01. 대사증후군_연도,시도코드,시도이름,인원.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "296ad483",
   "metadata": {},
   "source": [
    "# 01. 대사증후군"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "36d9056a",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "017d7a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "# matplotlib.rcParams['font.family'] = 'Malgun Gothic' # Windows\n",
    "matplotlib.rcParams['font.family'] = 'AppleGothic' # Mac\n",
    "matplotlib.rcParams['font.size'] = 15\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb455a1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_dic = {} # 딕셔너리 생성"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad254619",
   "metadata": {},
   "source": [
    "## 2011년도 대사증후군 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "06df894b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:3: DtypeWarning: Columns (31) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(f'NHIS{year}.CSV', encoding='CP949')\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/3874212694.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "시도 코드 47: 대사증후군 조건 충족 행의 개수 = 8682\n",
      "시도 코드 26: 대사증후군 조건 충족 행의 개수 = 9385\n",
      "시도 코드 27: 대사증후군 조건 충족 행의 개수 = 6122\n",
      "시도 코드 46: 대사증후군 조건 충족 행의 개수 = 7112\n",
      "시도 코드 31: 대사증후군 조건 충족 행의 개수 = 3705\n",
      "시도 코드 45: 대사증후군 조건 충족 행의 개수 = 6041\n",
      "시도 코드 41: 대사증후군 조건 충족 행의 개수 = 31536\n",
      "시도 코드 44: 대사증후군 조건 충족 행의 개수 = 7309\n",
      "시도 코드 29: 대사증후군 조건 충족 행의 개수 = 3712\n",
      "시도 코드 43: 대사증후군 조건 충족 행의 개수 = 5211\n",
      "시도 코드 11: 대사증후군 조건 충족 행의 개수 = 24507\n",
      "시도 코드 42: 대사증후군 조건 충족 행의 개수 = 5284\n",
      "시도 코드 30: 대사증후군 조건 충족 행의 개수 = 4058\n",
      "시도 코드 48: 대사증후군 조건 충족 행의 개수 = 9152\n",
      "시도 코드 49: 대사증후군 조건 충족 행의 개수 = 1311\n",
      "시도 코드 28: 대사증후군 조건 충족 행의 개수 = 7859\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>기준년도</th>\n",
       "      <th>가입자일련번호</th>\n",
       "      <th>성별코드</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "      <th>시도코드</th>\n",
       "      <th>신장(5Cm단위)</th>\n",
       "      <th>체중(5Kg 단위)</th>\n",
       "      <th>허리둘레</th>\n",
       "      <th>시력(좌)</th>\n",
       "      <th>시력(우)</th>\n",
       "      <th>...</th>\n",
       "      <th>감마지티피</th>\n",
       "      <th>흡연상태</th>\n",
       "      <th>음주여부</th>\n",
       "      <th>구강검진 수검여부</th>\n",
       "      <th>치아우식증유무</th>\n",
       "      <th>결손치유무</th>\n",
       "      <th>치아마모증유무</th>\n",
       "      <th>제3대구치(사랑니)이상</th>\n",
       "      <th>치석</th>\n",
       "      <th>데이터공개일자</th>\n",
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       "      <td>2011</td>\n",
       "      <td>86261</td>\n",
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       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>155</td>\n",
       "      <td>50</td>\n",
       "      <td>66</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.2</td>\n",
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       "      <td>11</td>\n",
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       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-20</td>\n",
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       "      <th>337</th>\n",
       "      <td>2011</td>\n",
       "      <td>919976</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.9</td>\n",
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       "      <td>2011</td>\n",
       "      <td>494059</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>155</td>\n",
       "      <td>60</td>\n",
       "      <td>78</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>386</th>\n",
       "      <td>2011</td>\n",
       "      <td>241261</td>\n",
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       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>155</td>\n",
       "      <td>70</td>\n",
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       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
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       "      <th>387</th>\n",
       "      <td>2011</td>\n",
       "      <td>293893</td>\n",
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       "      <td>1</td>\n",
       "      <td>30</td>\n",
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       "      <td>2011</td>\n",
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       "      <th>999982</th>\n",
       "      <td>2011</td>\n",
       "      <td>561236</td>\n",
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       "      <td>14</td>\n",
       "      <td>11</td>\n",
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       "      <td>0.2</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2011</td>\n",
       "      <td>164194</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2015-12-20</td>\n",
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       "    <tr>\n",
       "      <th>999986</th>\n",
       "      <td>2011</td>\n",
       "      <td>266398</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
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       "      <td>40</td>\n",
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       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-20</td>\n",
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       "    <tr>\n",
       "      <th>999992</th>\n",
       "      <td>2011</td>\n",
       "      <td>279066</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>47</td>\n",
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       "      <td>0.4</td>\n",
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       "      <td>34</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-20</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>140986 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        기준년도  가입자일련번호  성별코드  연령대코드(5세단위)  시도코드  신장(5Cm단위)  체중(5Kg 단위)  허리둘레  \\\n",
       "221     2011    86261     2            1    28        155          50    66   \n",
       "337     2011   919976     2            1    11        155          55    70   \n",
       "353     2011   494059     2            1    31        155          60    78   \n",
       "386     2011   241261     2            1    42        155          70    83   \n",
       "387     2011   293893     2            1    30        155          70    90   \n",
       "...      ...      ...   ...          ...   ...        ...         ...   ...   \n",
       "999981  2011   394477     2           14    44        140          35    70   \n",
       "999982  2011   561236     2           14    11        140          35    60   \n",
       "999984  2011   164194     2           14    46        140          40    60   \n",
       "999986  2011   266398     2           14    26        145          40    79   \n",
       "999992  2011   279066     2           14    47        150          50    89   \n",
       "\n",
       "        시력(좌)  시력(우)  ...  감마지티피  흡연상태  음주여부  구강검진 수검여부  치아우식증유무  결손치유무  \\\n",
       "221       1.0    1.2  ...     11   1.0     N          N      NaN    NaN   \n",
       "337       0.9    0.8  ...     11   3.0     N          Y        Y      N   \n",
       "353       0.7    0.6  ...     17   1.0     N          N      NaN    NaN   \n",
       "386       1.2    1.2  ...     22   1.0     Y          Y        Y      N   \n",
       "387       0.4    0.3  ...     23   1.0     Y          Y      NaN      N   \n",
       "...       ...    ...  ...    ...   ...   ...        ...      ...    ...   \n",
       "999981    0.1    0.2  ...     23   1.0     N          N      NaN    NaN   \n",
       "999982    0.2    0.2  ...     23   1.0     N          N      NaN    NaN   \n",
       "999984    0.1    0.1  ...     26   1.0     N          N      NaN    NaN   \n",
       "999986    0.2    0.1  ...     12   1.0     N          N      NaN    NaN   \n",
       "999992    0.4    0.4  ...     34   1.0     N          N      NaN    NaN   \n",
       "\n",
       "        치아마모증유무  제3대구치(사랑니)이상   치석     데이터공개일자  \n",
       "221         NaN           NaN  NaN  2015-12-20  \n",
       "337           N             N    Y  2015-12-20  \n",
       "353         NaN           NaN  NaN  2015-12-20  \n",
       "386           N             N    Y  2015-12-20  \n",
       "387           Y             N    Y  2015-12-20  \n",
       "...         ...           ...  ...         ...  \n",
       "999981      NaN           NaN  NaN  2015-12-20  \n",
       "999982      NaN           NaN  NaN  2015-12-20  \n",
       "999984      NaN           NaN  NaN  2015-12-20  \n",
       "999986      NaN           NaN  NaN  2015-12-20  \n",
       "999992      NaN           NaN  NaN  2015-12-20  \n",
       "\n",
       "[140986 rows x 34 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = '2011'\n",
    "\n",
    "df = pd.read_csv(f'NHIS{year}.CSV', encoding='CP949')\n",
    "df.head(100)\n",
    "\n",
    "unique_sido_codes = df['시도코드'].unique()\n",
    "sido_dic = {}\n",
    "\n",
    "for sido_code in unique_sido_codes:\n",
    "    data_for_sido = df[df['시도코드'] == sido_code]\n",
    "\n",
    "    # 1. 복부 둘레 증가\n",
    "    filt1 = ((df['허리둘레'] >= 101) & (df['성별코드'] == 1)) | ((df['허리둘레'] >= 88) & (df['성별코드'] == 2))\n",
    "    df.loc[filt1]\n",
    "\n",
    "    # 2. 중성 지방 수치 상승\n",
    "    filt2 = (df['트리글리세라이드'] >= 150)\n",
    "    df.loc[filt2]\n",
    "\n",
    "    # 3. HDL-콜레스테롤 감소\n",
    "    filt3 = ((df['HDL콜레스테롤'] < 40) & (df['성별코드'] == 1) | (df['HDL콜레스테롤'] < 50) & (df['성별코드'] == 2))\n",
    "    df.loc[filt3]\n",
    "\n",
    "    # 4. 혈당 상승\n",
    "    filt4 = (df['식전혈당(공복혈당)'] >= 100)\n",
    "    df.loc[filt4]\n",
    "\n",
    "    # 5. 혈압 상승\n",
    "    filt5 = ((df['수축기혈압'] >= 130) | (df['이완기혈압'] >= 85))\n",
    "    df.loc[filt5]\n",
    "\n",
    "    # 3개 이상의 조건 만족\n",
    "    filt_combined = (filt1.astype(int) + filt2.astype(int) + filt3.astype(int) + filt4.astype(int) + filt5.astype(int)) >= 3\n",
    "\n",
    "        # 조건을 충족하는 행을 추출\n",
    "    result_data = data_for_sido[filt_combined]\n",
    "\n",
    "    # 조건을 충족하는 행의 개수를 계산\n",
    "    count = result_data.shape[0]\n",
    "    \n",
    "    # 결과를 딕셔너리에 저장\n",
    "    sido_dic[sido_code] = count\n",
    "    \n",
    "\n",
    "# 결과\n",
    "for sido_code, count in sido_dic.items():\n",
    "    print(f\"시도 코드 {sido_code}: 대사증후군 조건 충족 행의 개수 = {count}\")\n",
    "\n",
    "    \n",
    "result_df = df.loc[filt_combined]\n",
    "# row_count = result_df.shape[0]\n",
    "result_dic[2011] = sido_dic;\n",
    "\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5a74eba",
   "metadata": {},
   "source": [
    "## 2012년도 대사증후군 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1fa4cb7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "시도 코드 46: 대사증후군 조건 충족 행의 개수 = 7202\n",
      "시도 코드 45: 대사증후군 조건 충족 행의 개수 = 6200\n",
      "시도 코드 26: 대사증후군 조건 충족 행의 개수 = 9333\n",
      "시도 코드 11: 대사증후군 조건 충족 행의 개수 = 24173\n",
      "시도 코드 28: 대사증후군 조건 충족 행의 개수 = 7982\n",
      "시도 코드 41: 대사증후군 조건 충족 행의 개수 = 31128\n",
      "시도 코드 47: 대사증후군 조건 충족 행의 개수 = 8620\n",
      "시도 코드 44: 대사증후군 조건 충족 행의 개수 = 6921\n",
      "시도 코드 43: 대사증후군 조건 충족 행의 개수 = 5428\n",
      "시도 코드 27: 대사증후군 조건 충족 행의 개수 = 6130\n",
      "시도 코드 42: 대사증후군 조건 충족 행의 개수 = 5281\n",
      "시도 코드 30: 대사증후군 조건 충족 행의 개수 = 4091\n",
      "시도 코드 48: 대사증후군 조건 충족 행의 개수 = 9170\n",
      "시도 코드 31: 대사증후군 조건 충족 행의 개수 = 3576\n",
      "시도 코드 29: 대사증후군 조건 충족 행의 개수 = 3820\n",
      "시도 코드 49: 대사증후군 조건 충족 행의 개수 = 1565\n",
      "시도 코드 36: 대사증후군 조건 충족 행의 개수 = 342\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/875683983.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>기준년도</th>\n",
       "      <th>가입자일련번호</th>\n",
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       "      <th>치아우식증유무</th>\n",
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      ],
      "text/plain": [
       "        기준년도  가입자일련번호  성별코드  연령대코드(5세단위)  시도코드  신장(5Cm단위)  체중(5Kg 단위)  허리둘레  \\\n",
       "327     2012   902101     2            1    41        155          55    76   \n",
       "374     2012   883987     2            1    41        155          70    80   \n",
       "599     2012    38544     1            1    41        160          50    68   \n",
       "718     2012   387436     2            1    47        160          60    70   \n",
       "865     2012   995794     1            1    41        165          50    72   \n",
       "...      ...      ...   ...          ...   ...        ...         ...   ...   \n",
       "999983  2012    36863     2           14    11        145          50    88   \n",
       "999984  2012   254388     2           14    11        150          45    76   \n",
       "999992  2012   865661     2           14    43        140          35    67   \n",
       "999993  2012   218995     2           14    30        140          35    82   \n",
       "999997  2012   518208     2           14    41        145          40    73   \n",
       "\n",
       "        시력(좌)  시력(우)  ...  감마지티피  흡연상태  음주여부  구강검진 수검여부  치아우식증유무  결손치유무  \\\n",
       "327       0.7    0.8  ...     12   3.0     N          Y        Y      N   \n",
       "374       0.7    0.7  ...     65   1.0     Y          Y        Y      N   \n",
       "599       0.4    0.4  ...     15   3.0     Y          Y        N      N   \n",
       "718       1.5    1.2  ...     29   1.0     Y          Y        Y      N   \n",
       "865       1.2    0.9  ...     27   1.0     Y          Y        Y      Y   \n",
       "...       ...    ...  ...    ...   ...   ...        ...      ...    ...   \n",
       "999983    0.4    0.2  ...     12   1.0     N          N      NaN    NaN   \n",
       "999984    0.8    0.8  ...     13   1.0     N          Y        Y      Y   \n",
       "999992    0.3    0.2  ...      9   1.0     N          N      NaN    NaN   \n",
       "999993    9.9    0.2  ...     15   1.0     N          N      NaN    NaN   \n",
       "999997    0.5    0.5  ...     16   1.0     N          N      NaN    NaN   \n",
       "\n",
       "        치아마모증유무  제3대구치(사랑니)이상   치석     데이터공개일자  \n",
       "327           N             N    Y  2015-12-20  \n",
       "374           N             N    Y  2015-12-20  \n",
       "599           N             N    N  2015-12-20  \n",
       "718           N             N    N  2015-12-20  \n",
       "865           N             N    Y  2015-12-20  \n",
       "...         ...           ...  ...         ...  \n",
       "999983      NaN           NaN  NaN  2015-12-20  \n",
       "999984        N             N    Y  2015-12-20  \n",
       "999992      NaN           NaN  NaN  2015-12-20  \n",
       "999993      NaN           NaN  NaN  2015-12-20  \n",
       "999997      NaN           NaN  NaN  2015-12-20  \n",
       "\n",
       "[140962 rows x 34 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = '2012'\n",
    "\n",
    "df = pd.read_csv(f'NHIS{year}.CSV', encoding='CP949')\n",
    "df.head(100)\n",
    "\n",
    "unique_sido_codes = df['시도코드'].unique()\n",
    "sido_dic = {}\n",
    "\n",
    "for sido_code in unique_sido_codes:\n",
    "    data_for_sido = df[df['시도코드'] == sido_code]\n",
    "\n",
    "    # 1. 복부 둘레 증가\n",
    "    filt1 = ((df['허리둘레'] >= 101) & (df['성별코드'] == 1)) | ((df['허리둘레'] >= 88) & (df['성별코드'] == 2))\n",
    "    df.loc[filt1]\n",
    "\n",
    "    # 2. 중성 지방 수치 상승\n",
    "    filt2 = (df['트리글리세라이드'] >= 150)\n",
    "    df.loc[filt2]\n",
    "\n",
    "    # 3. HDL-콜레스테롤 감소\n",
    "    filt3 = ((df['HDL콜레스테롤'] < 40) & (df['성별코드'] == 1) | (df['HDL콜레스테롤'] < 50) & (df['성별코드'] == 2))\n",
    "    df.loc[filt3]\n",
    "\n",
    "    # 4. 혈당 상승\n",
    "    filt4 = (df['식전혈당(공복혈당)'] >= 100)\n",
    "    df.loc[filt4]\n",
    "\n",
    "    # 5. 혈압 상승\n",
    "    filt5 = ((df['수축기혈압'] >= 130) | (df['이완기혈압'] >= 85))\n",
    "    df.loc[filt5]\n",
    "\n",
    "    # 3개 이상의 조건 만족\n",
    "    filt_combined = (filt1.astype(int) + filt2.astype(int) + filt3.astype(int) + filt4.astype(int) + filt5.astype(int)) >= 3\n",
    "\n",
    "        # 조건을 충족하는 행을 추출\n",
    "    result_data = data_for_sido[filt_combined]\n",
    "\n",
    "    # 조건을 충족하는 행의 개수를 계산\n",
    "    count = result_data.shape[0]\n",
    "    \n",
    "    # 결과를 딕셔너리에 저장\n",
    "    sido_dic[sido_code] = count\n",
    "    \n",
    "\n",
    "# 결과\n",
    "for sido_code, count in sido_dic.items():\n",
    "    print(f\"시도 코드 {sido_code}: 대사증후군 조건 충족 행의 개수 = {count}\")\n",
    "\n",
    "    \n",
    "result_df = df.loc[filt_combined]\n",
    "# row_count = result_df.shape[0]\n",
    "result_dic[2012] = sido_dic;\n",
    "\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52f44052",
   "metadata": {},
   "source": [
    "## 2013년도 대사증후군 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3097bf26",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:3: DtypeWarning: Columns (35) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(f'NHIS{year}.CSV', encoding='CP949')\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "시도 코드 11: 대사증후군 조건 충족 행의 개수 = 24349\n",
      "시도 코드 27: 대사증후군 조건 충족 행의 개수 = 6559\n",
      "시도 코드 29: 대사증후군 조건 충족 행의 개수 = 4024\n",
      "시도 코드 43: 대사증후군 조건 충족 행의 개수 = 5385\n",
      "시도 코드 41: 대사증후군 조건 충족 행의 개수 = 32264\n",
      "시도 코드 47: 대사증후군 조건 충족 행의 개수 = 8963\n",
      "시도 코드 48: 대사증후군 조건 충족 행의 개수 = 9507\n",
      "시도 코드 45: 대사증후군 조건 충족 행의 개수 = 6336\n",
      "시도 코드 26: 대사증후군 조건 충족 행의 개수 = 10122\n",
      "시도 코드 44: 대사증후군 조건 충족 행의 개수 = 7204\n",
      "시도 코드 42: 대사증후군 조건 충족 행의 개수 = 5133\n",
      "시도 코드 28: 대사증후군 조건 충족 행의 개수 = 8545\n",
      "시도 코드 49: 대사증후군 조건 충족 행의 개수 = 1573\n",
      "시도 코드 46: 대사증후군 조건 충족 행의 개수 = 7239\n",
      "시도 코드 31: 대사증후군 조건 충족 행의 개수 = 3627\n",
      "시도 코드 30: 대사증후군 조건 충족 행의 개수 = 4268\n",
      "시도 코드 36: 대사증후군 조건 충족 행의 개수 = 403\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/163748477.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>기준년도</th>\n",
       "      <th>가입자일련번호</th>\n",
       "      <th>성별코드</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "      <th>시도코드</th>\n",
       "      <th>신장(5Cm단위)</th>\n",
       "      <th>체중(5Kg 단위)</th>\n",
       "      <th>허리둘레</th>\n",
       "      <th>시력(좌)</th>\n",
       "      <th>시력(우)</th>\n",
       "      <th>...</th>\n",
       "      <th>음주여부</th>\n",
       "      <th>구강검진 수검여부</th>\n",
       "      <th>치아우식증유무</th>\n",
       "      <th>결손치유무</th>\n",
       "      <th>치아마모증유무</th>\n",
       "      <th>제3대구치(사랑니)이상</th>\n",
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       "      <td>24193</td>\n",
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       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>150</td>\n",
       "      <td>65</td>\n",
       "      <td>79</td>\n",
       "      <td>1.2</td>\n",
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       "      <td>N</td>\n",
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       "      <td>140</td>\n",
       "      <td>40</td>\n",
       "      <td>80</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999982</th>\n",
       "      <td>2013</td>\n",
       "      <td>532686</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>41</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>85</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999987</th>\n",
       "      <td>2013</td>\n",
       "      <td>481860</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>46</td>\n",
       "      <td>150</td>\n",
       "      <td>40</td>\n",
       "      <td>72</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999991</th>\n",
       "      <td>2013</td>\n",
       "      <td>587149</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>41</td>\n",
       "      <td>150</td>\n",
       "      <td>50</td>\n",
       "      <td>83</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999999</th>\n",
       "      <td>2013</td>\n",
       "      <td>487152</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>27</td>\n",
       "      <td>145</td>\n",
       "      <td>45</td>\n",
       "      <td>85</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145501 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        기준년도  가입자일련번호  성별코드  연령대코드(5세단위)  시도코드  신장(5Cm단위)  체중(5Kg 단위)  허리둘레  \\\n",
       "0       2013    24193     2           14    11        145          35    83   \n",
       "66      2013    10290     1            1    27        150          50    81   \n",
       "93      2013   436989     2            1    41        150          65    79   \n",
       "408     2013   230766     2            1    26        155          80    98   \n",
       "757     2013   874569     2            1    41        160          60    85   \n",
       "...      ...      ...   ...          ...   ...        ...         ...   ...   \n",
       "999974  2013   315109     2           14    44        140          40    80   \n",
       "999982  2013   532686     2           14    41        145          50    85   \n",
       "999987  2013   481860     2           14    46        150          40    72   \n",
       "999991  2013   587149     2           14    41        150          50    83   \n",
       "999999  2013   487152     2           14    27        145          45    85   \n",
       "\n",
       "        시력(좌)  시력(우)  ...  음주여부  구강검진 수검여부  치아우식증유무  결손치유무  치아마모증유무  \\\n",
       "0         0.1    0.1  ...     N          N      NaN    NaN      NaN   \n",
       "66        0.8    0.8  ...     N          N      NaN    NaN      NaN   \n",
       "93        1.2    1.0  ...     Y          Y        N      N        N   \n",
       "408       0.2    0.2  ...     N          N      NaN    NaN      NaN   \n",
       "757       1.0    0.9  ...     N          N      NaN    NaN      NaN   \n",
       "...       ...    ...  ...   ...        ...      ...    ...      ...   \n",
       "999974    0.1    0.1  ...     N          N      NaN    NaN      NaN   \n",
       "999982    0.3    0.3  ...     N          N      NaN    NaN      NaN   \n",
       "999987    0.5    0.6  ...     N          N      NaN    NaN      NaN   \n",
       "999991    0.1    0.1  ...     N          N      NaN    NaN      NaN   \n",
       "999999    0.5    0.9  ...     N          N      NaN    NaN      NaN   \n",
       "\n",
       "        제3대구치(사랑니)이상   치석     데이터공개일자  Unnamed: 34  Unnamed: 35  \n",
       "0                NaN  NaN  2015-12-20          NaN          NaN  \n",
       "66               NaN  NaN  2015-12-20          NaN          NaN  \n",
       "93                 N    Y  2015-12-20          NaN          NaN  \n",
       "408              NaN  NaN  2015-12-20          NaN          NaN  \n",
       "757              NaN  NaN  2015-12-20          NaN          NaN  \n",
       "...              ...  ...         ...          ...          ...  \n",
       "999974           NaN  NaN  2015-12-20          NaN          NaN  \n",
       "999982           NaN  NaN  2015-12-20          NaN          NaN  \n",
       "999987           NaN  NaN  2015-12-20          NaN          NaN  \n",
       "999991           NaN  NaN  2015-12-20          NaN          NaN  \n",
       "999999           NaN  NaN  2015-12-20          NaN          NaN  \n",
       "\n",
       "[145501 rows x 36 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = '2013'\n",
    "\n",
    "df = pd.read_csv(f'NHIS{year}.CSV', encoding='CP949')\n",
    "df.head(100)\n",
    "\n",
    "unique_sido_codes = df['시도코드'].unique()\n",
    "sido_dic = {}\n",
    "\n",
    "for sido_code in unique_sido_codes:\n",
    "    data_for_sido = df[df['시도코드'] == sido_code]\n",
    "\n",
    "    # 1. 복부 둘레 증가\n",
    "    filt1 = ((df['허리둘레'] >= 101) & (df['성별코드'] == 1)) | ((df['허리둘레'] >= 88) & (df['성별코드'] == 2))\n",
    "    df.loc[filt1]\n",
    "\n",
    "    # 2. 중성 지방 수치 상승\n",
    "    filt2 = (df['트리글리세라이드'] >= 150)\n",
    "    df.loc[filt2]\n",
    "\n",
    "    # 3. HDL-콜레스테롤 감소\n",
    "    filt3 = ((df['HDL콜레스테롤'] < 40) & (df['성별코드'] == 1) | (df['HDL콜레스테롤'] < 50) & (df['성별코드'] == 2))\n",
    "    df.loc[filt3]\n",
    "\n",
    "    # 4. 혈당 상승\n",
    "    filt4 = (df['식전혈당(공복혈당)'] >= 100)\n",
    "    df.loc[filt4]\n",
    "\n",
    "    # 5. 혈압 상승\n",
    "    filt5 = ((df['수축기혈압'] >= 130) | (df['이완기혈압'] >= 85))\n",
    "    df.loc[filt5]\n",
    "\n",
    "    # 3개 이상의 조건 만족\n",
    "    filt_combined = (filt1.astype(int) + filt2.astype(int) + filt3.astype(int) + filt4.astype(int) + filt5.astype(int)) >= 3\n",
    "\n",
    "        # 조건을 충족하는 행을 추출\n",
    "    result_data = data_for_sido[filt_combined]\n",
    "\n",
    "    # 조건을 충족하는 행의 개수를 계산\n",
    "    count = result_data.shape[0]\n",
    "    \n",
    "    # 결과를 딕셔너리에 저장\n",
    "    sido_dic[sido_code] = count\n",
    "    \n",
    "\n",
    "# 결과\n",
    "for sido_code, count in sido_dic.items():\n",
    "    print(f\"시도 코드 {sido_code}: 대사증후군 조건 충족 행의 개수 = {count}\")\n",
    "\n",
    "    \n",
    "result_df = df.loc[filt_combined]\n",
    "# row_count = result_df.shape[0]\n",
    "result_dic[2013] = sido_dic;\n",
    "\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a53f7417",
   "metadata": {},
   "source": [
    "## 2014년도 대사증후군 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6be464da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "시도 코드 42: 대사증후군 조건 충족 행의 개수 = 5315\n",
      "시도 코드 41: 대사증후군 조건 충족 행의 개수 = 33338\n",
      "시도 코드 44: 대사증후군 조건 충족 행의 개수 = 7399\n",
      "시도 코드 47: 대사증후군 조건 충족 행의 개수 = 9127\n",
      "시도 코드 45: 대사증후군 조건 충족 행의 개수 = 6365\n",
      "시도 코드 26: 대사증후군 조건 충족 행의 개수 = 9666\n",
      "시도 코드 46: 대사증후군 조건 충족 행의 개수 = 7267\n",
      "시도 코드 29: 대사증후군 조건 충족 행의 개수 = 4025\n",
      "시도 코드 48: 대사증후군 조건 충족 행의 개수 = 9683\n",
      "시도 코드 43: 대사증후군 조건 충족 행의 개수 = 5260\n",
      "시도 코드 11: 대사증후군 조건 충족 행의 개수 = 24280\n",
      "시도 코드 30: 대사증후군 조건 충족 행의 개수 = 4468\n",
      "시도 코드 28: 대사증후군 조건 충족 행의 개수 = 8827\n",
      "시도 코드 31: 대사증후군 조건 충족 행의 개수 = 3504\n",
      "시도 코드 27: 대사증후군 조건 충족 행의 개수 = 6465\n",
      "시도 코드 36: 대사증후군 조건 충족 행의 개수 = 375\n",
      "시도 코드 49: 대사증후군 조건 충족 행의 개수 = 1609\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/3898266739.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>기준년도</th>\n",
       "      <th>가입자일련번호</th>\n",
       "      <th>성별코드</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "      <th>시도코드</th>\n",
       "      <th>신장(5Cm단위)</th>\n",
       "      <th>체중(5Kg 단위)</th>\n",
       "      <th>허리둘레</th>\n",
       "      <th>시력(좌)</th>\n",
       "      <th>시력(우)</th>\n",
       "      <th>...</th>\n",
       "      <th>혈청크레아티닌</th>\n",
       "      <th>(혈청지오티)AST</th>\n",
       "      <th>(혈청지오티)ALT</th>\n",
       "      <th>감마지티피</th>\n",
       "      <th>흡연상태</th>\n",
       "      <th>음주여부</th>\n",
       "      <th>구강검진 수검여부</th>\n",
       "      <th>치아우식증유무</th>\n",
       "      <th>치석유무</th>\n",
       "      <th>데이터 기준일자</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>165</td>\n",
       "      <td>80</td>\n",
       "      <td>104</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>19</td>\n",
       "      <td>14</td>\n",
       "      <td>21</td>\n",
       "      <td>1.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "      <td>145</td>\n",
       "      <td>55</td>\n",
       "      <td>93</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "      <td>175</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>150</td>\n",
       "      <td>65</td>\n",
       "      <td>95</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>24</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>41</td>\n",
       "      <td>155</td>\n",
       "      <td>60</td>\n",
       "      <td>86</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>26</td>\n",
       "      <td>155</td>\n",
       "      <td>70</td>\n",
       "      <td>84</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>37</td>\n",
       "      <td>36</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999970</th>\n",
       "      <td>2014</td>\n",
       "      <td>999971</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>43</td>\n",
       "      <td>170</td>\n",
       "      <td>75</td>\n",
       "      <td>85</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1</td>\n",
       "      <td>19</td>\n",
       "      <td>11</td>\n",
       "      <td>27</td>\n",
       "      <td>3.0</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999974</th>\n",
       "      <td>2014</td>\n",
       "      <td>999975</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>43</td>\n",
       "      <td>180</td>\n",
       "      <td>70</td>\n",
       "      <td>93</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>90</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999977</th>\n",
       "      <td>2014</td>\n",
       "      <td>999978</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>170</td>\n",
       "      <td>70</td>\n",
       "      <td>80</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999984</th>\n",
       "      <td>2014</td>\n",
       "      <td>999985</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>36</td>\n",
       "      <td>165</td>\n",
       "      <td>70</td>\n",
       "      <td>82</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999985</th>\n",
       "      <td>2014</td>\n",
       "      <td>999986</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>53</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>146973 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        기준년도  가입자일련번호  성별코드  연령대코드(5세단위)  시도코드  신장(5Cm단위)  체중(5Kg 단위)  허리둘레  \\\n",
       "3       2014        4     2           11    41        165          80   104   \n",
       "7       2014        8     2           11    41        145          55    93   \n",
       "26      2014       27     2           18    11        150          65    95   \n",
       "27      2014       28     2           18    41        155          60    86   \n",
       "29      2014       30     2           11    26        155          70    84   \n",
       "...      ...      ...   ...          ...   ...        ...         ...   ...   \n",
       "999970  2014   999971     1           11    43        170          75    85   \n",
       "999974  2014   999975     1            8    43        180          70    93   \n",
       "999977  2014   999978     1            7    11        170          70    80   \n",
       "999984  2014   999985     2           10    36        165          70    82   \n",
       "999985  2014   999986     1            8    41        180          80    90   \n",
       "\n",
       "        시력(좌)  시력(우)  ...  혈청크레아티닌  (혈청지오티)AST  (혈청지오티)ALT  감마지티피  흡연상태  음주여부  \\\n",
       "3         0.8    0.2  ...      0.9          19          14     21   1.0     N   \n",
       "7         0.9    0.9  ...      1.2          34          16    175   1.0     Y   \n",
       "26        0.4    0.3  ...      0.9          24          13     26   1.0     Y   \n",
       "27        0.6    0.6  ...      1.1          18          11     13   1.0     Y   \n",
       "29        0.7    0.7  ...      0.9          37          36     23   1.0     Y   \n",
       "...       ...    ...  ...      ...         ...         ...    ...   ...   ...   \n",
       "999970    0.5    1.0  ...      1.1          19          11     27   3.0     N   \n",
       "999974    1.2    0.9  ...      0.9          23          23     90   3.0     Y   \n",
       "999977    0.5    0.8  ...      0.8          17          15     17   1.0     Y   \n",
       "999984    1.2    0.7  ...      1.0          20          20     16   1.0     Y   \n",
       "999985    1.0    0.9  ...      1.1          14          18     53   3.0     Y   \n",
       "\n",
       "        구강검진 수검여부  치아우식증유무  치석유무    데이터 기준일자  \n",
       "3               Y        N     Y  2016-12-20  \n",
       "7               N      NaN   NaN  2016-12-20  \n",
       "26              Y      NaN     N  2016-12-20  \n",
       "27              N      NaN   NaN  2016-12-20  \n",
       "29              N      NaN   NaN  2016-12-20  \n",
       "...           ...      ...   ...         ...  \n",
       "999970          N      NaN   NaN  2016-12-20  \n",
       "999974          N      NaN   NaN  2016-12-20  \n",
       "999977          N      NaN   NaN  2016-12-20  \n",
       "999984          N      NaN   NaN  2016-12-20  \n",
       "999985          N      NaN   NaN  2016-12-20  \n",
       "\n",
       "[146973 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = '2014'\n",
    "\n",
    "df = pd.read_csv(f'NHIS{year}.CSV', encoding='CP949')\n",
    "df.head(100)\n",
    "\n",
    "unique_sido_codes = df['시도코드'].unique()\n",
    "sido_dic = {}\n",
    "\n",
    "for sido_code in unique_sido_codes:\n",
    "    data_for_sido = df[df['시도코드'] == sido_code]\n",
    "\n",
    "    # 1. 복부 둘레 증가\n",
    "    filt1 = ((df['허리둘레'] >= 101) & (df['성별코드'] == 1)) | ((df['허리둘레'] >= 88) & (df['성별코드'] == 2))\n",
    "    df.loc[filt1]\n",
    "\n",
    "    # 2. 중성 지방 수치 상승\n",
    "    filt2 = (df['트리글리세라이드'] >= 150)\n",
    "    df.loc[filt2]\n",
    "\n",
    "    # 3. HDL-콜레스테롤 감소\n",
    "    filt3 = ((df['HDL콜레스테롤'] < 40) & (df['성별코드'] == 1) | (df['HDL콜레스테롤'] < 50) & (df['성별코드'] == 2))\n",
    "    df.loc[filt3]\n",
    "\n",
    "    # 4. 혈당 상승\n",
    "    filt4 = (df['식전혈당(공복혈당)'] >= 100)\n",
    "    df.loc[filt4]\n",
    "\n",
    "    # 5. 혈압 상승\n",
    "    filt5 = ((df['수축기혈압'] >= 130) | (df['이완기혈압'] >= 85))\n",
    "    df.loc[filt5]\n",
    "\n",
    "    # 3개 이상의 조건 만족\n",
    "    filt_combined = (filt1.astype(int) + filt2.astype(int) + filt3.astype(int) + filt4.astype(int) + filt5.astype(int)) >= 3\n",
    "\n",
    "        # 조건을 충족하는 행을 추출\n",
    "    result_data = data_for_sido[filt_combined]\n",
    "\n",
    "    # 조건을 충족하는 행의 개수를 계산\n",
    "    count = result_data.shape[0]\n",
    "    \n",
    "    # 결과를 딕셔너리에 저장\n",
    "    sido_dic[sido_code] = count\n",
    "    \n",
    "\n",
    "# 결과\n",
    "for sido_code, count in sido_dic.items():\n",
    "    print(f\"시도 코드 {sido_code}: 대사증후군 조건 충족 행의 개수 = {count}\")\n",
    "\n",
    "    \n",
    "result_df = df.loc[filt_combined]\n",
    "# row_count = result_df.shape[0]\n",
    "result_dic[2014] = sido_dic;\n",
    "\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "853cf1fa",
   "metadata": {},
   "source": [
    "## 2015년도 대사증후군 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2e371890",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "시도 코드 45: 대사증후군 조건 충족 행의 개수 = 6556\n",
      "시도 코드 11: 대사증후군 조건 충족 행의 개수 = 25938\n",
      "시도 코드 26: 대사증후군 조건 충족 행의 개수 = 9978\n",
      "시도 코드 41: 대사증후군 조건 충족 행의 개수 = 35123\n",
      "시도 코드 47: 대사증후군 조건 충족 행의 개수 = 8777\n",
      "시도 코드 44: 대사증후군 조건 충족 행의 개수 = 7249\n",
      "시도 코드 43: 대사증후군 조건 충족 행의 개수 = 5242\n",
      "시도 코드 46: 대사증후군 조건 충족 행의 개수 = 7367\n",
      "시도 코드 30: 대사증후군 조건 충족 행의 개수 = 4462\n",
      "시도 코드 48: 대사증후군 조건 충족 행의 개수 = 9631\n",
      "시도 코드 49: 대사증후군 조건 충족 행의 개수 = 1564\n",
      "시도 코드 28: 대사증후군 조건 충족 행의 개수 = 9056\n",
      "시도 코드 29: 대사증후군 조건 충족 행의 개수 = 4232\n",
      "시도 코드 27: 대사증후군 조건 충족 행의 개수 = 6746\n",
      "시도 코드 42: 대사증후군 조건 충족 행의 개수 = 5461\n",
      "시도 코드 31: 대사증후군 조건 충족 행의 개수 = 3726\n",
      "시도 코드 36: 대사증후군 조건 충족 행의 개수 = 409\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/1370111115.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>기준년도</th>\n",
       "      <th>가입자일련번호</th>\n",
       "      <th>성별코드</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "      <th>시도코드</th>\n",
       "      <th>신장(5Cm단위)</th>\n",
       "      <th>체중(5Kg 단위)</th>\n",
       "      <th>허리둘레</th>\n",
       "      <th>시력(좌)</th>\n",
       "      <th>시력(우)</th>\n",
       "      <th>...</th>\n",
       "      <th>혈청크레아티닌</th>\n",
       "      <th>(혈청지오티)AST</th>\n",
       "      <th>(혈청지오티)ALT</th>\n",
       "      <th>감마지티피</th>\n",
       "      <th>흡연상태</th>\n",
       "      <th>음주여부</th>\n",
       "      <th>구강검진 수검여부</th>\n",
       "      <th>치아우식증유무</th>\n",
       "      <th>치석유무</th>\n",
       "      <th>데이터 기준일자</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>45</td>\n",
       "      <td>155</td>\n",
       "      <td>60</td>\n",
       "      <td>88</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
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       "      <th>1</th>\n",
       "      <td>2015</td>\n",
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       "      <td>15</td>\n",
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       "      <td>155</td>\n",
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       "      <td>0.6</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
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       "      <td>2016-12-20</td>\n",
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       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "      <td>175</td>\n",
       "      <td>75</td>\n",
       "      <td>89</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>30</td>\n",
       "      <td>160</td>\n",
       "      <td>60</td>\n",
       "      <td>85</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1</td>\n",
       "      <td>28</td>\n",
       "      <td>20</td>\n",
       "      <td>43</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>2016-12-20</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "      <td>150</td>\n",
       "      <td>55</td>\n",
       "      <td>82</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>45</td>\n",
       "      <td>37</td>\n",
       "      <td>69</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>999882</th>\n",
       "      <td>2015</td>\n",
       "      <td>999883</td>\n",
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       "      <td>165</td>\n",
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       "      <td>0.8</td>\n",
       "      <td>31</td>\n",
       "      <td>18</td>\n",
       "      <td>59</td>\n",
       "      <td>3.0</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>2016-12-20</td>\n",
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       "    <tr>\n",
       "      <th>999883</th>\n",
       "      <td>2015</td>\n",
       "      <td>999884</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>165</td>\n",
       "      <td>60</td>\n",
       "      <td>74</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.9</td>\n",
       "      <td>31</td>\n",
       "      <td>41</td>\n",
       "      <td>22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999891</th>\n",
       "      <td>2015</td>\n",
       "      <td>999892</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>41</td>\n",
       "      <td>180</td>\n",
       "      <td>105</td>\n",
       "      <td>99</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>57</td>\n",
       "      <td>55</td>\n",
       "      <td>139</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999966</th>\n",
       "      <td>2015</td>\n",
       "      <td>999967</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>180</td>\n",
       "      <td>85</td>\n",
       "      <td>87</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>58</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999984</th>\n",
       "      <td>2015</td>\n",
       "      <td>999985</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>47</td>\n",
       "      <td>170</td>\n",
       "      <td>75</td>\n",
       "      <td>90</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>0.8</td>\n",
       "      <td>24</td>\n",
       "      <td>52</td>\n",
       "      <td>43</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-12-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151517 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        기준년도  가입자일련번호  성별코드  연령대코드(5세단위)  시도코드  신장(5Cm단위)  체중(5Kg 단위)  허리둘레  \\\n",
       "0       2015        1     2           15    45        155          60    88   \n",
       "1       2015        2     2           15    11        155          65    98   \n",
       "3       2015        4     1            9    41        175          75    89   \n",
       "13      2015       14     1           18    30        160          60    85   \n",
       "14      2015       15     2           18    46        150          55    82   \n",
       "...      ...      ...   ...          ...   ...        ...         ...   ...   \n",
       "999882  2015   999883     1           13    44        165          55    75   \n",
       "999883  2015   999884     1            8    41        165          60    74   \n",
       "999891  2015   999892     1            7    41        180         105    99   \n",
       "999966  2015   999967     1            6    43        180          85    87   \n",
       "999984  2015   999985     1            8    47        170          75    90   \n",
       "\n",
       "        시력(좌)  시력(우)  ...  혈청크레아티닌  (혈청지오티)AST  (혈청지오티)ALT  감마지티피  흡연상태  음주여부  \\\n",
       "0         0.6    0.7  ...      1.1          17          28     23   1.0     Y   \n",
       "1         1.2    0.6  ...      1.0          25          30     41   1.0     Y   \n",
       "3         0.2    0.2  ...      0.9          28          40     50   1.0     Y   \n",
       "13        0.7    0.6  ...      1.1          28          20     43   3.0     Y   \n",
       "14        0.8    0.5  ...      0.8          45          37     69   1.0     Y   \n",
       "...       ...    ...  ...      ...         ...         ...    ...   ...   ...   \n",
       "999882    0.3    0.5  ...      0.8          31          18     59   3.0     N   \n",
       "999883    1.2    1.0  ...      0.9          31          41     22   1.0     Y   \n",
       "999891    1.2    1.5  ...      1.2          57          55    139   3.0     Y   \n",
       "999966    1.5    0.8  ...      1.1          22          24     58   1.0     Y   \n",
       "999984    0.8    0.8  ...      0.8          24          52     43   3.0     Y   \n",
       "\n",
       "        구강검진 수검여부  치아우식증유무  치석유무    데이터 기준일자  \n",
       "0               N      NaN   NaN  2016-12-20  \n",
       "1               Y      NaN     N  2016-12-20  \n",
       "3               Y        N   NaN  2016-12-20  \n",
       "13              Y      NaN     N  2016-12-20  \n",
       "14              N      NaN   NaN  2016-12-20  \n",
       "...           ...      ...   ...         ...  \n",
       "999882          Y      NaN     Y  2016-12-20  \n",
       "999883          N      NaN   NaN  2016-12-20  \n",
       "999891          N      NaN   NaN  2016-12-20  \n",
       "999966          Y      NaN   NaN  2016-12-20  \n",
       "999984          Y      NaN   NaN  2016-12-20  \n",
       "\n",
       "[151517 rows x 31 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = '2015'\n",
    "\n",
    "df = pd.read_csv(f'NHIS{year}.CSV', encoding='CP949')\n",
    "df.head(100)\n",
    "\n",
    "unique_sido_codes = df['시도코드'].unique()\n",
    "sido_dic = {}\n",
    "\n",
    "for sido_code in unique_sido_codes:\n",
    "    data_for_sido = df[df['시도코드'] == sido_code]\n",
    "\n",
    "    # 1. 복부 둘레 증가\n",
    "    filt1 = ((df['허리둘레'] >= 101) & (df['성별코드'] == 1)) | ((df['허리둘레'] >= 88) & (df['성별코드'] == 2))\n",
    "    df.loc[filt1]\n",
    "\n",
    "    # 2. 중성 지방 수치 상승\n",
    "    filt2 = (df['트리글리세라이드'] >= 150)\n",
    "    df.loc[filt2]\n",
    "\n",
    "    # 3. HDL-콜레스테롤 감소\n",
    "    filt3 = ((df['HDL콜레스테롤'] < 40) & (df['성별코드'] == 1) | (df['HDL콜레스테롤'] < 50) & (df['성별코드'] == 2))\n",
    "    df.loc[filt3]\n",
    "\n",
    "    # 4. 혈당 상승\n",
    "    filt4 = (df['식전혈당(공복혈당)'] >= 100)\n",
    "    df.loc[filt4]\n",
    "\n",
    "    # 5. 혈압 상승\n",
    "    filt5 = ((df['수축기혈압'] >= 130) | (df['이완기혈압'] >= 85))\n",
    "    df.loc[filt5]\n",
    "\n",
    "    # 3개 이상의 조건 만족\n",
    "    filt_combined = (filt1.astype(int) + filt2.astype(int) + filt3.astype(int) + filt4.astype(int) + filt5.astype(int)) >= 3\n",
    "\n",
    "        # 조건을 충족하는 행을 추출\n",
    "    result_data = data_for_sido[filt_combined]\n",
    "\n",
    "    # 조건을 충족하는 행의 개수를 계산\n",
    "    count = result_data.shape[0]\n",
    "    \n",
    "    # 결과를 딕셔너리에 저장\n",
    "    sido_dic[sido_code] = count\n",
    "    \n",
    "\n",
    "# 결과\n",
    "for sido_code, count in sido_dic.items():\n",
    "    print(f\"시도 코드 {sido_code}: 대사증후군 조건 충족 행의 개수 = {count}\")\n",
    "\n",
    "    \n",
    "result_df = df.loc[filt_combined]\n",
    "# row_count = result_df.shape[0]\n",
    "result_dic[2015] = sido_dic;\n",
    "\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2784227",
   "metadata": {},
   "source": [
    "## 2016년도 대사증후군 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e46e5e02",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "시도 코드 27: 대사증후군 조건 충족 행의 개수 = 7165\n",
      "시도 코드 41: 대사증후군 조건 충족 행의 개수 = 37027\n",
      "시도 코드 43: 대사증후군 조건 충족 행의 개수 = 5296\n",
      "시도 코드 48: 대사증후군 조건 충족 행의 개수 = 9599\n",
      "시도 코드 11: 대사증후군 조건 충족 행의 개수 = 26172\n",
      "시도 코드 46: 대사증후군 조건 충족 행의 개수 = 7578\n",
      "시도 코드 31: 대사증후군 조건 충족 행의 개수 = 3904\n",
      "시도 코드 28: 대사증후군 조건 충족 행의 개수 = 9371\n",
      "시도 코드 26: 대사증후군 조건 충족 행의 개수 = 9687\n",
      "시도 코드 30: 대사증후군 조건 충족 행의 개수 = 4391\n",
      "시도 코드 42: 대사증후군 조건 충족 행의 개수 = 5572\n",
      "시도 코드 29: 대사증후군 조건 충족 행의 개수 = 4435\n",
      "시도 코드 44: 대사증후군 조건 충족 행의 개수 = 7498\n",
      "시도 코드 45: 대사증후군 조건 충족 행의 개수 = 6840\n",
      "시도 코드 47: 대사증후군 조건 충족 행의 개수 = 8933\n",
      "시도 코드 49: 대사증후군 조건 충족 행의 개수 = 1752\n",
      "시도 코드 36: 대사증후군 조건 충족 행의 개수 = 640\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_46316/2726883083.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>기준년도</th>\n",
       "      <th>가입자일련번호</th>\n",
       "      <th>성별코드</th>\n",
       "      <th>연령대코드(5세단위)</th>\n",
       "      <th>시도코드</th>\n",
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       "      <th>체중(5Kg단위)</th>\n",
       "      <th>허리둘레</th>\n",
       "      <th>시력(좌)</th>\n",
       "      <th>시력(우)</th>\n",
       "      <th>...</th>\n",
       "      <th>감마지티피</th>\n",
       "      <th>흡연상태</th>\n",
       "      <th>음주여부</th>\n",
       "      <th>구강검진 수검여부</th>\n",
       "      <th>치아우식증유무</th>\n",
       "      <th>결손치유무</th>\n",
       "      <th>치아마모증유무</th>\n",
       "      <th>제3대구치(사랑니)이상</th>\n",
       "      <th>치석</th>\n",
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       "      <td>2016</td>\n",
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       "      <td>65.0</td>\n",
       "      <td>85.0</td>\n",
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       "      <td>11</td>\n",
       "      <td>170.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
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       "      <td>2016</td>\n",
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       "      <td>27</td>\n",
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       "      <td>2017-12-19</td>\n",
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       "<p>155860 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        기준년도  가입자일련번호  성별코드  연령대코드(5세단위)  시도코드  신장(5Cm단위)  체중(5Kg단위)  허리둘레  \\\n",
       "1       2016        2     2           11    41      160.0       65.0  85.0   \n",
       "7       2016        8     2           16    48      155.0       60.0  81.0   \n",
       "24      2016       25     1           13    11      170.0       70.0  92.0   \n",
       "26      2016       27     1           16    27      165.0       65.0  95.0   \n",
       "30      2016       31     1            9    11      175.0       85.0  97.0   \n",
       "...      ...      ...   ...          ...   ...        ...        ...   ...   \n",
       "999975  2016   999976     1           13    26      170.0       75.0  85.0   \n",
       "999983  2016   999984     1           11    11      175.0       80.0  83.0   \n",
       "999988  2016   999989     1            9    45      170.0       80.0  90.0   \n",
       "999990  2016   999991     2           11    41      150.0       75.0  99.0   \n",
       "999995  2016   999996     1           10    42      170.0       75.0  86.0   \n",
       "\n",
       "        시력(좌)  시력(우)  ...  감마지티피  흡연상태  음주여부  구강검진 수검여부  치아우식증유무  결손치유무  \\\n",
       "1         1.5    1.2  ...   19.0   1.0   NaN          N      NaN    NaN   \n",
       "7         0.7    0.5  ...   14.0   1.0   NaN          N      NaN    NaN   \n",
       "24        0.9    1.2  ...   61.0   2.0   NaN          N      NaN    NaN   \n",
       "26        0.8    0.8  ...   14.0   1.0   NaN          N      NaN    NaN   \n",
       "30        1.2    1.2  ...  105.0   2.0   NaN          N      NaN    NaN   \n",
       "...       ...    ...  ...    ...   ...   ...        ...      ...    ...   \n",
       "999975    1.0    1.2  ...   21.0   1.0   NaN          N      NaN    NaN   \n",
       "999983    1.0    1.2  ...   25.0   1.0   NaN          Y      NaN    NaN   \n",
       "999988    2.0    1.5  ...   52.0   3.0   NaN          N      NaN    NaN   \n",
       "999990    0.5    0.5  ...   31.0   1.0   NaN          N      NaN    NaN   \n",
       "999995    0.8    0.8  ...   44.0   3.0   NaN          Y      NaN    NaN   \n",
       "\n",
       "        치아마모증유무  제3대구치(사랑니)이상   치석     데이터공개일자  \n",
       "1           NaN           NaN  NaN  2017-12-19  \n",
       "7           NaN           NaN  NaN  2017-12-19  \n",
       "24          NaN           NaN  NaN  2017-12-19  \n",
       "26          NaN           NaN  NaN  2017-12-19  \n",
       "30          NaN           NaN  NaN  2017-12-19  \n",
       "...         ...           ...  ...         ...  \n",
       "999975      NaN           NaN  NaN  2017-12-19  \n",
       "999983      NaN           NaN    Y  2017-12-19  \n",
       "999988      NaN           NaN  NaN  2017-12-19  \n",
       "999990      NaN           NaN  NaN  2017-12-19  \n",
       "999995      NaN           NaN  NaN  2017-12-19  \n",
       "\n",
       "[155860 rows x 34 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = '2016'\n",
    "\n",
    "df = pd.read_csv(f'NHIS{year}.CSV', encoding='CP949')\n",
    "df.head(100)\n",
    "\n",
    "unique_sido_codes = df['시도코드'].unique()\n",
    "sido_dic = {}\n",
    "\n",
    "for sido_code in unique_sido_codes:\n",
    "    data_for_sido = df[df['시도코드'] == sido_code]\n",
    "\n",
    "    # 1. 복부 둘레 증가\n",
    "    filt1 = ((df['허리둘레'] >= 101) & (df['성별코드'] == 1)) | ((df['허리둘레'] >= 88) & (df['성별코드'] == 2))\n",
    "    df.loc[filt1]\n",
    "\n",
    "    # 2. 중성 지방 수치 상승\n",
    "    filt2 = (df['트리글리세라이드'] >= 150)\n",
    "    df.loc[filt2]\n",
    "\n",
    "    # 3. HDL-콜레스테롤 감소\n",
    "    filt3 = ((df['HDL콜레스테롤'] < 40) & (df['성별코드'] == 1) | (df['HDL콜레스테롤'] < 50) & (df['성별코드'] == 2))\n",
    "    df.loc[filt3]\n",
    "\n",
    "    # 4. 혈당 상승\n",
    "    filt4 = (df['식전혈당(공복혈당)'] >= 100)\n",
    "    df.loc[filt4]\n",
    "\n",
    "    # 5. 혈압 상승\n",
    "    filt5 = ((df['수축기혈압'] >= 130) | (df['이완기혈압'] >= 85))\n",
    "    df.loc[filt5]\n",
    "\n",
    "    # 3개 이상의 조건 만족\n",
    "    filt_combined = (filt1.astype(int) + filt2.astype(int) + filt3.astype(int) + filt4.astype(int) + filt5.astype(int)) >= 3\n",
    "\n",
    "        # 조건을 충족하는 행을 추출\n",
    "    result_data = data_for_sido[filt_combined]\n",
    "\n",
    "    # 조건을 충족하는 행의 개수를 계산\n",
    "    count = result_data.shape[0]\n",
    "    \n",
    "    # 결과를 딕셔너리에 저장\n",
    "    sido_dic[sido_code] = count\n",
    "    \n",
    "\n",
    "# 결과\n",
    "for sido_code, count in sido_dic.items():\n",
    "    print(f\"시도 코드 {sido_code}: 대사증후군 조건 충족 행의 개수 = {count}\")\n",
    "\n",
    "    \n",
    "result_df = df.loc[filt_combined]\n",
    "# row_count = result_df.shape[0]\n",
    "result_dic[2016] = sido_dic;\n",
    "\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b64418da",
   "metadata": {},
   "source": [
    "## 2017년도 대사증후군 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7b0e5abf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n",
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "시도 코드 43: 대사증후군 조건 충족 행의 개수 = 5665\n",
      "시도 코드 11: 대사증후군 조건 충족 행의 개수 = 26234\n",
      "시도 코드 41: 대사증후군 조건 충족 행의 개수 = 38374\n",
      "시도 코드 48: 대사증후군 조건 충족 행의 개수 = 9586\n",
      "시도 코드 30: 대사증후군 조건 충족 행의 개수 = 4568\n",
      "시도 코드 27: 대사증후군 조건 충족 행의 개수 = 6830\n",
      "시도 코드 47: 대사증후군 조건 충족 행의 개수 = 8948\n",
      "시도 코드 42: 대사증후군 조건 충족 행의 개수 = 5522\n",
      "시도 코드 28: 대사증후군 조건 충족 행의 개수 = 9383\n",
      "시도 코드 44: 대사증후군 조건 충족 행의 개수 = 7690\n",
      "시도 코드 45: 대사증후군 조건 충족 행의 개수 = 6425\n",
      "시도 코드 26: 대사증후군 조건 충족 행의 개수 = 9522\n",
      "시도 코드 29: 대사증후군 조건 충족 행의 개수 = 4305\n",
      "시도 코드 31: 대사증후군 조건 충족 행의 개수 = 3800\n",
      "시도 코드 46: 대사증후군 조건 충족 행의 개수 = 7495\n",
      "시도 코드 36: 대사증후군 조건 충족 행의 개수 = 625\n",
      "시도 코드 50: 대사증후군 조건 충족 행의 개수 = 1643\n",
      "156615\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/45/ys98fp91091_09_qm35k9bm80000gn/T/ipykernel_50793/1462688573.py:36: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  result_data = data_for_sido[filt_combined]\n"
     ]
    },
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       "      <th>기준년도</th>\n",
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       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-11-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156615 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        기준년도  가입자일련번호  성별코드  연령대코드(5세단위)  시도코드  신장(5Cm단위)  체중(5Kg단위)  허리둘레  \\\n",
       "12      2017       13     2           12    41        150         55  72.3   \n",
       "19      2017       20     1           13    44        165         60  85.0   \n",
       "22      2017       23     2           10    45        155         50  62.0   \n",
       "37      2017       38     2           13    42        150         50  77.0   \n",
       "41      2017       42     2            9    41        160         55  69.0   \n",
       "...      ...      ...   ...          ...   ...        ...        ...   ...   \n",
       "999969  2017   999970     2           12    47        160         70  91.2   \n",
       "999973  2017   999974     2           13    11        155         55  70.0   \n",
       "999974  2017   999975     2           13    11        155         50  69.5   \n",
       "999977  2017   999978     1            8    27        175         85  96.0   \n",
       "999999  2017  1000000     1           11    41        160         70  90.5   \n",
       "\n",
       "        시력(좌)  시력(우)  ...  감마지티피  흡연상태  음주여부  구강검진수검여부  치아우식증유무  결손치유무  \\\n",
       "12        1.2    0.9  ...   31.0   1.0     N         Y      NaN    NaN   \n",
       "19        0.3    0.7  ...   48.0   3.0     Y         N      NaN    NaN   \n",
       "22        0.5    0.7  ...   60.0   1.0     N         N      NaN    NaN   \n",
       "37        1.2    1.2  ...   48.0   1.0     N         N      NaN    NaN   \n",
       "41        0.9    1.2  ...   23.0   1.0     N         N      NaN    NaN   \n",
       "...       ...    ...  ...    ...   ...   ...       ...      ...    ...   \n",
       "999969    1.2    1.0  ...   25.0   1.0     N         N      NaN    NaN   \n",
       "999973    1.2    1.0  ...   19.0   1.0     N         N      NaN    NaN   \n",
       "999974    0.9    0.9  ...   25.0   1.0     N         N      NaN    NaN   \n",
       "999977    1.0    1.2  ...   37.0   1.0     Y         Y      NaN    NaN   \n",
       "999999    1.0    1.5  ...   36.0   3.0     Y         N      NaN    NaN   \n",
       "\n",
       "        치아마모증유무  제3대구치(사랑니)이상   치석     데이터공개일자  \n",
       "12          NaN           NaN    Y  2018-11-26  \n",
       "19          NaN           NaN  NaN  2018-11-26  \n",
       "22          NaN           NaN  NaN  2018-11-26  \n",
       "37          NaN           NaN  NaN  2018-11-26  \n",
       "41          NaN           NaN  NaN  2018-11-26  \n",
       "...         ...           ...  ...         ...  \n",
       "999969      NaN           NaN  NaN  2018-11-26  \n",
       "999973      NaN           NaN  NaN  2018-11-26  \n",
       "999974      NaN           NaN  NaN  2018-11-26  \n",
       "999977      NaN           NaN    Y  2018-11-26  \n",
       "999999      NaN           NaN  NaN  2018-11-26  \n",
       "\n",
       "[156615 rows x 34 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = '2017'\n",
    "\n",
    "df = pd.read_csv(f'NHIS{year}.CSV', encoding='CP949')\n",
    "df.head(100)\n",
    "\n",
    "unique_sido_codes = df['시도코드'].unique()\n",
    "sido_dic = {}\n",
    "\n",
    "for sido_code in unique_sido_codes:\n",
    "    data_for_sido = df[df['시도코드'] == sido_code]\n",
    "\n",
    "    # 1. 복부 둘레 증가\n",
    "    filt1 = ((df['허리둘레'] >= 101) & (df['성별코드'] == 1)) | ((df['허리둘레'] >= 88) & (df['성별코드'] == 2))\n",
    "    df.loc[filt1]\n",
    "\n",
    "    # 2. 중성 지방 수치 상승\n",
    "    filt2 = (df['트리글리세라이드'] >= 150)\n",
    "    df.loc[filt2]\n",
    "\n",
    "    # 3. HDL-콜레스테롤 감소\n",
    "    filt3 = ((df['HDL콜레스테롤'] < 40) & (df['성별코드'] == 1) | (df['HDL콜레스테롤'] < 50) & (df['성별코드'] == 2))\n",
    "    df.loc[filt3]\n",
    "\n",
    "    # 4. 혈당 상승\n",
    "    filt4 = (df['식전혈당(공복혈당)'] >= 100)\n",
    "    df.loc[filt4]\n",
    "\n",
    "    # 5. 혈압 상승\n",
    "    filt5 = ((df['수축기혈압'] >= 130) | (df['이완기혈압'] >= 85))\n",
    "    df.loc[filt5]\n",
    "\n",
    "    # 3개 이상의 조건 만족\n",
    "    filt_combined = (filt1.astype(int) + filt2.astype(int) + filt3.astype(int) + filt4.astype(int) + filt5.astype(int)) >= 3\n",
    "\n",
    "        # 조건을 충족하는 행을 추출\n",
    "    result_data = data_for_sido[filt_combined]\n",
    "\n",
    "    # 조건을 충족하는 행의 개수를 계산\n",
    "    count = result_data.shape[0]\n",
    "    \n",
    "    # 결과를 딕셔너리에 저장\n",
    "    sido_dic[sido_code] = count\n",
    "    \n",
    "res = 0;\n",
    "# 결과\n",
    "for sido_code, count in sido_dic.items():\n",
    "    print(f\"시도 코드 {sido_code}: 대사증후군 조건 충족 행의 개수 = {count}\")\n",
    "    res += count;\n",
    "    \n",
    "result_df = df.loc[filt_combined]\n",
    "# row_count = result_df.shape[0]\n",
    "result_dic[2017] = sido_dic;\n",
    "print(res);\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad6c7f59",
   "metadata": {},
   "source": [
    "## 결과: 대사증후군 증가율"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "08d73d5a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{2011: {47: 8682, 26: 9385, 27: 6122, 46: 7112, 31: 3705, 45: 6041, 41: 31536, 44: 7309, 29: 3712, 43: 5211, 11: 24507, 42: 5284, 30: 4058, 48: 9152, 49: 1311, 28: 7859}, 2017: {43: 5665, 11: 26234, 41: 38374, 48: 9586, 30: 4568, 27: 6830, 47: 8948, 42: 5522, 28: 9383, 44: 7690, 45: 6425, 26: 9522, 29: 4305, 31: 3800, 46: 7495, 36: 625, 50: 1643}}\n",
      "{11: '서울특별시', 26: '부산광역시', 27: '대구광역시', 28: '인천광역시', 29: '광주광역시', 30: '대전광역시', 31: '울산광역시', 36: '세종특별자치시', 41: '경기도', 42: '강원도', 43: '충청북도', 44: '충청남도', 45: '전라북도', 46: '전라남도', 47: '경상북도', 48: '경상남도', 49: '제주특별자치도'}\n"
     ]
    }
   ],
   "source": [
    "year_list = [2011, 2012, 2013, 2014, 2015, 2016, 2017]\n",
    "sido_name_dic = {\n",
    "    11: '서울특별시',\n",
    "    26: '부산광역시',\n",
    "    27: '대구광역시',\n",
    "    28: '인천광역시',\n",
    "    29: '광주광역시',\n",
    "    30: '대전광역시',\n",
    "    31: '울산광역시',\n",
    "    36: '세종특별자치시',\n",
    "    41: '경기도',\n",
    "    42: '강원도',\n",
    "    43: '충청북도',\n",
    "    44: '충청남도',\n",
    "    45: '전라북도',\n",
    "    46: '전라남도',\n",
    "    47: '경상북도',\n",
    "    48: '경상남도',\n",
    "    49: '제주특별자치도'\n",
    "}\n",
    "\n",
    "result_dic\n",
    "sido_dic\n",
    "sido_name_dic\n",
    "\n",
    "print(result_dic);\n",
    "print(sido_name_dic);\n",
    "\n",
    "# plt.figure(figsize=(10, 8))\n",
    "# plt.plot(year_list, result_list, marker='o')\n",
    "\n",
    "# for idx, txt in enumerate(result_list):\n",
    "#     plt.text(year_list[idx], result_list[idx] + 500, txt, ha='center')\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aa2bb96",
   "metadata": {},
   "source": [
    "## 결과: CSV저장"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d7ca4f2e",
   "metadata": {},
   "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>시도코드</th>\n",
       "      <th>시도이름</th>\n",
       "      <th>2011-2017대사증후군증가율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>7.046966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>부산광역시</td>\n",
       "      <td>1.459776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>대구광역시</td>\n",
       "      <td>11.564848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>인천광역시</td>\n",
       "      <td>19.391780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>광주광역시</td>\n",
       "      <td>15.975216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>대전광역시</td>\n",
       "      <td>12.567767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>31</td>\n",
       "      <td>울산광역시</td>\n",
       "      <td>2.564103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>36</td>\n",
       "      <td>세종특별자치시</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>41</td>\n",
       "      <td>경기도</td>\n",
       "      <td>21.683156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>강원도</td>\n",
       "      <td>4.504164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>43</td>\n",
       "      <td>충청북도</td>\n",
       "      <td>8.712339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>44</td>\n",
       "      <td>충청남도</td>\n",
       "      <td>5.212751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>45</td>\n",
       "      <td>전라북도</td>\n",
       "      <td>6.356563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>46</td>\n",
       "      <td>전라남도</td>\n",
       "      <td>5.385264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>47</td>\n",
       "      <td>경상북도</td>\n",
       "      <td>3.063810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>48</td>\n",
       "      <td>경상남도</td>\n",
       "      <td>4.742133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>49</td>\n",
       "      <td>제주특별자치도</td>\n",
       "      <td>-100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    시도코드     시도이름  2011-2017대사증후군증가율\n",
       "0     11    서울특별시           7.046966\n",
       "1     26    부산광역시           1.459776\n",
       "2     27    대구광역시          11.564848\n",
       "3     28    인천광역시          19.391780\n",
       "4     29    광주광역시          15.975216\n",
       "5     30    대전광역시          12.567767\n",
       "6     31    울산광역시           2.564103\n",
       "7     36  세종특별자치시           0.000000\n",
       "8     41      경기도          21.683156\n",
       "9     42      강원도           4.504164\n",
       "10    43     충청북도           8.712339\n",
       "11    44     충청남도           5.212751\n",
       "12    45     전라북도           6.356563\n",
       "13    46     전라남도           5.385264\n",
       "14    47     경상북도           3.063810\n",
       "15    48     경상남도           4.742133\n",
       "16    49  제주특별자치도        -100.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2011~2017 실행 후 실행시 연도,시도코드,시도이름,인원수 나옴\n",
    "# data = []\n",
    "# for year, year_data in result_dic.items():\n",
    "#     for sido_code, count in year_data.items():\n",
    "#         # sido_name_dic에서 sido_code 키가 존재하는지 확인\n",
    "#         if sido_code in sido_name_dic:\n",
    "#             sido_name = sido_name_dic[sido_code]\n",
    "#             data.append([year, sido_code, sido_name, count])\n",
    "\n",
    "# # 데이터프레임 생성\n",
    "# df = pd.DataFrame(data, columns=['연도', '시도코드', '시도이름', '인원'])\n",
    "\n",
    "# # 데이터프레임을 CSV 파일로 저장\n",
    "# df.to_csv('result_data.csv', index=False, encoding='utf-8')\n",
    "# df\n",
    "\n",
    "#ㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡㅡ\n",
    "\n",
    "# 2011~2017만 돌리고 실행시 시도코드, 시도이름, 확률 나옴\n",
    "data = []\n",
    "for sido_code, sido_name in sido_name_dic.items():\n",
    "    # 2011년과 2017년의 인원수\n",
    "    count_2011 = result_dic.get(2011, {}).get(sido_code, 0)\n",
    "    count_2017 = result_dic.get(2017, {}).get(sido_code, 0)\n",
    "    \n",
    "    # 퍼센트 계산\n",
    "    percent = ((count_2017 - count_2011) / count_2011) * 100 if count_2011 > 0 else 0\n",
    "    \n",
    "    data.append([sido_code, sido_name, percent])\n",
    "\n",
    "# 데이터프레임 생성\n",
    "df = pd.DataFrame(data, columns=['시도코드', '시도이름', '2011-2017대사증후군증가율'])\n",
    "\n",
    "# 데이터프레임을 CSV 파일로 저장\n",
    "df.to_csv('result_data.csv', index=False, encoding='utf-8')\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e16541a1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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