, ignore_index=True)
    \n # \u7b5b\u9009\u6709\u6548\u7ed3\u5b58（\u6570\u91cf>0），\u53bb\u9664\u5197\u4f59
    \n df_end_balance_valid = df_end_balance_valid[df_end_balance_valid['\u7ed3\u5b58\u6570\u91cf'] > 0].reset_index(drop=True)
    \n # \u8c03\u6574\u5217\u987a\u5e8f，\u4fbf\u4e8e\u9605\u8bfb
    \n df_end_balance_valid = df_end_balance_valid[['\u7269\u6599\u7f16\u7801', '\u4ed3\u5e93\u7f16\u7801', '\u6279\u6b21\u53f7', '\u7ed3\u5b58\u6570\u91cf', '\u7ed3\u5b58\u91d1\u989d', '\u7ed3\u5b58\u5355\u4ef7']]
    \n else:
    \n df_end_balance_valid = pd.DataFrame(
    \n columns=['\u7269\u6599\u7f16\u7801', '\u4ed3\u5e93\u7f16\u7801', '\u6279\u6b21\u53f7', '\u7ed3\u5b58\u6570\u91cf', '\u7ed3\u5b58\u91d1\u989d', '\u7ed3\u5b58\u5355\u4ef7']
    \n )<\/p>\n

    # 6. \u4fdd\u5b58\u6838\u7b97\u7ed3\u679c\u5230Excel（\u6309\u6708\u4efd\u547d\u540d，\u4fbf\u4e8e\u8ffd\u6eaf\u548c\u6eda\u52a8）
    \n summary_save_path = os.path.join(data_dir, f'FIFO_\u6536\u53d1\u5b58\u6c47\u603b_{month_str}.xlsx')
    \n balance_save_path = os.path.join(data_dir, f'FIFO_\u5b58\u8d27\u7ed3\u5b58_{month_str}.xlsx')<\/p>\n

    df_final_summary.to_excel(summary_save_path, index=False, engine='openpyxl')
    \n df_end_balance_valid.to_excel(balance_save_path, index=False, engine='openpyxl')<\/p>\n

    # 7. \u6253\u5370\u6838\u7b97\u5b8c\u6210\u65e5\u5fd7
    \n print(f"\u2705 {month_str} \u5148\u8fdb\u5148\u51fa\u6cd5（FIFO）\u6838\u7b97\u5b8c\u6210")
    \n print(f" \u5b8c\u6574\u6c47\u603b\u8868\u4fdd\u5b58\u8def\u5f84：{summary_save_path}")
    \n print(f" \u6708\u672b\u7ed3\u5b58\u8868（\u4e0b\u6708\u671f\u521d）\u4fdd\u5b58\u8def\u5f84：{balance_save_path}")<\/p>\n

    return df_final_summary, df_end_balance_valid<\/p>\n

    def batch_monthly_rolling_fifo_costing(data_dir, start_month, end_month):
    \n """
    \n \u6279\u91cf\u6267\u884c\u5148\u8fdb\u5148\u51fa\u6cd5\u6708\u5ea6\u6eda\u52a8\u6838\u7b97（\u904d\u5386\u6307\u5b9a\u6708\u4efd\u8303\u56f4，\u81ea\u52a8\u627f\u63a5\u4e0a\u6708\u7ed3\u5b58）
    \n """
    \n # \u786e\u4fdd\u76ee\u5f55\u5b58\u5728
    \n ensure_directory_exists(data_dir)
    \n # \u751f\u6210\u5f85\u6838\u7b97\u6708\u4efd\u5217\u8868
    \n month_calc_list = generate_month_list(start_month, end_month)<\/p>\n

    # \u9010\u6708\u7d2f\u52a0\u6838\u7b97
    \n for month in month_calc_list:
    \n try:
    \n fifo_costing_single(month, data_dir)
    \n except Exception as e:
    \n print(f"\u274c {month} \u5148\u8fdb\u5148\u51fa\u6cd5（FIFO）\u6838\u7b97\u5931\u8d25，\u9519\u8bef\u4fe1\u606f：{str(e)}")
    \n continue<\/p>\n

    # ===================== \u6267\u884c\u5148\u8fdb\u5148\u51fa\u6cd5\u6279\u91cf\u6eda\u52a8\u6838\u7b97 =====================
    \nif __name__ == "__main__":
    \n batch_monthly_rolling_fifo_costing(target_dir, start_month, end_month)<\/p>\n

    \u5173\u952e\u8bf4\u660e<\/h3>\n

    1. \u914d\u7f6e\u9879\u6ce8\u610f\u4e8b\u9879<\/h4>\n
      \n
    • target_dir：\u5fc5\u987b\u4e0e\u4e4b\u524d\u751f\u6210\u6d41\u6c34\u6570\u636e\u7684\u76ee\u5f55\u5b8c\u5168\u4e00\u81f4，\u5426\u5219\u4f1a\u63d0\u793a\u6d41\u6c34\u6587\u4ef6\u7f3a\u5931，\u65e0\u6cd5\u8fdb\u884c\u6838\u7b97\u3002<\/li>\n
    • start_month\/end_month：\u9700\u5bf9\u5e94\u4e4b\u524d\u751f\u6210\u7684 2 \u5e74\u6d41\u6c34\u6570\u636e\u6708\u4efd（\u4f8b\u5982 202401-202512），\u76f4\u63a5\u590d\u7528\u5373\u53ef\u5b9e\u73b0\u5168\u5468\u671f\u6eda\u52a8\u3002<\/li>\n
    • decimal_places：\u56fa\u5b9a\u4e3a 2 \u4f4d\u5c0f\u6570，\u7b26\u5408\u8d22\u52a1\u6838\u7b97\u7684\u91d1\u989d\u7cbe\u5ea6\u8981\u6c42，\u89c4\u907f\u6d6e\u70b9\u8fd0\u7b97\u5e26\u6765\u7684\u5c3e\u5dee\u95ee\u9898\u3002<\/li>\n<\/ul>\n

      2. FIFO \u6838\u5fc3\u5b9e\u73b0\u8981\u70b9<\/h4>\n
        \n
      • \u6392\u5e8f\u524d\u63d0：\u6d41\u6c34\u6570\u636e\u5148\u6309\u300c\u7269\u6599\u7f16\u7801\u2192\u4ed3\u5e93\u7f16\u7801\u2192\u5355\u636e\u65e5\u671f\u2192\u6279\u6b21\u53f7\u300d\u6392\u5e8f，\u4fdd\u8bc1\u5165\u5e93\u6279\u6b21\u7684\u65f6\u95f4\u987a\u5e8f\u6b63\u786e，\u8fd9\u662f FIFO \u6838\u7b97\u7684\u57fa\u7840\u3002<\/li>\n
      • \u9010\u6279\u6263\u51cf：\u51fa\u5e93\u65f6\u904d\u5386\u5f53\u524d\u7ed3\u5b58\u6279\u6b21，\u4f18\u5148\u6263\u51cf\u6700\u65e9\u5165\u5e93\u6279\u6b21，\u5206\u300c\u6279\u6b21\u8db3\u591f\u6263\u51cf\u300d\u548c\u300c\u6279\u6b21\u4e0d\u8db3\u6263\u51cf\u300d\u4e24\u79cd\u573a\u666f\u5904\u7406，\u786e\u4fdd\u903b\u8f91\u4e25\u8c28\u3002<\/li>\n
      • \u91d1\u989d\u52fe\u7a3d：\u672c\u6708\u53d1\u51fa\u91d1\u989d\u901a\u8fc7\u300c\u671f\u521d\u91d1\u989d + \u672c\u6708\u6536\u5165\u91d1\u989d – \u671f\u672b\u91d1\u989d\u300d\u5012\u7b97，\u907f\u514d\u76f4\u63a5\u7d2f\u52a0\u6263\u51cf\u91d1\u989d\u5e26\u6765\u7684\u7cbe\u5ea6\u8bef\u5dee，\u4fdd\u8bc1\u6570\u636e\u52fe\u7a3d\u5173\u7cfb\u6b63\u786e\u3002<\/li>\n<\/ul>\n

        3. \u6708\u5ea6\u6eda\u52a8\u7684\u5b9e\u73b0\u6838\u5fc3<\/h4>\n
          \n
        • \u6bcf\u6708\u6838\u7b97\u5b8c\u6210\u540e，\u751f\u6210\u300cFIFO_\u5b58\u8d27\u7ed3\u5b58_YYYYMM.xlsx\u300d，\u8bb0\u5f55\u5269\u4f59\u6709\u6548\u6279\u6b21\u7684\u300c\u7269\u6599 + \u4ed3\u5e93 + \u6279\u6b21 + \u6570\u91cf + \u91d1\u989d + \u5355\u4ef7\u300d\u3002<\/li>\n
        • \u4e0b\u6708\u6838\u7b97\u65f6，\u81ea\u52a8\u8bfb\u53d6\u4e0a\u6708\u7ed3\u5b58\u6587\u4ef6，\u5c06\u8be5\u6279\u6b21\u6570\u636e\u4f5c\u4e3a\u671f\u521d\u53ef\u7528\u5e93\u5b58，\u4fdd\u6301\u6279\u6b21\u7684\u65f6\u95f4\u987a\u5e8f，\u5b9e\u73b0\u6570\u636e\u7684\u8fde\u7eed\u627f\u63a5\u3002<\/li>\n
        • \u9996\u6b21\u6838\u7b97\u65e0\u4e0a\u6708\u7ed3\u5b58\u65f6，\u81ea\u52a8\u521d\u59cb\u5316\u7a7a\u7ed3\u5b58\u8868，\u4e0d\u5f71\u54cd\u6838\u7b97\u6d41\u7a0b，\u786e\u4fdd\u9996\u6b21\u8fd0\u884c\u5373\u53ef\u6b63\u5e38\u4ea7\u51fa\u7ed3\u679c\u3002<\/li>\n<\/ul>\n

          4. \u6587\u4ef6\u547d\u540d\u89c4\u5219（\u6e05\u6670\u53ef\u8ffd\u6eaf）<\/h4>\n\n\u6587\u4ef6\u7c7b\u578b\u6587\u4ef6\u540d\u683c\u5f0f\u7528\u9014<\/tr>\n\n\n\n
          \u6536\u53d1\u5b58\u6c47\u603b\u8868<\/td>\nFIFO_\u6536\u53d1\u5b58\u6c47\u603b_YYYYMM.xlsx<\/td>\n\u6309\u300c\u7269\u6599 + \u4ed3\u5e93\u300d\u5c55\u793a\u8be5\u6708\u671f\u521d\u3001\u6536\u53d1\u3001\u671f\u672b\u6570\u636e，\u7528\u4e8e\u5bf9\u8d26\u548c\u62a5\u8868\u751f\u6210<\/td>\n<\/tr>\n
          \u6708\u672b\u7ed3\u5b58\u8868<\/td>\nFIFO_\u5b58\u8d27\u7ed3\u5b58_YYYYMM.xlsx<\/td>\n\u6309\u300c\u7269\u6599 + \u4ed3\u5e93 + \u6279\u6b21\u300d\u5c55\u793a\u6708\u672b\u6709\u6548\u7ed3\u5b58，\u4f5c\u4e3a\u4e0b\u6708\u671f\u521d\u6570\u636e，\u652f\u6491\u6eda\u52a8\u8ba1\u7b97<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

          5. \u5f02\u5e38\u5904\u7406\u4e0e\u6570\u636e\u517c\u5bb9<\/h4>\n
            \n
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