{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fde41640",
   "metadata": {},
   "source": [
    "# 03 결과 도출 — 전망 표·그림, 이전 판 대비 변화, 전달 산출물, 검증\n",
    "\n",
    "`02_분석.ipynb`의 산출물로 5장의 표·그림을 만들고, 이전 판(2026-09-03 공표)과 비교하며, 한국고용정보원 전달 엑셀 형식의 재현본(62분류 부가가치, 2025–2035)과 비교 카드 자료(02 10절의 산업별 성장 요인 분해 포함)를 작성한다.\n",
    "전달 엑셀은 패키지 `output/`에만 만들며, 전달문서 폴더의 `_신규.xlsx`(사용자가 서식을 정리한 전달본)는 읽기만 한다.\n",
    "마지막 셀은 정비판 기록 산출물(`data/published/`)·핵심 수치와 계산 결과를 대조하고, 이전 판 전달본이 이전 판 전망으로 재현되는지도 확인한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "26d42025",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:20.362748Z",
     "iopub.status.busy": "2026-10-08T07:24:20.362748Z",
     "iopub.status.idle": "2026-10-08T07:24:21.282325Z",
     "shell.execute_reply": "2026-10-08T07:24:21.281821Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.12.10 · pandas 2.2.3 · numpy 2.4.6 · openpyxl 3.1.5 · matplotlib 3.11.0\n",
      "  드리프트 재설정 목표 1.8% · 수축 가중치 w=0.4(이전 판 0.45) · 원본 엑셀 대조: 없음\n",
      "  기존 전망 62     있음  data\\inputs\\prev_keis_va_62.csv\n",
      "  전달 템플릿       있음  data\\inputs\\template_62_VA_v1.xlsx\n",
      "  분기 자료(상류)    있음  data\\inputs\\upstream\n",
      "  정비판 기록       있음  data\\published\n",
      "  이전 판 공표      있음  data\\published_prev\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"1\")   # numpy보다 먼저 지정한다(config.py 주석 참조)\n",
    "import json\n",
    "import numpy as np, pandas as pd\n",
    "import config as C\n",
    "import gva_results as R\n",
    "pd.set_option(\"display.float_format\", lambda v: f\"{v:,.3f}\")\n",
    "C.setup()\n",
    "import matplotlib.pyplot as plt\n",
    "import koreanize_matplotlib  # noqa: F401  (한글 글꼴)\n",
    "rec = {r: R.read_long(os.path.join(C.OUTPUT, f\"forecast_annual_{r}.csv\")) for r in C.RES}\n",
    "bu = {r: R.read_long(os.path.join(C.OUTPUT, f\"bottomup_annual_{r}.csv\")) for r in C.RES}\n",
    "bu_raw = {r: R.read_long(os.path.join(C.OUTPUT, f\"bottomup_raw_annual_{r}.csv\")) for r in C.RES}\n",
    "agg = R.read_long(os.path.join(C.OUTPUT, \"topdown_annual.csv\"))\n",
    "prevrec = {r: R.read_long(os.path.join(C.PREV_DIR, f\"forecast_annual_{r}.csv\")) for r in C.RES}\n",
    "prev = pd.read_csv(C.PREV_KEIS, encoding=\"utf-8-sig\")\n",
    "fac = pd.read_csv(os.path.join(C.OUTPUT, \"reconcile_factors.csv\"), encoding=\"utf-8-sig\")\n",
    "bench = pd.read_csv(os.path.join(C.OUTPUT, \"benchmark_only_totals.csv\"), encoding=\"utf-8-sig\")\n",
    "wcur = pd.read_csv(os.path.join(C.OUTPUT, \"shrink_w_validation.csv\"), encoding=\"utf-8-sig\")\n",
    "wj = json.load(open(os.path.join(C.OUTPUT, \"wstar.json\"), encoding=\"utf-8\"))\n",
    "T = {r: R.totals(rec[r]) / 1e3 for r in C.RES}            # 조원\n",
    "TB = {r: R.totals(bu[r]) / 1e3 for r in C.RES}\n",
    "TBR = {r: R.totals(bu_raw[r]) / 1e3 for r in C.RES}\n",
    "TP = {r: R.totals(prevrec[r]) / 1e3 for r in C.RES}\n",
    "a25 = T[62].loc[C.BASE_YEAR, \"실적\"]\n",
    "LBL = {s: f\"{s} {C.SCN_NAME[s]}\" for s in C.SCENARIOS}\n",
    "UNIT = \"조 원(2020년 연쇄가격)\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5a52705",
   "metadata": {},
   "source": [
    "## 표 1. 시나리오별 전산업 실질부가가치 전망(62분류 합, 정합화 후) — 정비판과 이전 판"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0cdd5a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:21.283356Z",
     "iopub.status.busy": "2026-10-08T07:24:21.282824Z",
     "iopub.status.idle": "2026-10-08T07:24:21.324360Z",
     "shell.execute_reply": "2026-10-08T07:24:21.324360Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_bf9e2\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_bf9e2_level0_col0\" class=\"col_heading level0 col0\" >시나리오</th>\n",
       "      <th id=\"T_bf9e2_level0_col1\" class=\"col_heading level0 col1\" >2025(조원)</th>\n",
       "      <th id=\"T_bf9e2_level0_col2\" class=\"col_heading level0 col2\" >2030(조원)</th>\n",
       "      <th id=\"T_bf9e2_level0_col3\" class=\"col_heading level0 col3\" >2035(조원)</th>\n",
       "      <th id=\"T_bf9e2_level0_col4\" class=\"col_heading level0 col4\" >연평균 2025–35(%)</th>\n",
       "      <th id=\"T_bf9e2_level0_col5\" class=\"col_heading level0 col5\" >이전 판 2035(조원)</th>\n",
       "      <th id=\"T_bf9e2_level0_col6\" class=\"col_heading level0 col6\" >이전 판 연평균(%)</th>\n",
       "      <th id=\"T_bf9e2_level0_col7\" class=\"col_heading level0 col7\" >2035 변화(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_bf9e2_row0_col0\" class=\"data row0 col0\" >S1 기준</td>\n",
       "      <td id=\"T_bf9e2_row0_col1\" class=\"data row0 col1\" >2,178</td>\n",
       "      <td id=\"T_bf9e2_row0_col2\" class=\"data row0 col2\" >2,393</td>\n",
       "      <td id=\"T_bf9e2_row0_col3\" class=\"data row0 col3\" >2,609</td>\n",
       "      <td id=\"T_bf9e2_row0_col4\" class=\"data row0 col4\" >1.82</td>\n",
       "      <td id=\"T_bf9e2_row0_col5\" class=\"data row0 col5\" >2,689</td>\n",
       "      <td id=\"T_bf9e2_row0_col6\" class=\"data row0 col6\" >2.13</td>\n",
       "      <td id=\"T_bf9e2_row0_col7\" class=\"data row0 col7\" >-2.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_bf9e2_row1_col0\" class=\"data row1 col0\" >S2 공급제약</td>\n",
       "      <td id=\"T_bf9e2_row1_col1\" class=\"data row1 col1\" >2,178</td>\n",
       "      <td id=\"T_bf9e2_row1_col2\" class=\"data row1 col2\" >2,391</td>\n",
       "      <td id=\"T_bf9e2_row1_col3\" class=\"data row1 col3\" >2,606</td>\n",
       "      <td id=\"T_bf9e2_row1_col4\" class=\"data row1 col4\" >1.81</td>\n",
       "      <td id=\"T_bf9e2_row1_col5\" class=\"data row1 col5\" >2,650</td>\n",
       "      <td id=\"T_bf9e2_row1_col6\" class=\"data row1 col6\" >1.98</td>\n",
       "      <td id=\"T_bf9e2_row1_col7\" class=\"data row1 col7\" >-1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_bf9e2_row2_col0\" class=\"data row2 col0\" >S3 제약완화</td>\n",
       "      <td id=\"T_bf9e2_row2_col1\" class=\"data row2 col1\" >2,178</td>\n",
       "      <td id=\"T_bf9e2_row2_col2\" class=\"data row2 col2\" >2,394</td>\n",
       "      <td id=\"T_bf9e2_row2_col3\" class=\"data row2 col3\" >2,612</td>\n",
       "      <td id=\"T_bf9e2_row2_col4\" class=\"data row2 col4\" >1.83</td>\n",
       "      <td id=\"T_bf9e2_row2_col5\" class=\"data row2 col5\" >2,709</td>\n",
       "      <td id=\"T_bf9e2_row2_col6\" class=\"data row2 col6\" >2.20</td>\n",
       "      <td id=\"T_bf9e2_row2_col7\" class=\"data row2 col7\" >-3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_bf9e2_row3_col0\" class=\"data row3 col0\" >S4 기술도약</td>\n",
       "      <td id=\"T_bf9e2_row3_col1\" class=\"data row3 col1\" >2,178</td>\n",
       "      <td id=\"T_bf9e2_row3_col2\" class=\"data row3 col2\" >2,400</td>\n",
       "      <td id=\"T_bf9e2_row3_col3\" class=\"data row3 col3\" >2,608</td>\n",
       "      <td id=\"T_bf9e2_row3_col4\" class=\"data row3 col4\" >1.81</td>\n",
       "      <td id=\"T_bf9e2_row3_col5\" class=\"data row3 col5\" >2,741</td>\n",
       "      <td id=\"T_bf9e2_row3_col6\" class=\"data row3 col6\" >2.32</td>\n",
       "      <td id=\"T_bf9e2_row3_col7\" class=\"data row3 col7\" >-4.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_bf9e2_row4_col0\" class=\"data row4 col0\" >S5 지정학</td>\n",
       "      <td id=\"T_bf9e2_row4_col1\" class=\"data row4 col1\" >2,178</td>\n",
       "      <td id=\"T_bf9e2_row4_col2\" class=\"data row4 col2\" >2,394</td>\n",
       "      <td id=\"T_bf9e2_row4_col3\" class=\"data row4 col3\" >2,610</td>\n",
       "      <td id=\"T_bf9e2_row4_col4\" class=\"data row4 col4\" >1.82</td>\n",
       "      <td id=\"T_bf9e2_row4_col5\" class=\"data row4 col5\" >2,664</td>\n",
       "      <td id=\"T_bf9e2_row4_col6\" class=\"data row4 col6\" >2.03</td>\n",
       "      <td id=\"T_bf9e2_row4_col7\" class=\"data row4 col7\" >-2.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x118af0ccef0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = pd.DataFrame([{\"시나리오\": LBL[s], \"2025(조원)\": a25, \"2030(조원)\": T[62].loc[2030, s], \"2035(조원)\": T[62].loc[2035, s],\n",
    "                    \"연평균 2025–35(%)\": R.cagr(a25, T[62].loc[2035, s], 10),\n",
    "                    \"이전 판 2035(조원)\": TP[62].loc[2035, s], \"이전 판 연평균(%)\": R.cagr(a25, TP[62].loc[2035, s], 10),\n",
    "                    \"2035 변화(%)\": (T[62].loc[2035, s] / TP[62].loc[2035, s] - 1) * 100} for s in C.SCENARIOS])\n",
    "t1.to_csv(os.path.join(C.OUTPUT, \"table1_scenarios.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "t1.style.format({c: (\"{:,.0f}\" if \"조원\" in c else \"{:.2f}\") for c in t1.columns if c != \"시나리오\"}).hide(axis=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a79cb595",
   "metadata": {},
   "source": [
    "정비판의 시나리오 간 차이는 이전 판보다 훨씬 작다. 이전 판에서 시나리오를 가른 것은 주로 실질금리 수준 경로였다(6장 진단: 실질금리가 오르는 시나리오일수록 성장률이 높게 계산되었다).\n",
    "실질금리를 차분으로 넣으면 금리 경로의 일시적 차이는 일시적 효과만 남기고, 충격이 감쇠하는 레버 설계에서는 2035년의 수준 차이가 작다. 영구적인 차이는 인구 레버(노동공급)와 유가 수준에서 생긴다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e592679",
   "metadata": {},
   "source": [
    "## 그림 1. 전산업 경로 — 실적, 바텀업(재설정 전·후), 최종 전망, 이전 판, 기존 전망"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "04c2478d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:21.325365Z",
     "iopub.status.busy": "2026-10-08T07:24:21.325365Z",
     "iopub.status.idle": "2026-10-08T07:24:21.520340Z",
     "shell.execute_reply": "2026-10-08T07:24:21.520340Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(9, 5))\n",
    "h = T[62][\"실적\"].dropna()\n",
    "ax.plot(h.index, h.values, color=\"black\", lw=2, label=\"실적\")\n",
    "def joined(s):\n",
    "    x = s.copy(); x.loc[C.BASE_YEAR] = a25; return x.sort_index().loc[C.BASE_YEAR:]\n",
    "ax.plot(joined(TBR[62][\"S1\"]).index, joined(TBR[62][\"S1\"]).values, color=\"#bbbbbb\", ls=\"--\", label=\"바텀업(재설정 전, S1)\")\n",
    "ax.plot(joined(TB[62][\"S1\"]).index, joined(TB[62][\"S1\"]).values, color=\"#777777\", ls=\"--\", label=\"바텀업(드리프트 재설정, S1)\")\n",
    "ax.plot(joined(TP[62][\"S1\"]).index, joined(TP[62][\"S1\"]).values, color=\"#7aa6c2\", ls=\":\", lw=2, label=\"이전 판 최종(S1)\")\n",
    "cols = {\"S1\": \"#1f4e79\", \"S2\": \"#c0504d\", \"S3\": \"#9bbb59\", \"S4\": \"#a9781f\", \"S5\": \"#8064a2\"}\n",
    "for s in C.SCENARIOS:\n",
    "    ax.plot(joined(T[62][s]).index, joined(T[62][s]).values, color=cols[s], lw=1.6 if s != \"S1\" else 2.4, label=f\"최종 {LBL[s]}\")\n",
    "gp = pd.Series(R.prior_factors(prev)) * a25\n",
    "ax.plot(gp.index, gp.values, color=\"#555555\", lw=1, marker=\"o\", ms=3, label=\"기존 전망 성장률(2025 실적에 접합)\")\n",
    "ax.axvline(C.BASE_YEAR + 0.5, color=\"gray\", lw=0.8, ls=\"--\")\n",
    "ax.set_ylabel(UNIT); ax.set_xlim(2010, 2035.5); ax.set_ylim(1300, 3400); ax.grid(alpha=.3)\n",
    "ax.legend(fontsize=8, ncol=2, frameon=False, loc=\"upper left\")\n",
    "ax.set_title(\"62분류 전산업 실질부가가치: 바텀업·최종 전망과 이전 판\", loc=\"left\")\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig1_total_paths.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70f1ec5a",
   "metadata": {},
   "source": [
    "## 표 2. 정합화 단계별 2035년 전산업과 연평균(62분류)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ee34f5d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:21.521343Z",
     "iopub.status.busy": "2026-10-08T07:24:21.521343Z",
     "iopub.status.idle": "2026-10-08T07:24:21.529217Z",
     "shell.execute_reply": "2026-10-08T07:24:21.529217Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_01be7\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_01be7_level0_col0\" class=\"col_heading level0 col0\" >시나리오</th>\n",
       "      <th id=\"T_01be7_level0_col1\" class=\"col_heading level0 col1\" >바텀업 재설정 전(조원)</th>\n",
       "      <th id=\"T_01be7_level0_col2\" class=\"col_heading level0 col2\" >재설정 전(%)</th>\n",
       "      <th id=\"T_01be7_level0_col3\" class=\"col_heading level0 col3\" >바텀업(조원)</th>\n",
       "      <th id=\"T_01be7_level0_col4\" class=\"col_heading level0 col4\" >바텀업(%)</th>\n",
       "      <th id=\"T_01be7_level0_col5\" class=\"col_heading level0 col5\" >총량 벤치마킹 w=0(조원)</th>\n",
       "      <th id=\"T_01be7_level0_col6\" class=\"col_heading level0 col6\" >총량 벤치마킹 w=0(%)</th>\n",
       "      <th id=\"T_01be7_level0_col7\" class=\"col_heading level0 col7\" >최종 w=0.4(조원)</th>\n",
       "      <th id=\"T_01be7_level0_col8\" class=\"col_heading level0 col8\" >최종 w=0.4(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_01be7_row0_col0\" class=\"data row0 col0\" >S1 기준</td>\n",
       "      <td id=\"T_01be7_row0_col1\" class=\"data row0 col1\" >3,223</td>\n",
       "      <td id=\"T_01be7_row0_col2\" class=\"data row0 col2\" >4.00</td>\n",
       "      <td id=\"T_01be7_row0_col3\" class=\"data row0 col3\" >2,794</td>\n",
       "      <td id=\"T_01be7_row0_col4\" class=\"data row0 col4\" >2.52</td>\n",
       "      <td id=\"T_01be7_row0_col5\" class=\"data row0 col5\" >2,633</td>\n",
       "      <td id=\"T_01be7_row0_col6\" class=\"data row0 col6\" >1.91</td>\n",
       "      <td id=\"T_01be7_row0_col7\" class=\"data row0 col7\" >2,609</td>\n",
       "      <td id=\"T_01be7_row0_col8\" class=\"data row0 col8\" >1.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_01be7_row1_col0\" class=\"data row1 col0\" >S2 공급제약</td>\n",
       "      <td id=\"T_01be7_row1_col1\" class=\"data row1 col1\" >3,223</td>\n",
       "      <td id=\"T_01be7_row1_col2\" class=\"data row1 col2\" >3.99</td>\n",
       "      <td id=\"T_01be7_row1_col3\" class=\"data row1 col3\" >2,793</td>\n",
       "      <td id=\"T_01be7_row1_col4\" class=\"data row1 col4\" >2.52</td>\n",
       "      <td id=\"T_01be7_row1_col5\" class=\"data row1 col5\" >2,628</td>\n",
       "      <td id=\"T_01be7_row1_col6\" class=\"data row1 col6\" >1.89</td>\n",
       "      <td id=\"T_01be7_row1_col7\" class=\"data row1 col7\" >2,606</td>\n",
       "      <td id=\"T_01be7_row1_col8\" class=\"data row1 col8\" >1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_01be7_row2_col0\" class=\"data row2 col0\" >S3 제약완화</td>\n",
       "      <td id=\"T_01be7_row2_col1\" class=\"data row2 col1\" >3,222</td>\n",
       "      <td id=\"T_01be7_row2_col2\" class=\"data row2 col2\" >3.99</td>\n",
       "      <td id=\"T_01be7_row2_col3\" class=\"data row2 col3\" >2,793</td>\n",
       "      <td id=\"T_01be7_row2_col4\" class=\"data row2 col4\" >2.52</td>\n",
       "      <td id=\"T_01be7_row2_col5\" class=\"data row2 col5\" >2,638</td>\n",
       "      <td id=\"T_01be7_row2_col6\" class=\"data row2 col6\" >1.93</td>\n",
       "      <td id=\"T_01be7_row2_col7\" class=\"data row2 col7\" >2,612</td>\n",
       "      <td id=\"T_01be7_row2_col8\" class=\"data row2 col8\" >1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_01be7_row3_col0\" class=\"data row3 col0\" >S4 기술도약</td>\n",
       "      <td id=\"T_01be7_row3_col1\" class=\"data row3 col1\" >3,220</td>\n",
       "      <td id=\"T_01be7_row3_col2\" class=\"data row3 col2\" >3.99</td>\n",
       "      <td id=\"T_01be7_row3_col3\" class=\"data row3 col3\" >2,791</td>\n",
       "      <td id=\"T_01be7_row3_col4\" class=\"data row3 col4\" >2.51</td>\n",
       "      <td id=\"T_01be7_row3_col5\" class=\"data row3 col5\" >2,631</td>\n",
       "      <td id=\"T_01be7_row3_col6\" class=\"data row3 col6\" >1.90</td>\n",
       "      <td id=\"T_01be7_row3_col7\" class=\"data row3 col7\" >2,608</td>\n",
       "      <td id=\"T_01be7_row3_col8\" class=\"data row3 col8\" >1.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_01be7_row4_col0\" class=\"data row4 col0\" >S5 지정학</td>\n",
       "      <td id=\"T_01be7_row4_col1\" class=\"data row4 col1\" >3,232</td>\n",
       "      <td id=\"T_01be7_row4_col2\" class=\"data row4 col2\" >4.02</td>\n",
       "      <td id=\"T_01be7_row4_col3\" class=\"data row4 col3\" >2,801</td>\n",
       "      <td id=\"T_01be7_row4_col4\" class=\"data row4 col4\" >2.55</td>\n",
       "      <td id=\"T_01be7_row4_col5\" class=\"data row4 col5\" >2,635</td>\n",
       "      <td id=\"T_01be7_row4_col6\" class=\"data row4 col6\" >1.92</td>\n",
       "      <td id=\"T_01be7_row4_col7\" class=\"data row4 col7\" >2,610</td>\n",
       "      <td id=\"T_01be7_row4_col8\" class=\"data row4 col8\" >1.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x118adf9dd00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = []\n",
    "for s in C.SCENARIOS:\n",
    "    v0r, v0 = TBR[62].loc[2035, s], TB[62].loc[2035, s]\n",
    "    v1 = bench[(bench[\"분류\"] == 62) & (bench[\"시나리오\"] == s) & (bench[\"연도\"] == 2035)][\"합계_십억원\"].iloc[0] / 1e3\n",
    "    v2 = T[62].loc[2035, s]\n",
    "    rows.append({\"시나리오\": LBL[s], \"바텀업 재설정 전(조원)\": v0r, \"재설정 전(%)\": R.cagr(a25, v0r, 10),\n",
    "                 \"바텀업(조원)\": v0, \"바텀업(%)\": R.cagr(a25, v0, 10),\n",
    "                 \"총량 벤치마킹 w=0(조원)\": v1, \"총량 벤치마킹 w=0(%)\": R.cagr(a25, v1, 10),\n",
    "                 f\"최종 w={C.W_PRIOR}(조원)\": v2, f\"최종 w={C.W_PRIOR}(%)\": R.cagr(a25, v2, 10)})\n",
    "t2 = pd.DataFrame(rows)\n",
    "t2.to_csv(os.path.join(C.OUTPUT, \"table2_reconcile_steps.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "t2.style.format({c: (\"{:,.0f}\" if \"조원\" in c else \"{:.2f}\") for c in t2.columns if c != \"시나리오\"}).hide(axis=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75a9628",
   "metadata": {},
   "source": [
    "## 그림 2. 성장인자 — 통제 총량, 기존 전망, 결합(2025=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e241ed1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:21.530731Z",
     "iopub.status.busy": "2026-10-08T07:24:21.530227Z",
     "iopub.status.idle": "2026-10-08T07:24:21.812986Z",
     "shell.execute_reply": "2026-10-08T07:24:21.812478Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4.2))\n",
    "f62 = fac[fac[\"분류\"] == 62]\n",
    "def from25(x):                                    # 2025년(=1)을 앞에 붙인다\n",
    "    return pd.concat([pd.Series({C.BASE_YEAR: 1.0}), x])\n",
    "for s in C.SCENARIOS:\n",
    "    x = f62[f62[\"시나리오\"] == s].set_index(\"연도\")\n",
    "    ax.plot(from25(x[\"총량모형_gM\"]), color=cols[s], ls=\"--\", lw=1, label=f\"통제 총량 $g^M$ {s}\")\n",
    "    ax.plot(from25(x[\"결합_g\"]), color=cols[s], lw=2 if s == \"S1\" else 1.2, label=f\"결합 $g$ {s}\")\n",
    "x = f62[f62[\"시나리오\"] == \"S1\"].set_index(\"연도\")\n",
    "ax.plot(from25(x[\"기존전망_gP\"]), color=\"black\", marker=\"o\", ms=3, lw=1.2, label=\"기존 전망 $g^P$\")\n",
    "ax.set_ylabel(\"2025년 = 1\"); ax.grid(alpha=.3); ax.legend(fontsize=7, ncol=3, frameon=False)\n",
    "ax.set_title(r\"통제 총량의 성장인자: $g=\\exp\\,[\\,w\\ln g^P+(1-w)\\ln g^M\\,]$, $w=\" + str(C.W_PRIOR) + \"$\", loc=\"left\")\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig2_growth_factors.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7a7c66c",
   "metadata": {},
   "source": [
    "## 표 3. 36·62·69 분류 간 일관성\n",
    "\n",
    "세 분류는 같은 통제 총량에 맞추므로 정합화 후 전산업 합계가 같다. 반면 개별 산업 값은 분류마다 별도의 VARX로 전망되므로 같은 산업이라도 다를 수 있다.\n",
    "62분류와 69분류에 공통인 55개 산업(이름이 같은 산업)과, 69분류에서 분화된 산업 쌍의 합을 62분류 모산업과 비교한다(S1, 2035년)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1fbfbdf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:21.813983Z",
     "iopub.status.busy": "2026-10-08T07:24:21.813983Z",
     "iopub.status.idle": "2026-10-08T07:24:21.827332Z",
     "shell.execute_reply": "2026-10-08T07:24:21.826828Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "공통 55개 산업의 69/62 차이(S1 2035): 중앙값 -0.01%, 범위 -1.86% ~ +1.19% (최대: 14.석탄ㆍ석유제품)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_650c8\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_650c8_level0_col0\" class=\"col_heading level0 col0\" >구분</th>\n",
       "      <th id=\"T_650c8_level0_col1\" class=\"col_heading level0 col1\" >정합화 전</th>\n",
       "      <th id=\"T_650c8_level0_col2\" class=\"col_heading level0 col2\" >정합화 후</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_650c8_row0_col0\" class=\"data row0 col0\" >36분류 전산업 2035 S1(조원)</td>\n",
       "      <td id=\"T_650c8_row0_col1\" class=\"data row0 col1\" >2,777.0</td>\n",
       "      <td id=\"T_650c8_row0_col2\" class=\"data row0 col2\" >2,609.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_650c8_row1_col0\" class=\"data row1 col0\" >62분류 전산업 2035 S1(조원)</td>\n",
       "      <td id=\"T_650c8_row1_col1\" class=\"data row1 col1\" >2,793.7</td>\n",
       "      <td id=\"T_650c8_row1_col2\" class=\"data row1 col2\" >2,609.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_650c8_row2_col0\" class=\"data row2 col0\" >69분류 전산업 2035 S1(조원)</td>\n",
       "      <td id=\"T_650c8_row2_col1\" class=\"data row2 col1\" >2,795.5</td>\n",
       "      <td id=\"T_650c8_row2_col2\" class=\"data row2 col2\" >2,609.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x118b1324e00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_4b50d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_4b50d_level0_col0\" class=\"col_heading level0 col0\" >62분류 모산업</th>\n",
       "      <th id=\"T_4b50d_level0_col1\" class=\"col_heading level0 col1\" >62분류(조원)</th>\n",
       "      <th id=\"T_4b50d_level0_col2\" class=\"col_heading level0 col2\" >69분류 분화 합(조원)</th>\n",
       "      <th id=\"T_4b50d_level0_col3\" class=\"col_heading level0 col3\" >차이(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_4b50d_row0_col0\" class=\"data row0 col0\" >20.1차 금속</td>\n",
       "      <td id=\"T_4b50d_row0_col1\" class=\"data row0 col1\" >17.3</td>\n",
       "      <td id=\"T_4b50d_row0_col2\" class=\"data row0 col2\" >17.3</td>\n",
       "      <td id=\"T_4b50d_row0_col3\" class=\"data row0 col3\" >-0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4b50d_row1_col0\" class=\"data row1 col0\" >22.전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비</td>\n",
       "      <td id=\"T_4b50d_row1_col1\" class=\"data row1 col1\" >417.1</td>\n",
       "      <td id=\"T_4b50d_row1_col2\" class=\"data row1 col2\" >415.1</td>\n",
       "      <td id=\"T_4b50d_row1_col3\" class=\"data row1 col3\" >-0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4b50d_row2_col0\" class=\"data row2 col0\" >24.전기장비</td>\n",
       "      <td id=\"T_4b50d_row2_col1\" class=\"data row2 col1\" >38.6</td>\n",
       "      <td id=\"T_4b50d_row2_col2\" class=\"data row2 col2\" >40.6</td>\n",
       "      <td id=\"T_4b50d_row2_col3\" class=\"data row2 col3\" >+5.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4b50d_row3_col0\" class=\"data row3 col0\" >27.기타 운송장비</td>\n",
       "      <td id=\"T_4b50d_row3_col1\" class=\"data row3 col1\" >16.5</td>\n",
       "      <td id=\"T_4b50d_row3_col2\" class=\"data row3 col2\" >16.4</td>\n",
       "      <td id=\"T_4b50d_row3_col3\" class=\"data row3 col3\" >-0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4b50d_row4_col0\" class=\"data row4 col0\" >29.전기ㆍ가스ㆍ증기ㆍ공기조절공급업</td>\n",
       "      <td id=\"T_4b50d_row4_col1\" class=\"data row4 col1\" >45.1</td>\n",
       "      <td id=\"T_4b50d_row4_col2\" class=\"data row4 col2\" >47.0</td>\n",
       "      <td id=\"T_4b50d_row4_col3\" class=\"data row4 col3\" >+4.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4b50d_row5_col0\" class=\"data row5 col0\" >33.건설업</td>\n",
       "      <td id=\"T_4b50d_row5_col1\" class=\"data row5 col1\" >78.8</td>\n",
       "      <td id=\"T_4b50d_row5_col2\" class=\"data row5 col2\" >79.6</td>\n",
       "      <td id=\"T_4b50d_row5_col3\" class=\"data row5 col3\" >+1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_4b50d_row6_col0\" class=\"data row6 col0\" >38.창고ㆍ운송보조서비스업</td>\n",
       "      <td id=\"T_4b50d_row6_col1\" class=\"data row6 col1\" >59.5</td>\n",
       "      <td id=\"T_4b50d_row6_col2\" class=\"data row6 col2\" >59.2</td>\n",
       "      <td id=\"T_4b50d_row6_col3\" class=\"data row6 col3\" >-0.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x118b1324e00>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = [{\"구분\": f\"{r}분류 전산업 2035 S1(조원)\", \"정합화 전\": TB[r].loc[2035, \"S1\"], \"정합화 후\": T[r].loc[2035, \"S1\"]} for r in C.RES]\n",
    "t3a = pd.DataFrame(rows)\n",
    "x62 = rec[62][(rec[62][\"시나리오\"] == \"S1\") & (rec[62][\"연도\"] == 2035)].set_index(\"산업\")[R.VAL]\n",
    "x69 = rec[69][(rec[69][\"시나리오\"] == \"S1\") & (rec[69][\"연도\"] == 2035)].set_index(\"산업\")[R.VAL]\n",
    "common = sorted(set(x62.index) & set(x69.index))\n",
    "rel = (x69[common] / x62[common] - 1) * 100\n",
    "PAIRS = {\"20.1차 금속\": [\"철강\", \"20.1차 금속(잔여)\"],\n",
    "         \"22.전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비\": [\"반도체\", \"22.전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비(잔여)\"],\n",
    "         \"24.전기장비\": [\"이차전지\", \"24.전기장비(잔여)\"], \"27.기타 운송장비\": [\"조선업\", \"27.기타 운송장비(잔여)\"],\n",
    "         \"29.전기ㆍ가스ㆍ증기ㆍ공기조절공급업\": [\"신재생에너지\", \"29.전기ㆍ가스ㆍ증기ㆍ공기조절공급업(잔여)\"],\n",
    "         \"33.건설업\": [\"건물건설건축보수\", \"토목건설\"], \"38.창고ㆍ운송보조서비스업\": [\"택배\", \"38.창고ㆍ운송보조서비스업(잔여)\"]}\n",
    "assert len(common) == 55 and set(x69.index) == set(common) | {c for v in PAIRS.values() for c in v}\n",
    "t3b = pd.DataFrame([{\"62분류 모산업\": p, \"62분류(조원)\": x62[p] / 1e3, \"69분류 분화 합(조원)\": x69[c].sum() / 1e3,\n",
    "                     \"차이(%)\": (x69[c].sum() / x62[p] - 1) * 100} for p, c in PAIRS.items()])\n",
    "pd.concat([t3a.assign(표=\"전산업\"), t3b.assign(표=\"분화 산업\")]).to_csv(os.path.join(C.OUTPUT, \"table3_consistency.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "print(f\"공통 55개 산업의 69/62 차이(S1 2035): 중앙값 {rel.median():+.2f}%, 범위 {rel.min():+.2f}% ~ {rel.max():+.2f}%\"\n",
    "      f\" (최대: {rel.abs().idxmax()})\")\n",
    "display(t3a.style.format({\"정합화 전\": \"{:,.1f}\", \"정합화 후\": \"{:,.1f}\"}).hide(axis=\"index\"))\n",
    "t3b.style.format({\"62분류(조원)\": \"{:,.1f}\", \"69분류 분화 합(조원)\": \"{:,.1f}\", \"차이(%)\": \"{:+.2f}\"}).hide(axis=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f52ea776",
   "metadata": {},
   "source": [
    "## 표 4. 62분류 산업별 연평균 증가율(S1, 2025–2035) — 상·하위 10개, 이전 판 병기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9cf7c9a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:21.827831Z",
     "iopub.status.busy": "2026-10-08T07:24:21.827831Z",
     "iopub.status.idle": "2026-10-08T07:24:21.837574Z",
     "shell.execute_reply": "2026-10-08T07:24:21.837574Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_56066\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_56066_level0_col0\" class=\"col_heading level0 col0\" >구분</th>\n",
       "      <th id=\"T_56066_level0_col1\" class=\"col_heading level0 col1\" >2025(조원)</th>\n",
       "      <th id=\"T_56066_level0_col2\" class=\"col_heading level0 col2\" >2035(조원)</th>\n",
       "      <th id=\"T_56066_level0_col3\" class=\"col_heading level0 col3\" >연평균(%)</th>\n",
       "      <th id=\"T_56066_level0_col4\" class=\"col_heading level0 col4\" >이전 판 연평균(%)</th>\n",
       "      <th id=\"T_56066_level0_col5\" class=\"col_heading level0 col5\" >변화(%p)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >산업</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row0\" class=\"row_heading level0 row0\" >23.의료ㆍ정밀ㆍ광학기기ㆍ시계</th>\n",
       "      <td id=\"T_56066_row0_col0\" class=\"data row0 col0\" >상위</td>\n",
       "      <td id=\"T_56066_row0_col1\" class=\"data row0 col1\" >26.45</td>\n",
       "      <td id=\"T_56066_row0_col2\" class=\"data row0 col2\" >52.64</td>\n",
       "      <td id=\"T_56066_row0_col3\" class=\"data row0 col3\" >+7.12</td>\n",
       "      <td id=\"T_56066_row0_col4\" class=\"data row0 col4\" >+8.33</td>\n",
       "      <td id=\"T_56066_row0_col5\" class=\"data row0 col5\" >-1.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row1\" class=\"row_heading level0 row1\" >22.전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비</th>\n",
       "      <td id=\"T_56066_row1_col0\" class=\"data row1 col0\" >상위</td>\n",
       "      <td id=\"T_56066_row1_col1\" class=\"data row1 col1\" >209.91</td>\n",
       "      <td id=\"T_56066_row1_col2\" class=\"data row1 col2\" >417.11</td>\n",
       "      <td id=\"T_56066_row1_col3\" class=\"data row1 col3\" >+7.11</td>\n",
       "      <td id=\"T_56066_row1_col4\" class=\"data row1 col4\" >+8.37</td>\n",
       "      <td id=\"T_56066_row1_col5\" class=\"data row1 col5\" >-1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row2\" class=\"row_heading level0 row2\" >58.사회복지 서비스업</th>\n",
       "      <td id=\"T_56066_row2_col0\" class=\"data row2 col0\" >상위</td>\n",
       "      <td id=\"T_56066_row2_col1\" class=\"data row2 col1\" >34.15</td>\n",
       "      <td id=\"T_56066_row2_col2\" class=\"data row2 col2\" >51.12</td>\n",
       "      <td id=\"T_56066_row2_col3\" class=\"data row2 col3\" >+4.12</td>\n",
       "      <td id=\"T_56066_row2_col4\" class=\"data row2 col4\" >+3.87</td>\n",
       "      <td id=\"T_56066_row2_col5\" class=\"data row2 col5\" >+0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row3\" class=\"row_heading level0 row3\" >57.의료ㆍ보건업</th>\n",
       "      <td id=\"T_56066_row3_col0\" class=\"data row3 col0\" >상위</td>\n",
       "      <td id=\"T_56066_row3_col1\" class=\"data row3 col1\" >101.03</td>\n",
       "      <td id=\"T_56066_row3_col2\" class=\"data row3 col2\" >150.25</td>\n",
       "      <td id=\"T_56066_row3_col3\" class=\"data row3 col3\" >+4.05</td>\n",
       "      <td id=\"T_56066_row3_col4\" class=\"data row3 col4\" >+3.85</td>\n",
       "      <td id=\"T_56066_row3_col5\" class=\"data row3 col5\" >+0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row4\" class=\"row_heading level0 row4\" >47.보험ㆍ연금업</th>\n",
       "      <td id=\"T_56066_row4_col0\" class=\"data row4 col0\" >상위</td>\n",
       "      <td id=\"T_56066_row4_col1\" class=\"data row4 col1\" >17.88</td>\n",
       "      <td id=\"T_56066_row4_col2\" class=\"data row4 col2\" >26.51</td>\n",
       "      <td id=\"T_56066_row4_col3\" class=\"data row4 col3\" >+4.02</td>\n",
       "      <td id=\"T_56066_row4_col4\" class=\"data row4 col4\" >+4.14</td>\n",
       "      <td id=\"T_56066_row4_col5\" class=\"data row4 col5\" >-0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row5\" class=\"row_heading level0 row5\" >48.금융ㆍ보험관련서비스업</th>\n",
       "      <td id=\"T_56066_row5_col0\" class=\"data row5 col0\" >상위</td>\n",
       "      <td id=\"T_56066_row5_col1\" class=\"data row5 col1\" >36.29</td>\n",
       "      <td id=\"T_56066_row5_col2\" class=\"data row5 col2\" >53.16</td>\n",
       "      <td id=\"T_56066_row5_col3\" class=\"data row5 col3\" >+3.89</td>\n",
       "      <td id=\"T_56066_row5_col4\" class=\"data row5 col4\" >+4.00</td>\n",
       "      <td id=\"T_56066_row5_col5\" class=\"data row5 col5\" >-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row6\" class=\"row_heading level0 row6\" >46.금융업</th>\n",
       "      <td id=\"T_56066_row6_col0\" class=\"data row6 col0\" >상위</td>\n",
       "      <td id=\"T_56066_row6_col1\" class=\"data row6 col1\" >77.47</td>\n",
       "      <td id=\"T_56066_row6_col2\" class=\"data row6 col2\" >113.36</td>\n",
       "      <td id=\"T_56066_row6_col3\" class=\"data row6 col3\" >+3.88</td>\n",
       "      <td id=\"T_56066_row6_col4\" class=\"data row6 col4\" >+3.96</td>\n",
       "      <td id=\"T_56066_row6_col5\" class=\"data row6 col5\" >-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row7\" class=\"row_heading level0 row7\" >43.통신업</th>\n",
       "      <td id=\"T_56066_row7_col0\" class=\"data row7 col0\" >상위</td>\n",
       "      <td id=\"T_56066_row7_col1\" class=\"data row7 col1\" >29.88</td>\n",
       "      <td id=\"T_56066_row7_col2\" class=\"data row7 col2\" >40.65</td>\n",
       "      <td id=\"T_56066_row7_col3\" class=\"data row7 col3\" >+3.13</td>\n",
       "      <td id=\"T_56066_row7_col4\" class=\"data row7 col4\" >+1.57</td>\n",
       "      <td id=\"T_56066_row7_col5\" class=\"data row7 col5\" >+1.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row8\" class=\"row_heading level0 row8\" >40.출판업</th>\n",
       "      <td id=\"T_56066_row8_col0\" class=\"data row8 col0\" >상위</td>\n",
       "      <td id=\"T_56066_row8_col1\" class=\"data row8 col1\" >3.54</td>\n",
       "      <td id=\"T_56066_row8_col2\" class=\"data row8 col2\" >4.77</td>\n",
       "      <td id=\"T_56066_row8_col3\" class=\"data row8 col3\" >+3.04</td>\n",
       "      <td id=\"T_56066_row8_col4\" class=\"data row8 col4\" >+2.91</td>\n",
       "      <td id=\"T_56066_row8_col5\" class=\"data row8 col5\" >+0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row9\" class=\"row_heading level0 row9\" >41.영상ㆍ오디오기록물제작 배급업</th>\n",
       "      <td id=\"T_56066_row9_col0\" class=\"data row9 col0\" >상위</td>\n",
       "      <td id=\"T_56066_row9_col1\" class=\"data row9 col1\" >4.57</td>\n",
       "      <td id=\"T_56066_row9_col2\" class=\"data row9 col2\" >6.16</td>\n",
       "      <td id=\"T_56066_row9_col3\" class=\"data row9 col3\" >+3.03</td>\n",
       "      <td id=\"T_56066_row9_col4\" class=\"data row9 col4\" >+2.99</td>\n",
       "      <td id=\"T_56066_row9_col5\" class=\"data row9 col5\" >+0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row10\" class=\"row_heading level0 row10\" >11.목재ㆍ목제품</th>\n",
       "      <td id=\"T_56066_row10_col0\" class=\"data row10 col0\" >하위</td>\n",
       "      <td id=\"T_56066_row10_col1\" class=\"data row10 col1\" >2.09</td>\n",
       "      <td id=\"T_56066_row10_col2\" class=\"data row10 col2\" >1.68</td>\n",
       "      <td id=\"T_56066_row10_col3\" class=\"data row10 col3\" >-2.18</td>\n",
       "      <td id=\"T_56066_row10_col4\" class=\"data row10 col4\" >-1.31</td>\n",
       "      <td id=\"T_56066_row10_col5\" class=\"data row10 col5\" >-0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row11\" class=\"row_heading level0 row11\" >12.펄프ㆍ종이제품</th>\n",
       "      <td id=\"T_56066_row11_col0\" class=\"data row11 col0\" >하위</td>\n",
       "      <td id=\"T_56066_row11_col1\" class=\"data row11 col1\" >7.52</td>\n",
       "      <td id=\"T_56066_row11_col2\" class=\"data row11 col2\" >6.03</td>\n",
       "      <td id=\"T_56066_row11_col3\" class=\"data row11 col3\" >-2.19</td>\n",
       "      <td id=\"T_56066_row11_col4\" class=\"data row11 col4\" >-1.35</td>\n",
       "      <td id=\"T_56066_row11_col5\" class=\"data row11 col5\" >-0.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row12\" class=\"row_heading level0 row12\" >13.인쇄ㆍ복제</th>\n",
       "      <td id=\"T_56066_row12_col0\" class=\"data row12 col0\" >하위</td>\n",
       "      <td id=\"T_56066_row12_col1\" class=\"data row12 col1\" >3.79</td>\n",
       "      <td id=\"T_56066_row12_col2\" class=\"data row12 col2\" >3.04</td>\n",
       "      <td id=\"T_56066_row12_col3\" class=\"data row12 col3\" >-2.19</td>\n",
       "      <td id=\"T_56066_row12_col4\" class=\"data row12 col4\" >-1.45</td>\n",
       "      <td id=\"T_56066_row12_col5\" class=\"data row12 col5\" >-0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row13\" class=\"row_heading level0 row13\" >19.비금속광물제품</th>\n",
       "      <td id=\"T_56066_row13_col0\" class=\"data row13 col0\" >하위</td>\n",
       "      <td id=\"T_56066_row13_col1\" class=\"data row13 col1\" >9.62</td>\n",
       "      <td id=\"T_56066_row13_col2\" class=\"data row13 col2\" >7.55</td>\n",
       "      <td id=\"T_56066_row13_col3\" class=\"data row13 col3\" >-2.39</td>\n",
       "      <td id=\"T_56066_row13_col4\" class=\"data row13 col4\" >-1.93</td>\n",
       "      <td id=\"T_56066_row13_col5\" class=\"data row13 col5\" >-0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row14\" class=\"row_heading level0 row14\" >20.1차 금속</th>\n",
       "      <td id=\"T_56066_row14_col0\" class=\"data row14 col0\" >하위</td>\n",
       "      <td id=\"T_56066_row14_col1\" class=\"data row14 col1\" >24.09</td>\n",
       "      <td id=\"T_56066_row14_col2\" class=\"data row14 col2\" >17.34</td>\n",
       "      <td id=\"T_56066_row14_col3\" class=\"data row14 col3\" >-3.23</td>\n",
       "      <td id=\"T_56066_row14_col4\" class=\"data row14 col4\" >-2.80</td>\n",
       "      <td id=\"T_56066_row14_col5\" class=\"data row14 col5\" >-0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row15\" class=\"row_heading level0 row15\" >9.섬유ㆍ의복</th>\n",
       "      <td id=\"T_56066_row15_col0\" class=\"data row15 col0\" >하위</td>\n",
       "      <td id=\"T_56066_row15_col1\" class=\"data row15 col1\" >13.83</td>\n",
       "      <td id=\"T_56066_row15_col2\" class=\"data row15 col2\" >9.37</td>\n",
       "      <td id=\"T_56066_row15_col3\" class=\"data row15 col3\" >-3.82</td>\n",
       "      <td id=\"T_56066_row15_col4\" class=\"data row15 col4\" >-2.18</td>\n",
       "      <td id=\"T_56066_row15_col5\" class=\"data row15 col5\" >-1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row16\" class=\"row_heading level0 row16\" >10.가죽제품</th>\n",
       "      <td id=\"T_56066_row16_col0\" class=\"data row16 col0\" >하위</td>\n",
       "      <td id=\"T_56066_row16_col1\" class=\"data row16 col1\" >1.12</td>\n",
       "      <td id=\"T_56066_row16_col2\" class=\"data row16 col2\" >0.76</td>\n",
       "      <td id=\"T_56066_row16_col3\" class=\"data row16 col3\" >-3.86</td>\n",
       "      <td id=\"T_56066_row16_col4\" class=\"data row16 col4\" >-2.23</td>\n",
       "      <td id=\"T_56066_row16_col5\" class=\"data row16 col5\" >-1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row17\" class=\"row_heading level0 row17\" >5.금속ㆍ비금속광업</th>\n",
       "      <td id=\"T_56066_row17_col0\" class=\"data row17 col0\" >하위</td>\n",
       "      <td id=\"T_56066_row17_col1\" class=\"data row17 col1\" >1.38</td>\n",
       "      <td id=\"T_56066_row17_col2\" class=\"data row17 col2\" >0.90</td>\n",
       "      <td id=\"T_56066_row17_col3\" class=\"data row17 col3\" >-4.19</td>\n",
       "      <td id=\"T_56066_row17_col4\" class=\"data row17 col4\" >-5.49</td>\n",
       "      <td id=\"T_56066_row17_col5\" class=\"data row17 col5\" >+1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row18\" class=\"row_heading level0 row18\" >4.석탄ㆍ원유ㆍ천연가스</th>\n",
       "      <td id=\"T_56066_row18_col0\" class=\"data row18 col0\" >하위</td>\n",
       "      <td id=\"T_56066_row18_col1\" class=\"data row18 col1\" >0.02</td>\n",
       "      <td id=\"T_56066_row18_col2\" class=\"data row18 col2\" >0.01</td>\n",
       "      <td id=\"T_56066_row18_col3\" class=\"data row18 col3\" >-4.19</td>\n",
       "      <td id=\"T_56066_row18_col4\" class=\"data row18 col4\" >-5.60</td>\n",
       "      <td id=\"T_56066_row18_col5\" class=\"data row18 col5\" >+1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56066_level0_row19\" class=\"row_heading level0 row19\" >14.석탄ㆍ석유제품</th>\n",
       "      <td id=\"T_56066_row19_col0\" class=\"data row19 col0\" >하위</td>\n",
       "      <td id=\"T_56066_row19_col1\" class=\"data row19 col1\" >9.52</td>\n",
       "      <td id=\"T_56066_row19_col2\" class=\"data row19 col2\" >5.05</td>\n",
       "      <td id=\"T_56066_row19_col3\" class=\"data row19 col3\" >-6.14</td>\n",
       "      <td id=\"T_56066_row19_col4\" class=\"data row19 col4\" >-3.43</td>\n",
       "      <td id=\"T_56066_row19_col5\" class=\"data row19 col5\" >-2.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x118b16628d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r62 = rec[62]                                    # 요약표(소수 셋째 자리)가 아니라 반올림 전 값으로 계산한다\n",
    "v25 = r62[(r62[\"시나리오\"] == \"실적\") & (r62[\"연도\"] == C.BASE_YEAR)].set_index(\"산업\")[R.VAL]\n",
    "v35 = r62[(r62[\"시나리오\"] == \"S1\") & (r62[\"연도\"] == C.END_YEAR)].set_index(\"산업\")[R.VAL]\n",
    "p35 = prevrec[62][(prevrec[62][\"시나리오\"] == \"S1\") & (prevrec[62][\"연도\"] == C.END_YEAR)].set_index(\"산업\")[R.VAL]\n",
    "s1 = pd.DataFrame({\"2025(조원)\": v25 / 1e3, \"2035(조원)\": v35 / 1e3, \"연평균(%)\": ((v35 / v25) ** 0.1 - 1) * 100,\n",
    "                   \"이전 판 연평균(%)\": ((p35 / v25) ** 0.1 - 1) * 100})\n",
    "s1[\"변화(%p)\"] = s1[\"연평균(%)\"] - s1[\"이전 판 연평균(%)\"]\n",
    "s1 = s1.sort_values(\"연평균(%)\", ascending=False)\n",
    "t4 = pd.concat([s1.head(10).assign(구분=\"상위\"), s1.tail(10).assign(구분=\"하위\")])[[\"구분\", \"2025(조원)\", \"2035(조원)\", \"연평균(%)\", \"이전 판 연평균(%)\", \"변화(%p)\"]]\n",
    "t4.to_csv(os.path.join(C.OUTPUT, \"table4_industry_top_bottom.csv\"), encoding=\"utf-8-sig\")\n",
    "t4.style.format({\"2025(조원)\": \"{:,.2f}\", \"2035(조원)\": \"{:,.2f}\", \"연평균(%)\": \"{:+.2f}\", \"이전 판 연평균(%)\": \"{:+.2f}\", \"변화(%p)\": \"{:+.2f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0661476",
   "metadata": {},
   "source": [
    "## 그림 3. 62개 산업 연평균 증가율(S1, 2025–2035) — 정비판과 이전 판"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1eec896a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:21.838579Z",
     "iopub.status.busy": "2026-10-08T07:24:21.838579Z",
     "iopub.status.idle": "2026-10-08T07:24:22.276106Z",
     "shell.execute_reply": "2026-10-08T07:24:22.275603Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1100 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q = s1.sort_values(\"연평균(%)\")\n",
    "fig, ax = plt.subplots(figsize=(8, 11))\n",
    "yl = [s.split(\".\", 1)[1] for s in q.index]\n",
    "ax.barh(yl, q[\"연평균(%)\"].values, color=[\"#c0504d\" if v < 0 else \"#1f4e79\" for v in q[\"연평균(%)\"].values], label=\"정비판\")\n",
    "ax.scatter(q[\"이전 판 연평균(%)\"].values, yl, color=\"#a9781f\", s=10, zorder=3, label=\"이전 판\")\n",
    "ax.axvline(R.cagr(a25, T[62].loc[2035, \"S1\"], 10), color=\"#1f4e79\", lw=1, ls=\"--\", label=f\"전산업 {R.cagr(a25, T[62].loc[2035, 'S1'], 10):.2f}%\")\n",
    "ax.set_xlabel(\"연 %\"); ax.grid(axis=\"x\", alpha=.3); ax.tick_params(axis=\"y\", labelsize=7); ax.legend(frameon=False)\n",
    "ax.set_title(\"62개 산업 실질부가가치 연평균 증가율(S1, 정합화 후)\", loc=\"left\")\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig3_industry_cagr_62.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f0881c7",
   "metadata": {},
   "source": [
    "## 전달 엑셀 형식 — 62분류 부가가치(2025–2035, VA만)\n",
    "\n",
    "KEIS 전달 템플릿(2000–2034)을 복사해 2024–2035년을 채운다. 각 산업의 값은 **템플릿 2023년 수준 × (정합화 S1 전망의 t년 값 / 2023년 실적)** 이다.\n",
    "즉 수준은 템플릿을, 성장은 이 연구의 전망을 따른다. 2035년 열(AM)을 새로 만들고 전산업 행(65행)은 62개 산업의 합으로, 대분류 소계(68–76행)는 2034년 수식을 복제한다.\n",
    "결과는 `output/sector62_VA_2025-2035_재현.xlsx`이며, 전달문서 폴더의 전달본(`_신규.xlsx`)을 바꾸지 않는다(전달본 교체는 사용자 확인 후).\n",
    "\n",
    "이전 판 전달본의 재현도 함께 확인한다. 이전 판 공표 전망으로 같은 논리를 실행하면 전달본(`_신규.xlsx`)의 값과 일치해야 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0386a411",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:22.277605Z",
     "iopub.status.busy": "2026-10-08T07:24:22.277106Z",
     "iopub.status.idle": "2026-10-08T07:24:22.434783Z",
     "shell.execute_reply": "2026-10-08T07:24:22.434280Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "채운 파일의 성장인자 대 전망 성장인자 최대 절대차 8.9e-16 · 전산업 행 − 산업 합 최대차 9.5e-13조 원\n",
      "이전 판 전망으로 채운 값 대 이전 판 전달본(_신규.xlsx): 최대 상대차 2.1e-16\n"
     ]
    }
   ],
   "source": [
    "XLSX = os.path.join(C.OUTPUT, \"sector62_VA_2025-2035_재현.xlsx\")\n",
    "fill = R.fill_template(C.TEMPLATE, rec[62], XLSX)\n",
    "fv = R.read_filled_values(XLSX)\n",
    "our = R.s1_with_actuals(rec[62]); our.columns = [int(c.split(\".\")[0]) for c in our.columns]\n",
    "w = fv[fv[\"번호\"] > 0].pivot(index=\"연도\", columns=\"번호\", values=\"VA_백만원\")\n",
    "g_x = (w.loc[2024:2035] / w.loc[2023]); g_f = (our.loc[2024:2035] / our.loc[2023])[w.columns]\n",
    "d_growth = float((g_x - g_f).abs().max().max())\n",
    "d_sum = float((fv[fv[\"번호\"] == 0].set_index(\"연도\")[\"VA_백만원\"] - w.sum(axis=1)).abs().max() / 1e6)\n",
    "print(f\"채운 파일의 성장인자 대 전망 성장인자 최대 절대차 {d_growth:.1e} · 전산업 행 − 산업 합 최대차 {d_sum:.1e}조 원\")\n",
    "\n",
    "fill_p = R.fill_template(C.TEMPLATE, prevrec[62], os.path.join(C.INTERIM, \"fill_check_prev.xlsx\"), copy_style=False)\n",
    "deliv = pd.read_csv(os.path.join(C.PREV_DIR, \"delivered_62_VA_2025-2035.csv\"), encoding=\"utf-8-sig\")\n",
    "m = R.read_filled_values(os.path.join(C.INTERIM, \"fill_check_prev.xlsx\")).merge(deliv, on=[\"번호\", \"연도\"], suffixes=(\"_계산\", \"_전달\"))\n",
    "assert len(m) == len(deliv) and (m[\"산업_계산\"] == m[\"산업_전달\"]).all()\n",
    "d_deliv_prev = float(((m[\"VA_백만원_계산\"] - m[\"VA_백만원_전달\"]).abs() / m[\"VA_백만원_전달\"].abs()).max())\n",
    "print(f\"이전 판 전망으로 채운 값 대 이전 판 전달본(_신규.xlsx): 최대 상대차 {d_deliv_prev:.1e}\")\n",
    "\n",
    "names = {int(c.split(\".\")[0]): c.split(\".\", 1)[1] for c in rec[62][\"산업\"].unique()}\n",
    "card = R.card_data(fill, names); R.json_dump(card, os.path.join(C.OUTPUT, \"card_2025_2035.json\"))\n",
    "shap62 = json.load(open(os.path.join(C.OUTPUT, \"shap_62.json\"), encoding=\"utf-8\"))     # 02 10절 성장 요인 분해\n",
    "detail = R.va_detail(C.TEMPLATE, XLSX, shap62); R.json_dump(detail, os.path.join(C.OUTPUT, \"va_detail_62.json\"))\n",
    "card_prev = json.load(open(os.path.join(C.PREV_DIR, \"card_2025_2035.json\"), encoding=\"utf-8\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b2c2f30",
   "metadata": {},
   "source": [
    "## 표 5. 기존 전망(2024–2034판)·이전 판 대비 — 전달 템플릿 수준\n",
    "\n",
    "전달 엑셀은 템플릿의 수준(2023년 전산업 2,247조 원)을 쓰므로 전망 파일(2023년 2,090조 원)과 수준이 다르다. 비교는 같은 수준의 기존 전망·이전 판 전달본과 한다.\n",
    "산업별 상·하위는 **신규 − 기존 연평균(2024–2034) 차이**를 기준으로 6개씩 고른다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fedc566",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:22.435286Z",
     "iopub.status.busy": "2026-10-08T07:24:22.435286Z",
     "iopub.status.idle": "2026-10-08T07:24:22.446579Z",
     "shell.execute_reply": "2026-10-08T07:24:22.446579Z"
    }
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th id=\"T_fc35e_level0_col0\" class=\"col_heading level0 col0\" >구분</th>\n",
       "      <th id=\"T_fc35e_level0_col1\" class=\"col_heading level0 col1\" >2024(조원)</th>\n",
       "      <th id=\"T_fc35e_level0_col2\" class=\"col_heading level0 col2\" >2034(조원)</th>\n",
       "      <th id=\"T_fc35e_level0_col3\" class=\"col_heading level0 col3\" >2035(조원)</th>\n",
       "      <th id=\"T_fc35e_level0_col4\" class=\"col_heading level0 col4\" >연평균 2024–34(%)</th>\n",
       "      <th id=\"T_fc35e_level0_col5\" class=\"col_heading level0 col5\" >연평균 2025–35(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_fc35e_row0_col0\" class=\"data row0 col0\" >기존 전망(2024–2034판)</td>\n",
       "      <td id=\"T_fc35e_row0_col1\" class=\"data row0 col1\" >2,292.20</td>\n",
       "      <td id=\"T_fc35e_row0_col2\" class=\"data row0 col2\" >2,694.46</td>\n",
       "      <td id=\"T_fc35e_row0_col3\" class=\"data row0 col3\" >—</td>\n",
       "      <td id=\"T_fc35e_row0_col4\" class=\"data row0 col4\" >1.63</td>\n",
       "      <td id=\"T_fc35e_row0_col5\" class=\"data row0 col5\" >—</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_fc35e_row1_col0\" class=\"data row1 col0\" >이전 판 전달본(2026-09)</td>\n",
       "      <td id=\"T_fc35e_row1_col1\" class=\"data row1 col1\" >2,305.83</td>\n",
       "      <td id=\"T_fc35e_row1_col2\" class=\"data row1 col2\" >2,823.58</td>\n",
       "      <td id=\"T_fc35e_row1_col3\" class=\"data row1 col3\" >2,880.25</td>\n",
       "      <td id=\"T_fc35e_row1_col4\" class=\"data row1 col4\" >2.05</td>\n",
       "      <td id=\"T_fc35e_row1_col5\" class=\"data row1 col5\" >2.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_fc35e_row2_col0\" class=\"data row2 col0\" >정비판(정합화 S1)</td>\n",
       "      <td id=\"T_fc35e_row2_col1\" class=\"data row2 col1\" >2,305.83</td>\n",
       "      <td id=\"T_fc35e_row2_col2\" class=\"data row2 col2\" >2,751.46</td>\n",
       "      <td id=\"T_fc35e_row2_col3\" class=\"data row2 col3\" >2,797.26</td>\n",
       "      <td id=\"T_fc35e_row2_col4\" class=\"data row2 col4\" >1.78</td>\n",
       "      <td id=\"T_fc35e_row2_col5\" class=\"data row2 col5\" >1.80</td>\n",
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       "<style type=\"text/css\">\n",
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       "<table id=\"T_2d4ff\">\n",
       "  <thead>\n",
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       "      <th id=\"T_2d4ff_level0_col0\" class=\"col_heading level0 col0\" >구분</th>\n",
       "      <th id=\"T_2d4ff_level0_col1\" class=\"col_heading level0 col1\" >번호</th>\n",
       "      <th id=\"T_2d4ff_level0_col2\" class=\"col_heading level0 col2\" >산업</th>\n",
       "      <th id=\"T_2d4ff_level0_col3\" class=\"col_heading level0 col3\" >기존 연평균(%)</th>\n",
       "      <th id=\"T_2d4ff_level0_col4\" class=\"col_heading level0 col4\" >신규 연평균(%)</th>\n",
       "      <th id=\"T_2d4ff_level0_col5\" class=\"col_heading level0 col5\" >차이(%p)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td id=\"T_2d4ff_row0_col0\" class=\"data row0 col0\" >상위</td>\n",
       "      <td id=\"T_2d4ff_row0_col1\" class=\"data row0 col1\" >23</td>\n",
       "      <td id=\"T_2d4ff_row0_col2\" class=\"data row0 col2\" >의료ㆍ정밀ㆍ광학기기ㆍ시계</td>\n",
       "      <td id=\"T_2d4ff_row0_col3\" class=\"data row0 col3\" >+1.94</td>\n",
       "      <td id=\"T_2d4ff_row0_col4\" class=\"data row0 col4\" >+7.45</td>\n",
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       "      <td id=\"T_2d4ff_row1_col0\" class=\"data row1 col0\" >상위</td>\n",
       "      <td id=\"T_2d4ff_row1_col1\" class=\"data row1 col1\" >22</td>\n",
       "      <td id=\"T_2d4ff_row1_col2\" class=\"data row1 col2\" >전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비</td>\n",
       "      <td id=\"T_2d4ff_row1_col3\" class=\"data row1 col3\" >+2.68</td>\n",
       "      <td id=\"T_2d4ff_row1_col4\" class=\"data row1 col4\" >+7.44</td>\n",
       "      <td id=\"T_2d4ff_row1_col5\" class=\"data row1 col5\" >+4.77</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_2d4ff_row2_col0\" class=\"data row2 col0\" >상위</td>\n",
       "      <td id=\"T_2d4ff_row2_col1\" class=\"data row2 col1\" >16</td>\n",
       "      <td id=\"T_2d4ff_row2_col2\" class=\"data row2 col2\" >화학섬유</td>\n",
       "      <td id=\"T_2d4ff_row2_col3\" class=\"data row2 col3\" >-2.75</td>\n",
       "      <td id=\"T_2d4ff_row2_col4\" class=\"data row2 col4\" >+0.24</td>\n",
       "      <td id=\"T_2d4ff_row2_col5\" class=\"data row2 col5\" >+2.99</td>\n",
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       "      <td id=\"T_2d4ff_row3_col2\" class=\"data row3 col2\" >보험ㆍ연금업</td>\n",
       "      <td id=\"T_2d4ff_row3_col3\" class=\"data row3 col3\" >+1.81</td>\n",
       "      <td id=\"T_2d4ff_row3_col4\" class=\"data row3 col4\" >+4.20</td>\n",
       "      <td id=\"T_2d4ff_row3_col5\" class=\"data row3 col5\" >+2.39</td>\n",
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       "    <tr>\n",
       "      <td id=\"T_2d4ff_row4_col0\" class=\"data row4 col0\" >상위</td>\n",
       "      <td id=\"T_2d4ff_row4_col1\" class=\"data row4 col1\" >48</td>\n",
       "      <td id=\"T_2d4ff_row4_col2\" class=\"data row4 col2\" >금융ㆍ보험관련서비스업</td>\n",
       "      <td id=\"T_2d4ff_row4_col3\" class=\"data row4 col3\" >+1.70</td>\n",
       "      <td id=\"T_2d4ff_row4_col4\" class=\"data row4 col4\" >+4.08</td>\n",
       "      <td id=\"T_2d4ff_row4_col5\" class=\"data row4 col5\" >+2.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2d4ff_row5_col0\" class=\"data row5 col0\" >상위</td>\n",
       "      <td id=\"T_2d4ff_row5_col1\" class=\"data row5 col1\" >57</td>\n",
       "      <td id=\"T_2d4ff_row5_col2\" class=\"data row5 col2\" >의료ㆍ보건업</td>\n",
       "      <td id=\"T_2d4ff_row5_col3\" class=\"data row5 col3\" >+2.30</td>\n",
       "      <td id=\"T_2d4ff_row5_col4\" class=\"data row5 col4\" >+4.27</td>\n",
       "      <td id=\"T_2d4ff_row5_col5\" class=\"data row5 col5\" >+1.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2d4ff_row6_col0\" class=\"data row6 col0\" >하위</td>\n",
       "      <td id=\"T_2d4ff_row6_col1\" class=\"data row6 col1\" >33</td>\n",
       "      <td id=\"T_2d4ff_row6_col2\" class=\"data row6 col2\" >건설업</td>\n",
       "      <td id=\"T_2d4ff_row6_col3\" class=\"data row6 col3\" >+0.78</td>\n",
       "      <td id=\"T_2d4ff_row6_col4\" class=\"data row6 col4\" >-2.69</td>\n",
       "      <td id=\"T_2d4ff_row6_col5\" class=\"data row6 col5\" >-3.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2d4ff_row7_col0\" class=\"data row7 col0\" >하위</td>\n",
       "      <td id=\"T_2d4ff_row7_col1\" class=\"data row7 col1\" >9</td>\n",
       "      <td id=\"T_2d4ff_row7_col2\" class=\"data row7 col2\" >섬유ㆍ의복</td>\n",
       "      <td id=\"T_2d4ff_row7_col3\" class=\"data row7 col3\" >+0.16</td>\n",
       "      <td id=\"T_2d4ff_row7_col4\" class=\"data row7 col4\" >-3.35</td>\n",
       "      <td id=\"T_2d4ff_row7_col5\" class=\"data row7 col5\" >-3.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2d4ff_row8_col0\" class=\"data row8 col0\" >하위</td>\n",
       "      <td id=\"T_2d4ff_row8_col1\" class=\"data row8 col1\" >20</td>\n",
       "      <td id=\"T_2d4ff_row8_col2\" class=\"data row8 col2\" >1차 금속</td>\n",
       "      <td id=\"T_2d4ff_row8_col3\" class=\"data row8 col3\" >+0.50</td>\n",
       "      <td id=\"T_2d4ff_row8_col4\" class=\"data row8 col4\" >-3.03</td>\n",
       "      <td id=\"T_2d4ff_row8_col5\" class=\"data row8 col5\" >-3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2d4ff_row9_col0\" class=\"data row9 col0\" >하위</td>\n",
       "      <td id=\"T_2d4ff_row9_col1\" class=\"data row9 col1\" >19</td>\n",
       "      <td id=\"T_2d4ff_row9_col2\" class=\"data row9 col2\" >비금속광물제품</td>\n",
       "      <td id=\"T_2d4ff_row9_col3\" class=\"data row9 col3\" >+0.62</td>\n",
       "      <td id=\"T_2d4ff_row9_col4\" class=\"data row9 col4\" >-3.02</td>\n",
       "      <td id=\"T_2d4ff_row9_col5\" class=\"data row9 col5\" >-3.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2d4ff_row10_col0\" class=\"data row10 col0\" >하위</td>\n",
       "      <td id=\"T_2d4ff_row10_col1\" class=\"data row10 col1\" >5</td>\n",
       "      <td id=\"T_2d4ff_row10_col2\" class=\"data row10 col2\" >금속ㆍ비금속광업</td>\n",
       "      <td id=\"T_2d4ff_row10_col3\" class=\"data row10 col3\" >+0.10</td>\n",
       "      <td id=\"T_2d4ff_row10_col4\" class=\"data row10 col4\" >-4.41</td>\n",
       "      <td id=\"T_2d4ff_row10_col5\" class=\"data row10 col5\" >-4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2d4ff_row11_col0\" class=\"data row11 col0\" >하위</td>\n",
       "      <td id=\"T_2d4ff_row11_col1\" class=\"data row11 col1\" >14</td>\n",
       "      <td id=\"T_2d4ff_row11_col2\" class=\"data row11 col2\" >석탄ㆍ석유제품</td>\n",
       "      <td id=\"T_2d4ff_row11_col3\" class=\"data row11 col3\" >-0.01</td>\n",
       "      <td id=\"T_2d4ff_row11_col4\" class=\"data row11 col4\" >-5.63</td>\n",
       "      <td id=\"T_2d4ff_row11_col5\" class=\"data row11 col5\" >-5.62</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ag, agp = card[\"agg\"], card_prev[\"agg\"]\n",
    "t5a = pd.DataFrame([{\"구분\": \"기존 전망(2024–2034판)\", \"2024(조원)\": ag[\"old_path\"][\"2024\"], \"2034(조원)\": ag[\"old_2034\"], \"2035(조원)\": np.nan,\n",
    "                     \"연평균 2024–34(%)\": ag[\"old_cagr_24_34\"], \"연평균 2025–35(%)\": np.nan},\n",
    "                    {\"구분\": \"이전 판 전달본(2026-09)\", \"2024(조원)\": agp[\"new_path\"][\"2024\"], \"2034(조원)\": agp[\"new_2034\"], \"2035(조원)\": agp[\"new_2035\"],\n",
    "                     \"연평균 2024–34(%)\": agp[\"new_cagr_24_34\"], \"연평균 2025–35(%)\": agp[\"new_cagr_25_35\"]},\n",
    "                    {\"구분\": \"정비판(정합화 S1)\", \"2024(조원)\": ag[\"new_path\"][\"2024\"], \"2034(조원)\": ag[\"new_2034\"], \"2035(조원)\": ag[\"new_2035\"],\n",
    "                     \"연평균 2024–34(%)\": ag[\"new_cagr_24_34\"], \"연평균 2025–35(%)\": ag[\"new_cagr_25_35\"]}])\n",
    "ind = pd.DataFrame(card[\"inds\"]).assign(차이=lambda d: d[\"new_cagr\"] - d[\"old_cagr\"]).sort_values(\"차이\", ascending=False)\n",
    "t5b = pd.concat([ind.head(6).assign(구분=\"상위\"), ind.tail(6).assign(구분=\"하위\")])[[\"구분\", \"num\", \"name\", \"old_cagr\", \"new_cagr\", \"차이\"]]\n",
    "t5b.columns = [\"구분\", \"번호\", \"산업\", \"기존 연평균(%)\", \"신규 연평균(%)\", \"차이(%p)\"]\n",
    "pd.concat([t5a.assign(표=\"전산업\"), t5b.assign(표=\"산업별\")]).to_csv(os.path.join(C.OUTPUT, \"table5_vs_previous.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "display(t5a.style.format({c: \"{:,.2f}\" for c in t5a.columns if c != \"구분\"}, na_rep=\"—\").hide(axis=\"index\"))\n",
    "t5b.style.format({\"기존 연평균(%)\": \"{:+.2f}\", \"신규 연평균(%)\": \"{:+.2f}\", \"차이(%p)\": \"{:+.2f}\"}).hide(axis=\"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f0e75f",
   "metadata": {},
   "source": [
    "## 표 6. 이전 판 대비 변화 요약 — 전산업·바텀업·총량·가중치\n",
    "\n",
    "변경 전후의 핵심 수치를 한 표로 모은다. 이전 판 값은 `data/published_prev/`와 `config.PREVIOUS`에서 읽는다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "13df6af8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:22.447584Z",
     "iopub.status.busy": "2026-10-08T07:24:22.447584Z",
     "iopub.status.idle": "2026-10-08T07:24:22.460974Z",
     "shell.execute_reply": "2026-10-08T07:24:22.460974Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_cdb62\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cdb62_level0_col0\" class=\"col_heading level0 col0\" >정비판</th>\n",
       "      <th id=\"T_cdb62_level0_col1\" class=\"col_heading level0 col1\" >이전 판</th>\n",
       "      <th id=\"T_cdb62_level0_col2\" class=\"col_heading level0 col2\" >변화</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >항목</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cdb62_level0_row0\" class=\"row_heading level0 row0\" >전산업 S1 2035(조원, 62분류 합)</th>\n",
       "      <td id=\"T_cdb62_row0_col0\" class=\"data row0 col0\" >2,609.028</td>\n",
       "      <td id=\"T_cdb62_row0_col1\" class=\"data row0 col1\" >2,688.518</td>\n",
       "      <td id=\"T_cdb62_row0_col2\" class=\"data row0 col2\" >-79.490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdb62_level0_row1\" class=\"row_heading level0 row1\" >전산업 S1 연평균 2025–35(%)</th>\n",
       "      <td id=\"T_cdb62_row1_col0\" class=\"data row1 col0\" >1.820</td>\n",
       "      <td id=\"T_cdb62_row1_col1\" class=\"data row1 col1\" >2.126</td>\n",
       "      <td id=\"T_cdb62_row1_col2\" class=\"data row1 col2\" >-0.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdb62_level0_row2\" class=\"row_heading level0 row2\" >시나리오 범위 2035(조원, 최대−최소)</th>\n",
       "      <td id=\"T_cdb62_row2_col0\" class=\"data row2 col0\" >6.321</td>\n",
       "      <td id=\"T_cdb62_row2_col1\" class=\"data row2 col1\" >90.404</td>\n",
       "      <td id=\"T_cdb62_row2_col2\" class=\"data row2 col2\" >-84.083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdb62_level0_row3\" class=\"row_heading level0 row3\" >바텀업 62 S1 연평균(%)</th>\n",
       "      <td id=\"T_cdb62_row3_col0\" class=\"data row3 col0\" >2.519</td>\n",
       "      <td id=\"T_cdb62_row3_col1\" class=\"data row3 col1\" >4.880</td>\n",
       "      <td id=\"T_cdb62_row3_col2\" class=\"data row3 col2\" >-2.362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdb62_level0_row4\" class=\"row_heading level0 row4\" >바텀업 62 S1 연평균, 드리프트 재설정 전(%)</th>\n",
       "      <td id=\"T_cdb62_row4_col0\" class=\"data row4 col0\" >3.996</td>\n",
       "      <td id=\"T_cdb62_row4_col1\" class=\"data row4 col1\" >4.880</td>\n",
       "      <td id=\"T_cdb62_row4_col2\" class=\"data row4 col2\" >-0.885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdb62_level0_row5\" class=\"row_heading level0 row5\" >통제 총량 S1 연평균(%)</th>\n",
       "      <td id=\"T_cdb62_row5_col0\" class=\"data row5 col0\" >1.913</td>\n",
       "      <td id=\"T_cdb62_row5_col1\" class=\"data row5 col1\" >2.492</td>\n",
       "      <td id=\"T_cdb62_row5_col2\" class=\"data row5 col2\" >-0.579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdb62_level0_row6\" class=\"row_heading level0 row6\" >비례계수 62 S1 2035</th>\n",
       "      <td id=\"T_cdb62_row6_col0\" class=\"data row6 col0\" >0.934</td>\n",
       "      <td id=\"T_cdb62_row6_col1\" class=\"data row6 col1\" >0.766</td>\n",
       "      <td id=\"T_cdb62_row6_col2\" class=\"data row6 col2\" >0.168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdb62_level0_row7\" class=\"row_heading level0 row7\" >결합 가중치 w</th>\n",
       "      <td id=\"T_cdb62_row7_col0\" class=\"data row7 col0\" >0.400</td>\n",
       "      <td id=\"T_cdb62_row7_col1\" class=\"data row7 col1\" >0.450</td>\n",
       "      <td id=\"T_cdb62_row7_col2\" class=\"data row7 col2\" >-0.050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdb62_level0_row8\" class=\"row_heading level0 row8\" >전달 템플릿 수준 2035(조원)</th>\n",
       "      <td id=\"T_cdb62_row8_col0\" class=\"data row8 col0\" >2,797.257</td>\n",
       "      <td id=\"T_cdb62_row8_col1\" class=\"data row8 col1\" >2,880.248</td>\n",
       "      <td id=\"T_cdb62_row8_col2\" class=\"data row8 col2\" >-82.991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cdb62_level0_row9\" class=\"row_heading level0 row9\" >전달 템플릿 수준 연평균 2025–35(%)</th>\n",
       "      <td id=\"T_cdb62_row9_col0\" class=\"data row9 col0\" >1.799</td>\n",
       "      <td id=\"T_cdb62_row9_col1\" class=\"data row9 col1\" >2.097</td>\n",
       "      <td id=\"T_cdb62_row9_col2\" class=\"data row9 col2\" >-0.298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x118b1dbe540>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv = pd.read_csv(os.path.join(C.PREV_DIR, \"topdown_annual_AGG.csv\"), encoding=\"utf-8-sig\")\n",
    "tpv = R.totals(pv)\n",
    "tpb = R.totals(R.read_long(os.path.join(C.PREV_DIR, \"bottomup_annual_62.csv\"))) / 1e3\n",
    "tagg = R.totals(agg)\n",
    "rows = [\n",
    "    (\"전산업 S1 2035(조원, 62분류 합)\", T[62].loc[2035, \"S1\"], TP[62].loc[2035, \"S1\"]),\n",
    "    (\"전산업 S1 연평균 2025–35(%)\", R.cagr(a25, T[62].loc[2035, \"S1\"], 10), R.cagr(a25, TP[62].loc[2035, \"S1\"], 10)),\n",
    "    (\"시나리오 범위 2035(조원, 최대−최소)\", T[62].loc[2035, C.SCENARIOS].max() - T[62].loc[2035, C.SCENARIOS].min(),\n",
    "     TP[62].loc[2035, C.SCENARIOS].max() - TP[62].loc[2035, C.SCENARIOS].min()),\n",
    "    (\"바텀업 62 S1 연평균(%)\", R.cagr(a25, TB[62].loc[2035, \"S1\"], 10), R.cagr(a25, tpb.loc[2035, \"S1\"], 10)),\n",
    "    (\"바텀업 62 S1 연평균, 드리프트 재설정 전(%)\", R.cagr(a25, TBR[62].loc[2035, \"S1\"], 10), R.cagr(a25, tpb.loc[2035, \"S1\"], 10)),\n",
    "    (\"통제 총량 S1 연평균(%)\", R.cagr(tagg.loc[2025, \"실적\"], tagg.loc[2035, \"S1\"], 10), R.cagr(tpv.loc[2025, \"실적\"], tpv.loc[2035, \"S1\"], 10)),\n",
    "    (\"비례계수 62 S1 2035\", float(fac[(fac[\"분류\"] == 62) & (fac[\"시나리오\"] == \"S1\") & (fac[\"연도\"] == 2035)][\"비례계수\"].iloc[0]),\n",
    "     (TP[62].loc[2035, \"S1\"] / tpb.loc[2035, \"S1\"])),\n",
    "    (\"결합 가중치 w\", C.W_PRIOR, C.PREVIOUS[\"w\"]),\n",
    "    (\"전달 템플릿 수준 2035(조원)\", ag[\"new_2035\"], agp[\"new_2035\"]),\n",
    "    (\"전달 템플릿 수준 연평균 2025–35(%)\", ag[\"new_cagr_25_35\"], agp[\"new_cagr_25_35\"]),\n",
    "]\n",
    "t6 = pd.DataFrame(rows, columns=[\"항목\", \"정비판\", \"이전 판\"]).set_index(\"항목\")\n",
    "t6[\"변화\"] = t6[\"정비판\"] - t6[\"이전 판\"]\n",
    "t6.to_csv(os.path.join(C.OUTPUT, \"table6_change_summary.csv\"), encoding=\"utf-8-sig\")\n",
    "t6.style.format(\"{:,.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb1336b8",
   "metadata": {},
   "source": [
    "## 그림 4. 결합 가중치 검증 — 검증·시험 RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a7c503e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:22.461978Z",
     "iopub.status.busy": "2026-10-08T07:24:22.461978Z",
     "iopub.status.idle": "2026-10-08T07:24:22.591031Z",
     "shell.execute_reply": "2026-10-08T07:24:22.591031Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x380 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 3.8))\n",
    "v, t = wcur[\"검증 RMSE(2020–22)\"], wcur[\"시험 RMSE(2023–25)\"]\n",
    "ax.plot(wcur[\"w\"], v / v.iloc[0] * 100, marker=\"o\", ms=3, color=\"#1f4e79\", label=f\"검증 2020–22 (w=0: {v.iloc[0]:,.0f}십억 원)\")\n",
    "ax.plot(wcur[\"w\"], t / t.iloc[0] * 100, marker=\"s\", ms=3, color=\"#a9781f\", label=f\"시험 2023–25 (w=0: {t.iloc[0]:,.0f}십억 원)\")\n",
    "ax.axvline(wj[\"wstar\"], color=\"gray\", ls=\"--\", lw=0.8)\n",
    "ax.annotate(f\"w* = {wj['wstar']}\", (wj[\"wstar\"], 100), xytext=(6, 4), textcoords=\"offset points\", fontsize=8)\n",
    "ax.set_xlabel(\"기존 전망(추세) 가중치 w\"); ax.set_ylabel(\"RMSE(w=0 = 100)\"); ax.grid(alpha=.3); ax.legend(frameon=False, fontsize=8)\n",
    "ax.set_title(\"결합 가중치의 유사표본외 선택(62분류)\", loc=\"left\")\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig4_shrink_w.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8283445",
   "metadata": {},
   "source": [
    "## 표 7. 산업별 성장 요인 분해(S1, 62분류) — 10년 로그 성장 상·하위 8개\n",
    "\n",
    "02 10절의 분해(정합화 이전 바텀업, 드리프트 재설정 후, 2025Q4→2035Q4, %p)이다. 전망 갱신 카드의 요인 분해 막대와 자동 해설이 같은 값(`va_detail_62.json`의 `shap`)을 쓴다.\n",
    "통제 총량 맞춤은 모든 산업에 같은 값으로 더해지므로(`shap_62.json`의 `meta.reconcile_2035`) 산업 간 순위를 바꾸지 않는다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9bc50c8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:22.592034Z",
     "iopub.status.busy": "2026-10-08T07:24:22.592034Z",
     "iopub.status.idle": "2026-10-08T07:24:22.639990Z",
     "shell.execute_reply": "2026-10-08T07:24:22.639990Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "통제 총량 맞춤(모든 산업 공통): -6.84%p · 드리프트 재설정 δ = 연 1.524%p\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_7fada\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7fada_level0_col0\" class=\"col_heading level0 col0\" >구분</th>\n",
       "      <th id=\"T_7fada_level0_col1\" class=\"col_heading level0 col1\" >합계</th>\n",
       "      <th id=\"T_7fada_level0_col2\" class=\"col_heading level0 col2\" >과거추세</th>\n",
       "      <th id=\"T_7fada_level0_col3\" class=\"col_heading level0 col3\" >산업 동학</th>\n",
       "      <th id=\"T_7fada_level0_col4\" class=\"col_heading level0 col4\" >유가</th>\n",
       "      <th id=\"T_7fada_level0_col5\" class=\"col_heading level0 col5\" >세계수요</th>\n",
       "      <th id=\"T_7fada_level0_col6\" class=\"col_heading level0 col6\" >실질금리</th>\n",
       "      <th id=\"T_7fada_level0_col7\" class=\"col_heading level0 col7\" >실질환율</th>\n",
       "      <th id=\"T_7fada_level0_col8\" class=\"col_heading level0 col8\" >취업자 수</th>\n",
       "      <th id=\"T_7fada_level0_col9\" class=\"col_heading level0 col9\" >국내 경기</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >산업</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row0\" class=\"row_heading level0 row0\" >23.의료ㆍ정밀ㆍ광학기기ㆍ시계</th>\n",
       "      <td id=\"T_7fada_row0_col0\" class=\"data row0 col0\" >상위</td>\n",
       "      <td id=\"T_7fada_row0_col1\" class=\"data row0 col1\" >+74.96</td>\n",
       "      <td id=\"T_7fada_row0_col2\" class=\"data row0 col2\" >+97.78</td>\n",
       "      <td id=\"T_7fada_row0_col3\" class=\"data row0 col3\" >-24.45</td>\n",
       "      <td id=\"T_7fada_row0_col4\" class=\"data row0 col4\" >+1.14</td>\n",
       "      <td id=\"T_7fada_row0_col5\" class=\"data row0 col5\" >+0.50</td>\n",
       "      <td id=\"T_7fada_row0_col6\" class=\"data row0 col6\" >+0.03</td>\n",
       "      <td id=\"T_7fada_row0_col7\" class=\"data row0 col7\" >-0.57</td>\n",
       "      <td id=\"T_7fada_row0_col8\" class=\"data row0 col8\" >-0.01</td>\n",
       "      <td id=\"T_7fada_row0_col9\" class=\"data row0 col9\" >+0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row1\" class=\"row_heading level0 row1\" >22.전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비</th>\n",
       "      <td id=\"T_7fada_row1_col0\" class=\"data row1 col0\" >상위</td>\n",
       "      <td id=\"T_7fada_row1_col1\" class=\"data row1 col1\" >+74.79</td>\n",
       "      <td id=\"T_7fada_row1_col2\" class=\"data row1 col2\" >+78.00</td>\n",
       "      <td id=\"T_7fada_row1_col3\" class=\"data row1 col3\" >-3.88</td>\n",
       "      <td id=\"T_7fada_row1_col4\" class=\"data row1 col4\" >+0.85</td>\n",
       "      <td id=\"T_7fada_row1_col5\" class=\"data row1 col5\" >+0.20</td>\n",
       "      <td id=\"T_7fada_row1_col6\" class=\"data row1 col6\" >+0.07</td>\n",
       "      <td id=\"T_7fada_row1_col7\" class=\"data row1 col7\" >-0.86</td>\n",
       "      <td id=\"T_7fada_row1_col8\" class=\"data row1 col8\" >-0.01</td>\n",
       "      <td id=\"T_7fada_row1_col9\" class=\"data row1 col9\" >+0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row2\" class=\"row_heading level0 row2\" >58.사회복지 서비스업</th>\n",
       "      <td id=\"T_7fada_row2_col0\" class=\"data row2 col0\" >상위</td>\n",
       "      <td id=\"T_7fada_row2_col1\" class=\"data row2 col1\" >+45.46</td>\n",
       "      <td id=\"T_7fada_row2_col2\" class=\"data row2 col2\" >+52.72</td>\n",
       "      <td id=\"T_7fada_row2_col3\" class=\"data row2 col3\" >-7.65</td>\n",
       "      <td id=\"T_7fada_row2_col4\" class=\"data row2 col4\" >+0.17</td>\n",
       "      <td id=\"T_7fada_row2_col5\" class=\"data row2 col5\" >-0.33</td>\n",
       "      <td id=\"T_7fada_row2_col6\" class=\"data row2 col6\" >-0.08</td>\n",
       "      <td id=\"T_7fada_row2_col7\" class=\"data row2 col7\" >+0.45</td>\n",
       "      <td id=\"T_7fada_row2_col8\" class=\"data row2 col8\" >-0.03</td>\n",
       "      <td id=\"T_7fada_row2_col9\" class=\"data row2 col9\" >+0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row3\" class=\"row_heading level0 row3\" >57.의료ㆍ보건업</th>\n",
       "      <td id=\"T_7fada_row3_col0\" class=\"data row3 col0\" >상위</td>\n",
       "      <td id=\"T_7fada_row3_col1\" class=\"data row3 col1\" >+44.80</td>\n",
       "      <td id=\"T_7fada_row3_col2\" class=\"data row3 col2\" >+51.46</td>\n",
       "      <td id=\"T_7fada_row3_col3\" class=\"data row3 col3\" >-7.06</td>\n",
       "      <td id=\"T_7fada_row3_col4\" class=\"data row3 col4\" >+0.21</td>\n",
       "      <td id=\"T_7fada_row3_col5\" class=\"data row3 col5\" >-0.35</td>\n",
       "      <td id=\"T_7fada_row3_col6\" class=\"data row3 col6\" >-0.07</td>\n",
       "      <td id=\"T_7fada_row3_col7\" class=\"data row3 col7\" >+0.42</td>\n",
       "      <td id=\"T_7fada_row3_col8\" class=\"data row3 col8\" >-0.02</td>\n",
       "      <td id=\"T_7fada_row3_col9\" class=\"data row3 col9\" >+0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row4\" class=\"row_heading level0 row4\" >47.보험ㆍ연금업</th>\n",
       "      <td id=\"T_7fada_row4_col0\" class=\"data row4 col0\" >상위</td>\n",
       "      <td id=\"T_7fada_row4_col1\" class=\"data row4 col1\" >+44.54</td>\n",
       "      <td id=\"T_7fada_row4_col2\" class=\"data row4 col2\" >+30.15</td>\n",
       "      <td id=\"T_7fada_row4_col3\" class=\"data row4 col3\" >+15.73</td>\n",
       "      <td id=\"T_7fada_row4_col4\" class=\"data row4 col4\" >-0.54</td>\n",
       "      <td id=\"T_7fada_row4_col5\" class=\"data row4 col5\" >+0.19</td>\n",
       "      <td id=\"T_7fada_row4_col6\" class=\"data row4 col6\" >+0.14</td>\n",
       "      <td id=\"T_7fada_row4_col7\" class=\"data row4 col7\" >-0.22</td>\n",
       "      <td id=\"T_7fada_row4_col8\" class=\"data row4 col8\" >+0.02</td>\n",
       "      <td id=\"T_7fada_row4_col9\" class=\"data row4 col9\" >-0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row5\" class=\"row_heading level0 row5\" >48.금융ㆍ보험관련서비스업</th>\n",
       "      <td id=\"T_7fada_row5_col0\" class=\"data row5 col0\" >상위</td>\n",
       "      <td id=\"T_7fada_row5_col1\" class=\"data row5 col1\" >+43.32</td>\n",
       "      <td id=\"T_7fada_row5_col2\" class=\"data row5 col2\" >+24.77</td>\n",
       "      <td id=\"T_7fada_row5_col3\" class=\"data row5 col3\" >+19.71</td>\n",
       "      <td id=\"T_7fada_row5_col4\" class=\"data row5 col4\" >-0.60</td>\n",
       "      <td id=\"T_7fada_row5_col5\" class=\"data row5 col5\" >+0.22</td>\n",
       "      <td id=\"T_7fada_row5_col6\" class=\"data row5 col6\" >+0.16</td>\n",
       "      <td id=\"T_7fada_row5_col7\" class=\"data row5 col7\" >-0.26</td>\n",
       "      <td id=\"T_7fada_row5_col8\" class=\"data row5 col8\" >+0.02</td>\n",
       "      <td id=\"T_7fada_row5_col9\" class=\"data row5 col9\" >-0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row6\" class=\"row_heading level0 row6\" >46.금융업</th>\n",
       "      <td id=\"T_7fada_row6_col0\" class=\"data row6 col0\" >상위</td>\n",
       "      <td id=\"T_7fada_row6_col1\" class=\"data row6 col1\" >+43.20</td>\n",
       "      <td id=\"T_7fada_row6_col2\" class=\"data row6 col2\" >+24.43</td>\n",
       "      <td id=\"T_7fada_row6_col3\" class=\"data row6 col3\" >+19.98</td>\n",
       "      <td id=\"T_7fada_row6_col4\" class=\"data row6 col4\" >-0.64</td>\n",
       "      <td id=\"T_7fada_row6_col5\" class=\"data row6 col5\" >+0.25</td>\n",
       "      <td id=\"T_7fada_row6_col6\" class=\"data row6 col6\" >+0.14</td>\n",
       "      <td id=\"T_7fada_row6_col7\" class=\"data row6 col7\" >-0.32</td>\n",
       "      <td id=\"T_7fada_row6_col8\" class=\"data row6 col8\" >+0.03</td>\n",
       "      <td id=\"T_7fada_row6_col9\" class=\"data row6 col9\" >-0.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row7\" class=\"row_heading level0 row7\" >43.통신업</th>\n",
       "      <td id=\"T_7fada_row7_col0\" class=\"data row7 col0\" >상위</td>\n",
       "      <td id=\"T_7fada_row7_col1\" class=\"data row7 col1\" >+37.59</td>\n",
       "      <td id=\"T_7fada_row7_col2\" class=\"data row7 col2\" >+13.02</td>\n",
       "      <td id=\"T_7fada_row7_col3\" class=\"data row7 col3\" >+27.59</td>\n",
       "      <td id=\"T_7fada_row7_col4\" class=\"data row7 col4\" >-0.90</td>\n",
       "      <td id=\"T_7fada_row7_col5\" class=\"data row7 col5\" >+0.14</td>\n",
       "      <td id=\"T_7fada_row7_col6\" class=\"data row7 col6\" >-0.04</td>\n",
       "      <td id=\"T_7fada_row7_col7\" class=\"data row7 col7\" >-1.34</td>\n",
       "      <td id=\"T_7fada_row7_col8\" class=\"data row7 col8\" >+0.00</td>\n",
       "      <td id=\"T_7fada_row7_col9\" class=\"data row7 col9\" >-0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row8\" class=\"row_heading level0 row8\" >13.인쇄ㆍ복제</th>\n",
       "      <td id=\"T_7fada_row8_col0\" class=\"data row8 col0\" >하위</td>\n",
       "      <td id=\"T_7fada_row8_col1\" class=\"data row8 col1\" >-13.56</td>\n",
       "      <td id=\"T_7fada_row8_col2\" class=\"data row8 col2\" >-43.63</td>\n",
       "      <td id=\"T_7fada_row8_col3\" class=\"data row8 col3\" >+28.43</td>\n",
       "      <td id=\"T_7fada_row8_col4\" class=\"data row8 col4\" >+1.17</td>\n",
       "      <td id=\"T_7fada_row8_col5\" class=\"data row8 col5\" >-1.08</td>\n",
       "      <td id=\"T_7fada_row8_col6\" class=\"data row8 col6\" >-0.04</td>\n",
       "      <td id=\"T_7fada_row8_col7\" class=\"data row8 col7\" >+0.96</td>\n",
       "      <td id=\"T_7fada_row8_col8\" class=\"data row8 col8\" >-0.01</td>\n",
       "      <td id=\"T_7fada_row8_col9\" class=\"data row8 col9\" >+0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row9\" class=\"row_heading level0 row9\" >39.음식점ㆍ숙박업</th>\n",
       "      <td id=\"T_7fada_row9_col0\" class=\"data row9 col0\" >하위</td>\n",
       "      <td id=\"T_7fada_row9_col1\" class=\"data row9 col1\" >-13.56</td>\n",
       "      <td id=\"T_7fada_row9_col2\" class=\"data row9 col2\" >-7.80</td>\n",
       "      <td id=\"T_7fada_row9_col3\" class=\"data row9 col3\" >-8.29</td>\n",
       "      <td id=\"T_7fada_row9_col4\" class=\"data row9 col4\" >+0.95</td>\n",
       "      <td id=\"T_7fada_row9_col5\" class=\"data row9 col5\" >+0.43</td>\n",
       "      <td id=\"T_7fada_row9_col6\" class=\"data row9 col6\" >-0.06</td>\n",
       "      <td id=\"T_7fada_row9_col7\" class=\"data row9 col7\" >+1.13</td>\n",
       "      <td id=\"T_7fada_row9_col8\" class=\"data row9 col8\" >-0.01</td>\n",
       "      <td id=\"T_7fada_row9_col9\" class=\"data row9 col9\" >+0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row10\" class=\"row_heading level0 row10\" >20.1차 금속</th>\n",
       "      <td id=\"T_7fada_row10_col0\" class=\"data row10 col0\" >하위</td>\n",
       "      <td id=\"T_7fada_row10_col1\" class=\"data row10 col1\" >-25.95</td>\n",
       "      <td id=\"T_7fada_row10_col2\" class=\"data row10 col2\" >-3.12</td>\n",
       "      <td id=\"T_7fada_row10_col3\" class=\"data row10 col3\" >-16.17</td>\n",
       "      <td id=\"T_7fada_row10_col4\" class=\"data row10 col4\" >-0.35</td>\n",
       "      <td id=\"T_7fada_row10_col5\" class=\"data row10 col5\" >-1.73</td>\n",
       "      <td id=\"T_7fada_row10_col6\" class=\"data row10 col6\" >-0.02</td>\n",
       "      <td id=\"T_7fada_row10_col7\" class=\"data row10 col7\" >-3.11</td>\n",
       "      <td id=\"T_7fada_row10_col8\" class=\"data row10 col8\" >-0.02</td>\n",
       "      <td id=\"T_7fada_row10_col9\" class=\"data row10 col9\" >-1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row11\" class=\"row_heading level0 row11\" >9.섬유ㆍ의복</th>\n",
       "      <td id=\"T_7fada_row11_col0\" class=\"data row11 col0\" >하위</td>\n",
       "      <td id=\"T_7fada_row11_col1\" class=\"data row11 col1\" >-33.15</td>\n",
       "      <td id=\"T_7fada_row11_col2\" class=\"data row11 col2\" >-30.67</td>\n",
       "      <td id=\"T_7fada_row11_col3\" class=\"data row11 col3\" >-4.30</td>\n",
       "      <td id=\"T_7fada_row11_col4\" class=\"data row11 col4\" >+1.78</td>\n",
       "      <td id=\"T_7fada_row11_col5\" class=\"data row11 col5\" >-0.42</td>\n",
       "      <td id=\"T_7fada_row11_col6\" class=\"data row11 col6\" >+0.05</td>\n",
       "      <td id=\"T_7fada_row11_col7\" class=\"data row11 col7\" >-0.58</td>\n",
       "      <td id=\"T_7fada_row11_col8\" class=\"data row11 col8\" >-0.03</td>\n",
       "      <td id=\"T_7fada_row11_col9\" class=\"data row11 col9\" >+1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row12\" class=\"row_heading level0 row12\" >10.가죽제품</th>\n",
       "      <td id=\"T_7fada_row12_col0\" class=\"data row12 col0\" >하위</td>\n",
       "      <td id=\"T_7fada_row12_col1\" class=\"data row12 col1\" >-33.59</td>\n",
       "      <td id=\"T_7fada_row12_col2\" class=\"data row12 col2\" >-32.81</td>\n",
       "      <td id=\"T_7fada_row12_col3\" class=\"data row12 col3\" >-2.54</td>\n",
       "      <td id=\"T_7fada_row12_col4\" class=\"data row12 col4\" >+1.80</td>\n",
       "      <td id=\"T_7fada_row12_col5\" class=\"data row12 col5\" >-0.49</td>\n",
       "      <td id=\"T_7fada_row12_col6\" class=\"data row12 col6\" >+0.05</td>\n",
       "      <td id=\"T_7fada_row12_col7\" class=\"data row12 col7\" >-0.57</td>\n",
       "      <td id=\"T_7fada_row12_col8\" class=\"data row12 col8\" >-0.03</td>\n",
       "      <td id=\"T_7fada_row12_col9\" class=\"data row12 col9\" >+1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row13\" class=\"row_heading level0 row13\" >5.금속ㆍ비금속광업</th>\n",
       "      <td id=\"T_7fada_row13_col0\" class=\"data row13 col0\" >하위</td>\n",
       "      <td id=\"T_7fada_row13_col1\" class=\"data row13 col1\" >-34.12</td>\n",
       "      <td id=\"T_7fada_row13_col2\" class=\"data row13 col2\" >-48.83</td>\n",
       "      <td id=\"T_7fada_row13_col3\" class=\"data row13 col3\" >+11.40</td>\n",
       "      <td id=\"T_7fada_row13_col4\" class=\"data row13 col4\" >+0.06</td>\n",
       "      <td id=\"T_7fada_row13_col5\" class=\"data row13 col5\" >+0.38</td>\n",
       "      <td id=\"T_7fada_row13_col6\" class=\"data row13 col6\" >-0.01</td>\n",
       "      <td id=\"T_7fada_row13_col7\" class=\"data row13 col7\" >+1.62</td>\n",
       "      <td id=\"T_7fada_row13_col8\" class=\"data row13 col8\" >-0.01</td>\n",
       "      <td id=\"T_7fada_row13_col9\" class=\"data row13 col9\" >+1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row14\" class=\"row_heading level0 row14\" >4.석탄ㆍ원유ㆍ천연가스</th>\n",
       "      <td id=\"T_7fada_row14_col0\" class=\"data row14 col0\" >하위</td>\n",
       "      <td id=\"T_7fada_row14_col1\" class=\"data row14 col1\" >-34.15</td>\n",
       "      <td id=\"T_7fada_row14_col2\" class=\"data row14 col2\" >-61.67</td>\n",
       "      <td id=\"T_7fada_row14_col3\" class=\"data row14 col3\" >+23.77</td>\n",
       "      <td id=\"T_7fada_row14_col4\" class=\"data row14 col4\" >+0.18</td>\n",
       "      <td id=\"T_7fada_row14_col5\" class=\"data row14 col5\" >+0.34</td>\n",
       "      <td id=\"T_7fada_row14_col6\" class=\"data row14 col6\" >-0.00</td>\n",
       "      <td id=\"T_7fada_row14_col7\" class=\"data row14 col7\" >+1.55</td>\n",
       "      <td id=\"T_7fada_row14_col8\" class=\"data row14 col8\" >-0.01</td>\n",
       "      <td id=\"T_7fada_row14_col9\" class=\"data row14 col9\" >+1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7fada_level0_row15\" class=\"row_heading level0 row15\" >14.석탄ㆍ석유제품</th>\n",
       "      <td id=\"T_7fada_row15_col0\" class=\"data row15 col0\" >하위</td>\n",
       "      <td id=\"T_7fada_row15_col1\" class=\"data row15 col1\" >-60.60</td>\n",
       "      <td id=\"T_7fada_row15_col2\" class=\"data row15 col2\" >-46.44</td>\n",
       "      <td id=\"T_7fada_row15_col3\" class=\"data row15 col3\" >-19.62</td>\n",
       "      <td id=\"T_7fada_row15_col4\" class=\"data row15 col4\" >+6.21</td>\n",
       "      <td id=\"T_7fada_row15_col5\" class=\"data row15 col5\" >-2.07</td>\n",
       "      <td id=\"T_7fada_row15_col6\" class=\"data row15 col6\" >-0.00</td>\n",
       "      <td id=\"T_7fada_row15_col7\" class=\"data row15 col7\" >-0.27</td>\n",
       "      <td id=\"T_7fada_row15_col8\" class=\"data row15 col8\" >-0.00</td>\n",
       "      <td id=\"T_7fada_row15_col9\" class=\"data row15 col9\" >+1.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x118b235cd70>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh62 = pd.read_csv(os.path.join(C.OUTPUT, \"shap_62.csv\"), index_col=0, encoding=\"utf-8-sig\", float_precision=\"round_trip\")\n",
    "EXN = {\"고용_취업자\": \"취업자 수\", \"우리경기\": \"국내 경기\"}\n",
    "t7 = sh62.rename(columns={\"total\": \"합계\", \"drift\": \"과거추세\", \"dynamics\": \"산업 동학\", **EXN}).sort_values(\"합계\", ascending=False)\n",
    "t7 = pd.concat([t7.head(8).assign(구분=\"상위\"), t7.tail(8).assign(구분=\"하위\")])\n",
    "t7 = t7[[\"구분\"] + [c for c in t7.columns if c != \"구분\"]]\n",
    "t7.to_csv(os.path.join(C.OUTPUT, \"table7_growth_factors.csv\"), encoding=\"utf-8-sig\")\n",
    "print(f\"통제 총량 맞춤(모든 산업 공통): {shap62['meta']['reconcile_2035']:+.2f}%p · 드리프트 재설정 δ = 연 {shap62['meta']['delta']:.3f}%p\")\n",
    "t7.style.format({c: \"{:+.2f}\" for c in t7.columns if c != \"구분\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a55339d0",
   "metadata": {},
   "source": [
    "## 검증 — 정비판 기록 산출물·핵심 수치, 이전 판 전달본 재현\n",
    "\n",
    "정비판 기록 산출물(`data/published/`)은 2026-10 정비 실행의 `output/` 사본이다. 파일은 행 단위로 대조하며 허용오차는 파일의 반올림 단위(소수 셋째 자리 0.0005십억 원 등)이다.\n",
    "핵심 수치는 정수 조 원, 소수 둘째 자리 %의 반올림 범위를 허용오차로 둔다. 이전 판 전달본(`_신규.xlsx`)은 이전 판 전망으로 같은 채움 논리를 실행해 재현되는지 확인한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1a327398",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T07:24:22.640995Z",
     "iopub.status.busy": "2026-10-08T07:24:22.640995Z",
     "iopub.status.idle": "2026-10-08T07:24:22.761998Z",
     "shell.execute_reply": "2026-10-08T07:24:22.761998Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<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>기록</th>\n",
       "      <th>허용오차</th>\n",
       "      <th>통과</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S1 2025 전산업(조원)</td>\n",
       "      <td>2,178.49</td>\n",
       "      <td>2,178</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S1 2035 전산업(조원)</td>\n",
       "      <td>2,609.03</td>\n",
       "      <td>2,609</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S1 연평균 2025–35(%)</td>\n",
       "      <td>1.81982</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S2 2035 전산업(조원)</td>\n",
       "      <td>2,605.86</td>\n",
       "      <td>2,606</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S2 연평균(%)</td>\n",
       "      <td>1.80746</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>S3 2035 전산업(조원)</td>\n",
       "      <td>2,612.18</td>\n",
       "      <td>2,612</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>S3 연평균(%)</td>\n",
       "      <td>1.83213</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>S4 2035 전산업(조원)</td>\n",
       "      <td>2,607.7</td>\n",
       "      <td>2,608</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>S4 연평균(%)</td>\n",
       "      <td>1.81463</td>\n",
       "      <td>1.81</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>S5 2035 전산업(조원)</td>\n",
       "      <td>2,610.23</td>\n",
       "      <td>2,610</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>S5 연평균(%)</td>\n",
       "      <td>1.82453</td>\n",
       "      <td>1.82</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>S1 2030년 증가율(%)</td>\n",
       "      <td>1.84637</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>S1 2035년 증가율(%)</td>\n",
       "      <td>1.69119</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>정합화 후 36분류 S1 2035(조원)</td>\n",
       "      <td>2,609.03</td>\n",
       "      <td>2,609</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>정합화 후 62분류 S1 2035(조원)</td>\n",
       "      <td>2,609.03</td>\n",
       "      <td>2,609</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>정합화 후 69분류 S1 2035(조원)</td>\n",
       "      <td>2,609.03</td>\n",
       "      <td>2,609</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>분류 간 전산업 합계 최대차(십억원)</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0</td>\n",
       "      <td>0.035</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>바텀업 36분류 S1 연평균(%)</td>\n",
       "      <td>2.45703</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>바텀업 62분류 S1 연평균(%)</td>\n",
       "      <td>2.51871</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>바텀업 69분류 S1 연평균(%)</td>\n",
       "      <td>2.52502</td>\n",
       "      <td>2.53</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>바텀업(재설정 전) 36분류 S1 연평균(%)</td>\n",
       "      <td>3.96517</td>\n",
       "      <td>3.97</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>바텀업(재설정 전) 62분류 S1 연평균(%)</td>\n",
       "      <td>3.99576</td>\n",
       "      <td>4</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>바텀업(재설정 전) 69분류 S1 연평균(%)</td>\n",
       "      <td>4.00208</td>\n",
       "      <td>4</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>통제 총량 S1 연평균(%)</td>\n",
       "      <td>1.9134</td>\n",
       "      <td>1.91</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>비례계수 62 S1 2035</td>\n",
       "      <td>0.933882</td>\n",
       "      <td>0.934</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>결합 가중치 w*</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>검증 RMSE(w*)</td>\n",
       "      <td>3,195.3</td>\n",
       "      <td>3,195.3</td>\n",
       "      <td>0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>시험 RMSE 모형만</td>\n",
       "      <td>6,552.6</td>\n",
       "      <td>6,552.6</td>\n",
       "      <td>0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>시험 RMSE 결합</td>\n",
       "      <td>6,866.8</td>\n",
       "      <td>6,866.8</td>\n",
       "      <td>0.05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>기존 전망 연평균 2024–34(%)</td>\n",
       "      <td>1.62997</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>전달 템플릿 수준 2034(조원)</td>\n",
       "      <td>2,751.46</td>\n",
       "      <td>2,751</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>전달 템플릿 수준 2035(조원)</td>\n",
       "      <td>2,797.26</td>\n",
       "      <td>2,797</td>\n",
       "      <td>0.5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>전달 템플릿 수준 연평균 2024–34(%)</td>\n",
       "      <td>1.7826</td>\n",
       "      <td>1.78</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>전달 템플릿 수준 연평균 2025–35(%)</td>\n",
       "      <td>1.79894</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>전달 엑셀 성장인자 = 전망 성장인자(최대차)</td>\n",
       "      <td>8.88178e-16</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-12</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>이전 판 전망 → 이전 판 전달본 _신규.xlsx 재현(최대 상대차)</td>\n",
       "      <td>2.06563e-16</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>forecast_annual_36.csv 2,736행 최대차(십억원)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000500001</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>bottomup_annual_36.csv 2,736행 최대차(십억원)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000500001</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>bottomup_raw_annual_36.csv 2,736행 최대차(십억원)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000500001</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>forecast_summary_36.csv 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>40</th>\n",
       "      <td>forecast_total_36.csv 최대차(조원)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>forecast_annual_62.csv 4,712행 최대차(십억원)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000500001</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>bottomup_annual_62.csv 4,712행 최대차(십억원)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000500001</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>bottomup_raw_annual_62.csv 4,712행 최대차(십억원)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000500001</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>forecast_summary_62.csv 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>45</th>\n",
       "      <td>forecast_total_62.csv 최대차(조원)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>forecast_annual_69.csv 5,244행 최대차(십억원)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000500001</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>bottomup_annual_69.csv 5,244행 최대차(십억원)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000500001</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>bottomup_raw_annual_69.csv 5,244행 최대차(십억원)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000500001</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>forecast_summary_69.csv 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>50</th>\n",
       "      <td>forecast_total_69.csv 최대차(조원)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>topdown_annual.csv 76행 최대차</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>topdown_varx_annual.csv 76행 최대차</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>base_va_62.csv 682행 최대차</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>base_va_69.csv 759행 최대차</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>card_2025_2035.json 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>56</th>\n",
       "      <td>va_detail_62.json 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>57</th>\n",
       "      <td>산업 연평균 S1 의료ㆍ정밀ㆍ광학기기ㆍ시계(%)</td>\n",
       "      <td>7.12451</td>\n",
       "      <td>7.12</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>산업 연평균 S1 전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비(%)</td>\n",
       "      <td>7.10782</td>\n",
       "      <td>7.11</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>산업 연평균 S1 사회복지 서비스업(%)</td>\n",
       "      <td>4.11543</td>\n",
       "      <td>4.12</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>산업 연평균 S1 의료ㆍ보건업(%)</td>\n",
       "      <td>4.04915</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>산업 연평균 S1 보험ㆍ연금업(%)</td>\n",
       "      <td>4.01668</td>\n",
       "      <td>4.02</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>산업 연평균 S1 금융ㆍ보험관련서비스업(%)</td>\n",
       "      <td>3.89181</td>\n",
       "      <td>3.89</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>산업 연평균 S1 금융업(%)</td>\n",
       "      <td>3.8805</td>\n",
       "      <td>3.88</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>산업 연평균 S1 통신업(%)</td>\n",
       "      <td>3.12505</td>\n",
       "      <td>3.13</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>산업 연평균 S1 1차 금속(%)</td>\n",
       "      <td>-3.23348</td>\n",
       "      <td>-3.23</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>산업 연평균 S1 섬유ㆍ의복(%)</td>\n",
       "      <td>-3.81665</td>\n",
       "      <td>-3.82</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>산업 연평균 S1 가죽제품(%)</td>\n",
       "      <td>-3.85814</td>\n",
       "      <td>-3.86</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>산업 연평균 S1 금속ㆍ비금속광업(%)</td>\n",
       "      <td>-4.19051</td>\n",
       "      <td>-4.19</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>산업 연평균 S1 석탄ㆍ원유ㆍ천연가스(%)</td>\n",
       "      <td>-4.19181</td>\n",
       "      <td>-4.19</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>산업 연평균 S1 석탄ㆍ석유제품(%)</td>\n",
       "      <td>-6.14113</td>\n",
       "      <td>-6.14</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>shap_62.csv 62개 산업 × 9항목 최대차(%p)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-06</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>성장 요인 분해 점검 (가) 성분 합 − 합계, 최대 절대차(%p)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>성장 요인 분해 점검 (나) 합계 − 100·ln(경로 2035Q4/2025Q4),...</td>\n",
       "      <td>3.89022e-13</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>성장 요인 분해 점검 (다) 분해 경로 연간 합 − 바텀업 S1(bottomup_a...</td>\n",
       "      <td>0.000499155</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000500001</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>성장 요인 분해 점검 (라) 과거추세 재설정분 − (−10(1−ΣA)δ), 최대 절...</td>\n",
       "      <td>8.17124e-14</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>성장 요인 분해 통제 총량 맞춤 2035(%p)</td>\n",
       "      <td>-6.84</td>\n",
       "      <td>-6.84</td>\n",
       "      <td>1e-09</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>성장 요인 분해 전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비 total(%p)</td>\n",
       "      <td>74.7876</td>\n",
       "      <td>74.79</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>성장 요인 분해 전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비 drift(%p)</td>\n",
       "      <td>78.0049</td>\n",
       "      <td>78</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>성장 요인 분해 전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비 dynamics(%p)</td>\n",
       "      <td>-3.87526</td>\n",
       "      <td>-3.88</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>성장 요인 분해 의료ㆍ정밀ㆍ광학기기ㆍ시계 total(%p)</td>\n",
       "      <td>74.9586</td>\n",
       "      <td>74.96</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>성장 요인 분해 의료ㆍ정밀ㆍ광학기기ㆍ시계 drift(%p)</td>\n",
       "      <td>97.7768</td>\n",
       "      <td>97.78</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>성장 요인 분해 의료ㆍ정밀ㆍ광학기기ㆍ시계 dynamics(%p)</td>\n",
       "      <td>-24.4451</td>\n",
       "      <td>-24.45</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>성장 요인 분해 석탄ㆍ석유제품 total(%p)</td>\n",
       "      <td>-60.5991</td>\n",
       "      <td>-60.6</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>성장 요인 분해 석탄ㆍ석유제품 drift(%p)</td>\n",
       "      <td>-46.4373</td>\n",
       "      <td>-46.44</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>성장 요인 분해 석탄ㆍ석유제품 유가(%p)</td>\n",
       "      <td>6.20835</td>\n",
       "      <td>6.21</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>성장 요인 분해 실질금리 최대 절대 기여(%p)</td>\n",
       "      <td>0.157161</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.005</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>카드 자료(va_detail_62.json)의 분해 = shap_62.csv 반올림...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1e-12</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   항목          계산      기록  \\\n",
       "0                                     S1 2025 전산업(조원)    2,178.49   2,178   \n",
       "1                                     S1 2035 전산업(조원)    2,609.03   2,609   \n",
       "2                                   S1 연평균 2025–35(%)     1.81982    1.82   \n",
       "3                                     S2 2035 전산업(조원)    2,605.86   2,606   \n",
       "4                                           S2 연평균(%)     1.80746    1.81   \n",
       "5                                     S3 2035 전산업(조원)    2,612.18   2,612   \n",
       "6                                           S3 연평균(%)     1.83213    1.83   \n",
       "7                                     S4 2035 전산업(조원)     2,607.7   2,608   \n",
       "8                                           S4 연평균(%)     1.81463    1.81   \n",
       "9                                     S5 2035 전산업(조원)    2,610.23   2,610   \n",
       "10                                          S5 연평균(%)     1.82453    1.82   \n",
       "11                                    S1 2030년 증가율(%)     1.84637    1.85   \n",
       "12                                    S1 2035년 증가율(%)     1.69119    1.69   \n",
       "13                             정합화 후 36분류 S1 2035(조원)    2,609.03   2,609   \n",
       "14                             정합화 후 62분류 S1 2035(조원)    2,609.03   2,609   \n",
       "15                             정합화 후 69분류 S1 2035(조원)    2,609.03   2,609   \n",
       "16                               분류 간 전산업 합계 최대차(십억원)       0.008       0   \n",
       "17                                 바텀업 36분류 S1 연평균(%)     2.45703    2.46   \n",
       "18                                 바텀업 62분류 S1 연평균(%)     2.51871    2.52   \n",
       "19                                 바텀업 69분류 S1 연평균(%)     2.52502    2.53   \n",
       "20                          바텀업(재설정 전) 36분류 S1 연평균(%)     3.96517    3.97   \n",
       "21                          바텀업(재설정 전) 62분류 S1 연평균(%)     3.99576       4   \n",
       "22                          바텀업(재설정 전) 69분류 S1 연평균(%)     4.00208       4   \n",
       "23                                    통제 총량 S1 연평균(%)      1.9134    1.91   \n",
       "24                                    비례계수 62 S1 2035    0.933882   0.934   \n",
       "25                                          결합 가중치 w*         0.4     0.4   \n",
       "26                                        검증 RMSE(w*)     3,195.3 3,195.3   \n",
       "27                                        시험 RMSE 모형만     6,552.6 6,552.6   \n",
       "28                                         시험 RMSE 결합     6,866.8 6,866.8   \n",
       "29                               기존 전망 연평균 2024–34(%)     1.62997    1.63   \n",
       "30                                 전달 템플릿 수준 2034(조원)    2,751.46   2,751   \n",
       "31                                 전달 템플릿 수준 2035(조원)    2,797.26   2,797   \n",
       "32                           전달 템플릿 수준 연평균 2024–34(%)      1.7826    1.78   \n",
       "33                           전달 템플릿 수준 연평균 2025–35(%)     1.79894     1.8   \n",
       "34                          전달 엑셀 성장인자 = 전망 성장인자(최대차) 8.88178e-16       0   \n",
       "35             이전 판 전망 → 이전 판 전달본 _신규.xlsx 재현(최대 상대차) 2.06563e-16       0   \n",
       "36             forecast_annual_36.csv 2,736행 최대차(십억원)           0       0   \n",
       "37             bottomup_annual_36.csv 2,736행 최대차(십억원)           0       0   \n",
       "38         bottomup_raw_annual_36.csv 2,736행 최대차(십억원)           0       0   \n",
       "39                        forecast_summary_36.csv 최대차           0       0   \n",
       "40                      forecast_total_36.csv 최대차(조원)           0       0   \n",
       "41             forecast_annual_62.csv 4,712행 최대차(십억원)           0       0   \n",
       "42             bottomup_annual_62.csv 4,712행 최대차(십억원)           0       0   \n",
       "43         bottomup_raw_annual_62.csv 4,712행 최대차(십억원)           0       0   \n",
       "44                        forecast_summary_62.csv 최대차           0       0   \n",
       "45                      forecast_total_62.csv 최대차(조원)           0       0   \n",
       "46             forecast_annual_69.csv 5,244행 최대차(십억원)           0       0   \n",
       "47             bottomup_annual_69.csv 5,244행 최대차(십억원)           0       0   \n",
       "48         bottomup_raw_annual_69.csv 5,244행 최대차(십억원)           0       0   \n",
       "49                        forecast_summary_69.csv 최대차           0       0   \n",
       "50                      forecast_total_69.csv 최대차(조원)           0       0   \n",
       "51                         topdown_annual.csv 76행 최대차           0       0   \n",
       "52                    topdown_varx_annual.csv 76행 최대차           0       0   \n",
       "53                            base_va_62.csv 682행 최대차           0       0   \n",
       "54                            base_va_69.csv 759행 최대차           0       0   \n",
       "55                            card_2025_2035.json 최대차           0       0   \n",
       "56                              va_detail_62.json 최대차           0       0   \n",
       "57                         산업 연평균 S1 의료ㆍ정밀ㆍ광학기기ㆍ시계(%)     7.12451    7.12   \n",
       "58                   산업 연평균 S1 전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비(%)     7.10782    7.11   \n",
       "59                             산업 연평균 S1 사회복지 서비스업(%)     4.11543    4.12   \n",
       "60                                산업 연평균 S1 의료ㆍ보건업(%)     4.04915    4.05   \n",
       "61                                산업 연평균 S1 보험ㆍ연금업(%)     4.01668    4.02   \n",
       "62                           산업 연평균 S1 금융ㆍ보험관련서비스업(%)     3.89181    3.89   \n",
       "63                                   산업 연평균 S1 금융업(%)      3.8805    3.88   \n",
       "64                                   산업 연평균 S1 통신업(%)     3.12505    3.13   \n",
       "65                                 산업 연평균 S1 1차 금속(%)    -3.23348   -3.23   \n",
       "66                                 산업 연평균 S1 섬유ㆍ의복(%)    -3.81665   -3.82   \n",
       "67                                  산업 연평균 S1 가죽제품(%)    -3.85814   -3.86   \n",
       "68                              산업 연평균 S1 금속ㆍ비금속광업(%)    -4.19051   -4.19   \n",
       "69                            산업 연평균 S1 석탄ㆍ원유ㆍ천연가스(%)    -4.19181   -4.19   \n",
       "70                               산업 연평균 S1 석탄ㆍ석유제품(%)    -6.14113   -6.14   \n",
       "71                   shap_62.csv 62개 산업 × 9항목 최대차(%p)           0       0   \n",
       "72              성장 요인 분해 점검 (가) 성분 합 − 합계, 최대 절대차(%p)           0       0   \n",
       "73  성장 요인 분해 점검 (나) 합계 − 100·ln(경로 2035Q4/2025Q4),... 3.89022e-13       0   \n",
       "74  성장 요인 분해 점검 (다) 분해 경로 연간 합 − 바텀업 S1(bottomup_a... 0.000499155       0   \n",
       "75  성장 요인 분해 점검 (라) 과거추세 재설정분 − (−10(1−ΣA)δ), 최대 절... 8.17124e-14       0   \n",
       "76                         성장 요인 분해 통제 총량 맞춤 2035(%p)       -6.84   -6.84   \n",
       "77             성장 요인 분해 전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비 total(%p)     74.7876   74.79   \n",
       "78             성장 요인 분해 전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비 drift(%p)     78.0049      78   \n",
       "79          성장 요인 분해 전자부품ㆍ컴퓨터ㆍ영상ㆍ음향ㆍ통신장비 dynamics(%p)    -3.87526   -3.88   \n",
       "80                   성장 요인 분해 의료ㆍ정밀ㆍ광학기기ㆍ시계 total(%p)     74.9586   74.96   \n",
       "81                   성장 요인 분해 의료ㆍ정밀ㆍ광학기기ㆍ시계 drift(%p)     97.7768   97.78   \n",
       "82                성장 요인 분해 의료ㆍ정밀ㆍ광학기기ㆍ시계 dynamics(%p)    -24.4451  -24.45   \n",
       "83                         성장 요인 분해 석탄ㆍ석유제품 total(%p)    -60.5991   -60.6   \n",
       "84                         성장 요인 분해 석탄ㆍ석유제품 drift(%p)    -46.4373  -46.44   \n",
       "85                            성장 요인 분해 석탄ㆍ석유제품 유가(%p)     6.20835    6.21   \n",
       "86                         성장 요인 분해 실질금리 최대 절대 기여(%p)    0.157161    0.16   \n",
       "87  카드 자료(va_detail_62.json)의 분해 = shap_62.csv 반올림...           0       0   \n",
       "\n",
       "          허용오차    통과  \n",
       "0          0.5  True  \n",
       "1          0.5  True  \n",
       "2        0.005  True  \n",
       "3          0.5  True  \n",
       "4        0.005  True  \n",
       "5          0.5  True  \n",
       "6        0.005  True  \n",
       "7          0.5  True  \n",
       "8        0.005  True  \n",
       "9          0.5  True  \n",
       "10       0.005  True  \n",
       "11       0.005  True  \n",
       "12       0.005  True  \n",
       "13         0.5  True  \n",
       "14         0.5  True  \n",
       "15         0.5  True  \n",
       "16       0.035  True  \n",
       "17       0.005  True  \n",
       "18       0.005  True  \n",
       "19       0.005  True  \n",
       "20       0.005  True  \n",
       "21       0.005  True  \n",
       "22       0.005  True  \n",
       "23       0.005  True  \n",
       "24      0.0005  True  \n",
       "25       1e-09  True  \n",
       "26        0.05  True  \n",
       "27        0.05  True  \n",
       "28        0.05  True  \n",
       "29       0.005  True  \n",
       "30         0.5  True  \n",
       "31         0.5  True  \n",
       "32       0.005  True  \n",
       "33       0.005  True  \n",
       "34       1e-12  True  \n",
       "35       1e-09  True  \n",
       "36 0.000500001  True  \n",
       "37 0.000500001  True  \n",
       "38 0.000500001  True  \n",
       "39       1e-09  True  \n",
       "40       1e-09  True  \n",
       "41 0.000500001  True  \n",
       "42 0.000500001  True  \n",
       "43 0.000500001  True  \n",
       "44       1e-09  True  \n",
       "45       1e-09  True  \n",
       "46 0.000500001  True  \n",
       "47 0.000500001  True  \n",
       "48 0.000500001  True  \n",
       "49       1e-09  True  \n",
       "50       1e-09  True  \n",
       "51       1e-06  True  \n",
       "52       1e-06  True  \n",
       "53       1e-06  True  \n",
       "54       1e-06  True  \n",
       "55       1e-09  True  \n",
       "56       1e-09  True  \n",
       "57       0.005  True  \n",
       "58       0.005  True  \n",
       "59       0.005  True  \n",
       "60       0.005  True  \n",
       "61       0.005  True  \n",
       "62       0.005  True  \n",
       "63       0.005  True  \n",
       "64       0.005  True  \n",
       "65       0.005  True  \n",
       "66       0.005  True  \n",
       "67       0.005  True  \n",
       "68       0.005  True  \n",
       "69       0.005  True  \n",
       "70       0.005  True  \n",
       "71       1e-06  True  \n",
       "72       1e-09  True  \n",
       "73       1e-09  True  \n",
       "74 0.000500001  True  \n",
       "75       1e-09  True  \n",
       "76       1e-09  True  \n",
       "77       0.005  True  \n",
       "78       0.005  True  \n",
       "79       0.005  True  \n",
       "80       0.005  True  \n",
       "81       0.005  True  \n",
       "82       0.005  True  \n",
       "83       0.005  True  \n",
       "84       0.005  True  \n",
       "85       0.005  True  \n",
       "86       0.005  True  \n",
       "87       1e-12  True  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "검증 통과: 88개 항목 — 정비판 전망(바텀업·통제 총량·정합화·요약·총계·전달 형식·카드 자료·산업별 성장 요인 분해)과 이전 판 전달본 재현.\n"
     ]
    }
   ],
   "source": [
    "P = C.PUBLISHED\n",
    "g = lambda s, y: (T[62].loc[y, s] / T[62].loc[y - 1, s] - 1) * 100\n",
    "tagg = R.totals(agg)\n",
    "checks = [\n",
    "    (\"S1 2025 전산업(조원)\", a25, P[\"S1_2025_조\"], 0.5),\n",
    "    (\"S1 2035 전산업(조원)\", T[62].loc[2035, \"S1\"], P[\"S1_2035_조\"], 0.5),\n",
    "    (\"S1 연평균 2025–35(%)\", R.cagr(a25, T[62].loc[2035, \"S1\"], 10), P[\"S1_CAGR\"], 0.005),\n",
    "]\n",
    "for s in [\"S2\", \"S3\", \"S4\", \"S5\"]:\n",
    "    checks += [(f\"{s} 2035 전산업(조원)\", T[62].loc[2035, s], P[f\"{s}_2035_조\"], 0.5),\n",
    "               (f\"{s} 연평균(%)\", R.cagr(a25, T[62].loc[2035, s], 10), P[f\"{s}_CAGR\"], 0.005)]\n",
    "checks += [(\"S1 2030년 증가율(%)\", g(\"S1\", 2030), P[\"S1_증가율_2030\"], 0.005),\n",
    "           (\"S1 2035년 증가율(%)\", g(\"S1\", 2035), P[\"S1_증가율_2035\"], 0.005)]\n",
    "checks += [(f\"정합화 후 {r}분류 S1 2035(조원)\", T[r].loc[2035, \"S1\"], P[\"S1_2035_조\"], 0.5) for r in C.RES]\n",
    "checks += [(\"분류 간 전산업 합계 최대차(십억원)\", max((T[r] - T[62]).abs().max().max() for r in C.RES) * 1e3, 0.0, 0.035)]\n",
    "checks += [(f\"바텀업 {r}분류 S1 연평균(%)\", R.cagr(a25, TB[r].loc[2035, \"S1\"], 10), P[\"바텀업_CAGR\"][r], 0.005) for r in C.RES]\n",
    "checks += [(f\"바텀업(재설정 전) {r}분류 S1 연평균(%)\", R.cagr(a25, TBR[r].loc[2035, \"S1\"], 10), P[\"바텀업원값_CAGR\"][r], 0.005) for r in C.RES]\n",
    "checks += [\n",
    "    (\"통제 총량 S1 연평균(%)\", R.cagr(tagg.loc[2025, \"실적\"], tagg.loc[2035, \"S1\"], 10), P[\"통제총량_CAGR\"], 0.005),\n",
    "    (\"비례계수 62 S1 2035\", float(fac[(fac[\"분류\"] == 62) & (fac[\"시나리오\"] == \"S1\") & (fac[\"연도\"] == 2035)][\"비례계수\"].iloc[0]), P[\"비례계수_2035\"], 0.0005),\n",
    "    (\"결합 가중치 w*\", wj[\"wstar\"], P[\"w\"], 1e-9),\n",
    "    (\"검증 RMSE(w*)\", min(wj[\"val\"]), P[\"검증RMSE_w\"], 0.05),\n",
    "    (\"시험 RMSE 모형만\", wj[\"test\"][\"model\"], P[\"시험RMSE_모형\"], 0.05),\n",
    "    (\"시험 RMSE 결합\", wj[\"test\"][\"shrink\"], P[\"시험RMSE_수축\"], 0.05),\n",
    "    (\"기존 전망 연평균 2024–34(%)\", card[\"agg\"][\"old_cagr_24_34\"], C.PREVIOUS[\"기존_CAGR_24_34\"], 0.005),\n",
    "    (\"전달 템플릿 수준 2034(조원)\", card[\"agg\"][\"new_2034\"], P[\"전달_2034_조\"], 0.5),\n",
    "    (\"전달 템플릿 수준 2035(조원)\", card[\"agg\"][\"new_2035\"], P[\"전달_2035_조\"], 0.5),\n",
    "    (\"전달 템플릿 수준 연평균 2024–34(%)\", card[\"agg\"][\"new_cagr_24_34\"], P[\"전달_CAGR_24_34\"], 0.005),\n",
    "    (\"전달 템플릿 수준 연평균 2025–35(%)\", card[\"agg\"][\"new_cagr_25_35\"], P[\"전달_CAGR_25_35\"], 0.005),\n",
    "    (\"전달 엑셀 성장인자 = 전망 성장인자(최대차)\", d_growth, 0.0, 1e-12),\n",
    "    (\"이전 판 전망 → 이전 판 전달본 _신규.xlsx 재현(최대 상대차)\", d_deliv_prev, 0.0, 1e-9),\n",
    "]\n",
    "def file_check(name, keys, cols, tol):\n",
    "    c = pd.read_csv(os.path.join(C.OUTPUT, name), encoding=\"utf-8-sig\"); p = pd.read_csv(os.path.join(C.PUB_DIR, name), encoding=\"utf-8-sig\")\n",
    "    d = R.compare_long(c, p, keys, cols)\n",
    "    assert d[\"키불일치\"] == 0 and d[\"행_계산\"] == d[\"행_공표\"], name\n",
    "    return max(d[k] for k in cols), d[\"행_계산\"]\n",
    "for r in C.RES:\n",
    "    for f, tol in [(f\"forecast_annual_{r}.csv\", 0.0005), (f\"bottomup_annual_{r}.csv\", 0.0005), (f\"bottomup_raw_annual_{r}.csv\", 0.0005)]:\n",
    "        v, n = file_check(f, [\"시나리오\", \"연도\", \"산업\"], [R.VAL], tol)\n",
    "        checks.append((f\"{f} {n:,}행 최대차(십억원)\", v, 0.0, tol + 1e-9))\n",
    "    v, n = file_check(f\"forecast_summary_{r}.csv\", [\"시나리오\", \"산업\"], [\"VA2025\", \"VA2035\", \"CAGR_26_35(%)\"], 0.0)\n",
    "    checks.append((f\"forecast_summary_{r}.csv 최대차\", v, 0.0, 1e-9))\n",
    "    tc = pd.read_csv(os.path.join(C.OUTPUT, f\"forecast_total_{r}.csv\"), index_col=0, encoding=\"utf-8-sig\")\n",
    "    tp = pd.read_csv(os.path.join(C.PUB_DIR, f\"forecast_total_{r}.csv\"), index_col=0, encoding=\"utf-8-sig\")\n",
    "    checks.append((f\"forecast_total_{r}.csv 최대차(조원)\", float((tc - tp).abs().max().max()), 0.0, 1e-9))\n",
    "for f in [\"topdown_annual.csv\", \"topdown_varx_annual.csv\", \"base_va_62.csv\", \"base_va_69.csv\"]:\n",
    "    v, n = file_check(f, [\"시나리오\", \"연도\", \"산업\"], [R.VAL], 0.0)\n",
    "    checks.append((f\"{f} {n:,}행 최대차\", v, 0.0, 1e-6))\n",
    "for f, tol in [(\"card_2025_2035.json\", 1e-9), (\"va_detail_62.json\", 1e-9)]:              # va_detail은 성장 요인 분해(shap)까지 대조\n",
    "    checks.append((f\"{f} 최대차\", R.max_abs_json_diff(json.load(open(os.path.join(C.OUTPUT, f), encoding=\"utf-8\")),\n",
    "                                                        json.load(open(os.path.join(C.PUB_DIR, f), encoding=\"utf-8\")), skip=()), 0.0, tol))\n",
    "by_num = {int(i.split(\".\")[0]): i for i in s1.index}\n",
    "checks += [(f\"산업 연평균 S1 {by_num[k].split('.', 1)[1]}(%)\", s1.loc[by_num[k], \"연평균(%)\"], v, 0.005) for k, v in P[\"산업_CAGR_S1\"].items()]\n",
    "# 성장 요인 분해(02 10절): 기록 파일 대조, 분해 점검 4종, 카드가 쓰는 대표 수치\n",
    "sh_p = pd.read_csv(os.path.join(C.PUB_DIR, \"shap_62.csv\"), index_col=0, encoding=\"utf-8-sig\", float_precision=\"round_trip\")\n",
    "assert list(sh62.index) == list(sh_p.index) and list(sh62.columns) == list(sh_p.columns), \"shap_62.csv 행·열이 기록과 다르다\"\n",
    "checks.append((f\"shap_62.csv {sh62.shape[0]}개 산업 × {sh62.shape[1]}항목 최대차(%p)\", float((sh62 - sh_p).abs().max().max()), 0.0, 1e-6))\n",
    "sc = pd.read_csv(os.path.join(C.OUTPUT, \"shap_check.csv\"), encoding=\"utf-8-sig\")\n",
    "checks += [(f\"성장 요인 분해 점검 {r['점검']}\", r[\"값\"], 0.0, r[\"허용오차\"]) for _, r in sc.iterrows()]\n",
    "SP = P[\"성장요인\"]\n",
    "checks.append((\"성장 요인 분해 통제 총량 맞춤 2035(%p)\", shap62[\"meta\"][\"reconcile_2035\"], SP[\"통제총량_맞춤_2035\"], 1e-9))\n",
    "for k, d in SP[\"산업\"].items():\n",
    "    checks += [(f\"성장 요인 분해 {by_num[k].split('.', 1)[1]} {c}(%p)\", sh62.loc[by_num[k], c], v, 0.005) for c, v in d.items()]\n",
    "checks.append((\"성장 요인 분해 실질금리 최대 절대 기여(%p)\", sh62[\"실질금리\"].abs().max(), SP[\"실질금리_최대절대\"], 0.005))\n",
    "d_card = max(abs(x[\"shap\"][c] - round(float(sh62.loc[by_num[x[\"num\"]], c]), 2)) for x in detail[\"inds\"] for c in (\"total\", \"drift\", \"dynamics\")) +          max(abs(x[\"shap\"][\"exog\"][k] - round(float(sh62.loc[by_num[x[\"num\"]], k]), 2)) for x in detail[\"inds\"] for k in shap62[\"exog\"])\n",
    "checks.append((\"카드 자료(va_detail_62.json)의 분해 = shap_62.csv 반올림값(최대차)\", d_card, 0.0, 1e-12))\n",
    "res = pd.DataFrame([{\"항목\": k, \"계산\": float(v), \"기록\": float(p), \"허용오차\": tol, \"통과\": 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.max_rows\", 120, \"display.float_format\", lambda v: f\"{v:,.6g}\"):\n",
    "    display(res)\n",
    "assert res[\"통과\"].all(), \"기록 수치와 계산 결과가 다르다 — 위 표에서 통과=False 항목을 확인한다\"\n",
    "print(f\"검증 통과: {len(res)}개 항목 — 정비판 전망(바텀업·통제 총량·정합화·요약·총계·전달 형식·카드 자료·산업별 성장 요인 분해)과 이전 판 전달본 재현.\")"
   ]
  }
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