{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ee575f21",
   "metadata": {},
   "source": [
    "# 03 결과 도출 — 패널 요약·분화 몫·연쇄가중 재현과 검증\n",
    "\n",
    "`02_분석.ipynb`의 산출물로 2장의 표와 그림을 만든다. 마지막 셀은 계산 결과를 공표 산출물(`data/published/`의 패널·몫·연계 행렬 사본)과\n",
    "공표 수치(`config.PUBLISHED`)에 대조한다. 허용오차는 공표 자릿수의 반올림 범위이며, 파일 대조는 부동소수점 오차 범위이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cc6ec7b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T20:32:56.334967Z",
     "iopub.status.busy": "2026-10-07T20:32:56.334967Z",
     "iopub.status.idle": "2026-10-07T20:32:57.290507Z",
     "shell.execute_reply": "2026-10-07T20:32:57.290008Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.12.10 · pandas 2.2.3 · numpy 2.4.6\n",
      "  ONLINE=0 (원천 재수집)  FULL=0  ECOS 키 없음 · KOSIS 키 없음\n",
      "  36개 산업 분기      있음  data\\inputs\\bok36_va_q.csv\n",
      "  총부가가치          있음  data\\inputs\\gva_total_q.csv\n",
      "  실질 GDP         있음  data\\inputs\\gdp_quarterly_sa.csv\n",
      "  IO 중분류 부가가치    있음  data\\inputs\\io_benchmark_va_83ind.csv\n",
      "  IO 생산유발계수      있음  data\\inputs\\io_M_mid_2020.csv\n",
      "  IO 소분류 37x     있음  data\\inputs\\io_small_37x_va.csv\n",
      "  생산지수 C28·C282  있음  data\\inputs\\mfg_prod_c28_c282.csv\n",
      "  발전량 KPX        있음  data\\inputs\\power_gen_kpx.csv\n",
      "  발전량 KEPCO      있음  data\\inputs\\power_gen_kepco.csv\n",
      "  대조표 62         있음  data\\crosswalk_62.json\n",
      "  분화 규칙 69       있음  data\\carveout_rules.csv\n"
     ]
    }
   ],
   "source": [
    "import os, glob\n",
    "import numpy as np, pandas as pd\n",
    "import config as C\n",
    "import gva_data as F\n",
    "pd.set_option(\"display.float_format\", lambda v: f\"{v:,.4f}\")\n",
    "pd.set_option(\"display.width\", 160)\n",
    "C.setup()\n",
    "import matplotlib.pyplot as plt\n",
    "import koreanize_matplotlib  # noqa: F401  (한글 글꼴)\n",
    "rd = lambda n, **kw: pd.read_csv(os.path.join(C.OUTPUT, n), **kw)\n",
    "real = pd.read_csv(os.path.join(C.INTERIM, \"va36_real_q.csv\"), index_col=0)\n",
    "L62, L69 = rd(\"levels_62.csv\", index_col=0), rd(\"levels_69.csv\", index_col=0)\n",
    "mapping, carve_log = rd(\"mapping_62_official.csv\"), rd(\"carve_log.csv\")\n",
    "share_b, share_r = rd(\"share_battery.csv\", index_col=0).iloc[:, 0], rd(\"share_renew.csv\", index_col=0).iloc[:, 0]\n",
    "act, rq, ra = rd(\"renew_gen_actual.csv\"), rd(\"recon_total_q.csv\", index_col=0), rd(\"recon_total_a.csv\", index_col=0)\n",
    "rules = pd.read_csv(C.CARVE_RULES, dtype={\"IO분자\": str, \"IO분모\": str})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d38c422",
   "metadata": {},
   "source": [
    "## 표 1. 산업 해상도별 분기 실질부가가치 패널(2020년 연쇄가격)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a92dc8f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T20:32:57.291507Z",
     "iopub.status.busy": "2026-10-07T20:32:57.291507Z",
     "iopub.status.idle": "2026-10-07T20:32:57.339950Z",
     "shell.execute_reply": "2026-10-07T20:32:57.339444Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_78edc\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_78edc_level0_col0\" class=\"col_heading level0 col0\" >분류</th>\n",
       "      <th id=\"T_78edc_level0_col1\" class=\"col_heading level0 col1\" >산업 수</th>\n",
       "      <th id=\"T_78edc_level0_col2\" class=\"col_heading level0 col2\" >기간</th>\n",
       "      <th id=\"T_78edc_level0_col3\" class=\"col_heading level0 col3\" >2000Q1 합계(조원)</th>\n",
       "      <th id=\"T_78edc_level0_col4\" class=\"col_heading level0 col4\" >2020Q1 합계(조원)</th>\n",
       "      <th id=\"T_78edc_level0_col5\" class=\"col_heading level0 col5\" >2025Q4 합계(조원)</th>\n",
       "      <th id=\"T_78edc_level0_col6\" class=\"col_heading level0 col6\" >36합 대비 최대오차(십억원)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_78edc_row0_col0\" class=\"data row0 col0\" >36(한국은행)</td>\n",
       "      <td id=\"T_78edc_row0_col1\" class=\"data row0 col1\" >36</td>\n",
       "      <td id=\"T_78edc_row0_col2\" class=\"data row0 col2\" >2000Q1–2025Q4</td>\n",
       "      <td id=\"T_78edc_row0_col3\" class=\"data row0 col3\" >233.4</td>\n",
       "      <td id=\"T_78edc_row0_col4\" class=\"data row0 col4\" >475.9</td>\n",
       "      <td id=\"T_78edc_row0_col5\" class=\"data row0 col5\" >549.4</td>\n",
       "      <td id=\"T_78edc_row0_col6\" class=\"data row0 col6\" >0.0e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_78edc_row1_col0\" class=\"data row1 col0\" >62(KEIT)</td>\n",
       "      <td id=\"T_78edc_row1_col1\" class=\"data row1 col1\" >62</td>\n",
       "      <td id=\"T_78edc_row1_col2\" class=\"data row1 col2\" >2000Q1–2025Q4</td>\n",
       "      <td id=\"T_78edc_row1_col3\" class=\"data row1 col3\" >233.4</td>\n",
       "      <td id=\"T_78edc_row1_col4\" class=\"data row1 col4\" >475.9</td>\n",
       "      <td id=\"T_78edc_row1_col5\" class=\"data row1 col5\" >549.4</td>\n",
       "      <td id=\"T_78edc_row1_col6\" class=\"data row1 col6\" >2.9e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_78edc_row2_col0\" class=\"data row2 col0\" >69(62+분화)</td>\n",
       "      <td id=\"T_78edc_row2_col1\" class=\"data row2 col1\" >69</td>\n",
       "      <td id=\"T_78edc_row2_col2\" class=\"data row2 col2\" >2000Q1–2025Q4</td>\n",
       "      <td id=\"T_78edc_row2_col3\" class=\"data row2 col3\" >233.4</td>\n",
       "      <td id=\"T_78edc_row2_col4\" class=\"data row2 col4\" >475.9</td>\n",
       "      <td id=\"T_78edc_row2_col5\" class=\"data row2 col5\" >549.4</td>\n",
       "      <td id=\"T_78edc_row2_col6\" class=\"data row2 col6\" >3.5e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ae06922900>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = F.panel_summary(real, L62, L69, [\"2000Q1\", \"2020Q1\", \"2025Q4\"])\n",
    "t1.to_csv(os.path.join(C.OUTPUT, \"table1_panels.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "t1.style.format({c: \"{:,.1f}\" for c in t1.columns if \"조원\" in c} | {\"36합 대비 최대오차(십억원)\": \"{:.1e}\"}).hide(axis=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "901b9b2f",
   "metadata": {},
   "source": [
    "## 표 2. 36→62 배분 — 모산업을 나누는 산업과 몫(2020년 산업연관표)\n",
    "\n",
    "62개 가운데 47개는 BOK36 산업 17개를 나누어 가지고(분할), 15개는 BOK36 산업 하나(13개) 또는 병합 묶음(전기업+가스·증기, 건설 4개 업종)과 1:1로 대응한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3128bce8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T20:32:57.341452Z",
     "iopub.status.busy": "2026-10-07T20:32:57.340951Z",
     "iopub.status.idle": "2026-10-07T20:32:57.353831Z",
     "shell.execute_reply": "2026-10-07T20:32:57.353324Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "유형\n",
      "분할        47\n",
      "1:1/병합    15\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>산업 수</th>\n",
       "      <th>구성(몫)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOK36부모</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>3</td>\n",
       "      <td>농업(0.820) · 임업(0.073) · 어업(0.108)</td>\n",
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       "    <tr>\n",
       "      <th>1102</th>\n",
       "      <td>2</td>\n",
       "      <td>석탄ㆍ원유ㆍ천연가스(0.016) · 금속ㆍ비금속광업(0.984)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110301</th>\n",
       "      <td>3</td>\n",
       "      <td>식료품(0.603) · 음료품(0.161) · 담배(0.236)</td>\n",
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       "    <tr>\n",
       "      <th>110302</th>\n",
       "      <td>2</td>\n",
       "      <td>섬유ㆍ의복(0.925) · 가죽제품(0.075)</td>\n",
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       "    <tr>\n",
       "      <th>110303</th>\n",
       "      <td>3</td>\n",
       "      <td>목재ㆍ목제품(0.156) · 펄프ㆍ종이제품(0.561) · 인쇄ㆍ복제(0.283)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110305</th>\n",
       "      <td>4</td>\n",
       "      <td>화학물질ㆍ제품(0.343) · 화학섬유(0.010) · 의료ㆍ의약품(0.195) ·...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110309</th>\n",
       "      <td>2</td>\n",
       "      <td>전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비(0.888) · 의료ㆍ정밀ㆍ광학기기ㆍ시계(0.112)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110312</th>\n",
       "      <td>2</td>\n",
       "      <td>자동차(0.836) · 기타 운송장비(0.164)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110403</th>\n",
       "      <td>3</td>\n",
       "      <td>수도사업(0.172) · 하수ㆍ폐수ㆍ분뇨처리업(0.195) · 폐기물ㆍ자원재활용서비...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>4</td>\n",
       "      <td>육상운송업(0.380) · 수상운송업(0.066) · 항공운송업(0.068) · 창...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1108</th>\n",
       "      <td>3</td>\n",
       "      <td>금융업(0.589) · 보험ㆍ연금업(0.136) · 금융ㆍ보험관련서비스업(0.276)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1112</th>\n",
       "      <td>2</td>\n",
       "      <td>의료ㆍ보건업(0.747) · 사회복지 서비스업(0.253)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11131</th>\n",
       "      <td>2</td>\n",
       "      <td>문화서비스업(0.292) · 스포츠ㆍ오락서비스업(0.708)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11132</th>\n",
       "      <td>2</td>\n",
       "      <td>사회단체(0.259) · 수리ㆍ개인서비스업(0.741)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11142</th>\n",
       "      <td>5</td>\n",
       "      <td>출판업(0.045) · 영상ㆍ오디오기록물제작 배급업(0.058) · 방송업(0.06...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111501</th>\n",
       "      <td>3</td>\n",
       "      <td>연구개발업(0.412) · 사업관련전문서비스업(0.272) · 과학기술관련전문서비스...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111502</th>\n",
       "      <td>2</td>\n",
       "      <td>장비ㆍ용품임대업(0.198) · 사업지원서비스업(0.802)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         산업 수                                              구성(몫)\n",
       "BOK36부모                                                         \n",
       "1101        3                  농업(0.820) · 임업(0.073) · 어업(0.108)\n",
       "1102        2                석탄ㆍ원유ㆍ천연가스(0.016) · 금속ㆍ비금속광업(0.984)\n",
       "110301      3                식료품(0.603) · 음료품(0.161) · 담배(0.236)\n",
       "110302      2                         섬유ㆍ의복(0.925) · 가죽제품(0.075)\n",
       "110303      3      목재ㆍ목제품(0.156) · 펄프ㆍ종이제품(0.561) · 인쇄ㆍ복제(0.283)\n",
       "110305      4  화학물질ㆍ제품(0.343) · 화학섬유(0.010) · 의료ㆍ의약품(0.195) ·...\n",
       "110309      2  전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비(0.888) · 의료ㆍ정밀ㆍ광학기기ㆍ시계(0.112)\n",
       "110312      2                        자동차(0.836) · 기타 운송장비(0.164)\n",
       "110403      3  수도사업(0.172) · 하수ㆍ폐수ㆍ분뇨처리업(0.195) · 폐기물ㆍ자원재활용서비...\n",
       "1107        4  육상운송업(0.380) · 수상운송업(0.066) · 항공운송업(0.068) · 창...\n",
       "1108        3    금융업(0.589) · 보험ㆍ연금업(0.136) · 금융ㆍ보험관련서비스업(0.276)\n",
       "1112        2                   의료ㆍ보건업(0.747) · 사회복지 서비스업(0.253)\n",
       "11131       2                  문화서비스업(0.292) · 스포츠ㆍ오락서비스업(0.708)\n",
       "11132       2                     사회단체(0.259) · 수리ㆍ개인서비스업(0.741)\n",
       "11142       5  출판업(0.045) · 영상ㆍ오디오기록물제작 배급업(0.058) · 방송업(0.06...\n",
       "111501      3  연구개발업(0.412) · 사업관련전문서비스업(0.272) · 과학기술관련전문서비스...\n",
       "111502      2                  장비ㆍ용품임대업(0.198) · 사업지원서비스업(0.802)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split = mapping[mapping[\"유형\"].str.startswith(\"분할\")].copy()\n",
    "t2 = split.groupby(\"BOK36부모\").apply(lambda g: pd.Series({\"산업 수\": len(g), \"구성(몫)\": \" · \".join(f\"{n}({s:.3f})\" for n, s in zip(g[\"산업명\"], g[\"몫\"]))}),\n",
    "                                    include_groups=False)\n",
    "t2.to_csv(os.path.join(C.OUTPUT, \"table2_split_62.csv\"), encoding=\"utf-8-sig\")\n",
    "print(mapping[\"유형\"].str.split(\"(\").str[0].value_counts().to_string())\n",
    "t2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b89b718a",
   "metadata": {},
   "source": [
    "## 표 3. 62→69 분화 — 몫과 2025Q4 수준"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5584e9b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T20:32:57.354836Z",
     "iopub.status.busy": "2026-10-07T20:32:57.354331Z",
     "iopub.status.idle": "2026-10-07T20:32:57.360558Z",
     "shell.execute_reply": "2026-10-07T20:32:57.360558Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_07837\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_07837_level0_col0\" class=\"col_heading level0 col0\" >신산업</th>\n",
       "      <th id=\"T_07837_level0_col1\" class=\"col_heading level0 col1\" >모산업</th>\n",
       "      <th id=\"T_07837_level0_col2\" class=\"col_heading level0 col2\" >나머지 열</th>\n",
       "      <th id=\"T_07837_level0_col3\" class=\"col_heading level0 col3\" >방식</th>\n",
       "      <th id=\"T_07837_level0_col4\" class=\"col_heading level0 col4\" >몫(평균)</th>\n",
       "      <th id=\"T_07837_level0_col5\" class=\"col_heading level0 col5\" >신산업 2025Q4(십억원)</th>\n",
       "      <th id=\"T_07837_level0_col6\" class=\"col_heading level0 col6\" >나머지 2025Q4(십억원)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_07837_row0_col0\" class=\"data row0 col0\" >반도체</td>\n",
       "      <td id=\"T_07837_row0_col1\" class=\"data row0 col1\" >22.전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비</td>\n",
       "      <td id=\"T_07837_row0_col2\" class=\"data row0 col2\" >22.전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비(잔여)</td>\n",
       "      <td id=\"T_07837_row0_col3\" class=\"data row0 col3\" >IO고정</td>\n",
       "      <td id=\"T_07837_row0_col4\" class=\"data row0 col4\" >0.5471</td>\n",
       "      <td id=\"T_07837_row0_col5\" class=\"data row0 col5\" >29,767.7</td>\n",
       "      <td id=\"T_07837_row0_col6\" class=\"data row0 col6\" >24,644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_07837_row1_col0\" class=\"data row1 col0\" >조선업</td>\n",
       "      <td id=\"T_07837_row1_col1\" class=\"data row1 col1\" >27.기타 운송장비</td>\n",
       "      <td id=\"T_07837_row1_col2\" class=\"data row1 col2\" >27.기타 운송장비(잔여)</td>\n",
       "      <td id=\"T_07837_row1_col3\" class=\"data row1 col3\" >IO고정</td>\n",
       "      <td id=\"T_07837_row1_col4\" class=\"data row1 col4\" >0.5703</td>\n",
       "      <td id=\"T_07837_row1_col5\" class=\"data row1 col5\" >2,069.3</td>\n",
       "      <td id=\"T_07837_row1_col6\" class=\"data row1 col6\" >1,559.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_07837_row2_col0\" class=\"data row2 col0\" >철강</td>\n",
       "      <td id=\"T_07837_row2_col1\" class=\"data row2 col1\" >20.1차 금속</td>\n",
       "      <td id=\"T_07837_row2_col2\" class=\"data row2 col2\" >20.1차 금속(잔여)</td>\n",
       "      <td id=\"T_07837_row2_col3\" class=\"data row2 col3\" >IO고정</td>\n",
       "      <td id=\"T_07837_row2_col4\" class=\"data row2 col4\" >0.7041</td>\n",
       "      <td id=\"T_07837_row2_col5\" class=\"data row2 col5\" >4,206.3</td>\n",
       "      <td id=\"T_07837_row2_col6\" class=\"data row2 col6\" >1,767.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_07837_row3_col0\" class=\"data row3 col0\" >택배</td>\n",
       "      <td id=\"T_07837_row3_col1\" class=\"data row3 col1\" >38.창고ㆍ운송보조서비스업</td>\n",
       "      <td id=\"T_07837_row3_col2\" class=\"data row3 col2\" >38.창고ㆍ운송보조서비스업(잔여)</td>\n",
       "      <td id=\"T_07837_row3_col3\" class=\"data row3 col3\" >IO고정</td>\n",
       "      <td id=\"T_07837_row3_col4\" class=\"data row3 col4\" >0.2066</td>\n",
       "      <td id=\"T_07837_row3_col5\" class=\"data row3 col5\" >2,796.5</td>\n",
       "      <td id=\"T_07837_row3_col6\" class=\"data row3 col6\" >10,740.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_07837_row4_col0\" class=\"data row4 col0\" >이차전지</td>\n",
       "      <td id=\"T_07837_row4_col1\" class=\"data row4 col1\" >24.전기장비</td>\n",
       "      <td id=\"T_07837_row4_col2\" class=\"data row4 col2\" >24.전기장비(잔여)</td>\n",
       "      <td id=\"T_07837_row4_col3\" class=\"data row4 col3\" >share_battery</td>\n",
       "      <td id=\"T_07837_row4_col4\" class=\"data row4 col4\" >0.1394</td>\n",
       "      <td id=\"T_07837_row4_col5\" class=\"data row4 col5\" >1,279.0</td>\n",
       "      <td id=\"T_07837_row4_col6\" class=\"data row4 col6\" >7,233.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_07837_row5_col0\" class=\"data row5 col0\" >신재생에너지</td>\n",
       "      <td id=\"T_07837_row5_col1\" class=\"data row5 col1\" >29.전기ㆍ가스ㆍ증기ㆍ공기조절공급업</td>\n",
       "      <td id=\"T_07837_row5_col2\" class=\"data row5 col2\" >29.전기ㆍ가스ㆍ증기ㆍ공기조절공급업(잔여)</td>\n",
       "      <td id=\"T_07837_row5_col3\" class=\"data row5 col3\" >share_renew</td>\n",
       "      <td id=\"T_07837_row5_col4\" class=\"data row5 col4\" >0.0335</td>\n",
       "      <td id=\"T_07837_row5_col5\" class=\"data row5 col5\" >831.8</td>\n",
       "      <td id=\"T_07837_row5_col6\" class=\"data row5 col6\" >8,255.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_07837_row6_col0\" class=\"data row6 col0\" >건물건설건축보수</td>\n",
       "      <td id=\"T_07837_row6_col1\" class=\"data row6 col1\" >33.건설업</td>\n",
       "      <td id=\"T_07837_row6_col2\" class=\"data row6 col2\" >토목건설</td>\n",
       "      <td id=\"T_07837_row6_col3\" class=\"data row6 col3\" >IO고정</td>\n",
       "      <td id=\"T_07837_row6_col4\" class=\"data row6 col4\" >0.7013</td>\n",
       "      <td id=\"T_07837_row6_col5\" class=\"data row6 col5\" >16,289.9</td>\n",
       "      <td id=\"T_07837_row6_col6\" class=\"data row6 col6\" >6,937.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ae07e23aa0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lv = L69.loc[\"2025Q4\"]\n",
    "t3 = carve_log.assign(**{\"신산업 2025Q4(십억원)\": [lv[n] for n in carve_log[\"신산업\"]],\n",
    "                         \"나머지 2025Q4(십억원)\": [lv[n] for n in carve_log[\"나머지 열\"]]})\n",
    "t3.to_csv(os.path.join(C.OUTPUT, \"table3_carveout_69.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "t3.style.format({\"몫(평균)\": \"{:.4f}\", \"신산업 2025Q4(십억원)\": \"{:,.1f}\", \"나머지 2025Q4(십억원)\": \"{:,.1f}\"}).hide(axis=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab06192",
   "metadata": {},
   "source": [
    "## 표 4. 시변 몫 — 연평균(이차전지/전기장비, 신재생/전기·가스·증기)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d1895ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T20:32:57.361563Z",
     "iopub.status.busy": "2026-10-07T20:32:57.361563Z",
     "iopub.status.idle": "2026-10-07T20:32:57.368424Z",
     "shell.execute_reply": "2026-10-07T20:32:57.367919Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_d8e28\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d8e28_level0_col0\" class=\"col_heading level0 col0\" >이차전지 몫</th>\n",
       "      <th id=\"T_d8e28_level0_col1\" class=\"col_heading level0 col1\" >신재생 몫</th>\n",
       "      <th id=\"T_d8e28_level0_col2\" class=\"col_heading level0 col2\" >신재생 발전비중(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e28_level0_row0\" class=\"row_heading level0 row0\" >2000</th>\n",
       "      <td id=\"T_d8e28_row0_col0\" class=\"data row0 col0\" >0.0278</td>\n",
       "      <td id=\"T_d8e28_row0_col1\" class=\"data row0 col1\" >0.0072</td>\n",
       "      <td id=\"T_d8e28_row0_col2\" class=\"data row0 col2\" >—</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e28_level0_row1\" class=\"row_heading level0 row1\" >2005</th>\n",
       "      <td id=\"T_d8e28_row1_col0\" class=\"data row1 col0\" >0.0896</td>\n",
       "      <td id=\"T_d8e28_row1_col1\" class=\"data row1 col1\" >0.0082</td>\n",
       "      <td id=\"T_d8e28_row1_col2\" class=\"data row1 col2\" >—</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e28_level0_row2\" class=\"row_heading level0 row2\" >2010</th>\n",
       "      <td id=\"T_d8e28_row2_col0\" class=\"data row2 col0\" >0.1661</td>\n",
       "      <td id=\"T_d8e28_row2_col1\" class=\"data row2 col1\" >0.0168</td>\n",
       "      <td id=\"T_d8e28_row2_col2\" class=\"data row2 col2\" >1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e28_level0_row3\" class=\"row_heading level0 row3\" >2015</th>\n",
       "      <td id=\"T_d8e28_row3_col0\" class=\"data row3 col0\" >0.1622</td>\n",
       "      <td id=\"T_d8e28_row3_col1\" class=\"data row3 col1\" >0.0360</td>\n",
       "      <td id=\"T_d8e28_row3_col2\" class=\"data row3 col2\" >4.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e28_level0_row4\" class=\"row_heading level0 row4\" >2020</th>\n",
       "      <td id=\"T_d8e28_row4_col0\" class=\"data row4 col0\" >0.1823</td>\n",
       "      <td id=\"T_d8e28_row4_col1\" class=\"data row4 col1\" >0.0558</td>\n",
       "      <td id=\"T_d8e28_row4_col2\" class=\"data row4 col2\" >6.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e28_level0_row5\" class=\"row_heading level0 row5\" >2021</th>\n",
       "      <td id=\"T_d8e28_row5_col0\" class=\"data row5 col0\" >0.1927</td>\n",
       "      <td id=\"T_d8e28_row5_col1\" class=\"data row5 col1\" >0.0642</td>\n",
       "      <td id=\"T_d8e28_row5_col2\" class=\"data row5 col2\" >7.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e28_level0_row6\" class=\"row_heading level0 row6\" >2022</th>\n",
       "      <td id=\"T_d8e28_row6_col0\" class=\"data row6 col0\" >0.2186</td>\n",
       "      <td id=\"T_d8e28_row6_col1\" class=\"data row6 col1\" >0.0736</td>\n",
       "      <td id=\"T_d8e28_row6_col2\" class=\"data row6 col2\" >8.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e28_level0_row7\" class=\"row_heading level0 row7\" >2023</th>\n",
       "      <td id=\"T_d8e28_row7_col0\" class=\"data row7 col0\" >0.2015</td>\n",
       "      <td id=\"T_d8e28_row7_col1\" class=\"data row7 col1\" >0.0799</td>\n",
       "      <td id=\"T_d8e28_row7_col2\" class=\"data row7 col2\" >9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e28_level0_row8\" class=\"row_heading level0 row8\" >2024</th>\n",
       "      <td id=\"T_d8e28_row8_col0\" class=\"data row8 col0\" >0.1573</td>\n",
       "      <td id=\"T_d8e28_row8_col1\" class=\"data row8 col1\" >0.0874</td>\n",
       "      <td id=\"T_d8e28_row8_col2\" class=\"data row8 col2\" >10.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d8e28_level0_row9\" class=\"row_heading level0 row9\" >2025</th>\n",
       "      <td id=\"T_d8e28_row9_col0\" class=\"data row9 col0\" >0.1474</td>\n",
       "      <td id=\"T_d8e28_row9_col1\" class=\"data row9 col1\" >0.0915</td>\n",
       "      <td id=\"T_d8e28_row9_col2\" class=\"data row9 col2\" >11.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ae06ca7320>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yr = lambda s: s.groupby(s.index.str[:4]).mean()\n",
    "t4 = pd.DataFrame({\"이차전지 몫\": yr(share_b), \"신재생 몫\": yr(share_r)}).loc[[\"2000\", \"2005\", \"2010\", \"2015\", \"2020\", \"2021\", \"2022\", \"2023\", \"2024\", \"2025\"]]\n",
    "t4[\"신재생 발전비중(%)\"] = act.set_index(act[\"year\"].astype(str))[\"renew_share\"].reindex(t4.index) * 100\n",
    "t4.to_csv(os.path.join(C.OUTPUT, \"table4_timevarying_shares.csv\"), encoding=\"utf-8-sig\")\n",
    "t4.style.format({\"이차전지 몫\": \"{:.4f}\", \"신재생 몫\": \"{:.4f}\", \"신재생 발전비중(%)\": \"{:.2f}\"}, na_rep=\"—\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ee166c4",
   "metadata": {},
   "source": [
    "## 그림 1. 이차전지·신재생에너지 시변 몫(분기)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec884118",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T20:32:57.369426Z",
     "iopub.status.busy": "2026-10-07T20:32:57.368923Z",
     "iopub.status.idle": "2026-10-07T20:32:57.514254Z",
     "shell.execute_reply": "2026-10-07T20:32:57.513746Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 3.8))\n",
    "x = range(len(share_b))\n",
    "ax.plot(x, share_b.values, color=\"#1f4e79\", label=\"이차전지 / 전기장비\")\n",
    "ax.plot(x, share_r.values, color=\"#a9781f\", label=\"신재생에너지 / 전기·가스·증기\")\n",
    "ticks = [i for i, q in enumerate(share_b.index) if q.endswith(\"Q1\") and int(q[:4]) % 5 == 0]\n",
    "ax.set_xticks(ticks, [share_b.index[i][:4] for i in ticks]); ax.set_ylabel(\"모산업 대비 몫\")\n",
    "ax.grid(alpha=.3); ax.legend(frameon=False); ax.set_title(\"분화 산업의 시변 몫\", loc=\"left\")\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig1_shares.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4a917e",
   "metadata": {},
   "source": [
    "## 표 5. 연쇄가중 총부가가치 재현 — 검증표\n",
    "\n",
    "'단순합 괴리'(포털 검증표의 '그냥 합 괴리')는 36개 산업 실질값 단순합의 총부가가치 대비 괴리, '재현값 괴리'는 2020Q1 기준 양방향 연쇄 재현값의 괴리이다(%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9b855bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T20:32:57.515252Z",
     "iopub.status.busy": "2026-10-07T20:32:57.515252Z",
     "iopub.status.idle": "2026-10-07T20:32:57.522695Z",
     "shell.execute_reply": "2026-10-07T20:32:57.522695Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_047cc\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_047cc_level0_col0\" class=\"col_heading level0 col0\" >단순합 괴리(%)</th>\n",
       "      <th id=\"T_047cc_level0_col1\" class=\"col_heading level0 col1\" >재현값 괴리(%)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >분기</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_047cc_level0_row0\" class=\"row_heading level0 row0\" >1980Q1</th>\n",
       "      <td id=\"T_047cc_row0_col0\" class=\"data row0 col0\" >+21.935</td>\n",
       "      <td id=\"T_047cc_row0_col1\" class=\"data row0 col1\" >+0.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_047cc_level0_row1\" class=\"row_heading level0 row1\" >2000Q1</th>\n",
       "      <td id=\"T_047cc_row1_col0\" class=\"data row1 col0\" >+5.716</td>\n",
       "      <td id=\"T_047cc_row1_col1\" class=\"data row1 col1\" >+0.2153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_047cc_level0_row2\" class=\"row_heading level0 row2\" >2019Q4</th>\n",
       "      <td id=\"T_047cc_row2_col0\" class=\"data row2 col0\" >+0.003</td>\n",
       "      <td id=\"T_047cc_row2_col1\" class=\"data row2 col1\" >+0.0380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_047cc_level0_row3\" class=\"row_heading level0 row3\" >2020Q1</th>\n",
       "      <td id=\"T_047cc_row3_col0\" class=\"data row3 col0\" >+0.027</td>\n",
       "      <td id=\"T_047cc_row3_col1\" class=\"data row3 col1\" >+0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_047cc_level0_row4\" class=\"row_heading level0 row4\" >2020Q4</th>\n",
       "      <td id=\"T_047cc_row4_col0\" class=\"data row4 col0\" >-0.031</td>\n",
       "      <td id=\"T_047cc_row4_col1\" class=\"data row4 col1\" >+0.0687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_047cc_level0_row5\" class=\"row_heading level0 row5\" >2025Q4</th>\n",
       "      <td id=\"T_047cc_row5_col0\" class=\"data row5 col0\" >+1.013</td>\n",
       "      <td id=\"T_047cc_row5_col1\" class=\"data row5 col1\" >+0.3278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_047cc_level0_row6\" class=\"row_heading level0 row6\" >전체(184분기) 최대 |괴리|</th>\n",
       "      <td id=\"T_047cc_row6_col0\" class=\"data row6 col0\" >+25.208</td>\n",
       "      <td id=\"T_047cc_row6_col1\" class=\"data row6 col1\" >+0.8232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_047cc_level0_row7\" class=\"row_heading level0 row7\" >전체(184분기) 평균 |괴리|</th>\n",
       "      <td id=\"T_047cc_row7_col0\" class=\"data row7 col0\" >+7.655</td>\n",
       "      <td id=\"T_047cc_row7_col1\" class=\"data row7 col1\" >+0.2324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_047cc_level0_row8\" class=\"row_heading level0 row8\" >연간(46년) 최대 |괴리|</th>\n",
       "      <td id=\"T_047cc_row8_col0\" class=\"data row8 col0\" >+23.096</td>\n",
       "      <td id=\"T_047cc_row8_col1\" class=\"data row8 col1\" >+0.0014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1ae06a683e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pts = [\"1980Q1\", \"2000Q1\", \"2019Q4\", \"2020Q1\", \"2020Q4\", \"2025Q4\"]\n",
    "t5 = rq.loc[pts, [\"단순합_괴리_pct\", \"재현_괴리_pct\"]].copy()\n",
    "t5.loc[\"전체(184분기) 최대 |괴리|\"] = rq[[\"단순합_괴리_pct\", \"재현_괴리_pct\"]].abs().max()\n",
    "t5.loc[\"전체(184분기) 평균 |괴리|\"] = rq[[\"단순합_괴리_pct\", \"재현_괴리_pct\"]].abs().mean()\n",
    "t5.loc[\"연간(46년) 최대 |괴리|\"] = ra[[\"단순합_괴리_pct\", \"재현_괴리_pct\"]].abs().max()\n",
    "t5.columns = [\"단순합 괴리(%)\", \"재현값 괴리(%)\"]\n",
    "t5.to_csv(os.path.join(C.OUTPUT, \"table5_recon.csv\"), encoding=\"utf-8-sig\")\n",
    "t5.style.format({\"단순합 괴리(%)\": \"{:+.3f}\", \"재현값 괴리(%)\": \"{:+.4f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42de4a11",
   "metadata": {},
   "source": [
    "## 그림 2. 36개 산업 단순합과 연쇄 재현값의 총부가가치 대비 괴리(분기)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "76684551",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T20:32:57.524200Z",
     "iopub.status.busy": "2026-10-07T20:32:57.524200Z",
     "iopub.status.idle": "2026-10-07T20:32:57.662584Z",
     "shell.execute_reply": "2026-10-07T20:32:57.662079Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 3.8))\n",
    "x = range(len(rq))\n",
    "ax.plot(x, rq[\"단순합_괴리_pct\"].values, color=\"#b03a2e\", label=\"36개 단순합\")\n",
    "ax.plot(x, rq[\"재현_괴리_pct\"].values, color=\"#1e8449\", label=\"연쇄 재현값(기준 2020Q1)\")\n",
    "ax.axhline(0, color=\"grey\", lw=.8)\n",
    "ticks = [i for i, q in enumerate(rq.index) if q.endswith(\"Q1\") and int(q[:4]) % 5 == 0]\n",
    "ax.set_xticks(ticks, [rq.index[i][:4] for i in ticks]); ax.set_ylabel(\"괴리(%)\")\n",
    "ax.grid(alpha=.3); ax.legend(frameon=False); ax.set_title(\"총부가가치 대비 괴리\", loc=\"left\")\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig2_recon.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0760658",
   "metadata": {},
   "source": [
    "## 검증 — 공표 산출물·공표 수치와 대조\n",
    "\n",
    "① 패널·몫·연계 행렬은 `data/published/`의 공표본 사본과 원소별로 비교한다(부동소수점 오차 범위).\n",
    "② 배분 유형·분화 몫·총량 정합은 원 노트북(`industry_pipeline/01_데이터입수_산업구분.ipynb`)의 실행 출력,\n",
    "③ 이차전지·신재생 몫 수치는 포털 「62개에서 69개로 — 7가지 분화 제안」, ④ 연쇄가중 재현 수치는 포털 「36개 산업 실질값으로 GVA 재현하기」의 검증표와 비교한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "895c4bc0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-07T20:32:57.663584Z",
     "iopub.status.busy": "2026-10-07T20:32:57.663584Z",
     "iopub.status.idle": "2026-10-07T20:32:57.717798Z",
     "shell.execute_reply": "2026-10-07T20:32:57.717294Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>항목</th>\n",
       "      <th>계산</th>\n",
       "      <th>공표</th>\n",
       "      <th>허용오차</th>\n",
       "      <th>통과</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>levels_62 공표본 대비 최대차(십억원)</td>\n",
       "      <td>1.16415e-10</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>levels_69 공표본 대비 최대차(십억원)</td>\n",
       "      <td>1.16415e-10</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>levels_agg 공표본 대비 최대차(십억원)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>share_battery 공표본 대비 최대차</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-12</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>share_renew 공표본 대비 최대차</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-12</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>io_linkage_62 공표본 대비 최대차</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>io_linkage_69 공표본 대비 최대차</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mapping_62 몫 공표본 대비 최대차</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>62합−36합 최대오차(십억원)</td>\n",
       "      <td>2.91038e-10</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>69합−36합 최대오차(십억원)</td>\n",
       "      <td>3.49246e-10</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>분화 항등식 최대오차(십억원)</td>\n",
       "      <td>7.27596e-12</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62분류 2025Q4 합계(십억원)</td>\n",
       "      <td>549,415</td>\n",
       "      <td>549,415</td>\n",
       "      <td>0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>분할 산업 수</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1:1·병합 산업 수</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>몫: 반도체</td>\n",
       "      <td>0.5471</td>\n",
       "      <td>0.5471</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>몫: 조선업</td>\n",
       "      <td>0.5703</td>\n",
       "      <td>0.5703</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>몫: 철강</td>\n",
       "      <td>0.7041</td>\n",
       "      <td>0.7041</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>몫: 택배</td>\n",
       "      <td>0.2066</td>\n",
       "      <td>0.2066</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>몫: 이차전지(평균)</td>\n",
       "      <td>0.1394</td>\n",
       "      <td>0.1394</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>몫: 신재생에너지(평균)</td>\n",
       "      <td>0.0335</td>\n",
       "      <td>0.0335</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>몫: 건물건설·건축보수</td>\n",
       "      <td>0.7013</td>\n",
       "      <td>0.7013</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>이차전지 κ</td>\n",
       "      <td>0.182221</td>\n",
       "      <td>0.1822</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>IO 373/37 비중 2020–23 평균(원 코드 상수)</td>\n",
       "      <td>0.19875</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>원 코드 상수 = 소분류표 비중 반올림(일치 연도 수)</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>신재생 발전비중 2006(%)</td>\n",
       "      <td>1.0439</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>신재생 발전비중 2016(%)</td>\n",
       "      <td>4.7805</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>신재생 발전비중 2025(%)</td>\n",
       "      <td>11.5004</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>전력부문 VA몫 IO45/(45+46)</td>\n",
       "      <td>0.795993</td>\n",
       "      <td>0.796</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>단순합 평균 |괴리|(%)</td>\n",
       "      <td>7.65469</td>\n",
       "      <td>7.655</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>단순합 최대 |괴리|(%)</td>\n",
       "      <td>25.2081</td>\n",
       "      <td>25.208</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>재현 평균 |괴리|(%)</td>\n",
       "      <td>0.232416</td>\n",
       "      <td>0.2324</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>재현 최대 |괴리|(%)</td>\n",
       "      <td>0.823244</td>\n",
       "      <td>0.8232</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>단순합 괴리 1980Q1(%)</td>\n",
       "      <td>21.9347</td>\n",
       "      <td>21.935</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>재현 괴리 1980Q1(%)</td>\n",
       "      <td>0.119986</td>\n",
       "      <td>0.12</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>단순합 괴리 2000Q1(%)</td>\n",
       "      <td>5.71561</td>\n",
       "      <td>5.716</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>재현 괴리 2000Q1(%)</td>\n",
       "      <td>0.215264</td>\n",
       "      <td>0.2153</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>단순합 괴리 2019Q4(%)</td>\n",
       "      <td>0.0027738</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>재현 괴리 2019Q4(%)</td>\n",
       "      <td>0.0380028</td>\n",
       "      <td>0.038</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>단순합 괴리 2020Q1(%)</td>\n",
       "      <td>0.0271549</td>\n",
       "      <td>0.027</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>재현 괴리 2020Q1(%)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>단순합 괴리 2020Q4(%)</td>\n",
       "      <td>-0.0305575</td>\n",
       "      <td>-0.031</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>재현 괴리 2020Q4(%)</td>\n",
       "      <td>0.068685</td>\n",
       "      <td>0.0687</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>단순합 괴리 2025Q4(%)</td>\n",
       "      <td>1.01251</td>\n",
       "      <td>1.013</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>재현 괴리 2025Q4(%)</td>\n",
       "      <td>0.327767</td>\n",
       "      <td>0.3278</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>연간 재현 최대 |괴리|(%)</td>\n",
       "      <td>0.00142224</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>분기별 단순합 괴리 184개 공표 대비 최대차(%p)</td>\n",
       "      <td>4.98075e-05</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>분기별 재현 괴리 184개 공표 대비 최대차(%p)</td>\n",
       "      <td>4.99501e-05</td>\n",
       "      <td>0</td>\n",
       "      <td>5e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  항목          계산      공표   허용오차    통과\n",
       "0          levels_62 공표본 대비 최대차(십억원) 1.16415e-10       0  1e-06  True\n",
       "1          levels_69 공표본 대비 최대차(십억원) 1.16415e-10       0  1e-06  True\n",
       "2         levels_agg 공표본 대비 최대차(십억원)           0       0  1e-06  True\n",
       "3           share_battery 공표본 대비 최대차           0       0  1e-12  True\n",
       "4             share_renew 공표본 대비 최대차           0       0  1e-12  True\n",
       "5           io_linkage_62 공표본 대비 최대차           0       0  1e-09  True\n",
       "6           io_linkage_69 공표본 대비 최대차           0       0  1e-09  True\n",
       "7            mapping_62 몫 공표본 대비 최대차           0       0      0  True\n",
       "8                  62합−36합 최대오차(십억원) 2.91038e-10       0  1e-06  True\n",
       "9                  69합−36합 최대오차(십억원) 3.49246e-10       0  1e-06  True\n",
       "10                  분화 항등식 최대오차(십억원) 7.27596e-12       0  1e-06  True\n",
       "11               62분류 2025Q4 합계(십억원)     549,415 549,415   0.05  True\n",
       "12                           분할 산업 수          47      47      0  True\n",
       "13                       1:1·병합 산업 수          15      15      0  True\n",
       "14                            몫: 반도체      0.5471  0.5471  5e-05  True\n",
       "15                            몫: 조선업      0.5703  0.5703  5e-05  True\n",
       "16                             몫: 철강      0.7041  0.7041  5e-05  True\n",
       "17                             몫: 택배      0.2066  0.2066  5e-05  True\n",
       "18                       몫: 이차전지(평균)      0.1394  0.1394  5e-05  True\n",
       "19                     몫: 신재생에너지(평균)      0.0335  0.0335  5e-05  True\n",
       "20                      몫: 건물건설·건축보수      0.7013  0.7013  5e-05  True\n",
       "21                            이차전지 κ    0.182221  0.1822  5e-05  True\n",
       "22  IO 373/37 비중 2020–23 평균(원 코드 상수)     0.19875   0.199 0.0005  True\n",
       "23    원 코드 상수 = 소분류표 비중 반올림(일치 연도 수)           4       4      0  True\n",
       "24                  신재생 발전비중 2006(%)      1.0439       1   0.05  True\n",
       "25                  신재생 발전비중 2016(%)      4.7805     4.8   0.05  True\n",
       "26                  신재생 발전비중 2025(%)     11.5004    11.5   0.05  True\n",
       "27             전력부문 VA몫 IO45/(45+46)    0.795993   0.796 0.0005  True\n",
       "28                    단순합 평균 |괴리|(%)     7.65469   7.655 0.0005  True\n",
       "29                    단순합 최대 |괴리|(%)     25.2081  25.208 0.0005  True\n",
       "30                     재현 평균 |괴리|(%)    0.232416  0.2324  5e-05  True\n",
       "31                     재현 최대 |괴리|(%)    0.823244  0.8232  5e-05  True\n",
       "32                  단순합 괴리 1980Q1(%)     21.9347  21.935 0.0005  True\n",
       "33                   재현 괴리 1980Q1(%)    0.119986    0.12  5e-05  True\n",
       "34                  단순합 괴리 2000Q1(%)     5.71561   5.716 0.0005  True\n",
       "35                   재현 괴리 2000Q1(%)    0.215264  0.2153  5e-05  True\n",
       "36                  단순합 괴리 2019Q4(%)   0.0027738   0.003 0.0005  True\n",
       "37                   재현 괴리 2019Q4(%)   0.0380028   0.038  5e-05  True\n",
       "38                  단순합 괴리 2020Q1(%)   0.0271549   0.027 0.0005  True\n",
       "39                   재현 괴리 2020Q1(%)           0       0  5e-05  True\n",
       "40                  단순합 괴리 2020Q4(%)  -0.0305575  -0.031 0.0005  True\n",
       "41                   재현 괴리 2020Q4(%)    0.068685  0.0687  5e-05  True\n",
       "42                  단순합 괴리 2025Q4(%)     1.01251   1.013 0.0005  True\n",
       "43                   재현 괴리 2025Q4(%)    0.327767  0.3278  5e-05  True\n",
       "44                  연간 재현 최대 |괴리|(%)  0.00142224  0.0014  5e-05  True\n",
       "45     분기별 단순합 괴리 184개 공표 대비 최대차(%p) 4.98075e-05       0  5e-05  True\n",
       "46      분기별 재현 괴리 184개 공표 대비 최대차(%p) 4.99501e-05       0  5e-05  True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "검증 통과: 47개 항목 — 62·69·총량 패널, 분화 몫, IO 연계 행렬, 연쇄가중 재현 수치를 재현했다.\n"
     ]
    }
   ],
   "source": [
    "P = C.PUBLISHED\n",
    "R = lambda d: 0.5 * 10 ** -d + 1e-12      # 소수 d자리 반올림 범위(부동소수점 여유 1e-12)\n",
    "pub = lambda n: os.path.join(C.PUB_DIR, n)\n",
    "fd = lambda n: F.max_abs_diff(rd(n, index_col=0), pub(n))[0]\n",
    "viewer = pd.read_csv(pub(\"recon_viewer.csv\"), index_col=0)\n",
    "map_pub = pd.read_csv(pub(\"mapping_62_official.csv\"))\n",
    "s36 = real.loc[L62.index].sum(axis=1)\n",
    "share = dict(zip(carve_log[\"신산업\"], carve_log[\"몫(평균)\"]))\n",
    "bench = F.io_battery_bench(pd.read_csv(C.IO_SMALL, dtype={\"소분류코드\": str}))\n",
    "kappa = np.mean(list(C.BATTERY_BENCH.values())) / (lambda m: (F.quarterly_mean(m[\"C282\"].dropna()) / F.quarterly_mean(m[\"C28\"].dropna())).loc[\"2020Q1\":\"2023Q4\"].mean())(F.read_mfg_prod(C.MFG_PROD))\n",
    "gen = act.set_index(\"year\")[\"renew_share\"] * 100\n",
    "nsplit = int(mapping[\"유형\"].str.startswith(\"분할\").sum())\n",
    "checks = [\n",
    "    (\"levels_62 공표본 대비 최대차(십억원)\", fd(\"levels_62.csv\"), 0.0, 1e-6),\n",
    "    (\"levels_69 공표본 대비 최대차(십억원)\", fd(\"levels_69.csv\"), 0.0, 1e-6),\n",
    "    (\"levels_agg 공표본 대비 최대차(십억원)\", fd(\"levels_agg.csv\"), 0.0, 1e-6),\n",
    "    (\"share_battery 공표본 대비 최대차\", fd(\"share_battery.csv\"), 0.0, 1e-12),\n",
    "    (\"share_renew 공표본 대비 최대차\", fd(\"share_renew.csv\"), 0.0, 1e-12),\n",
    "    (\"io_linkage_62 공표본 대비 최대차\", fd(\"io_linkage_62.csv\"), 0.0, 1e-9),\n",
    "    (\"io_linkage_69 공표본 대비 최대차\", fd(\"io_linkage_69.csv\"), 0.0, 1e-9),\n",
    "    (\"mapping_62 몫 공표본 대비 최대차\", float((mapping[\"몫\"] - map_pub[\"몫\"]).abs().max()), 0.0, 0.0),\n",
    "    (\"62합−36합 최대오차(십억원)\", float((L62.drop(columns=\"총량GDP\").sum(axis=1) - s36).abs().max()), P[\"62합-36합_최대오차\"], 1e-6),\n",
    "    (\"69합−36합 최대오차(십억원)\", float((L69.drop(columns=\"총량GDP\").sum(axis=1) - s36).abs().max()), P[\"69합-36합_최대오차\"], 1e-6),\n",
    "    (\"분화 항등식 최대오차(십억원)\", F.mece_gap(L62, L69, rules), 0.0, 1e-6),\n",
    "    (\"62분류 2025Q4 합계(십억원)\", float(L62.drop(columns=\"총량GDP\").loc[\"2025Q4\"].sum()), P[\"62_2025Q4_합계\"], R(1)),\n",
    "    (\"분할 산업 수\", nsplit, P[\"분할_산업수\"], 0),\n",
    "    (\"1:1·병합 산업 수\", len(mapping) - nsplit, P[\"1:1병합_산업수\"], 0),\n",
    "    (\"몫: 반도체\", share[\"반도체\"], P[\"몫_반도체\"], R(4)),\n",
    "    (\"몫: 조선업\", share[\"조선업\"], P[\"몫_조선업\"], R(4)),\n",
    "    (\"몫: 철강\", share[\"철강\"], P[\"몫_철강\"], R(4)),\n",
    "    (\"몫: 택배\", share[\"택배\"], P[\"몫_택배\"], R(4)),\n",
    "    (\"몫: 이차전지(평균)\", share[\"이차전지\"], P[\"몫_이차전지_평균\"], R(4)),\n",
    "    (\"몫: 신재생에너지(평균)\", share[\"신재생에너지\"], P[\"몫_신재생_평균\"], R(4)),\n",
    "    (\"몫: 건물건설·건축보수\", share[\"건물건설건축보수\"], P[\"몫_건물건설\"], R(4)),\n",
    "    (\"이차전지 κ\", kappa, P[\"이차전지_k\"], R(4)),\n",
    "    (\"IO 373/37 비중 2020–23 평균(원 코드 상수)\", np.mean(list(C.BATTERY_BENCH.values())), P[\"IO373_비중_2020_23평균\"], R(3)),\n",
    "    (\"원 코드 상수 = 소분류표 비중 반올림(일치 연도 수)\", int((bench.round(3) == pd.Series(C.BATTERY_BENCH)).sum()), 4, 0),\n",
    "    (\"신재생 발전비중 2006(%)\", gen[2006], P[\"발전비중_2006_pct\"], R(1)),\n",
    "    (\"신재생 발전비중 2016(%)\", gen[2016], P[\"발전비중_2016_pct\"], R(1)),\n",
    "    (\"신재생 발전비중 2025(%)\", gen[2025], P[\"발전비중_2025_pct\"], R(1)),\n",
    "    (\"전력부문 VA몫 IO45/(45+46)\", F.io_share(F.read_io_va83(C.IO_VA83), [45], [45, 46]), P[\"전력부문_VA몫\"], R(3)),\n",
    "    (\"단순합 평균 |괴리|(%)\", rq[\"단순합_괴리_pct\"].abs().mean(), P[\"단순합_평균괴리\"], R(3)),\n",
    "    (\"단순합 최대 |괴리|(%)\", rq[\"단순합_괴리_pct\"].abs().max(), P[\"단순합_최대괴리\"], R(3)),\n",
    "    (\"재현 평균 |괴리|(%)\", rq[\"재현_괴리_pct\"].abs().mean(), P[\"재현_평균괴리\"], R(4)),\n",
    "    (\"재현 최대 |괴리|(%)\", rq[\"재현_괴리_pct\"].abs().max(), P[\"재현_최대괴리\"], R(4)),\n",
    "]\n",
    "for q in [\"1980Q1\", \"2000Q1\", \"2019Q4\", \"2020Q1\", \"2020Q4\", \"2025Q4\"]:\n",
    "    checks.append((f\"단순합 괴리 {q}(%)\", rq.loc[q, \"단순합_괴리_pct\"], P[f\"단순합_{q}\"], R(3)))\n",
    "    checks.append((f\"재현 괴리 {q}(%)\", rq.loc[q, \"재현_괴리_pct\"], P[f\"재현_{q}\"], R(4)))\n",
    "checks += [\n",
    "    (\"연간 재현 최대 |괴리|(%)\", ra[\"재현_괴리_pct\"].abs().max(), P[\"연간_재현_최대괴리\"], R(4)),\n",
    "    (\"분기별 단순합 괴리 184개 공표 대비 최대차(%p)\", float((rq[\"단순합_괴리_pct\"] - viewer[\"단순합_괴리_pct\"]).abs().max()), 0.0, R(4)),\n",
    "    (\"분기별 재현 괴리 184개 공표 대비 최대차(%p)\", float((rq[\"재현_괴리_pct\"] - viewer[\"재현_괴리_pct\"]).abs().max()), 0.0, R(4)),\n",
    "]\n",
    "res = pd.DataFrame([{\"항목\": k, \"계산\": float(v), \"공표\": float(p), \"허용오차\": tol, \"통과\": bool(abs(float(v) - float(p)) <= tol)}\n",
    "                    for k, v, p, tol in checks])\n",
    "res.to_csv(os.path.join(C.OUTPUT, \"verification.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "with pd.option_context(\"display.float_format\", lambda v: f\"{v:,.6g}\", \"display.max_rows\", 100):\n",
    "    display(res)\n",
    "assert res[\"통과\"].all(), \"공표 수치와 계산 결과가 다르다 — 위 표에서 통과=False 항목을 확인한다\"\n",
    "print(f\"검증 통과: {len(res)}개 항목 — 62·69·총량 패널, 분화 몫, IO 연계 행렬, 연쇄가중 재현 수치를 재현했다.\")"
   ]
  }
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