{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f31fcdbf",
   "metadata": {},
   "source": [
    "# 03 결과 도출 — 원고 표·그림과 검증\n",
    "\n",
    "`02_분석.ipynb`의 주 산출물(5장 정비판 최종 전망 투입)로 7장 「인력수급 시사점」의 표와 그림을 만든다.\n",
    "산업별 증감은 2025년 실적(대분류 취업자의 부가가치 비중 배분)에서 2035년 S1 전망까지의 변화로 나타낸다.\n",
    "\n",
    "**2026-10 정비판.** 보고서1은 거시 기준선을 다시 작성하였다. BOKDPM의 장기 정상상태를 현재 여건에 맞추어 고치고(잠재성장률 1.8%, 물가목표 2%, 실질유가 일정),\n",
    "실질금리 외생 투입을 1차 차분으로 바꾸었으며, 외생 경로에 전달되지 않던 시나리오 레버를 연결한 뒤 산업 전망과 정합화를 다시 계산하였다.\n",
    "이 패키지는 그 결과인 5장 정비판 최종 전망으로 주 투입을 바꾸었다. 취업계수의 기준 수준·추세·계수 경로를 정하는 방법과 취업자·부가가치 이력 자료는 바꾸지 않았다.\n",
    "이전 판(2026-10-08 판)의 투입·산출 파일은 `data/published_prev/`에 보존하여 재현 점검과 비교에 쓴다.\n",
    "\n",
    "표 6·7은 정비판을 이전 판과 비교하고, 표 8은 인력수급 전망(고용노동부·한국고용정보원 「2024~2034 중장기 인력수급 전망」)의 취업자 증감과 비교한다.\n",
    "마지막 셀은 주 산출물을 `config.PUBLISHED`(7장 인용 수치)와 대조하고, 이전 판 산출 파일과 최초 판(v0, 2026-08-04 게시) 파일의 바이트 단위 재현을 확인한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a91c1fb5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:56.114122Z",
     "iopub.status.busy": "2026-10-08T00:07:56.113622Z",
     "iopub.status.idle": "2026-10-08T00:07:56.832084Z",
     "shell.execute_reply": "2026-10-08T00:07:56.832084Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.12.10 · pandas 2.2.3 · numpy 2.4.6 · matplotlib 3.11.0\n",
      "  스위치: ONLINE=0 · FULL=0 · API_KEY_BOK 없음\n",
      "  취업자(분기)            있음  data\\inputs\\emp_section_q.csv\n",
      "  실질VA 이력(분기)        있음  data\\inputs\\va36_hist_q.csv\n",
      "  주 투입: 정비판 VA       있음  data\\inputs\\va36_reconciled_annual.csv\n",
      "  이전 판 투입: VA        있음  data\\published_prev\\va36_reconciled_annual_prev.csv\n",
      "  이전 판 산출 JSON       있음  data\\published_prev\\labor_demand_viewer_data_prev.json\n",
      "  최초 판 투입: 분기 VA     있음  data\\inputs\\scenario_viewer_39q_data.json\n",
      "  최초 판 게시 JSON       있음  data\\published\\labor_demand_viewer_data_v0_pre-reconciliation.json\n",
      "  산업 대응표             있음  data\\section_map_36_19.csv\n"
     ]
    }
   ],
   "source": [
    "import os, json\n",
    "import numpy as np, pandas as pd\n",
    "import config as C\n",
    "import labor_demand as F\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",
    "ld = pd.read_csv(os.path.join(C.OUTPUT, \"labor_demand_36.csv\"))\n",
    "ld_prev = pd.read_csv(os.path.join(C.OUTPUT, \"labor_demand_36_prev.csv\"))\n",
    "ld0 = pd.read_csv(os.path.join(C.OUTPUT, \"labor_demand_36_v0.csv\"))\n",
    "act25 = pd.read_csv(os.path.join(C.OUTPUT, \"labor_actual_2025_36.csv\"), index_col=0)[\"취업자_천명\"]\n",
    "par = pd.read_csv(os.path.join(C.OUTPUT, \"coef_params.csv\"), index_col=0)\n",
    "emp_a = pd.read_csv(os.path.join(C.INTERIM, \"emp_section_annual_sa.csv\"), index_col=0)\n",
    "va_a = pd.read_csv(os.path.join(C.INTERIM, \"va36_hist_annual.csv\"), index_col=0)\n",
    "smap_raw = pd.read_csv(C.SECTION_MAP, dtype={\"산업코드\": str})\n",
    "smap = smap_raw.set_index(\"산업명\")\n",
    "sec_of = dict(zip(smap_raw[\"산업명\"], smap_raw[\"대분류\"]))\n",
    "sec_name = dict(zip(smap_raw[\"대분류\"], smap_raw[\"대분류명\"]))\n",
    "def wide(d, val):\n",
    "    return {s: g.pivot(index=\"연도\", columns=\"산업\", values=val) for s, g in d.groupby(\"시나리오\")}\n",
    "P, P_prev = wide(ld, \"취업자_천명\"), wide(ld_prev, \"취업자_천명\")\n",
    "VA = wide(pd.read_csv(os.path.join(C.INTERIM, \"va36_recon_annual.csv\")), \"실질VA_십억원\")\n",
    "VA_prev = wide(pd.read_csv(os.path.join(C.INTERIM, \"va36_recon_prev_annual.csv\")), \"실질VA_십억원\")\n",
    "tot = pd.DataFrame({s: p.sum(axis=1) for s, p in P.items()})\n",
    "tot_prev = pd.DataFrame({s: p.sum(axis=1) for s, p in P_prev.items()})\n",
    "tot_v0 = ld0.groupby([\"시나리오\", \"연도\"])[\"취업자_천명\"].sum().unstack(0)\n",
    "emp25 = act25.sum()\n",
    "COLORS = {\"S1\": \"#1f4e79\", \"S2\": \"#b5402f\", \"S3\": \"#2e7d32\", \"S4\": \"#a9781f\", \"S5\": \"#7b3fa0\"}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "151e5b84",
   "metadata": {},
   "source": [
    "## 표 1. 시나리오별 전산업 취업자 전망(천 명)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d5017df2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:56.833587Z",
     "iopub.status.busy": "2026-10-08T00:07:56.833587Z",
     "iopub.status.idle": "2026-10-08T00:07:56.875137Z",
     "shell.execute_reply": "2026-10-08T00:07:56.875137Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_afff9\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_afff9_level0_col0\" class=\"col_heading level0 col0\" >2025 실적</th>\n",
       "      <th id=\"T_afff9_level0_col1\" class=\"col_heading level0 col1\" >2026</th>\n",
       "      <th id=\"T_afff9_level0_col2\" class=\"col_heading level0 col2\" >2030</th>\n",
       "      <th id=\"T_afff9_level0_col3\" class=\"col_heading level0 col3\" >2035</th>\n",
       "      <th id=\"T_afff9_level0_col4\" class=\"col_heading level0 col4\" >증감 2025→2035</th>\n",
       "      <th id=\"T_afff9_level0_col5\" class=\"col_heading level0 col5\" >연평균 2025–35(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_afff9_level0_row0\" class=\"row_heading level0 row0\" >S1 기준</th>\n",
       "      <td id=\"T_afff9_row0_col0\" class=\"data row0 col0\" >28,681.5</td>\n",
       "      <td id=\"T_afff9_row0_col1\" class=\"data row0 col1\" >28,526.2</td>\n",
       "      <td id=\"T_afff9_row0_col2\" class=\"data row0 col2\" >28,670.8</td>\n",
       "      <td id=\"T_afff9_row0_col3\" class=\"data row0 col3\" >28,560.2</td>\n",
       "      <td id=\"T_afff9_row0_col4\" class=\"data row0 col4\" >-121.3</td>\n",
       "      <td id=\"T_afff9_row0_col5\" class=\"data row0 col5\" >-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_afff9_level0_row1\" class=\"row_heading level0 row1\" >S2 제약+위기</th>\n",
       "      <td id=\"T_afff9_row1_col0\" class=\"data row1 col0\" >28,681.5</td>\n",
       "      <td id=\"T_afff9_row1_col1\" class=\"data row1 col1\" >28,289.4</td>\n",
       "      <td id=\"T_afff9_row1_col2\" class=\"data row1 col2\" >28,648.0</td>\n",
       "      <td id=\"T_afff9_row1_col3\" class=\"data row1 col3\" >28,517.2</td>\n",
       "      <td id=\"T_afff9_row1_col4\" class=\"data row1 col4\" >-164.3</td>\n",
       "      <td id=\"T_afff9_row1_col5\" class=\"data row1 col5\" >-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_afff9_level0_row2\" class=\"row_heading level0 row2\" >S3 제약완화</th>\n",
       "      <td id=\"T_afff9_row2_col0\" class=\"data row2 col0\" >28,681.5</td>\n",
       "      <td id=\"T_afff9_row2_col1\" class=\"data row2 col1\" >28,656.1</td>\n",
       "      <td id=\"T_afff9_row2_col2\" class=\"data row2 col2\" >28,678.1</td>\n",
       "      <td id=\"T_afff9_row2_col3\" class=\"data row2 col3\" >28,595.3</td>\n",
       "      <td id=\"T_afff9_row2_col4\" class=\"data row2 col4\" >-86.2</td>\n",
       "      <td id=\"T_afff9_row2_col5\" class=\"data row2 col5\" >-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_afff9_level0_row3\" class=\"row_heading level0 row3\" >S4 기술도약</th>\n",
       "      <td id=\"T_afff9_row3_col0\" class=\"data row3 col0\" >28,681.5</td>\n",
       "      <td id=\"T_afff9_row3_col1\" class=\"data row3 col1\" >28,652.6</td>\n",
       "      <td id=\"T_afff9_row3_col2\" class=\"data row3 col2\" >28,681.0</td>\n",
       "      <td id=\"T_afff9_row3_col3\" class=\"data row3 col3\" >28,517.5</td>\n",
       "      <td id=\"T_afff9_row3_col4\" class=\"data row3 col4\" >-164.0</td>\n",
       "      <td id=\"T_afff9_row3_col5\" class=\"data row3 col5\" >-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_afff9_level0_row4\" class=\"row_heading level0 row4\" >S5 지정학</th>\n",
       "      <td id=\"T_afff9_row4_col0\" class=\"data row4 col0\" >28,681.5</td>\n",
       "      <td id=\"T_afff9_row4_col1\" class=\"data row4 col1\" >28,337.0</td>\n",
       "      <td id=\"T_afff9_row4_col2\" class=\"data row4 col2\" >28,729.0</td>\n",
       "      <td id=\"T_afff9_row4_col3\" class=\"data row4 col3\" >28,614.9</td>\n",
       "      <td id=\"T_afff9_row4_col4\" class=\"data row4 col4\" >-66.6</td>\n",
       "      <td id=\"T_afff9_row4_col5\" class=\"data row4 col5\" >-0.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2277c3c37a0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = pd.DataFrame({F.SCENARIO_LABELS[s]: {\"2025 실적\": emp25, \"2026\": tot.loc[2026, s], \"2030\": tot.loc[2030, s], \"2035\": tot.loc[2035, s],\n",
    "                                             \"증감 2025→2035\": tot.loc[2035, s] - emp25,\n",
    "                                             \"연평균 2025–35(%)\": F.cagr(emp25, tot.loc[2035, s], 10)} for s in C.SCENARIOS}).T\n",
    "t1.to_csv(os.path.join(C.OUTPUT, \"table1_totals.csv\"), encoding=\"utf-8-sig\")\n",
    "t1.style.format({\"2025 실적\": \"{:,.1f}\", \"2026\": \"{:,.1f}\", \"2030\": \"{:,.1f}\", \"2035\": \"{:,.1f}\",\n",
    "                 \"증감 2025→2035\": \"{:+,.1f}\", \"연평균 2025–35(%)\": \"{:+.2f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68537448",
   "metadata": {},
   "source": [
    "## 그림 1. 시나리오별 전산업 취업자 경로(2025 실적, 2026–2035 전망)\n",
    "\n",
    "점선은 이전 판(2026-10-08 판) S1이다. 최초 판(v0)은 수준이 크게 달라(2035년 S1 3,628만 명) 그림에서 뺐으며 표 9에 수치를 둔다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "50bd6f48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:56.876140Z",
     "iopub.status.busy": "2026-10-08T00:07:56.876140Z",
     "iopub.status.idle": "2026-10-08T00:07:57.052888Z",
     "shell.execute_reply": "2026-10-08T00:07:57.052384Z"
    }
   },
   "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",
    "for s in C.SCENARIOS:\n",
    "    ax.plot([2025] + list(tot.index), [emp25] + list(tot[s]), marker=\"o\", ms=3, color=COLORS[s], label=F.SCENARIO_LABELS[s])\n",
    "ax.plot([2025] + list(tot_prev.index), [emp25] + list(tot_prev[\"S1\"]), ls=\"--\", color=\"#8592a3\", lw=1.2, label=\"이전 판 S1(2026-10-08 판)\")\n",
    "ax.scatter([2025], [emp25], color=\"#1a2330\", zorder=3, label=\"2025 실적\")\n",
    "ax.set_ylabel(\"천 명\"); ax.grid(alpha=.3); ax.legend(frameon=False, ncol=3, fontsize=8)\n",
    "ax.set_title(\"전산업 취업자: 시나리오별 전망(정비판)\", loc=\"left\")\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig1_total_paths.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4729a76d",
   "metadata": {},
   "source": [
    "## 표 2. 인력수요 확대 성장산업(S1, 2025→2035 증가 폭 상위 8개)\n",
    "\n",
    "2025년 값은 대분류 실측 취업자를 2025년 실질부가가치 비중으로 나눈 값이다. 대분류가 산업 하나인 경우(보건복지·도소매 등 15개 대분류)에는 실측치와 같다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8333349c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:57.053888Z",
     "iopub.status.busy": "2026-10-08T00:07:57.053888Z",
     "iopub.status.idle": "2026-10-08T00:07:57.062263Z",
     "shell.execute_reply": "2026-10-08T00:07:57.062263Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "보건복지 비중: 2025년 11.1% → 2035년 S1 16.2%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_68b21\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_68b21_level0_col0\" class=\"col_heading level0 col0\" >대분류</th>\n",
       "      <th id=\"T_68b21_level0_col1\" class=\"col_heading level0 col1\" >2025 실적(천명)</th>\n",
       "      <th id=\"T_68b21_level0_col2\" class=\"col_heading level0 col2\" >2035 S1(천명)</th>\n",
       "      <th id=\"T_68b21_level0_col3\" class=\"col_heading level0 col3\" >증감(천명)</th>\n",
       "      <th id=\"T_68b21_level0_col4\" class=\"col_heading level0 col4\" >증감률(%)</th>\n",
       "      <th id=\"T_68b21_level0_col5\" class=\"col_heading level0 col5\" >연평균(%)</th>\n",
       "      <th id=\"T_68b21_level0_col6\" class=\"col_heading level0 col6\" >2035 비중(%)</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_68b21_level0_row0\" class=\"row_heading level0 row0\" >의료, 보건업 및 사회복지서비스업</th>\n",
       "      <td id=\"T_68b21_row0_col0\" class=\"data row0 col0\" >Q</td>\n",
       "      <td id=\"T_68b21_row0_col1\" class=\"data row0 col1\" >3,175.8</td>\n",
       "      <td id=\"T_68b21_row0_col2\" class=\"data row0 col2\" >4,626.7</td>\n",
       "      <td id=\"T_68b21_row0_col3\" class=\"data row0 col3\" >+1,450.9</td>\n",
       "      <td id=\"T_68b21_row0_col4\" class=\"data row0 col4\" >+45.7</td>\n",
       "      <td id=\"T_68b21_row0_col5\" class=\"data row0 col5\" >+3.83</td>\n",
       "      <td id=\"T_68b21_row0_col6\" class=\"data row0 col6\" >16.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b21_level0_row1\" class=\"row_heading level0 row1\" >컴퓨터, 전자 및 광학기기 제조업</th>\n",
       "      <td id=\"T_68b21_row1_col0\" class=\"data row1 col0\" >C</td>\n",
       "      <td id=\"T_68b21_row1_col1\" class=\"data row1 col1\" >1,597.7</td>\n",
       "      <td id=\"T_68b21_row1_col2\" class=\"data row1 col2\" >2,588.6</td>\n",
       "      <td id=\"T_68b21_row1_col3\" class=\"data row1 col3\" >+990.8</td>\n",
       "      <td id=\"T_68b21_row1_col4\" class=\"data row1 col4\" >+62.0</td>\n",
       "      <td id=\"T_68b21_row1_col5\" class=\"data row1 col5\" >+4.94</td>\n",
       "      <td id=\"T_68b21_row1_col6\" class=\"data row1 col6\" >9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b21_level0_row2\" class=\"row_heading level0 row2\" >출판, 방송, 영상, 정보서비스</th>\n",
       "      <td id=\"T_68b21_row2_col0\" class=\"data row2 col0\" >J</td>\n",
       "      <td id=\"T_68b21_row2_col1\" class=\"data row2 col1\" >825.2</td>\n",
       "      <td id=\"T_68b21_row2_col2\" class=\"data row2 col2\" >981.1</td>\n",
       "      <td id=\"T_68b21_row2_col3\" class=\"data row2 col3\" >+155.9</td>\n",
       "      <td id=\"T_68b21_row2_col4\" class=\"data row2 col4\" >+18.9</td>\n",
       "      <td id=\"T_68b21_row2_col5\" class=\"data row2 col5\" >+1.75</td>\n",
       "      <td id=\"T_68b21_row2_col6\" class=\"data row2 col6\" >3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b21_level0_row3\" class=\"row_heading level0 row3\" >통신업</th>\n",
       "      <td id=\"T_68b21_row3_col0\" class=\"data row3 col0\" >J</td>\n",
       "      <td id=\"T_68b21_row3_col1\" class=\"data row3 col1\" >314.1</td>\n",
       "      <td id=\"T_68b21_row3_col2\" class=\"data row3 col2\" >412.1</td>\n",
       "      <td id=\"T_68b21_row3_col3\" class=\"data row3 col3\" >+98.0</td>\n",
       "      <td id=\"T_68b21_row3_col4\" class=\"data row3 col4\" >+31.2</td>\n",
       "      <td id=\"T_68b21_row3_col5\" class=\"data row3 col5\" >+2.75</td>\n",
       "      <td id=\"T_68b21_row3_col6\" class=\"data row3 col6\" >1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b21_level0_row4\" class=\"row_heading level0 row4\" >공공행정, 국방 및 사회보장</th>\n",
       "      <td id=\"T_68b21_row4_col0\" class=\"data row4 col0\" >O</td>\n",
       "      <td id=\"T_68b21_row4_col1\" class=\"data row4 col1\" >1,329.8</td>\n",
       "      <td id=\"T_68b21_row4_col2\" class=\"data row4 col2\" >1,411.6</td>\n",
       "      <td id=\"T_68b21_row4_col3\" class=\"data row4 col3\" >+81.9</td>\n",
       "      <td id=\"T_68b21_row4_col4\" class=\"data row4 col4\" >+6.2</td>\n",
       "      <td id=\"T_68b21_row4_col5\" class=\"data row4 col5\" >+0.60</td>\n",
       "      <td id=\"T_68b21_row4_col6\" class=\"data row4 col6\" >4.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b21_level0_row5\" class=\"row_heading level0 row5\" >전문, 과학 및 기술관련 서비스업</th>\n",
       "      <td id=\"T_68b21_row5_col0\" class=\"data row5 col0\" >M</td>\n",
       "      <td id=\"T_68b21_row5_col1\" class=\"data row5 col1\" >1,475.8</td>\n",
       "      <td id=\"T_68b21_row5_col2\" class=\"data row5 col2\" >1,556.8</td>\n",
       "      <td id=\"T_68b21_row5_col3\" class=\"data row5 col3\" >+81.1</td>\n",
       "      <td id=\"T_68b21_row5_col4\" class=\"data row5 col4\" >+5.5</td>\n",
       "      <td id=\"T_68b21_row5_col5\" class=\"data row5 col5\" >+0.54</td>\n",
       "      <td id=\"T_68b21_row5_col6\" class=\"data row5 col6\" >5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b21_level0_row6\" class=\"row_heading level0 row6\" >수도, 하수 및 폐기물 처리, 원료 재생업</th>\n",
       "      <td id=\"T_68b21_row6_col0\" class=\"data row6 col0\" >E</td>\n",
       "      <td id=\"T_68b21_row6_col1\" class=\"data row6 col1\" >148.2</td>\n",
       "      <td id=\"T_68b21_row6_col2\" class=\"data row6 col2\" >193.9</td>\n",
       "      <td id=\"T_68b21_row6_col3\" class=\"data row6 col3\" >+45.6</td>\n",
       "      <td id=\"T_68b21_row6_col4\" class=\"data row6 col4\" >+30.8</td>\n",
       "      <td id=\"T_68b21_row6_col5\" class=\"data row6 col5\" >+2.72</td>\n",
       "      <td id=\"T_68b21_row6_col6\" class=\"data row6 col6\" >0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_68b21_level0_row7\" class=\"row_heading level0 row7\" >예술, 스포츠 및 여가관련 서비스업</th>\n",
       "      <td id=\"T_68b21_row7_col0\" class=\"data row7 col0\" >R</td>\n",
       "      <td id=\"T_68b21_row7_col1\" class=\"data row7 col1\" >553.2</td>\n",
       "      <td id=\"T_68b21_row7_col2\" class=\"data row7 col2\" >578.5</td>\n",
       "      <td id=\"T_68b21_row7_col3\" class=\"data row7 col3\" >+25.2</td>\n",
       "      <td id=\"T_68b21_row7_col4\" class=\"data row7 col4\" >+4.6</td>\n",
       "      <td id=\"T_68b21_row7_col5\" class=\"data row7 col5\" >+0.45</td>\n",
       "      <td id=\"T_68b21_row7_col6\" class=\"data row7 col6\" >2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2271eeef560>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = P[\"S1\"]\n",
    "chg = pd.DataFrame({\"대분류\": smap.loc[act25.index, \"대분류\"].values, \"2025 실적(천명)\": act25, \"2035 S1(천명)\": s1.loc[2035, act25.index]})\n",
    "chg[\"증감(천명)\"] = chg[\"2035 S1(천명)\"] - chg[\"2025 실적(천명)\"]\n",
    "chg[\"증감률(%)\"] = 100 * chg[\"증감(천명)\"] / chg[\"2025 실적(천명)\"]\n",
    "chg[\"연평균(%)\"] = 100 * ((chg[\"2035 S1(천명)\"] / chg[\"2025 실적(천명)\"]) ** 0.1 - 1)\n",
    "chg[\"2035 비중(%)\"] = 100 * chg[\"2035 S1(천명)\"] / chg[\"2035 S1(천명)\"].sum()\n",
    "chg = chg.sort_values(\"증감(천명)\", ascending=False)\n",
    "chg.to_csv(os.path.join(C.OUTPUT, \"table2_change_s1.csv\"), encoding=\"utf-8-sig\")\n",
    "fmt = {\"2025 실적(천명)\": \"{:,.1f}\", \"2035 S1(천명)\": \"{:,.1f}\", \"증감(천명)\": \"{:+,.1f}\", \"증감률(%)\": \"{:+.1f}\",\n",
    "       \"연평균(%)\": \"{:+.2f}\", \"2035 비중(%)\": \"{:.1f}\"}\n",
    "hw = \"의료, 보건업 및 사회복지서비스업\"\n",
    "print(f\"보건복지 비중: 2025년 {100 * act25[hw] / emp25:.1f}% → 2035년 S1 {chg.loc[hw, '2035 비중(%)']:.1f}%\")\n",
    "chg.head(8).style.format(fmt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "273a707a",
   "metadata": {},
   "source": [
    "## 표 3. 인력 재배치가 필요한 정체·감소산업(S1, 2025→2035 감소 폭 상위 6개)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c63226cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:57.063268Z",
     "iopub.status.busy": "2026-10-08T00:07:57.063268Z",
     "iopub.status.idle": "2026-10-08T00:07:57.067096Z",
     "shell.execute_reply": "2026-10-08T00:07:57.067096Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_e7a11\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e7a11_level0_col0\" class=\"col_heading level0 col0\" >대분류</th>\n",
       "      <th id=\"T_e7a11_level0_col1\" class=\"col_heading level0 col1\" >2025 실적(천명)</th>\n",
       "      <th id=\"T_e7a11_level0_col2\" class=\"col_heading level0 col2\" >2035 S1(천명)</th>\n",
       "      <th id=\"T_e7a11_level0_col3\" class=\"col_heading level0 col3\" >증감(천명)</th>\n",
       "      <th id=\"T_e7a11_level0_col4\" class=\"col_heading level0 col4\" >증감률(%)</th>\n",
       "      <th id=\"T_e7a11_level0_col5\" class=\"col_heading level0 col5\" >연평균(%)</th>\n",
       "      <th id=\"T_e7a11_level0_col6\" class=\"col_heading level0 col6\" >2035 비중(%)</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a11_level0_row0\" class=\"row_heading level0 row0\" >도소매업</th>\n",
       "      <td id=\"T_e7a11_row0_col0\" class=\"data row0 col0\" >G</td>\n",
       "      <td id=\"T_e7a11_row0_col1\" class=\"data row0 col1\" >3,213.5</td>\n",
       "      <td id=\"T_e7a11_row0_col2\" class=\"data row0 col2\" >2,226.2</td>\n",
       "      <td id=\"T_e7a11_row0_col3\" class=\"data row0 col3\" >-987.3</td>\n",
       "      <td id=\"T_e7a11_row0_col4\" class=\"data row0 col4\" >-30.7</td>\n",
       "      <td id=\"T_e7a11_row0_col5\" class=\"data row0 col5\" >-3.60</td>\n",
       "      <td id=\"T_e7a11_row0_col6\" class=\"data row0 col6\" >7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a11_level0_row1\" class=\"row_heading level0 row1\" >숙박 및 음식점업</th>\n",
       "      <td id=\"T_e7a11_row1_col0\" class=\"data row1 col0\" >I</td>\n",
       "      <td id=\"T_e7a11_row1_col1\" class=\"data row1 col1\" >2,319.2</td>\n",
       "      <td id=\"T_e7a11_row1_col2\" class=\"data row1 col2\" >1,844.5</td>\n",
       "      <td id=\"T_e7a11_row1_col3\" class=\"data row1 col3\" >-474.8</td>\n",
       "      <td id=\"T_e7a11_row1_col4\" class=\"data row1 col4\" >-20.5</td>\n",
       "      <td id=\"T_e7a11_row1_col5\" class=\"data row1 col5\" >-2.26</td>\n",
       "      <td id=\"T_e7a11_row1_col6\" class=\"data row1 col6\" >6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a11_level0_row2\" class=\"row_heading level0 row2\" >기타 서비스업</th>\n",
       "      <td id=\"T_e7a11_row2_col0\" class=\"data row2 col0\" >S</td>\n",
       "      <td id=\"T_e7a11_row2_col1\" class=\"data row2 col1\" >1,145.5</td>\n",
       "      <td id=\"T_e7a11_row2_col2\" class=\"data row2 col2\" >893.4</td>\n",
       "      <td id=\"T_e7a11_row2_col3\" class=\"data row2 col3\" >-252.1</td>\n",
       "      <td id=\"T_e7a11_row2_col4\" class=\"data row2 col4\" >-22.0</td>\n",
       "      <td id=\"T_e7a11_row2_col5\" class=\"data row2 col5\" >-2.46</td>\n",
       "      <td id=\"T_e7a11_row2_col6\" class=\"data row2 col6\" >3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a11_level0_row3\" class=\"row_heading level0 row3\" >교육서비스업</th>\n",
       "      <td id=\"T_e7a11_row3_col0\" class=\"data row3 col0\" >P</td>\n",
       "      <td id=\"T_e7a11_row3_col1\" class=\"data row3 col1\" >1,943.0</td>\n",
       "      <td id=\"T_e7a11_row3_col2\" class=\"data row3 col2\" >1,708.3</td>\n",
       "      <td id=\"T_e7a11_row3_col3\" class=\"data row3 col3\" >-234.7</td>\n",
       "      <td id=\"T_e7a11_row3_col4\" class=\"data row3 col4\" >-12.1</td>\n",
       "      <td id=\"T_e7a11_row3_col5\" class=\"data row3 col5\" >-1.28</td>\n",
       "      <td id=\"T_e7a11_row3_col6\" class=\"data row3 col6\" >6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a11_level0_row4\" class=\"row_heading level0 row4\" >금융 및 보험업</th>\n",
       "      <td id=\"T_e7a11_row4_col0\" class=\"data row4 col0\" >K</td>\n",
       "      <td id=\"T_e7a11_row4_col1\" class=\"data row4 col1\" >811.2</td>\n",
       "      <td id=\"T_e7a11_row4_col2\" class=\"data row4 col2\" >626.9</td>\n",
       "      <td id=\"T_e7a11_row4_col3\" class=\"data row4 col3\" >-184.3</td>\n",
       "      <td id=\"T_e7a11_row4_col4\" class=\"data row4 col4\" >-22.7</td>\n",
       "      <td id=\"T_e7a11_row4_col5\" class=\"data row4 col5\" >-2.54</td>\n",
       "      <td id=\"T_e7a11_row4_col6\" class=\"data row4 col6\" >2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e7a11_level0_row5\" class=\"row_heading level0 row5\" >토목건설업</th>\n",
       "      <td id=\"T_e7a11_row5_col0\" class=\"data row5 col0\" >F</td>\n",
       "      <td id=\"T_e7a11_row5_col1\" class=\"data row5 col1\" >562.0</td>\n",
       "      <td id=\"T_e7a11_row5_col2\" class=\"data row5 col2\" >438.1</td>\n",
       "      <td id=\"T_e7a11_row5_col3\" class=\"data row5 col3\" >-123.9</td>\n",
       "      <td id=\"T_e7a11_row5_col4\" class=\"data row5 col4\" >-22.0</td>\n",
       "      <td id=\"T_e7a11_row5_col5\" class=\"data row5 col5\" >-2.46</td>\n",
       "      <td id=\"T_e7a11_row5_col6\" class=\"data row5 col6\" >1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2271ee7b560>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chg.tail(6).sort_values(\"증감(천명)\").style.format(fmt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca54bbe",
   "metadata": {},
   "source": [
    "## 그림 2. 36개 산업 취업자 증감(S1, 2025 실적→2035)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab5f1145",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:57.068099Z",
     "iopub.status.busy": "2026-10-08T00:07:57.068099Z",
     "iopub.status.idle": "2026-10-08T00:07:57.333917Z",
     "shell.execute_reply": "2026-10-08T00:07:57.333917Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q = chg[\"증감(천명)\"].sort_values()\n",
    "fig, ax = plt.subplots(figsize=(8, 9))\n",
    "ax.barh(q.index, q.values, color=[\"#b5402f\" if v < 0 else \"#1f4e79\" for v in q.values])\n",
    "ax.set_xlabel(\"천 명\"); ax.grid(axis=\"x\", alpha=.3); ax.tick_params(axis=\"y\", labelsize=7)\n",
    "ax.set_title(\"산업별 취업자 증감(S1, 2025 실적→2035)\", loc=\"left\")\n",
    "fig.tight_layout(); fig.savefig(os.path.join(C.OUTPUT, \"fig2_change_s1.png\"), dpi=150)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "955165dd",
   "metadata": {},
   "source": [
    "## 표 4. 시나리오별 인력수급 위험(2035년, 시나리오 간 범위 상위 10개 산업)\n",
    "\n",
    "같은 산업이라도 거시 시나리오에 따라 2035년 취업자 전망이 달라진다. 범위(최고 − 최저)가 큰 산업일수록 시나리오에 따른 인력수급 불확실성이 크다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "14c40c69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:57.335422Z",
     "iopub.status.busy": "2026-10-08T00:07:57.335422Z",
     "iopub.status.idle": "2026-10-08T00:07:57.342931Z",
     "shell.execute_reply": "2026-10-08T00:07:57.342931Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_7c16d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7c16d_level0_col0\" class=\"col_heading level0 col0\" >S1(천명)</th>\n",
       "      <th id=\"T_7c16d_level0_col1\" class=\"col_heading level0 col1\" >최저</th>\n",
       "      <th id=\"T_7c16d_level0_col2\" class=\"col_heading level0 col2\" >최저(천명)</th>\n",
       "      <th id=\"T_7c16d_level0_col3\" class=\"col_heading level0 col3\" >최고</th>\n",
       "      <th id=\"T_7c16d_level0_col4\" class=\"col_heading level0 col4\" >최고(천명)</th>\n",
       "      <th id=\"T_7c16d_level0_col5\" class=\"col_heading level0 col5\" >범위(천명)</th>\n",
       "      <th id=\"T_7c16d_level0_col6\" class=\"col_heading level0 col6\" >범위/S1(%)</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7c16d_level0_row0\" class=\"row_heading level0 row0\" >사업지원 서비스업</th>\n",
       "      <td id=\"T_7c16d_row0_col0\" class=\"data row0 col0\" >1,393.3</td>\n",
       "      <td id=\"T_7c16d_row0_col1\" class=\"data row0 col1\" >S2</td>\n",
       "      <td id=\"T_7c16d_row0_col2\" class=\"data row0 col2\" >1,326.7</td>\n",
       "      <td id=\"T_7c16d_row0_col3\" class=\"data row0 col3\" >S4</td>\n",
       "      <td id=\"T_7c16d_row0_col4\" class=\"data row0 col4\" >1,467.4</td>\n",
       "      <td id=\"T_7c16d_row0_col5\" class=\"data row0 col5\" >140.7</td>\n",
       "      <td id=\"T_7c16d_row0_col6\" class=\"data row0 col6\" >10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c16d_level0_row1\" class=\"row_heading level0 row1\" >농림어업</th>\n",
       "      <td id=\"T_7c16d_row1_col0\" class=\"data row1 col0\" >1,374.1</td>\n",
       "      <td id=\"T_7c16d_row1_col1\" class=\"data row1 col1\" >S4</td>\n",
       "      <td id=\"T_7c16d_row1_col2\" class=\"data row1 col2\" >1,311.0</td>\n",
       "      <td id=\"T_7c16d_row1_col3\" class=\"data row1 col3\" >S5</td>\n",
       "      <td id=\"T_7c16d_row1_col4\" class=\"data row1 col4\" >1,437.4</td>\n",
       "      <td id=\"T_7c16d_row1_col5\" class=\"data row1 col5\" >126.4</td>\n",
       "      <td id=\"T_7c16d_row1_col6\" class=\"data row1 col6\" >9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c16d_level0_row2\" class=\"row_heading level0 row2\" >전문, 과학 및 기술관련 서비스업</th>\n",
       "      <td id=\"T_7c16d_row2_col0\" class=\"data row2 col0\" >1,556.8</td>\n",
       "      <td id=\"T_7c16d_row2_col1\" class=\"data row2 col1\" >S5</td>\n",
       "      <td id=\"T_7c16d_row2_col2\" class=\"data row2 col2\" >1,508.2</td>\n",
       "      <td id=\"T_7c16d_row2_col3\" class=\"data row2 col3\" >S4</td>\n",
       "      <td id=\"T_7c16d_row2_col4\" class=\"data row2 col4\" >1,591.5</td>\n",
       "      <td id=\"T_7c16d_row2_col5\" class=\"data row2 col5\" >83.3</td>\n",
       "      <td id=\"T_7c16d_row2_col6\" class=\"data row2 col6\" >5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c16d_level0_row3\" class=\"row_heading level0 row3\" >운수업</th>\n",
       "      <td id=\"T_7c16d_row3_col0\" class=\"data row3 col0\" >1,674.4</td>\n",
       "      <td id=\"T_7c16d_row3_col1\" class=\"data row3 col1\" >S2</td>\n",
       "      <td id=\"T_7c16d_row3_col2\" class=\"data row3 col2\" >1,654.5</td>\n",
       "      <td id=\"T_7c16d_row3_col3\" class=\"data row3 col3\" >S5</td>\n",
       "      <td id=\"T_7c16d_row3_col4\" class=\"data row3 col4\" >1,731.9</td>\n",
       "      <td id=\"T_7c16d_row3_col5\" class=\"data row3 col5\" >77.3</td>\n",
       "      <td id=\"T_7c16d_row3_col6\" class=\"data row3 col6\" >4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c16d_level0_row4\" class=\"row_heading level0 row4\" >숙박 및 음식점업</th>\n",
       "      <td id=\"T_7c16d_row4_col0\" class=\"data row4 col0\" >1,844.5</td>\n",
       "      <td id=\"T_7c16d_row4_col1\" class=\"data row4 col1\" >S2</td>\n",
       "      <td id=\"T_7c16d_row4_col2\" class=\"data row4 col2\" >1,818.4</td>\n",
       "      <td id=\"T_7c16d_row4_col3\" class=\"data row4 col3\" >S5</td>\n",
       "      <td id=\"T_7c16d_row4_col4\" class=\"data row4 col4\" >1,895.0</td>\n",
       "      <td id=\"T_7c16d_row4_col5\" class=\"data row4 col5\" >76.6</td>\n",
       "      <td id=\"T_7c16d_row4_col6\" class=\"data row4 col6\" >4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c16d_level0_row5\" class=\"row_heading level0 row5\" >의료, 보건업 및 사회복지서비스업</th>\n",
       "      <td id=\"T_7c16d_row5_col0\" class=\"data row5 col0\" >4,626.7</td>\n",
       "      <td id=\"T_7c16d_row5_col1\" class=\"data row5 col1\" >S4</td>\n",
       "      <td id=\"T_7c16d_row5_col2\" class=\"data row5 col2\" >4,588.4</td>\n",
       "      <td id=\"T_7c16d_row5_col3\" class=\"data row5 col3\" >S2</td>\n",
       "      <td id=\"T_7c16d_row5_col4\" class=\"data row5 col4\" >4,660.3</td>\n",
       "      <td id=\"T_7c16d_row5_col5\" class=\"data row5 col5\" >71.9</td>\n",
       "      <td id=\"T_7c16d_row5_col6\" class=\"data row5 col6\" >1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c16d_level0_row6\" class=\"row_heading level0 row6\" >도소매업</th>\n",
       "      <td id=\"T_7c16d_row6_col0\" class=\"data row6 col0\" >2,226.2</td>\n",
       "      <td id=\"T_7c16d_row6_col1\" class=\"data row6 col1\" >S4</td>\n",
       "      <td id=\"T_7c16d_row6_col2\" class=\"data row6 col2\" >2,189.8</td>\n",
       "      <td id=\"T_7c16d_row6_col3\" class=\"data row6 col3\" >S2</td>\n",
       "      <td id=\"T_7c16d_row6_col4\" class=\"data row6 col4\" >2,256.9</td>\n",
       "      <td id=\"T_7c16d_row6_col5\" class=\"data row6 col5\" >67.2</td>\n",
       "      <td id=\"T_7c16d_row6_col6\" class=\"data row6 col6\" >3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c16d_level0_row7\" class=\"row_heading level0 row7\" >비주거용 건물 건설업</th>\n",
       "      <td id=\"T_7c16d_row7_col0\" class=\"data row7 col0\" >650.1</td>\n",
       "      <td id=\"T_7c16d_row7_col1\" class=\"data row7 col1\" >S5</td>\n",
       "      <td id=\"T_7c16d_row7_col2\" class=\"data row7 col2\" >616.9</td>\n",
       "      <td id=\"T_7c16d_row7_col3\" class=\"data row7 col3\" >S4</td>\n",
       "      <td id=\"T_7c16d_row7_col4\" class=\"data row7 col4\" >681.8</td>\n",
       "      <td id=\"T_7c16d_row7_col5\" class=\"data row7 col5\" >65.0</td>\n",
       "      <td id=\"T_7c16d_row7_col6\" class=\"data row7 col6\" >10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c16d_level0_row8\" class=\"row_heading level0 row8\" >출판, 방송, 영상, 정보서비스</th>\n",
       "      <td id=\"T_7c16d_row8_col0\" class=\"data row8 col0\" >981.1</td>\n",
       "      <td id=\"T_7c16d_row8_col1\" class=\"data row8 col1\" >S4</td>\n",
       "      <td id=\"T_7c16d_row8_col2\" class=\"data row8 col2\" >948.5</td>\n",
       "      <td id=\"T_7c16d_row8_col3\" class=\"data row8 col3\" >S2</td>\n",
       "      <td id=\"T_7c16d_row8_col4\" class=\"data row8 col4\" >1,007.5</td>\n",
       "      <td id=\"T_7c16d_row8_col5\" class=\"data row8 col5\" >59.0</td>\n",
       "      <td id=\"T_7c16d_row8_col6\" class=\"data row8 col6\" >6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c16d_level0_row9\" class=\"row_heading level0 row9\" >주거용 건물 건설업</th>\n",
       "      <td id=\"T_7c16d_row9_col0\" class=\"data row9 col0\" >399.8</td>\n",
       "      <td id=\"T_7c16d_row9_col1\" class=\"data row9 col1\" >S4</td>\n",
       "      <td id=\"T_7c16d_row9_col2\" class=\"data row9 col2\" >380.5</td>\n",
       "      <td id=\"T_7c16d_row9_col3\" class=\"data row9 col3\" >S2</td>\n",
       "      <td id=\"T_7c16d_row9_col4\" class=\"data row9 col4\" >428.7</td>\n",
       "      <td id=\"T_7c16d_row9_col5\" class=\"data row9 col5\" >48.2</td>\n",
       "      <td id=\"T_7c16d_row9_col6\" class=\"data row9 col6\" >12.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2271f0a6b70>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y35 = pd.DataFrame({s: P[s].loc[2035] for s in C.SCENARIOS})\n",
    "t4 = pd.DataFrame({\"S1(천명)\": y35[\"S1\"], \"최저\": y35.idxmin(axis=1), \"최저(천명)\": y35.min(axis=1),\n",
    "                   \"최고\": y35.idxmax(axis=1), \"최고(천명)\": y35.max(axis=1)})\n",
    "t4[\"범위(천명)\"] = t4[\"최고(천명)\"] - t4[\"최저(천명)\"]\n",
    "t4[\"범위/S1(%)\"] = 100 * t4[\"범위(천명)\"] / t4[\"S1(천명)\"]\n",
    "t4 = t4.sort_values(\"범위(천명)\", ascending=False)\n",
    "t4.to_csv(os.path.join(C.OUTPUT, \"table4_scenario_range_2035.csv\"), encoding=\"utf-8-sig\")\n",
    "t4.head(10).style.format({\"S1(천명)\": \"{:,.1f}\", \"최저(천명)\": \"{:,.1f}\", \"최고(천명)\": \"{:,.1f}\", \"범위(천명)\": \"{:,.1f}\", \"범위/S1(%)\": \"{:.1f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93138db0",
   "metadata": {},
   "source": [
    "## 표 5. 대분류 취업계수의 기준 수준과 추세"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7294e3df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:57.343929Z",
     "iopub.status.busy": "2026-10-08T00:07:57.343929Z",
     "iopub.status.idle": "2026-10-08T00:07:57.349488Z",
     "shell.execute_reply": "2026-10-08T00:07:57.349488Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_ca568\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ca568_level0_col0\" class=\"col_heading level0 col0\" >대분류명</th>\n",
       "      <th id=\"T_ca568_level0_col1\" class=\"col_heading level0 col1\" >기준 수준(명/10억원)</th>\n",
       "      <th id=\"T_ca568_level0_col2\" class=\"col_heading level0 col2\" >연간 로그변화율(%)</th>\n",
       "      <th id=\"T_ca568_level0_col3\" class=\"col_heading level0 col3\" >추세 R²</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
       "      <td id=\"T_ca568_row0_col0\" class=\"data row0 col0\" >농업, 임업 및 어업</td>\n",
       "      <td id=\"T_ca568_row0_col1\" class=\"data row0 col1\" >43.62</td>\n",
       "      <td id=\"T_ca568_row0_col2\" class=\"data row0 col2\" >+1.03</td>\n",
       "      <td id=\"T_ca568_row0_col3\" class=\"data row0 col3\" >0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row1\" class=\"row_heading level0 row1\" >B</th>\n",
       "      <td id=\"T_ca568_row1_col0\" class=\"data row1 col0\" >광업</td>\n",
       "      <td id=\"T_ca568_row1_col1\" class=\"data row1 col1\" >5.15</td>\n",
       "      <td id=\"T_ca568_row1_col2\" class=\"data row1 col2\" >-3.48</td>\n",
       "      <td id=\"T_ca568_row1_col3\" class=\"data row1 col3\" >0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row2\" class=\"row_heading level0 row2\" >C</th>\n",
       "      <td id=\"T_ca568_row2_col0\" class=\"data row2 col0\" >제조업</td>\n",
       "      <td id=\"T_ca568_row2_col1\" class=\"data row2 col1\" >7.10</td>\n",
       "      <td id=\"T_ca568_row2_col2\" class=\"data row2 col2\" >-2.60</td>\n",
       "      <td id=\"T_ca568_row2_col3\" class=\"data row2 col3\" >0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row3\" class=\"row_heading level0 row3\" >D</th>\n",
       "      <td id=\"T_ca568_row3_col0\" class=\"data row3 col0\" >전기, 가스, 증기 및 공기조절 공급업</td>\n",
       "      <td id=\"T_ca568_row3_col1\" class=\"data row3 col1\" >2.49</td>\n",
       "      <td id=\"T_ca568_row3_col2\" class=\"data row3 col2\" >-1.94</td>\n",
       "      <td id=\"T_ca568_row3_col3\" class=\"data row3 col3\" >0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row4\" class=\"row_heading level0 row4\" >E</th>\n",
       "      <td id=\"T_ca568_row4_col0\" class=\"data row4 col0\" >수도, 하수 및 폐기물 처리, 원료 재생업</td>\n",
       "      <td id=\"T_ca568_row4_col1\" class=\"data row4 col1\" >10.59</td>\n",
       "      <td id=\"T_ca568_row4_col2\" class=\"data row4 col2\" >+3.43</td>\n",
       "      <td id=\"T_ca568_row4_col3\" class=\"data row4 col3\" >0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row5\" class=\"row_heading level0 row5\" >F</th>\n",
       "      <td id=\"T_ca568_row5_col0\" class=\"data row5 col0\" >건설업</td>\n",
       "      <td id=\"T_ca568_row5_col1\" class=\"data row5 col1\" >19.64</td>\n",
       "      <td id=\"T_ca568_row5_col2\" class=\"data row5 col2\" >+0.34</td>\n",
       "      <td id=\"T_ca568_row5_col3\" class=\"data row5 col3\" >0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row6\" class=\"row_heading level0 row6\" >G</th>\n",
       "      <td id=\"T_ca568_row6_col0\" class=\"data row6 col0\" >도매 및 소매업</td>\n",
       "      <td id=\"T_ca568_row6_col1\" class=\"data row6 col1\" >18.46</td>\n",
       "      <td id=\"T_ca568_row6_col2\" class=\"data row6 col2\" >-3.27</td>\n",
       "      <td id=\"T_ca568_row6_col3\" class=\"data row6 col3\" >0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row7\" class=\"row_heading level0 row7\" >H</th>\n",
       "      <td id=\"T_ca568_row7_col0\" class=\"data row7 col0\" >운수 및 창고업</td>\n",
       "      <td id=\"T_ca568_row7_col1\" class=\"data row7 col1\" >16.05</td>\n",
       "      <td id=\"T_ca568_row7_col2\" class=\"data row7 col2\" >-2.21</td>\n",
       "      <td id=\"T_ca568_row7_col3\" class=\"data row7 col3\" >0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row8\" class=\"row_heading level0 row8\" >I</th>\n",
       "      <td id=\"T_ca568_row8_col0\" class=\"data row8 col0\" >숙박 및 음식점업</td>\n",
       "      <td id=\"T_ca568_row8_col1\" class=\"data row8 col1\" >47.07</td>\n",
       "      <td id=\"T_ca568_row8_col2\" class=\"data row8 col2\" >-0.12</td>\n",
       "      <td id=\"T_ca568_row8_col3\" class=\"data row8 col3\" >0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row9\" class=\"row_heading level0 row9\" >J</th>\n",
       "      <td id=\"T_ca568_row9_col0\" class=\"data row9 col0\" >정보통신업</td>\n",
       "      <td id=\"T_ca568_row9_col1\" class=\"data row9 col1\" >10.29</td>\n",
       "      <td id=\"T_ca568_row9_col2\" class=\"data row9 col2\" >-0.40</td>\n",
       "      <td id=\"T_ca568_row9_col3\" class=\"data row9 col3\" >0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row10\" class=\"row_heading level0 row10\" >K</th>\n",
       "      <td id=\"T_ca568_row10_col0\" class=\"data row10 col0\" >금융 및 보험업</td>\n",
       "      <td id=\"T_ca568_row10_col1\" class=\"data row10 col1\" >6.15</td>\n",
       "      <td id=\"T_ca568_row10_col2\" class=\"data row10 col2\" >-5.15</td>\n",
       "      <td id=\"T_ca568_row10_col3\" class=\"data row10 col3\" >0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row11\" class=\"row_heading level0 row11\" >L</th>\n",
       "      <td id=\"T_ca568_row11_col0\" class=\"data row11 col0\" >부동산업</td>\n",
       "      <td id=\"T_ca568_row11_col1\" class=\"data row11 col1\" >3.26</td>\n",
       "      <td id=\"T_ca568_row11_col2\" class=\"data row11 col2\" >-0.50</td>\n",
       "      <td id=\"T_ca568_row11_col3\" class=\"data row11 col3\" >0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row12\" class=\"row_heading level0 row12\" >M</th>\n",
       "      <td id=\"T_ca568_row12_col0\" class=\"data row12 col0\" >전문, 과학 및 기술 서비스업</td>\n",
       "      <td id=\"T_ca568_row12_col1\" class=\"data row12 col1\" >14.26</td>\n",
       "      <td id=\"T_ca568_row12_col2\" class=\"data row12 col2\" >+0.32</td>\n",
       "      <td id=\"T_ca568_row12_col3\" class=\"data row12 col3\" >0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row13\" class=\"row_heading level0 row13\" >N</th>\n",
       "      <td id=\"T_ca568_row13_col0\" class=\"data row13 col0\" >사업시설 관리, 사업 지원 및 임대 서비스업</td>\n",
       "      <td id=\"T_ca568_row13_col1\" class=\"data row13 col1\" >19.85</td>\n",
       "      <td id=\"T_ca568_row13_col2\" class=\"data row13 col2\" >-1.47</td>\n",
       "      <td id=\"T_ca568_row13_col3\" class=\"data row13 col3\" >0.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row14\" class=\"row_heading level0 row14\" >O</th>\n",
       "      <td id=\"T_ca568_row14_col0\" class=\"data row14 col0\" >공공행정, 국방 및 사회보장 행정</td>\n",
       "      <td id=\"T_ca568_row14_col1\" class=\"data row14 col1\" >9.01</td>\n",
       "      <td id=\"T_ca568_row14_col2\" class=\"data row14 col2\" >-0.21</td>\n",
       "      <td id=\"T_ca568_row14_col3\" class=\"data row14 col3\" >0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row15\" class=\"row_heading level0 row15\" >P</th>\n",
       "      <td id=\"T_ca568_row15_col0\" class=\"data row15 col0\" >교육 서비스업</td>\n",
       "      <td id=\"T_ca568_row15_col1\" class=\"data row15 col1\" >19.07</td>\n",
       "      <td id=\"T_ca568_row15_col2\" class=\"data row15 col2\" >-1.07</td>\n",
       "      <td id=\"T_ca568_row15_col3\" class=\"data row15 col3\" >0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row16\" class=\"row_heading level0 row16\" >Q</th>\n",
       "      <td id=\"T_ca568_row16_col0\" class=\"data row16 col0\" >보건업 및 사회복지 서비스업</td>\n",
       "      <td id=\"T_ca568_row16_col1\" class=\"data row16 col1\" >23.26</td>\n",
       "      <td id=\"T_ca568_row16_col2\" class=\"data row16 col2\" >+0.01</td>\n",
       "      <td id=\"T_ca568_row16_col3\" class=\"data row16 col3\" >0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row17\" class=\"row_heading level0 row17\" >R</th>\n",
       "      <td id=\"T_ca568_row17_col0\" class=\"data row17 col0\" >예술, 스포츠 및 여가관련 서비스업</td>\n",
       "      <td id=\"T_ca568_row17_col1\" class=\"data row17 col1\" >18.88</td>\n",
       "      <td id=\"T_ca568_row17_col2\" class=\"data row17 col2\" >+0.67</td>\n",
       "      <td id=\"T_ca568_row17_col3\" class=\"data row17 col3\" >0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ca568_level0_row18\" class=\"row_heading level0 row18\" >S</th>\n",
       "      <td id=\"T_ca568_row18_col0\" class=\"data row18 col0\" >협회 및 단체, 수리 및 기타 개인 서비스업</td>\n",
       "      <td id=\"T_ca568_row18_col1\" class=\"data row18 col1\" >38.69</td>\n",
       "      <td id=\"T_ca568_row18_col2\" class=\"data row18 col2\" >-0.76</td>\n",
       "      <td id=\"T_ca568_row18_col3\" class=\"data row18 col3\" >0.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22721b765d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t5 = pd.DataFrame({\"대분류명\": par[\"대분류명\"], \"기준 수준(명/10억원)\": par[\"기준수준_b\"] * 1000,\n",
    "                   \"연간 로그변화율(%)\": par[\"연간로그변화율_g\"] * 100, \"추세 R²\": par[\"추세_R2\"]})\n",
    "t5.to_csv(os.path.join(C.OUTPUT, \"table5_coef_params.csv\"), encoding=\"utf-8-sig\")\n",
    "t5.style.format({\"기준 수준(명/10억원)\": \"{:.2f}\", \"연간 로그변화율(%)\": \"{:+.2f}\", \"추세 R²\": \"{:.2f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fee61408",
   "metadata": {},
   "source": [
    "## 표 6. 정비판과 이전 판의 비교 ① — 시나리오별 2035년 전산업 취업자(천 명)\n",
    "\n",
    "이전 판(2026-10-08 판)은 정비 이전 5장 최종 전망을, 정비판은 2026-10 정비판 최종 전망을 투입했다. 계수 경로가 같으므로 차이는 모두 투입 부가가치의 차이에서 생긴다.\n",
    "'부가가치 차이율'은 36개 산업 실질부가가치 합계(2035년)의 정비판 대비 이전 판 차이율이며, 취업자 차이율과의 차이는 산업 구성(대분류별 취업계수의 차이)에서 생긴다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "12db3437",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:57.350491Z",
     "iopub.status.busy": "2026-10-08T00:07:57.350491Z",
     "iopub.status.idle": "2026-10-08T00:07:57.357202Z",
     "shell.execute_reply": "2026-10-08T00:07:57.357202Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2035년 시나리오 간 범위(최고 − 최저): 이전 판 121.8천 명 → 정비판 97.6천 명\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_8cc52\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8cc52_level0_col0\" class=\"col_heading level0 col0\" >이전 판 2035</th>\n",
       "      <th id=\"T_8cc52_level0_col1\" class=\"col_heading level0 col1\" >정비판 2035</th>\n",
       "      <th id=\"T_8cc52_level0_col2\" class=\"col_heading level0 col2\" >차이</th>\n",
       "      <th id=\"T_8cc52_level0_col3\" class=\"col_heading level0 col3\" >차이율(%)</th>\n",
       "      <th id=\"T_8cc52_level0_col4\" class=\"col_heading level0 col4\" >부가가치 차이율(%)</th>\n",
       "      <th id=\"T_8cc52_level0_col5\" class=\"col_heading level0 col5\" >이전 판 증감 2025→2035</th>\n",
       "      <th id=\"T_8cc52_level0_col6\" class=\"col_heading level0 col6\" >정비판 증감 2025→2035</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc52_level0_row0\" class=\"row_heading level0 row0\" >S1 기준</th>\n",
       "      <td id=\"T_8cc52_row0_col0\" class=\"data row0 col0\" >29,040.1</td>\n",
       "      <td id=\"T_8cc52_row0_col1\" class=\"data row0 col1\" >28,560.2</td>\n",
       "      <td id=\"T_8cc52_row0_col2\" class=\"data row0 col2\" >-479.9</td>\n",
       "      <td id=\"T_8cc52_row0_col3\" class=\"data row0 col3\" >-1.65</td>\n",
       "      <td id=\"T_8cc52_row0_col4\" class=\"data row0 col4\" >-2.96</td>\n",
       "      <td id=\"T_8cc52_row0_col5\" class=\"data row0 col5\" >+358.6</td>\n",
       "      <td id=\"T_8cc52_row0_col6\" class=\"data row0 col6\" >-121.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc52_level0_row1\" class=\"row_heading level0 row1\" >S2 제약+위기</th>\n",
       "      <td id=\"T_8cc52_row1_col0\" class=\"data row1 col0\" >28,988.9</td>\n",
       "      <td id=\"T_8cc52_row1_col1\" class=\"data row1 col1\" >28,517.2</td>\n",
       "      <td id=\"T_8cc52_row1_col2\" class=\"data row1 col2\" >-471.7</td>\n",
       "      <td id=\"T_8cc52_row1_col3\" class=\"data row1 col3\" >-1.63</td>\n",
       "      <td id=\"T_8cc52_row1_col4\" class=\"data row1 col4\" >-1.68</td>\n",
       "      <td id=\"T_8cc52_row1_col5\" class=\"data row1 col5\" >+307.4</td>\n",
       "      <td id=\"T_8cc52_row1_col6\" class=\"data row1 col6\" >-164.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc52_level0_row2\" class=\"row_heading level0 row2\" >S3 제약완화</th>\n",
       "      <td id=\"T_8cc52_row2_col0\" class=\"data row2 col0\" >29,079.6</td>\n",
       "      <td id=\"T_8cc52_row2_col1\" class=\"data row2 col1\" >28,595.3</td>\n",
       "      <td id=\"T_8cc52_row2_col2\" class=\"data row2 col2\" >-484.3</td>\n",
       "      <td id=\"T_8cc52_row2_col3\" class=\"data row2 col3\" >-1.67</td>\n",
       "      <td id=\"T_8cc52_row2_col4\" class=\"data row2 col4\" >-3.56</td>\n",
       "      <td id=\"T_8cc52_row2_col5\" class=\"data row2 col5\" >+398.1</td>\n",
       "      <td id=\"T_8cc52_row2_col6\" class=\"data row2 col6\" >-86.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc52_level0_row3\" class=\"row_heading level0 row3\" >S4 기술도약</th>\n",
       "      <td id=\"T_8cc52_row3_col0\" class=\"data row3 col0\" >29,110.8</td>\n",
       "      <td id=\"T_8cc52_row3_col1\" class=\"data row3 col1\" >28,517.5</td>\n",
       "      <td id=\"T_8cc52_row3_col2\" class=\"data row3 col2\" >-593.3</td>\n",
       "      <td id=\"T_8cc52_row3_col3\" class=\"data row3 col3\" >-2.04</td>\n",
       "      <td id=\"T_8cc52_row3_col4\" class=\"data row3 col4\" >-4.85</td>\n",
       "      <td id=\"T_8cc52_row3_col5\" class=\"data row3 col5\" >+429.3</td>\n",
       "      <td id=\"T_8cc52_row3_col6\" class=\"data row3 col6\" >-164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8cc52_level0_row4\" class=\"row_heading level0 row4\" >S5 지정학</th>\n",
       "      <td id=\"T_8cc52_row4_col0\" class=\"data row4 col0\" >29,046.2</td>\n",
       "      <td id=\"T_8cc52_row4_col1\" class=\"data row4 col1\" >28,614.9</td>\n",
       "      <td id=\"T_8cc52_row4_col2\" class=\"data row4 col2\" >-431.3</td>\n",
       "      <td id=\"T_8cc52_row4_col3\" class=\"data row4 col3\" >-1.49</td>\n",
       "      <td id=\"T_8cc52_row4_col4\" class=\"data row4 col4\" >-2.03</td>\n",
       "      <td id=\"T_8cc52_row4_col5\" class=\"data row4 col5\" >+364.7</td>\n",
       "      <td id=\"T_8cc52_row4_col6\" class=\"data row4 col6\" >-66.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2271ef7c1a0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t6 = pd.DataFrame({F.SCENARIO_LABELS[s]: {\n",
    "    \"이전 판 2035\": tot_prev.loc[2035, s], \"정비판 2035\": tot.loc[2035, s],\n",
    "    \"차이\": tot.loc[2035, s] - tot_prev.loc[2035, s], \"차이율(%)\": 100 * (tot.loc[2035, s] / tot_prev.loc[2035, s] - 1),\n",
    "    \"부가가치 차이율(%)\": 100 * (VA[s].loc[2035].sum() / VA_prev[s].loc[2035].sum() - 1),\n",
    "    \"이전 판 증감 2025→2035\": tot_prev.loc[2035, s] - emp25, \"정비판 증감 2025→2035\": tot.loc[2035, s] - emp25} for s in C.SCENARIOS}).T\n",
    "t6.to_csv(os.path.join(C.OUTPUT, \"table6_vs_prev_scenario.csv\"), encoding=\"utf-8-sig\")\n",
    "r_prev, r_new = tot_prev.loc[2035].max() - tot_prev.loc[2035].min(), tot.loc[2035].max() - tot.loc[2035].min()\n",
    "print(f\"2035년 시나리오 간 범위(최고 − 최저): 이전 판 {r_prev:,.1f}천 명 → 정비판 {r_new:,.1f}천 명\")\n",
    "t6.style.format({\"이전 판 2035\": \"{:,.1f}\", \"정비판 2035\": \"{:,.1f}\", \"차이\": \"{:+,.1f}\", \"차이율(%)\": \"{:+.2f}\",\n",
    "                 \"부가가치 차이율(%)\": \"{:+.2f}\", \"이전 판 증감 2025→2035\": \"{:+,.1f}\", \"정비판 증감 2025→2035\": \"{:+,.1f}\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "495d44e6",
   "metadata": {},
   "source": [
    "## 표 7. 정비판과 이전 판의 비교 ② — 대분류별 2035년 취업자(천 명)\n",
    "\n",
    "S1의 이전 판·정비판 값과 차이를 보이고, 오른쪽 열에 S2–S5의 차이(정비판 − 이전 판)를 함께 둔다.\n",
    "같은 대분류 안에서는 계수가 같으므로 대분류별 취업자 차이율은 그 대분류 투입 부가가치의 차이율과 같다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf6e8a08",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:57.358705Z",
     "iopub.status.busy": "2026-10-08T00:07:57.358206Z",
     "iopub.status.idle": "2026-10-08T00:07:57.370076Z",
     "shell.execute_reply": "2026-10-08T00:07:57.370076Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_e3eec\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e3eec_level0_col0\" class=\"col_heading level0 col0\" >대분류명</th>\n",
       "      <th id=\"T_e3eec_level0_col1\" class=\"col_heading level0 col1\" >이전 판 S1</th>\n",
       "      <th id=\"T_e3eec_level0_col2\" class=\"col_heading level0 col2\" >정비판 S1</th>\n",
       "      <th id=\"T_e3eec_level0_col3\" class=\"col_heading level0 col3\" >차이 S1</th>\n",
       "      <th id=\"T_e3eec_level0_col4\" class=\"col_heading level0 col4\" >차이율 S1(%)</th>\n",
       "      <th id=\"T_e3eec_level0_col5\" class=\"col_heading level0 col5\" >차이 S2</th>\n",
       "      <th id=\"T_e3eec_level0_col6\" class=\"col_heading level0 col6\" >차이 S3</th>\n",
       "      <th id=\"T_e3eec_level0_col7\" class=\"col_heading level0 col7\" >차이 S4</th>\n",
       "      <th id=\"T_e3eec_level0_col8\" class=\"col_heading level0 col8\" >차이 S5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row0\" class=\"row_heading level0 row0\" >A</th>\n",
       "      <td id=\"T_e3eec_row0_col0\" class=\"data row0 col0\" >농업, 임업 및 어업</td>\n",
       "      <td id=\"T_e3eec_row0_col1\" class=\"data row0 col1\" >1,467.3</td>\n",
       "      <td id=\"T_e3eec_row0_col2\" class=\"data row0 col2\" >1,374.1</td>\n",
       "      <td id=\"T_e3eec_row0_col3\" class=\"data row0 col3\" >-93.2</td>\n",
       "      <td id=\"T_e3eec_row0_col4\" class=\"data row0 col4\" >-6.35</td>\n",
       "      <td id=\"T_e3eec_row0_col5\" class=\"data row0 col5\" >-56.1</td>\n",
       "      <td id=\"T_e3eec_row0_col6\" class=\"data row0 col6\" >-104.9</td>\n",
       "      <td id=\"T_e3eec_row0_col7\" class=\"data row0 col7\" >-130.6</td>\n",
       "      <td id=\"T_e3eec_row0_col8\" class=\"data row0 col8\" >-55.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row1\" class=\"row_heading level0 row1\" >B</th>\n",
       "      <td id=\"T_e3eec_row1_col0\" class=\"data row1 col0\" >광업</td>\n",
       "      <td id=\"T_e3eec_row1_col1\" class=\"data row1 col1\" >2.8</td>\n",
       "      <td id=\"T_e3eec_row1_col2\" class=\"data row1 col2\" >2.9</td>\n",
       "      <td id=\"T_e3eec_row1_col3\" class=\"data row1 col3\" >+0.1</td>\n",
       "      <td id=\"T_e3eec_row1_col4\" class=\"data row1 col4\" >+1.84</td>\n",
       "      <td id=\"T_e3eec_row1_col5\" class=\"data row1 col5\" >+0.2</td>\n",
       "      <td id=\"T_e3eec_row1_col6\" class=\"data row1 col6\" >-0.0</td>\n",
       "      <td id=\"T_e3eec_row1_col7\" class=\"data row1 col7\" >-0.0</td>\n",
       "      <td id=\"T_e3eec_row1_col8\" class=\"data row1 col8\" >+0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row2\" class=\"row_heading level0 row2\" >C</th>\n",
       "      <td id=\"T_e3eec_row2_col0\" class=\"data row2 col0\" >제조업</td>\n",
       "      <td id=\"T_e3eec_row2_col1\" class=\"data row2 col1\" >5,299.4</td>\n",
       "      <td id=\"T_e3eec_row2_col2\" class=\"data row2 col2\" >4,805.9</td>\n",
       "      <td id=\"T_e3eec_row2_col3\" class=\"data row2 col3\" >-493.5</td>\n",
       "      <td id=\"T_e3eec_row2_col4\" class=\"data row2 col4\" >-9.31</td>\n",
       "      <td id=\"T_e3eec_row2_col5\" class=\"data row2 col5\" >-238.1</td>\n",
       "      <td id=\"T_e3eec_row2_col6\" class=\"data row2 col6\" >-615.8</td>\n",
       "      <td id=\"T_e3eec_row2_col7\" class=\"data row2 col7\" >-847.5</td>\n",
       "      <td id=\"T_e3eec_row2_col8\" class=\"data row2 col8\" >-321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row3\" class=\"row_heading level0 row3\" >D</th>\n",
       "      <td id=\"T_e3eec_row3_col0\" class=\"data row3 col0\" >전기, 가스, 증기 및 공기조절 공급업</td>\n",
       "      <td id=\"T_e3eec_row3_col1\" class=\"data row3 col1\" >86.2</td>\n",
       "      <td id=\"T_e3eec_row3_col2\" class=\"data row3 col2\" >89.1</td>\n",
       "      <td id=\"T_e3eec_row3_col3\" class=\"data row3 col3\" >+2.9</td>\n",
       "      <td id=\"T_e3eec_row3_col4\" class=\"data row3 col4\" >+3.41</td>\n",
       "      <td id=\"T_e3eec_row3_col5\" class=\"data row3 col5\" >+0.8</td>\n",
       "      <td id=\"T_e3eec_row3_col6\" class=\"data row3 col6\" >+4.4</td>\n",
       "      <td id=\"T_e3eec_row3_col7\" class=\"data row3 col7\" >+5.8</td>\n",
       "      <td id=\"T_e3eec_row3_col8\" class=\"data row3 col8\" >+1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row4\" class=\"row_heading level0 row4\" >E</th>\n",
       "      <td id=\"T_e3eec_row4_col0\" class=\"data row4 col0\" >수도, 하수 및 폐기물 처리, 원료 재생업</td>\n",
       "      <td id=\"T_e3eec_row4_col1\" class=\"data row4 col1\" >212.8</td>\n",
       "      <td id=\"T_e3eec_row4_col2\" class=\"data row4 col2\" >193.9</td>\n",
       "      <td id=\"T_e3eec_row4_col3\" class=\"data row4 col3\" >-19.0</td>\n",
       "      <td id=\"T_e3eec_row4_col4\" class=\"data row4 col4\" >-8.91</td>\n",
       "      <td id=\"T_e3eec_row4_col5\" class=\"data row4 col5\" >-3.4</td>\n",
       "      <td id=\"T_e3eec_row4_col6\" class=\"data row4 col6\" >-28.4</td>\n",
       "      <td id=\"T_e3eec_row4_col7\" class=\"data row4 col7\" >-42.7</td>\n",
       "      <td id=\"T_e3eec_row4_col8\" class=\"data row4 col8\" >-8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row5\" class=\"row_heading level0 row5\" >F</th>\n",
       "      <td id=\"T_e3eec_row5_col0\" class=\"data row5 col0\" >건설업</td>\n",
       "      <td id=\"T_e3eec_row5_col1\" class=\"data row5 col1\" >1,537.1</td>\n",
       "      <td id=\"T_e3eec_row5_col2\" class=\"data row5 col2\" >1,618.9</td>\n",
       "      <td id=\"T_e3eec_row5_col3\" class=\"data row5 col3\" >+81.8</td>\n",
       "      <td id=\"T_e3eec_row5_col4\" class=\"data row5 col4\" >+5.32</td>\n",
       "      <td id=\"T_e3eec_row5_col5\" class=\"data row5 col5\" >+53.8</td>\n",
       "      <td id=\"T_e3eec_row5_col6\" class=\"data row5 col6\" >+93.7</td>\n",
       "      <td id=\"T_e3eec_row5_col7\" class=\"data row5 col7\" >+96.0</td>\n",
       "      <td id=\"T_e3eec_row5_col8\" class=\"data row5 col8\" >+61.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row6\" class=\"row_heading level0 row6\" >G</th>\n",
       "      <td id=\"T_e3eec_row6_col0\" class=\"data row6 col0\" >도매 및 소매업</td>\n",
       "      <td id=\"T_e3eec_row6_col1\" class=\"data row6 col1\" >2,180.5</td>\n",
       "      <td id=\"T_e3eec_row6_col2\" class=\"data row6 col2\" >2,226.2</td>\n",
       "      <td id=\"T_e3eec_row6_col3\" class=\"data row6 col3\" >+45.7</td>\n",
       "      <td id=\"T_e3eec_row6_col4\" class=\"data row6 col4\" >+2.10</td>\n",
       "      <td id=\"T_e3eec_row6_col5\" class=\"data row6 col5\" >+16.7</td>\n",
       "      <td id=\"T_e3eec_row6_col6\" class=\"data row6 col6\" >+48.8</td>\n",
       "      <td id=\"T_e3eec_row6_col7\" class=\"data row6 col7\" >+85.7</td>\n",
       "      <td id=\"T_e3eec_row6_col8\" class=\"data row6 col8\" >+30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row7\" class=\"row_heading level0 row7\" >H</th>\n",
       "      <td id=\"T_e3eec_row7_col0\" class=\"data row7 col0\" >운수 및 창고업</td>\n",
       "      <td id=\"T_e3eec_row7_col1\" class=\"data row7 col1\" >1,629.5</td>\n",
       "      <td id=\"T_e3eec_row7_col2\" class=\"data row7 col2\" >1,674.4</td>\n",
       "      <td id=\"T_e3eec_row7_col3\" class=\"data row7 col3\" >+44.9</td>\n",
       "      <td id=\"T_e3eec_row7_col4\" class=\"data row7 col4\" >+2.76</td>\n",
       "      <td id=\"T_e3eec_row7_col5\" class=\"data row7 col5\" >-92.1</td>\n",
       "      <td id=\"T_e3eec_row7_col6\" class=\"data row7 col6\" >+114.0</td>\n",
       "      <td id=\"T_e3eec_row7_col7\" class=\"data row7 col7\" >+207.5</td>\n",
       "      <td id=\"T_e3eec_row7_col8\" class=\"data row7 col8\" >-38.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row8\" class=\"row_heading level0 row8\" >I</th>\n",
       "      <td id=\"T_e3eec_row8_col0\" class=\"data row8 col0\" >숙박 및 음식점업</td>\n",
       "      <td id=\"T_e3eec_row8_col1\" class=\"data row8 col1\" >1,894.9</td>\n",
       "      <td id=\"T_e3eec_row8_col2\" class=\"data row8 col2\" >1,844.5</td>\n",
       "      <td id=\"T_e3eec_row8_col3\" class=\"data row8 col3\" >-50.5</td>\n",
       "      <td id=\"T_e3eec_row8_col4\" class=\"data row8 col4\" >-2.66</td>\n",
       "      <td id=\"T_e3eec_row8_col5\" class=\"data row8 col5\" >-117.4</td>\n",
       "      <td id=\"T_e3eec_row8_col6\" class=\"data row8 col6\" >-10.6</td>\n",
       "      <td id=\"T_e3eec_row8_col7\" class=\"data row8 col7\" >+23.0</td>\n",
       "      <td id=\"T_e3eec_row8_col8\" class=\"data row8 col8\" >-85.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row9\" class=\"row_heading level0 row9\" >J</th>\n",
       "      <td id=\"T_e3eec_row9_col0\" class=\"data row9 col0\" >정보통신업</td>\n",
       "      <td id=\"T_e3eec_row9_col1\" class=\"data row9 col1\" >1,367.3</td>\n",
       "      <td id=\"T_e3eec_row9_col2\" class=\"data row9 col2\" >1,393.2</td>\n",
       "      <td id=\"T_e3eec_row9_col3\" class=\"data row9 col3\" >+25.9</td>\n",
       "      <td id=\"T_e3eec_row9_col4\" class=\"data row9 col4\" >+1.90</td>\n",
       "      <td id=\"T_e3eec_row9_col5\" class=\"data row9 col5\" >+4.7</td>\n",
       "      <td id=\"T_e3eec_row9_col6\" class=\"data row9 col6\" >+40.3</td>\n",
       "      <td id=\"T_e3eec_row9_col7\" class=\"data row9 col7\" >+58.5</td>\n",
       "      <td id=\"T_e3eec_row9_col8\" class=\"data row9 col8\" >+14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row10\" class=\"row_heading level0 row10\" >K</th>\n",
       "      <td id=\"T_e3eec_row10_col0\" class=\"data row10 col0\" >금융 및 보험업</td>\n",
       "      <td id=\"T_e3eec_row10_col1\" class=\"data row10 col1\" >639.9</td>\n",
       "      <td id=\"T_e3eec_row10_col2\" class=\"data row10 col2\" >626.9</td>\n",
       "      <td id=\"T_e3eec_row10_col3\" class=\"data row10 col3\" >-13.0</td>\n",
       "      <td id=\"T_e3eec_row10_col4\" class=\"data row10 col4\" >-2.04</td>\n",
       "      <td id=\"T_e3eec_row10_col5\" class=\"data row10 col5\" >+10.3</td>\n",
       "      <td id=\"T_e3eec_row10_col6\" class=\"data row10 col6\" >-24.3</td>\n",
       "      <td id=\"T_e3eec_row10_col7\" class=\"data row10 col7\" >-47.1</td>\n",
       "      <td id=\"T_e3eec_row10_col8\" class=\"data row10 col8\" >+2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row11\" class=\"row_heading level0 row11\" >L</th>\n",
       "      <td id=\"T_e3eec_row11_col0\" class=\"data row11 col0\" >부동산업</td>\n",
       "      <td id=\"T_e3eec_row11_col1\" class=\"data row11 col1\" >521.5</td>\n",
       "      <td id=\"T_e3eec_row11_col2\" class=\"data row11 col2\" >541.6</td>\n",
       "      <td id=\"T_e3eec_row11_col3\" class=\"data row11 col3\" >+20.1</td>\n",
       "      <td id=\"T_e3eec_row11_col4\" class=\"data row11 col4\" >+3.85</td>\n",
       "      <td id=\"T_e3eec_row11_col5\" class=\"data row11 col5\" >+16.8</td>\n",
       "      <td id=\"T_e3eec_row11_col6\" class=\"data row11 col6\" >+22.3</td>\n",
       "      <td id=\"T_e3eec_row11_col7\" class=\"data row11 col7\" >+29.1</td>\n",
       "      <td id=\"T_e3eec_row11_col8\" class=\"data row11 col8\" >+16.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row12\" class=\"row_heading level0 row12\" >M</th>\n",
       "      <td id=\"T_e3eec_row12_col0\" class=\"data row12 col0\" >전문, 과학 및 기술 서비스업</td>\n",
       "      <td id=\"T_e3eec_row12_col1\" class=\"data row12 col1\" >1,636.6</td>\n",
       "      <td id=\"T_e3eec_row12_col2\" class=\"data row12 col2\" >1,556.8</td>\n",
       "      <td id=\"T_e3eec_row12_col3\" class=\"data row12 col3\" >-79.7</td>\n",
       "      <td id=\"T_e3eec_row12_col4\" class=\"data row12 col4\" >-4.87</td>\n",
       "      <td id=\"T_e3eec_row12_col5\" class=\"data row12 col5\" >-29.9</td>\n",
       "      <td id=\"T_e3eec_row12_col6\" class=\"data row12 col6\" >-98.4</td>\n",
       "      <td id=\"T_e3eec_row12_col7\" class=\"data row12 col7\" >-166.9</td>\n",
       "      <td id=\"T_e3eec_row12_col8\" class=\"data row12 col8\" >-42.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row13\" class=\"row_heading level0 row13\" >N</th>\n",
       "      <td id=\"T_e3eec_row13_col0\" class=\"data row13 col0\" >사업시설 관리, 사업 지원 및 임대 서비스업</td>\n",
       "      <td id=\"T_e3eec_row13_col1\" class=\"data row13 col1\" >1,466.6</td>\n",
       "      <td id=\"T_e3eec_row13_col2\" class=\"data row13 col2\" >1,393.3</td>\n",
       "      <td id=\"T_e3eec_row13_col3\" class=\"data row13 col3\" >-73.4</td>\n",
       "      <td id=\"T_e3eec_row13_col4\" class=\"data row13 col4\" >-5.00</td>\n",
       "      <td id=\"T_e3eec_row13_col5\" class=\"data row13 col5\" >-57.9</td>\n",
       "      <td id=\"T_e3eec_row13_col6\" class=\"data row13 col6\" >-88.5</td>\n",
       "      <td id=\"T_e3eec_row13_col7\" class=\"data row13 col7\" >-119.7</td>\n",
       "      <td id=\"T_e3eec_row13_col8\" class=\"data row13 col8\" >-70.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row14\" class=\"row_heading level0 row14\" >O</th>\n",
       "      <td id=\"T_e3eec_row14_col0\" class=\"data row14 col0\" >공공행정, 국방 및 사회보장 행정</td>\n",
       "      <td id=\"T_e3eec_row14_col1\" class=\"data row14 col1\" >1,358.3</td>\n",
       "      <td id=\"T_e3eec_row14_col2\" class=\"data row14 col2\" >1,411.6</td>\n",
       "      <td id=\"T_e3eec_row14_col3\" class=\"data row14 col3\" >+53.3</td>\n",
       "      <td id=\"T_e3eec_row14_col4\" class=\"data row14 col4\" >+3.93</td>\n",
       "      <td id=\"T_e3eec_row14_col5\" class=\"data row14 col5\" >+25.6</td>\n",
       "      <td id=\"T_e3eec_row14_col6\" class=\"data row14 col6\" >+66.0</td>\n",
       "      <td id=\"T_e3eec_row14_col7\" class=\"data row14 col7\" >+93.0</td>\n",
       "      <td id=\"T_e3eec_row14_col8\" class=\"data row14 col8\" >+38.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row15\" class=\"row_heading level0 row15\" >P</th>\n",
       "      <td id=\"T_e3eec_row15_col0\" class=\"data row15 col0\" >교육 서비스업</td>\n",
       "      <td id=\"T_e3eec_row15_col1\" class=\"data row15 col1\" >1,715.2</td>\n",
       "      <td id=\"T_e3eec_row15_col2\" class=\"data row15 col2\" >1,708.3</td>\n",
       "      <td id=\"T_e3eec_row15_col3\" class=\"data row15 col3\" >-6.9</td>\n",
       "      <td id=\"T_e3eec_row15_col4\" class=\"data row15 col4\" >-0.40</td>\n",
       "      <td id=\"T_e3eec_row15_col5\" class=\"data row15 col5\" >+43.4</td>\n",
       "      <td id=\"T_e3eec_row15_col6\" class=\"data row15 col6\" >-39.3</td>\n",
       "      <td id=\"T_e3eec_row15_col7\" class=\"data row15 col7\" >-63.1</td>\n",
       "      <td id=\"T_e3eec_row15_col8\" class=\"data row15 col8\" >+20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row16\" class=\"row_heading level0 row16\" >Q</th>\n",
       "      <td id=\"T_e3eec_row16_col0\" class=\"data row16 col0\" >보건업 및 사회복지 서비스업</td>\n",
       "      <td id=\"T_e3eec_row16_col1\" class=\"data row16 col1\" >4,491.3</td>\n",
       "      <td id=\"T_e3eec_row16_col2\" class=\"data row16 col2\" >4,626.7</td>\n",
       "      <td id=\"T_e3eec_row16_col3\" class=\"data row16 col3\" >+135.4</td>\n",
       "      <td id=\"T_e3eec_row16_col4\" class=\"data row16 col4\" >+3.01</td>\n",
       "      <td id=\"T_e3eec_row16_col5\" class=\"data row16 col5\" >+20.3</td>\n",
       "      <td id=\"T_e3eec_row16_col6\" class=\"data row16 col6\" >+186.4</td>\n",
       "      <td id=\"T_e3eec_row16_col7\" class=\"data row16 col7\" >+282.8</td>\n",
       "      <td id=\"T_e3eec_row16_col8\" class=\"data row16 col8\" >+69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row17\" class=\"row_heading level0 row17\" >R</th>\n",
       "      <td id=\"T_e3eec_row17_col0\" class=\"data row17 col0\" >예술, 스포츠 및 여가관련 서비스업</td>\n",
       "      <td id=\"T_e3eec_row17_col1\" class=\"data row17 col1\" >616.7</td>\n",
       "      <td id=\"T_e3eec_row17_col2\" class=\"data row17 col2\" >578.5</td>\n",
       "      <td id=\"T_e3eec_row17_col3\" class=\"data row17 col3\" >-38.2</td>\n",
       "      <td id=\"T_e3eec_row17_col4\" class=\"data row17 col4\" >-6.20</td>\n",
       "      <td id=\"T_e3eec_row17_col5\" class=\"data row17 col5\" >-37.3</td>\n",
       "      <td id=\"T_e3eec_row17_col6\" class=\"data row17 col6\" >-34.2</td>\n",
       "      <td id=\"T_e3eec_row17_col7\" class=\"data row17 col7\" >-39.7</td>\n",
       "      <td id=\"T_e3eec_row17_col8\" class=\"data row17 col8\" >-38.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row18\" class=\"row_heading level0 row18\" >S</th>\n",
       "      <td id=\"T_e3eec_row18_col0\" class=\"data row18 col0\" >협회 및 단체, 수리 및 기타 개인 서비스업</td>\n",
       "      <td id=\"T_e3eec_row18_col1\" class=\"data row18 col1\" >916.0</td>\n",
       "      <td id=\"T_e3eec_row18_col2\" class=\"data row18 col2\" >893.4</td>\n",
       "      <td id=\"T_e3eec_row18_col3\" class=\"data row18 col3\" >-22.6</td>\n",
       "      <td id=\"T_e3eec_row18_col4\" class=\"data row18 col4\" >-2.47</td>\n",
       "      <td id=\"T_e3eec_row18_col5\" class=\"data row18 col5\" >-32.0</td>\n",
       "      <td id=\"T_e3eec_row18_col6\" class=\"data row18 col6\" >-15.6</td>\n",
       "      <td id=\"T_e3eec_row18_col7\" class=\"data row18 col7\" >-17.5</td>\n",
       "      <td id=\"T_e3eec_row18_col8\" class=\"data row18 col8\" >-26.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3eec_level0_row19\" class=\"row_heading level0 row19\" >계</th>\n",
       "      <td id=\"T_e3eec_row19_col0\" class=\"data row19 col0\" >19개 대분류 합계</td>\n",
       "      <td id=\"T_e3eec_row19_col1\" class=\"data row19 col1\" >29,040.1</td>\n",
       "      <td id=\"T_e3eec_row19_col2\" class=\"data row19 col2\" >28,560.2</td>\n",
       "      <td id=\"T_e3eec_row19_col3\" class=\"data row19 col3\" >-479.9</td>\n",
       "      <td id=\"T_e3eec_row19_col4\" class=\"data row19 col4\" >-1.65</td>\n",
       "      <td id=\"T_e3eec_row19_col5\" class=\"data row19 col5\" >-471.7</td>\n",
       "      <td id=\"T_e3eec_row19_col6\" class=\"data row19 col6\" >-484.3</td>\n",
       "      <td id=\"T_e3eec_row19_col7\" class=\"data row19 col7\" >-593.3</td>\n",
       "      <td id=\"T_e3eec_row19_col8\" class=\"data row19 col8\" >-431.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2271f07ba40>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def by_sec(Pd, s, y=2035):\n",
    "    return Pd[s].loc[y].groupby(pd.Series(sec_of)).sum()\n",
    "t7 = pd.DataFrame({\"대분류명\": pd.Series(sec_name)}).loc[F.SECTIONS]\n",
    "t7[\"이전 판 S1\"] = by_sec(P_prev, \"S1\")\n",
    "t7[\"정비판 S1\"] = by_sec(P, \"S1\")\n",
    "t7[\"차이 S1\"] = t7[\"정비판 S1\"] - t7[\"이전 판 S1\"]\n",
    "t7[\"차이율 S1(%)\"] = 100 * (t7[\"정비판 S1\"] / t7[\"이전 판 S1\"] - 1)\n",
    "for s in C.SCENARIOS[1:]:\n",
    "    t7[f\"차이 {s}\"] = by_sec(P, s) - by_sec(P_prev, s)\n",
    "tsum = t7.drop(columns=\"대분류명\").sum()\n",
    "tsum[\"차이율 S1(%)\"] = 100 * (tsum[\"정비판 S1\"] / tsum[\"이전 판 S1\"] - 1)\n",
    "t7.loc[\"계\"] = pd.Series({\"대분류명\": \"19개 대분류 합계\", **tsum.to_dict()})\n",
    "t7.to_csv(os.path.join(C.OUTPUT, \"table7_vs_prev_section.csv\"), encoding=\"utf-8-sig\")\n",
    "f7 = {c: \"{:+,.1f}\" for c in t7.columns if c.startswith(\"차이 \")}\n",
    "f7.update({\"이전 판 S1\": \"{:,.1f}\", \"정비판 S1\": \"{:,.1f}\", \"차이율 S1(%)\": \"{:+.2f}\"})\n",
    "t7.style.format(f7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f957fbb0",
   "metadata": {},
   "source": [
    "## 표 8. 중장기 인력수급 전망과의 비교 — 취업자 증감(만 명)\n",
    "\n",
    "인력수급 전망은 고용노동부·한국고용정보원 「2024~2034 중장기 인력수급 전망」(2026-02-12)의 취업자 증감이다(2024→2029년 +36.7만 명, 2029→2034년 −30.3만 명, 2024→2034년 +6.4만 명).\n",
    "본 연구의 노동수요 전망은 이 값에 맞추어 조정하지 않았다. 이 표는 같은 기간의 증감을 나란히 놓고 방향과 크기를 비교하기 위한 것이며, 두 전망 사이에 제약 관계를 두지 않는다.\n",
    "\n",
    "- 인력수급 전망은 전체 취업자, 본 연구는 19개 대분류(A–S) 합계이다. 가구 내 고용활동(T)·국제 및 외국기관(U)은 전체 취업자의 0.3% 수준이므로 증감 비교에 주는 영향은 작다.\n",
    "- 본 연구의 2024년 값은 두 가지로 둔다. **실적 기준**은 경제활동인구조사 계절조정 계열의 2024년 평균이고, **계수 경로 기준**은 계수 경로의 2024년 값(기준 수준)에 2024년 실적 부가가치를 곱한 값이다.\n",
    "  기준 수준은 2023–2025년 평균 계수이므로 계수 경로 기준 값은 실적과 다르며, 그 차이만큼 2024년에서 출발하는 증감이 달라진다(README 알려진 문제 3). 2029→2034년 증감은 두 기준이 같다.\n",
    "- 본 연구의 값은 부가가치 전망에 취업계수를 곱한 노동수요이며, 인구 구조나 경제활동참가율 같은 인력공급 여건을 반영하지 않는다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e7236ecd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:57.371580Z",
     "iopub.status.busy": "2026-10-08T00:07:57.371079Z",
     "iopub.status.idle": "2026-10-08T00:07:57.380620Z",
     "shell.execute_reply": "2026-10-08T00:07:57.380620Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024년 19개 대분류 취업자: 실적 28,482.2천 명 · 계수 경로 기준 28,581.6천 명(차이 +99.4천 명)\n",
      "S1 정점: 2030년 28,670.8천 명 · 인력수급 전망: 2024→2029년 +36.7만 명, 2029→2034년 -30.3만 명 · 정비판 S1: +17.2만 명(계수 경로 기준 +7.2만 명), -5.2만 명\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_33d89\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_33d89_level0_col0\" class=\"col_heading level0 col0\" >인력수급 전망</th>\n",
       "      <th id=\"T_33d89_level0_col1\" class=\"col_heading level0 col1\" >정비판 S1(실적 기준)</th>\n",
       "      <th id=\"T_33d89_level0_col2\" class=\"col_heading level0 col2\" >차이(실적 기준)</th>\n",
       "      <th id=\"T_33d89_level0_col3\" class=\"col_heading level0 col3\" >정비판 S1(계수 경로 기준)</th>\n",
       "      <th id=\"T_33d89_level0_col4\" class=\"col_heading level0 col4\" >차이(계수 경로 기준)</th>\n",
       "      <th id=\"T_33d89_level0_col5\" class=\"col_heading level0 col5\" >정비판 S1–S5 최저</th>\n",
       "      <th id=\"T_33d89_level0_col6\" class=\"col_heading level0 col6\" >정비판 S1–S5 최고</th>\n",
       "      <th id=\"T_33d89_level0_col7\" class=\"col_heading level0 col7\" >이전 판 S1(실적 기준)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_33d89_level0_row0\" class=\"row_heading level0 row0\" >2024→2029</th>\n",
       "      <td id=\"T_33d89_row0_col0\" class=\"data row0 col0\" >+36.7</td>\n",
       "      <td id=\"T_33d89_row0_col1\" class=\"data row0 col1\" >+17.2</td>\n",
       "      <td id=\"T_33d89_row0_col2\" class=\"data row0 col2\" >-19.5</td>\n",
       "      <td id=\"T_33d89_row0_col3\" class=\"data row0 col3\" >+7.2</td>\n",
       "      <td id=\"T_33d89_row0_col4\" class=\"data row0 col4\" >-29.5</td>\n",
       "      <td id=\"T_33d89_row0_col5\" class=\"data row0 col5\" >+14.2</td>\n",
       "      <td id=\"T_33d89_row0_col6\" class=\"data row0 col6\" >+22.4</td>\n",
       "      <td id=\"T_33d89_row0_col7\" class=\"data row0 col7\" >+31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33d89_level0_row1\" class=\"row_heading level0 row1\" >2029→2034</th>\n",
       "      <td id=\"T_33d89_row1_col0\" class=\"data row1 col0\" >-30.3</td>\n",
       "      <td id=\"T_33d89_row1_col1\" class=\"data row1 col1\" >-5.2</td>\n",
       "      <td id=\"T_33d89_row1_col2\" class=\"data row1 col2\" >+25.1</td>\n",
       "      <td id=\"T_33d89_row1_col3\" class=\"data row1 col3\" >-5.2</td>\n",
       "      <td id=\"T_33d89_row1_col4\" class=\"data row1 col4\" >+25.1</td>\n",
       "      <td id=\"T_33d89_row1_col5\" class=\"data row1 col5\" >-13.7</td>\n",
       "      <td id=\"T_33d89_row1_col6\" class=\"data row1 col6\" >-3.1</td>\n",
       "      <td id=\"T_33d89_row1_col7\" class=\"data row1 col7\" >+26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_33d89_level0_row2\" class=\"row_heading level0 row2\" >2024→2034</th>\n",
       "      <td id=\"T_33d89_row2_col0\" class=\"data row2 col0\" >+6.4</td>\n",
       "      <td id=\"T_33d89_row2_col1\" class=\"data row2 col1\" >+11.9</td>\n",
       "      <td id=\"T_33d89_row2_col2\" class=\"data row2 col2\" >+5.5</td>\n",
       "      <td id=\"T_33d89_row2_col3\" class=\"data row2 col3\" >+2.0</td>\n",
       "      <td id=\"T_33d89_row2_col4\" class=\"data row2 col4\" >-4.4</td>\n",
       "      <td id=\"T_33d89_row2_col5\" class=\"data row2 col5\" >+7.7</td>\n",
       "      <td id=\"T_33d89_row2_col6\" class=\"data row2 col6\" >+17.6</td>\n",
       "      <td id=\"T_33d89_row2_col7\" class=\"data row2 col7\" >+57.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x22721b775c0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "periods = list(C.KEIS_EMP_CHANGE)\n",
    "ch_act, ch_path, L24, L24p = F.changes_from_base(tot, emp_a, va_a, par, sec_of, periods, 2024, C.ANCHOR)\n",
    "ch_prev = F.demand_changes(tot_prev, L24, 2024, periods)\n",
    "keis = pd.Series({f\"{a}→{b}\": v for (a, b), v in C.KEIS_EMP_CHANGE.items()})\n",
    "t8 = pd.DataFrame({\"인력수급 전망\": keis,\n",
    "                   \"정비판 S1(실적 기준)\": ch_act[\"S1\"] / 10, \"차이(실적 기준)\": ch_act[\"S1\"] / 10 - keis,\n",
    "                   \"정비판 S1(계수 경로 기준)\": ch_path[\"S1\"] / 10, \"차이(계수 경로 기준)\": ch_path[\"S1\"] / 10 - keis,\n",
    "                   \"정비판 S1–S5 최저\": ch_act.min(axis=1) / 10, \"정비판 S1–S5 최고\": ch_act.max(axis=1) / 10,\n",
    "                   \"이전 판 S1(실적 기준)\": ch_prev[\"S1\"] / 10})\n",
    "t8.to_csv(os.path.join(C.OUTPUT, \"table8_keis_comparison.csv\"), encoding=\"utf-8-sig\")\n",
    "kc = {\"S1_증감_2024_2029\": ch_act.loc[\"2024→2029\", \"S1\"], \"S1_증감_2029_2034\": ch_act.loc[\"2029→2034\", \"S1\"],\n",
    "      \"S1_증감_2024_2034\": ch_act.loc[\"2024→2034\", \"S1\"],\n",
    "      \"S1_증감_2024_2029_경로\": ch_path.loc[\"2024→2029\", \"S1\"], \"S1_증감_2024_2034_경로\": ch_path.loc[\"2024→2034\", \"S1\"]}\n",
    "print(f\"2024년 19개 대분류 취업자: 실적 {L24:,.1f}천 명 · 계수 경로 기준 {L24p:,.1f}천 명(차이 {L24p - L24:+,.1f}천 명)\")\n",
    "print(f\"S1 정점: {int(tot['S1'].idxmax())}년 {tot['S1'].max():,.1f}천 명 · 인력수급 전망: 2024→2029년 {keis.iloc[0]:+.1f}만 명, 2029→2034년 {keis.iloc[1]:+.1f}만 명 · \"\n",
    "      f\"정비판 S1: {ch_act['S1'].iloc[0] / 10:+.1f}만 명(계수 경로 기준 {ch_path['S1'].iloc[0] / 10:+.1f}만 명), {ch_act['S1'].iloc[1] / 10:+.1f}만 명\")\n",
    "t8.style.format(\"{:+,.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c51f8e",
   "metadata": {},
   "source": [
    "## 표 9. 판본 이력 — 2035년 전산업 취업자(천 명)\n",
    "\n",
    "최초 판(v0, 2026-08-04 게시)은 정합화 이전 부가가치 전망을 투입해 2035년 취업자를 크게 산정했다(README 정정 기록). 이전 판과 정비판은 같은 계수에 각 시점의 5장 최종 전망을 투입한 값이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "92705e5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:57.381618Z",
     "iopub.status.busy": "2026-10-08T00:07:57.381618Z",
     "iopub.status.idle": "2026-10-08T00:07:57.387092Z",
     "shell.execute_reply": "2026-10-08T00:07:57.386588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_208fc\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_208fc_level0_col0\" class=\"col_heading level0 col0\" >최초 판(v0)</th>\n",
       "      <th id=\"T_208fc_level0_col1\" class=\"col_heading level0 col1\" >이전 판(2026-10-08)</th>\n",
       "      <th id=\"T_208fc_level0_col2\" class=\"col_heading level0 col2\" >정비판</th>\n",
       "      <th id=\"T_208fc_level0_col3\" class=\"col_heading level0 col3\" >정비판 − 이전 판</th>\n",
       "      <th id=\"T_208fc_level0_col4\" class=\"col_heading level0 col4\" >정비판 − 최초 판</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_208fc_level0_row0\" class=\"row_heading level0 row0\" >S1 기준</th>\n",
       "      <td id=\"T_208fc_row0_col0\" class=\"data row0 col0\" >36,284.8</td>\n",
       "      <td id=\"T_208fc_row0_col1\" class=\"data row0 col1\" >29,040.1</td>\n",
       "      <td id=\"T_208fc_row0_col2\" class=\"data row0 col2\" >28,560.2</td>\n",
       "      <td id=\"T_208fc_row0_col3\" class=\"data row0 col3\" >-479.9</td>\n",
       "      <td id=\"T_208fc_row0_col4\" class=\"data row0 col4\" >-7,724.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208fc_level0_row1\" class=\"row_heading level0 row1\" >S2 제약+위기</th>\n",
       "      <td id=\"T_208fc_row1_col0\" class=\"data row1 col0\" >35,703.4</td>\n",
       "      <td id=\"T_208fc_row1_col1\" class=\"data row1 col1\" >28,988.9</td>\n",
       "      <td id=\"T_208fc_row1_col2\" class=\"data row1 col2\" >28,517.2</td>\n",
       "      <td id=\"T_208fc_row1_col3\" class=\"data row1 col3\" >-471.7</td>\n",
       "      <td id=\"T_208fc_row1_col4\" class=\"data row1 col4\" >-7,186.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208fc_level0_row2\" class=\"row_heading level0 row2\" >S3 제약완화</th>\n",
       "      <td id=\"T_208fc_row2_col0\" class=\"data row2 col0\" >36,569.3</td>\n",
       "      <td id=\"T_208fc_row2_col1\" class=\"data row2 col1\" >29,079.6</td>\n",
       "      <td id=\"T_208fc_row2_col2\" class=\"data row2 col2\" >28,595.3</td>\n",
       "      <td id=\"T_208fc_row2_col3\" class=\"data row2 col3\" >-484.3</td>\n",
       "      <td id=\"T_208fc_row2_col4\" class=\"data row2 col4\" >-7,974.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208fc_level0_row3\" class=\"row_heading level0 row3\" >S4 기술도약</th>\n",
       "      <td id=\"T_208fc_row3_col0\" class=\"data row3 col0\" >36,450.6</td>\n",
       "      <td id=\"T_208fc_row3_col1\" class=\"data row3 col1\" >29,110.8</td>\n",
       "      <td id=\"T_208fc_row3_col2\" class=\"data row3 col2\" >28,517.5</td>\n",
       "      <td id=\"T_208fc_row3_col3\" class=\"data row3 col3\" >-593.3</td>\n",
       "      <td id=\"T_208fc_row3_col4\" class=\"data row3 col4\" >-7,933.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208fc_level0_row4\" class=\"row_heading level0 row4\" >S5 지정학</th>\n",
       "      <td id=\"T_208fc_row4_col0\" class=\"data row4 col0\" >36,076.5</td>\n",
       "      <td id=\"T_208fc_row4_col1\" class=\"data row4 col1\" >29,046.2</td>\n",
       "      <td id=\"T_208fc_row4_col2\" class=\"data row4 col2\" >28,614.9</td>\n",
       "      <td id=\"T_208fc_row4_col3\" class=\"data row4 col3\" >-431.3</td>\n",
       "      <td id=\"T_208fc_row4_col4\" class=\"data row4 col4\" >-7,461.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2271ef41490>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t9 = pd.DataFrame({\"최초 판(v0)\": tot_v0.loc[2035], \"이전 판(2026-10-08)\": tot_prev.loc[2035], \"정비판\": tot.loc[2035]})\n",
    "t9[\"정비판 − 이전 판\"] = t9[\"정비판\"] - t9[\"이전 판(2026-10-08)\"]\n",
    "t9[\"정비판 − 최초 판\"] = t9[\"정비판\"] - t9[\"최초 판(v0)\"]\n",
    "t9.index = [F.SCENARIO_LABELS[s] for s in t9.index]\n",
    "t9.to_csv(os.path.join(C.OUTPUT, \"table9_vintages_2035.csv\"), encoding=\"utf-8-sig\")\n",
    "t9.style.format({c: (\"{:+,.1f}\" if \"−\" in c else \"{:,.1f}\") for c in t9.columns})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd1ab730",
   "metadata": {},
   "source": [
    "## 포털 게시본 대조(ONLINE=1)\n",
    "\n",
    "`01_입수.ipynb`가 내려받은 포털 노동수요 JSON을 산출물과 비교한다. 현행 주소의 파일은 정비판 주 산출물과, 최초 판 주소의 파일은 최초 판 게시 파일과 같아야 한다.\n",
    "포털이 아직 이전 판이나 최초 판을 현행 주소로 게시하고 있으면 그 사실을 보고하며, 이 비교는 실행을 멈추지 않는다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "749c20cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:57.387591Z",
     "iopub.status.busy": "2026-10-08T00:07:57.387591Z",
     "iopub.status.idle": "2026-10-08T00:07:57.392165Z",
     "shell.execute_reply": "2026-10-08T00:07:57.391659Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ONLINE=0: 포털 게시본 대조를 건너뛴다.\n"
     ]
    }
   ],
   "source": [
    "dl_main = os.path.join(C.PORTAL_DL, \"labor_demand_viewer_data.json\")\n",
    "dl_v0 = os.path.join(C.PORTAL_DL, \"labor_demand_viewer_data_v0_pre-reconciliation.json\")\n",
    "if not C.ONLINE:\n",
    "    print(\"ONLINE=0: 포털 게시본 대조를 건너뛴다.\")\n",
    "else:\n",
    "    known = {F.sha256(C.OUT_JSON): \"정비판\", F.sha256(C.PREV_JSON): \"이전 판(2026-10-08 판)\", F.sha256(C.V0_JSON): \"최초 판(v0)\"}\n",
    "    rows = []\n",
    "    for label, p, want, ref in [(\"현행 주소\", dl_main, \"정비판\", C.OUT_JSON), (\"최초 판 주소\", dl_v0, \"최초 판(v0)\", C.V0_JSON)]:\n",
    "        if not os.path.exists(p):\n",
    "            rows.append({\"포털 파일\": label, \"결과\": \"내려받지 못했다(아직 게시되지 않았거나 접속 실패)\"})\n",
    "            continue\n",
    "        h = F.sha256(p)\n",
    "        if h in known:\n",
    "            msg = f\"{known[h]}과 SHA-256 일치\" + (\"\" if known[h] == want else f\" — 기대한 판({want})이 아니므로 포털 갱신이 필요하다\")\n",
    "        else:\n",
    "            cmpv = F.compare_viewer(json.load(open(ref, encoding=\"utf-8\")), json.load(open(p, encoding=\"utf-8\")))\n",
    "            msg = f\"SHA-256 다름 · 산업값 최대 차이 {cmpv['산업값 최대절대오차']:.1f}천 명 · 합계 최대 차이 {cmpv['합계 최대절대오차']:.1f}천 명\"\n",
    "        rows.append({\"포털 파일\": label, \"결과\": msg})\n",
    "    display(pd.DataFrame(rows))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1df47fd",
   "metadata": {},
   "source": [
    "## 검증 — 정비판 주 산출물, 이전 판 재현, 최초 판 재현\n",
    "\n",
    "**정비판 주 산출물.** `output/labor_demand_viewer_data.json`을 `config.PUBLISHED`(7장 인용 수치)와 대조한다. 파일 SHA-256, 최초 판·이전 판 파일과의 구조 동일성(뷰어 호환),\n",
    "반올림 전 계산값과 JSON 값의 일치, 전산업 합계 15개, 주요 산업 6개, 표 8(인력수급 전망 비교)의 S1 증감 5개를 확인한다.\n",
    "\n",
    "**이전 판(2026-10-08 판) 재현.** `data/published_prev/labor_demand_viewer_data_prev.json`의 SHA-256이 `config.PREVIOUS`의 기록값과 같고, 재현본이 이 파일과 바이트 단위로 같으며,\n",
    "시나리오별 2035년 전산업 합계가 `config.PREVIOUS`와 같은지 확인한다.\n",
    "\n",
    "**최초 판(v0) 재현.** 2026-08-04 게시 파일(`data/published/labor_demand_viewer_data_v0_pre-reconciliation.json`)을 재현본과 바이트 단위로 대조한다.\n",
    "\n",
    "공표 값은 소수 첫째 자리(100명 단위)로 반올림되어 있으므로 허용오차는 0.05천 명이다. 반올림 전 오차의 최댓값은 반올림 단위의 절반(0.05)에 가깝게 나타나는 것이 정상이며,\n",
    "반올림 후 오차가 0이면 모든 값이 일치한다는 뜻이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "71713422",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-10-08T00:07:57.393163Z",
     "iopub.status.busy": "2026-10-08T00:07:57.393163Z",
     "iopub.status.idle": "2026-10-08T00:07:57.415535Z",
     "shell.execute_reply": "2026-10-08T00:07:57.415200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>항목</th>\n",
       "      <th>계산</th>\n",
       "      <th>공표</th>\n",
       "      <th>허용오차</th>\n",
       "      <th>통과</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[정비판 주 산출물] JSON SHA-256 = 기록값</td>\n",
       "      <td>776c43a570648a03</td>\n",
       "      <td>776c43a570648a03</td>\n",
       "      <td>동일</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[정비판 주 산출물] 구조(키·연도·시나리오·산업 순서) = 최초 판·이전 판 파일</td>\n",
       "      <td>동일</td>\n",
       "      <td>동일</td>\n",
       "      <td>동일</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[정비판 주 산출물] 산업 수</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[정비판 주 산출물] 연도 수</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[정비판 주 산출물] 시나리오 수</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[정비판 주 산출물] 산업값 1,800개: 반올림 전 계산값 대비 최대 절대오차(천명)</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[정비판 주 산출물] 시나리오 합계 50개: 반올림 전 계산값 대비 최대 절대오차(천명)</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S1 2026(천명)</td>\n",
       "      <td>28,526.159</td>\n",
       "      <td>28,526.200</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S1 2030(천명)</td>\n",
       "      <td>28,670.837</td>\n",
       "      <td>28,670.800</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S1 2035(천명)</td>\n",
       "      <td>28,560.194</td>\n",
       "      <td>28,560.200</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S2 2026(천명)</td>\n",
       "      <td>28,289.437</td>\n",
       "      <td>28,289.400</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S2 2030(천명)</td>\n",
       "      <td>28,647.957</td>\n",
       "      <td>28,648.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S2 2035(천명)</td>\n",
       "      <td>28,517.237</td>\n",
       "      <td>28,517.200</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S3 2026(천명)</td>\n",
       "      <td>28,656.081</td>\n",
       "      <td>28,656.100</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S3 2030(천명)</td>\n",
       "      <td>28,678.064</td>\n",
       "      <td>28,678.100</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S3 2035(천명)</td>\n",
       "      <td>28,595.314</td>\n",
       "      <td>28,595.300</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S4 2026(천명)</td>\n",
       "      <td>28,652.640</td>\n",
       "      <td>28,652.600</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S4 2030(천명)</td>\n",
       "      <td>28,680.978</td>\n",
       "      <td>28,681.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S4 2035(천명)</td>\n",
       "      <td>28,517.456</td>\n",
       "      <td>28,517.500</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S5 2026(천명)</td>\n",
       "      <td>28,337.017</td>\n",
       "      <td>28,337.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S5 2030(천명)</td>\n",
       "      <td>28,729.033</td>\n",
       "      <td>28,729.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>[정비판 주 산출물] 전산업 S5 2035(천명)</td>\n",
       "      <td>28,614.873</td>\n",
       "      <td>28,614.900</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>[정비판 주 산출물] 의료, 보건업 및 사회복지서비스업 S1 2035(천명)</td>\n",
       "      <td>4,626.675</td>\n",
       "      <td>4,626.700</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>[정비판 주 산출물] 도소매업 S1 2035(천명)</td>\n",
       "      <td>2,226.205</td>\n",
       "      <td>2,226.200</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>[정비판 주 산출물] 컴퓨터, 전자 및 광학기기 제조업 S1 2035(천명)</td>\n",
       "      <td>2,588.553</td>\n",
       "      <td>2,588.600</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>[정비판 주 산출물] 숙박 및 음식점업 S1 2035(천명)</td>\n",
       "      <td>1,844.457</td>\n",
       "      <td>1,844.500</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>[정비판 주 산출물] 금융 및 보험업 S1 2035(천명)</td>\n",
       "      <td>626.903</td>\n",
       "      <td>626.900</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>[정비판 주 산출물] 농림어업 S1 2035(천명)</td>\n",
       "      <td>1,374.148</td>\n",
       "      <td>1,374.100</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>[정비판 주 산출물] 표 8 S1_증감_2024_2029(천명)</td>\n",
       "      <td>171.620</td>\n",
       "      <td>171.600</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>[정비판 주 산출물] 표 8 S1_증감_2029_2034(천명)</td>\n",
       "      <td>-52.209</td>\n",
       "      <td>-52.200</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>[정비판 주 산출물] 표 8 S1_증감_2024_2034(천명)</td>\n",
       "      <td>119.411</td>\n",
       "      <td>119.400</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>[정비판 주 산출물] 표 8 S1_증감_2024_2029_경로(천명)</td>\n",
       "      <td>72.260</td>\n",
       "      <td>72.300</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>[정비판 주 산출물] 표 8 S1_증감_2024_2034_경로(천명)</td>\n",
       "      <td>20.051</td>\n",
       "      <td>20.100</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>[이전 판(2026-10-08 판) 재현] 보존 파일 SHA-256 = 기록값</td>\n",
       "      <td>f3346aee10c08f89</td>\n",
       "      <td>f3346aee10c08f89</td>\n",
       "      <td>동일</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>[이전 판(2026-10-08 판) 재현] 재현본 SHA-256 = 보존 파일</td>\n",
       "      <td>f3346aee10c08f89</td>\n",
       "      <td>f3346aee10c08f89</td>\n",
       "      <td>동일</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>[이전 판(2026-10-08 판) 재현] 산업값 1,800개: 반올림 후 최대 절...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>[이전 판(2026-10-08 판) 재현] 시나리오 합계 50개: 반올림 후 최대 ...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>[이전 판(2026-10-08 판) 재현] 전산업 S1 2035(천명)</td>\n",
       "      <td>29,040.139</td>\n",
       "      <td>29,040.100</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>[이전 판(2026-10-08 판) 재현] 전산업 S2 2035(천명)</td>\n",
       "      <td>28,988.941</td>\n",
       "      <td>28,988.900</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>[이전 판(2026-10-08 판) 재현] 전산업 S3 2035(천명)</td>\n",
       "      <td>29,079.629</td>\n",
       "      <td>29,079.600</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>[이전 판(2026-10-08 판) 재현] 전산업 S4 2035(천명)</td>\n",
       "      <td>29,110.763</td>\n",
       "      <td>29,110.800</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>[이전 판(2026-10-08 판) 재현] 전산업 S5 2035(천명)</td>\n",
       "      <td>29,046.214</td>\n",
       "      <td>29,046.200</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>[최초 판(v0) 재현] 게시 파일 SHA-256 = 기록값</td>\n",
       "      <td>31c1c9c5dfadd831</td>\n",
       "      <td>31c1c9c5dfadd831</td>\n",
       "      <td>동일</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>[최초 판(v0) 재현] 재현본 SHA-256 = 게시 파일</td>\n",
       "      <td>31c1c9c5dfadd831</td>\n",
       "      <td>31c1c9c5dfadd831</td>\n",
       "      <td>동일</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>[최초 판(v0) 재현] 산업값 1,800개: 반올림 후 최대 절대오차(천명)</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>[최초 판(v0) 재현] 시나리오 합계 50개: 반올림 후 최대 절대오차(천명)</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>[최초 판(v0) 재현] 전산업 S1 2035(천명)</td>\n",
       "      <td>36,284.817</td>\n",
       "      <td>36,284.800</td>\n",
       "      <td>0.050</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   항목                계산  \\\n",
       "0                      [정비판 주 산출물] JSON SHA-256 = 기록값  776c43a570648a03   \n",
       "1      [정비판 주 산출물] 구조(키·연도·시나리오·산업 순서) = 최초 판·이전 판 파일                동일   \n",
       "2                                    [정비판 주 산출물] 산업 수                36   \n",
       "3                                    [정비판 주 산출물] 연도 수                10   \n",
       "4                                  [정비판 주 산출물] 시나리오 수                 5   \n",
       "5    [정비판 주 산출물] 산업값 1,800개: 반올림 전 계산값 대비 최대 절대오차(천명)             0.050   \n",
       "6   [정비판 주 산출물] 시나리오 합계 50개: 반올림 전 계산값 대비 최대 절대오차(천명)             0.049   \n",
       "7                         [정비판 주 산출물] 전산업 S1 2026(천명)        28,526.159   \n",
       "8                         [정비판 주 산출물] 전산업 S1 2030(천명)        28,670.837   \n",
       "9                         [정비판 주 산출물] 전산업 S1 2035(천명)        28,560.194   \n",
       "10                        [정비판 주 산출물] 전산업 S2 2026(천명)        28,289.437   \n",
       "11                        [정비판 주 산출물] 전산업 S2 2030(천명)        28,647.957   \n",
       "12                        [정비판 주 산출물] 전산업 S2 2035(천명)        28,517.237   \n",
       "13                        [정비판 주 산출물] 전산업 S3 2026(천명)        28,656.081   \n",
       "14                        [정비판 주 산출물] 전산업 S3 2030(천명)        28,678.064   \n",
       "15                        [정비판 주 산출물] 전산업 S3 2035(천명)        28,595.314   \n",
       "16                        [정비판 주 산출물] 전산업 S4 2026(천명)        28,652.640   \n",
       "17                        [정비판 주 산출물] 전산업 S4 2030(천명)        28,680.978   \n",
       "18                        [정비판 주 산출물] 전산업 S4 2035(천명)        28,517.456   \n",
       "19                        [정비판 주 산출물] 전산업 S5 2026(천명)        28,337.017   \n",
       "20                        [정비판 주 산출물] 전산업 S5 2030(천명)        28,729.033   \n",
       "21                        [정비판 주 산출물] 전산업 S5 2035(천명)        28,614.873   \n",
       "22         [정비판 주 산출물] 의료, 보건업 및 사회복지서비스업 S1 2035(천명)         4,626.675   \n",
       "23                       [정비판 주 산출물] 도소매업 S1 2035(천명)         2,226.205   \n",
       "24         [정비판 주 산출물] 컴퓨터, 전자 및 광학기기 제조업 S1 2035(천명)         2,588.553   \n",
       "25                  [정비판 주 산출물] 숙박 및 음식점업 S1 2035(천명)         1,844.457   \n",
       "26                   [정비판 주 산출물] 금융 및 보험업 S1 2035(천명)           626.903   \n",
       "27                       [정비판 주 산출물] 농림어업 S1 2035(천명)         1,374.148   \n",
       "28                [정비판 주 산출물] 표 8 S1_증감_2024_2029(천명)           171.620   \n",
       "29                [정비판 주 산출물] 표 8 S1_증감_2029_2034(천명)           -52.209   \n",
       "30                [정비판 주 산출물] 표 8 S1_증감_2024_2034(천명)           119.411   \n",
       "31             [정비판 주 산출물] 표 8 S1_증감_2024_2029_경로(천명)            72.260   \n",
       "32             [정비판 주 산출물] 표 8 S1_증감_2024_2034_경로(천명)            20.051   \n",
       "33        [이전 판(2026-10-08 판) 재현] 보존 파일 SHA-256 = 기록값  f3346aee10c08f89   \n",
       "34        [이전 판(2026-10-08 판) 재현] 재현본 SHA-256 = 보존 파일  f3346aee10c08f89   \n",
       "35  [이전 판(2026-10-08 판) 재현] 산업값 1,800개: 반올림 후 최대 절...             0.000   \n",
       "36  [이전 판(2026-10-08 판) 재현] 시나리오 합계 50개: 반올림 후 최대 ...             0.000   \n",
       "37            [이전 판(2026-10-08 판) 재현] 전산업 S1 2035(천명)        29,040.139   \n",
       "38            [이전 판(2026-10-08 판) 재현] 전산업 S2 2035(천명)        28,988.941   \n",
       "39            [이전 판(2026-10-08 판) 재현] 전산업 S3 2035(천명)        29,079.629   \n",
       "40            [이전 판(2026-10-08 판) 재현] 전산업 S4 2035(천명)        29,110.763   \n",
       "41            [이전 판(2026-10-08 판) 재현] 전산업 S5 2035(천명)        29,046.214   \n",
       "42                  [최초 판(v0) 재현] 게시 파일 SHA-256 = 기록값  31c1c9c5dfadd831   \n",
       "43                  [최초 판(v0) 재현] 재현본 SHA-256 = 게시 파일  31c1c9c5dfadd831   \n",
       "44        [최초 판(v0) 재현] 산업값 1,800개: 반올림 후 최대 절대오차(천명)             0.000   \n",
       "45       [최초 판(v0) 재현] 시나리오 합계 50개: 반올림 후 최대 절대오차(천명)             0.000   \n",
       "46                      [최초 판(v0) 재현] 전산업 S1 2035(천명)        36,284.817   \n",
       "\n",
       "                  공표  허용오차    통과  \n",
       "0   776c43a570648a03    동일  True  \n",
       "1                 동일    동일  True  \n",
       "2                 36     0  True  \n",
       "3                 10     0  True  \n",
       "4                  5     0  True  \n",
       "5              0.000 0.050  True  \n",
       "6              0.000 0.050  True  \n",
       "7         28,526.200 0.050  True  \n",
       "8         28,670.800 0.050  True  \n",
       "9         28,560.200 0.050  True  \n",
       "10        28,289.400 0.050  True  \n",
       "11        28,648.000 0.050  True  \n",
       "12        28,517.200 0.050  True  \n",
       "13        28,656.100 0.050  True  \n",
       "14        28,678.100 0.050  True  \n",
       "15        28,595.300 0.050  True  \n",
       "16        28,652.600 0.050  True  \n",
       "17        28,681.000 0.050  True  \n",
       "18        28,517.500 0.050  True  \n",
       "19        28,337.000 0.050  True  \n",
       "20        28,729.000 0.050  True  \n",
       "21        28,614.900 0.050  True  \n",
       "22         4,626.700 0.050  True  \n",
       "23         2,226.200 0.050  True  \n",
       "24         2,588.600 0.050  True  \n",
       "25         1,844.500 0.050  True  \n",
       "26           626.900 0.050  True  \n",
       "27         1,374.100 0.050  True  \n",
       "28           171.600 0.050  True  \n",
       "29           -52.200 0.050  True  \n",
       "30           119.400 0.050  True  \n",
       "31            72.300 0.050  True  \n",
       "32            20.100 0.050  True  \n",
       "33  f3346aee10c08f89    동일  True  \n",
       "34  f3346aee10c08f89    동일  True  \n",
       "35             0.000 0.050  True  \n",
       "36             0.000 0.050  True  \n",
       "37        29,040.100 0.050  True  \n",
       "38        28,988.900 0.050  True  \n",
       "39        29,079.600 0.050  True  \n",
       "40        29,110.800 0.050  True  \n",
       "41        29,046.200 0.050  True  \n",
       "42  31c1c9c5dfadd831    동일  True  \n",
       "43  31c1c9c5dfadd831    동일  True  \n",
       "44             0.000 0.050  True  \n",
       "45             0.000 0.050  True  \n",
       "46        36,284.800 0.050  True  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "검증 통과(47개 항목): 정비판 주 산출물이 7장 인용 수치와 일치하고, 이전 판(2026-10-08 판) 산출 파일과 최초 판(v0, 2026-08-04 게시) 파일을 바이트 단위까지 재현했다.\n"
     ]
    }
   ],
   "source": [
    "PB, PV, P0 = C.PUBLISHED, C.PREVIOUS, C.PUBLISHED_V0\n",
    "main = json.load(open(C.OUT_JSON, encoding=\"utf-8\"))\n",
    "prev_pub = json.load(open(C.PREV_JSON, encoding=\"utf-8\"))\n",
    "prev_out = json.load(open(C.OUT_PREV_JSON, encoding=\"utf-8\"))\n",
    "v0_pub = json.load(open(C.V0_JSON, encoding=\"utf-8\"))\n",
    "v0_out = json.load(open(C.OUT_V0_JSON, encoding=\"utf-8\"))\n",
    "h_main = F.sha256(C.OUT_JSON)\n",
    "h_prevpub, h_prevout = F.sha256(C.PREV_JSON), F.sha256(C.OUT_PREV_JSON)\n",
    "h_v0pub, h_v0out = F.sha256(C.V0_JSON), F.sha256(C.OUT_V0_JSON)\n",
    "raw_err = max(abs(P[s].loc[y, i] - v) for s in main[\"scenarios\"] for i, vals in main[\"scenarios\"][s].items()\n",
    "              for y, v in zip(main[\"years\"], vals))\n",
    "raw_tot = max(abs(tot.loc[y, s] - v) for s, vals in main[\"scenarios_total\"].items() for y, v in zip(main[\"years\"], vals))\n",
    "same_st = F.same_structure(main, v0_pub) and F.same_structure(main, prev_pub)\n",
    "cmpp, cmp0 = F.compare_viewer(prev_out, prev_pub), F.compare_viewer(v0_out, v0_pub)\n",
    "M, R, V = \"[정비판 주 산출물] \", \"[이전 판(2026-10-08 판) 재현] \", \"[최초 판(v0) 재현] \"\n",
    "checks = [\n",
    "    (M + \"JSON SHA-256 = 기록값\", h_main[:16], PB[\"JSON_SHA256\"][:16], \"동일\", h_main == PB[\"JSON_SHA256\"]),\n",
    "    (M + \"구조(키·연도·시나리오·산업 순서) = 최초 판·이전 판 파일\", \"동일\" if same_st else \"다름\", \"동일\", \"동일\", same_st),\n",
    "    (M + \"산업 수\", len(main[\"scenarios\"][\"S1\"]), PB[\"산업수\"], 0, None),\n",
    "    (M + \"연도 수\", len(main[\"years\"]), PB[\"연도수\"], 0, None),\n",
    "    (M + \"시나리오 수\", len(main[\"scenarios\"]), PB[\"시나리오수\"], 0, None),\n",
    "    (M + \"산업값 1,800개: 반올림 전 계산값 대비 최대 절대오차(천명)\", raw_err, 0.0, 0.05, None),\n",
    "    (M + \"시나리오 합계 50개: 반올림 전 계산값 대비 최대 절대오차(천명)\", raw_tot, 0.0, 0.05, None),\n",
    "]\n",
    "for s in C.SCENARIOS:\n",
    "    for y in (2026, 2030, 2035):\n",
    "        checks.append((M + f\"전산업 {s} {y}(천명)\", tot.loc[y, s], PB[f\"전산업_{s}_{y}\"], 0.05, None))\n",
    "for k, name in C.INDUSTRY_KEYS.items():\n",
    "    checks.append((M + f\"{name} S1 2035(천명)\", P[\"S1\"].loc[2035, name], PB[f\"{k}_S1_2035\"], 0.05, None))\n",
    "for k, v in kc.items():\n",
    "    checks.append((M + f\"표 8 {k}(천명)\", v, PB[k], 0.05, None))\n",
    "checks += [\n",
    "    (R + \"보존 파일 SHA-256 = 기록값\", h_prevpub[:16], PV[\"JSON_SHA256\"][:16], \"동일\", h_prevpub == PV[\"JSON_SHA256\"]),\n",
    "    (R + \"재현본 SHA-256 = 보존 파일\", h_prevout[:16], h_prevpub[:16], \"동일\", h_prevout == h_prevpub),\n",
    "    (R + \"산업값 1,800개: 반올림 후 최대 절대오차(천명)\", cmpp[\"산업값 최대절대오차\"], 0.0, 0.05, None),\n",
    "    (R + \"시나리오 합계 50개: 반올림 후 최대 절대오차(천명)\", cmpp[\"합계 최대절대오차\"], 0.0, 0.05, None),\n",
    "]\n",
    "for s in C.SCENARIOS:\n",
    "    checks.append((R + f\"전산업 {s} 2035(천명)\", tot_prev.loc[2035, s], PV[f\"전산업_{s}_2035\"], 0.05, None))\n",
    "checks += [\n",
    "    (V + \"게시 파일 SHA-256 = 기록값\", h_v0pub[:16], P0[\"JSON_SHA256\"][:16], \"동일\", h_v0pub == P0[\"JSON_SHA256\"]),\n",
    "    (V + \"재현본 SHA-256 = 게시 파일\", h_v0out[:16], h_v0pub[:16], \"동일\", h_v0out == h_v0pub),\n",
    "    (V + \"산업값 1,800개: 반올림 후 최대 절대오차(천명)\", cmp0[\"산업값 최대절대오차\"], 0.0, 0.05, None),\n",
    "    (V + \"시나리오 합계 50개: 반올림 후 최대 절대오차(천명)\", cmp0[\"합계 최대절대오차\"], 0.0, 0.05, None),\n",
    "    (V + \"전산업 S1 2035(천명)\", tot_v0.loc[2035, \"S1\"], P0[\"전산업_S1_2035\"], 0.05, None),\n",
    "]\n",
    "res = pd.DataFrame([{\"항목\": k, \"계산\": v, \"공표\": p, \"허용오차\": tol,\n",
    "                     \"통과\": bool(ok) if ok is not None else bool(abs(v - p) <= tol)} for k, v, p, tol, ok in checks])\n",
    "res.to_csv(os.path.join(C.OUTPUT, \"verification.csv\"), index=False, encoding=\"utf-8-sig\")\n",
    "display(res)\n",
    "assert res[\"통과\"].all(), \"공표 수치와 계산 결과가 다르다 — 위 표에서 통과=False 항목을 확인한다\"\n",
    "print(f\"검증 통과({len(res)}개 항목): 정비판 주 산출물이 7장 인용 수치와 일치하고, 이전 판(2026-10-08 판) 산출 파일과 \"\n",
    "      \"최초 판(v0, 2026-08-04 게시) 파일을 바이트 단위까지 재현했다.\")"
   ]
  }
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