{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a4e89e1b",
   "metadata": {},
   "source": [
    "### 1. 패키지 설치하기와 로딩하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f7a925ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas            as pd  # 데이터 읽어오기/저장하기, 핸들링, 분석\n",
    "import matplotlib.pyplot as plt # 데이터 시각화(Data Visualization)\n",
    "import numpy             as np  # 수치 계산\n",
    "import seaborn           as sns # 데이터 시각화 : 고수준\n",
    "\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "# import 패키지 이름 as 애칭(별명)\n",
    "# 패키지를 메모리(RAM)에 올리는 기능 : 로딩하기(Loading)\n",
    "\n",
    "# 참고\n",
    "# 패키지 로딩하기는 사전에 패키지가 설치되어 있어야 함\n",
    "# pandas 패키지는 아나콘다가 이미 하드(HDD)에 설치했음\n",
    "# 패키지(package) : 함수들"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "71c1e977",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 그래프 한글 깨짐 해결\n",
    "plt.rcParams['font.family'] = 'Malgun Gothic'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7f6e676",
   "metadata": {},
   "source": [
    "### 2. 데이터 읽어오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "523ae51e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 파일위치/파일명.확장자\n",
    "file_path = r'D:\\NHIS\\Mental.xlsx'\n",
    "mental    = pd.read_excel(file_path, sheet_name = 0, header = 0)\n",
    "\n",
    "# 참고\n",
    "# pd : pandas 패키지\n",
    "# .  : 패키지의 함수를 불러옴(호출)\n",
    "# read_excel() : 함수\n",
    "# sheet_name, header : 함수의 파라미터(parameter)\n",
    "# sheet_name = 0    : 첫 번째 시트를 의미\n",
    "# header     = 0    : 첫 번째 행을 의미\n",
    "# =                 : 왼쪽 이름에 오른쪽의 작업을 저장하는 기능\n",
    "# file_path, mental : 파이썬 데이터의 이름, RAM에 저장됨"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e44af90d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LG\\AppData\\Local\\Temp\\ipykernel_15332\\3443638004.py:2: DtypeWarning: Columns (9,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  m20       = pd.read_csv(file_path, header = 0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RN_INDI</th>\n",
       "      <th>MDCARE_STRT_DT</th>\n",
       "      <th>RN_KEY</th>\n",
       "      <th>RN_INST</th>\n",
       "      <th>FORM_CD</th>\n",
       "      <th>MCARE_SUBJ_CD</th>\n",
       "      <th>SICK_SYM1</th>\n",
       "      <th>SICK_SYM2</th>\n",
       "      <th>HSPTZ_PATH_TYPE</th>\n",
       "      <th>OFIJ_TYPE</th>\n",
       "      <th>...</th>\n",
       "      <th>VSHSP_DD_CNT</th>\n",
       "      <th>TOT_PRSC_DD_CNT</th>\n",
       "      <th>MCARE_RSLT_TYPE</th>\n",
       "      <th>FST_HSPTZ_DT</th>\n",
       "      <th>EDC_ADD_RT</th>\n",
       "      <th>SPCF_SYM_TYPE</th>\n",
       "      <th>ED_RC_TOT_AMT</th>\n",
       "      <th>EDC_SBA</th>\n",
       "      <th>EDC_INSUR_BRDN_AMT</th>\n",
       "      <th>STD_YYYY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001347</td>\n",
       "      <td>20160128</td>\n",
       "      <td>2016010777797</td>\n",
       "      <td>53079</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>L239</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14160</td>\n",
       "      <td>1500</td>\n",
       "      <td>12660</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001347</td>\n",
       "      <td>20160114</td>\n",
       "      <td>2016012377871</td>\n",
       "      <td>55889</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>S134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14280</td>\n",
       "      <td>1500</td>\n",
       "      <td>12780</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001347</td>\n",
       "      <td>20160119</td>\n",
       "      <td>2016010890509</td>\n",
       "      <td>55889</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>S134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13980</td>\n",
       "      <td>1500</td>\n",
       "      <td>12480</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001347</td>\n",
       "      <td>20160127</td>\n",
       "      <td>2016010262074</td>\n",
       "      <td>55889</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>S134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13980</td>\n",
       "      <td>1500</td>\n",
       "      <td>12480</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001347</td>\n",
       "      <td>20160102</td>\n",
       "      <td>2016011681112</td>\n",
       "      <td>53079</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>L239</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14160</td>\n",
       "      <td>1500</td>\n",
       "      <td>12660</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RN_INDI  MDCARE_STRT_DT         RN_KEY  RN_INST  FORM_CD  MCARE_SUBJ_CD  \\\n",
       "0  1001347        20160128  2016010777797    53079        3             14   \n",
       "1  1001347        20160114  2016012377871    55889        3              5   \n",
       "2  1001347        20160119  2016010890509    55889        3              5   \n",
       "3  1001347        20160127  2016010262074    55889        3              5   \n",
       "4  1001347        20160102  2016011681112    53079        3             14   \n",
       "\n",
       "  SICK_SYM1 SICK_SYM2  HSPTZ_PATH_TYPE OFIJ_TYPE  ...  VSHSP_DD_CNT  \\\n",
       "0      L239       NaN              NaN         0  ...             1   \n",
       "1      S134       NaN              NaN         0  ...             1   \n",
       "2      S134       NaN              NaN         0  ...             1   \n",
       "3      S134       NaN              NaN         0  ...             1   \n",
       "4      L239       NaN              NaN         0  ...             1   \n",
       "\n",
       "   TOT_PRSC_DD_CNT  MCARE_RSLT_TYPE  FST_HSPTZ_DT  EDC_ADD_RT  SPCF_SYM_TYPE  \\\n",
       "0                0              1.0           NaN        0.15            NaN   \n",
       "1                0              1.0           NaN        0.15            NaN   \n",
       "2                0              1.0           NaN        0.15            NaN   \n",
       "3                0              1.0           NaN        0.15            NaN   \n",
       "4                0              1.0           NaN        0.15            NaN   \n",
       "\n",
       "   ED_RC_TOT_AMT EDC_SBA  EDC_INSUR_BRDN_AMT  STD_YYYY  \n",
       "0          14160    1500               12660      2016  \n",
       "1          14280    1500               12780      2016  \n",
       "2          13980    1500               12480      2016  \n",
       "3          13980    1500               12480      2016  \n",
       "4          14160    1500               12660      2016  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = \"D:/NHIS/NSC2_M20_1619.csv\"\n",
    "m20       = pd.read_csv(file_path, header = 0)\n",
    "m20.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75c4271e",
   "metadata": {},
   "source": [
    "### 3. 데이터 보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7fcacaaf",
   "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>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Schizophrenia_disorders</th>\n",
       "      <th>Depressive_disorders</th>\n",
       "      <th>Anxiety_disorders</th>\n",
       "      <th>Bipolar_disorders</th>\n",
       "      <th>Eating_disorders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.223206</td>\n",
       "      <td>4.996118</td>\n",
       "      <td>4.713314</td>\n",
       "      <td>0.703023</td>\n",
       "      <td>0.127700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1991</td>\n",
       "      <td>0.222454</td>\n",
       "      <td>4.989290</td>\n",
       "      <td>4.702100</td>\n",
       "      <td>0.702069</td>\n",
       "      <td>0.123256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.221751</td>\n",
       "      <td>4.981346</td>\n",
       "      <td>4.683743</td>\n",
       "      <td>0.700792</td>\n",
       "      <td>0.118844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.220987</td>\n",
       "      <td>4.976958</td>\n",
       "      <td>4.673549</td>\n",
       "      <td>0.700087</td>\n",
       "      <td>0.115089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>4.977782</td>\n",
       "      <td>4.670810</td>\n",
       "      <td>0.699898</td>\n",
       "      <td>0.111815</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Entity Code  Year  Schizophrenia_disorders  Depressive_disorders  \\\n",
       "0  Afghanistan  AFG  1990                 0.223206              4.996118   \n",
       "1  Afghanistan  AFG  1991                 0.222454              4.989290   \n",
       "2  Afghanistan  AFG  1992                 0.221751              4.981346   \n",
       "3  Afghanistan  AFG  1993                 0.220987              4.976958   \n",
       "4  Afghanistan  AFG  1994                 0.220183              4.977782   \n",
       "\n",
       "   Anxiety_disorders  Bipolar_disorders  Eating_disorders  \n",
       "0           4.713314           0.703023          0.127700  \n",
       "1           4.702100           0.702069          0.123256  \n",
       "2           4.683743           0.700792          0.118844  \n",
       "3           4.673549           0.700087          0.115089  \n",
       "4           4.670810           0.699898          0.111815  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mental.head()\n",
    "\n",
    "# 참고\n",
    "# mental : 데이터\n",
    "# head() : 함수(function), 5개의 행만 보여줌\n",
    "# head(n = 5) : default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c69e5c4a",
   "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>Entity</th>\n",
       "      <th>Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Schizophrenia_disorders</th>\n",
       "      <th>Depressive_disorders</th>\n",
       "      <th>Anxiety_disorders</th>\n",
       "      <th>Bipolar_disorders</th>\n",
       "      <th>Eating_disorders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1990</td>\n",
       "      <td>0.223206</td>\n",
       "      <td>4.996118</td>\n",
       "      <td>4.713314</td>\n",
       "      <td>0.703023</td>\n",
       "      <td>0.127700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1991</td>\n",
       "      <td>0.222454</td>\n",
       "      <td>4.989290</td>\n",
       "      <td>4.702100</td>\n",
       "      <td>0.702069</td>\n",
       "      <td>0.123256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.221751</td>\n",
       "      <td>4.981346</td>\n",
       "      <td>4.683743</td>\n",
       "      <td>0.700792</td>\n",
       "      <td>0.118844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.220987</td>\n",
       "      <td>4.976958</td>\n",
       "      <td>4.673549</td>\n",
       "      <td>0.700087</td>\n",
       "      <td>0.115089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.220183</td>\n",
       "      <td>4.977782</td>\n",
       "      <td>4.670810</td>\n",
       "      <td>0.699898</td>\n",
       "      <td>0.111815</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6415</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.201042</td>\n",
       "      <td>3.407624</td>\n",
       "      <td>3.184012</td>\n",
       "      <td>0.538596</td>\n",
       "      <td>0.095652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6416</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.201319</td>\n",
       "      <td>3.410755</td>\n",
       "      <td>3.187148</td>\n",
       "      <td>0.538593</td>\n",
       "      <td>0.096662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6417</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.201639</td>\n",
       "      <td>3.411965</td>\n",
       "      <td>3.188418</td>\n",
       "      <td>0.538589</td>\n",
       "      <td>0.097330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6418</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.201976</td>\n",
       "      <td>3.406929</td>\n",
       "      <td>3.172111</td>\n",
       "      <td>0.538585</td>\n",
       "      <td>0.097909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6419</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>2019</td>\n",
       "      <td>0.202482</td>\n",
       "      <td>3.395476</td>\n",
       "      <td>3.137017</td>\n",
       "      <td>0.538580</td>\n",
       "      <td>0.098295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6420 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Entity Code  Year  Schizophrenia_disorders  Depressive_disorders  \\\n",
       "0     Afghanistan  AFG  1990                 0.223206              4.996118   \n",
       "1     Afghanistan  AFG  1991                 0.222454              4.989290   \n",
       "2     Afghanistan  AFG  1992                 0.221751              4.981346   \n",
       "3     Afghanistan  AFG  1993                 0.220987              4.976958   \n",
       "4     Afghanistan  AFG  1994                 0.220183              4.977782   \n",
       "...           ...  ...   ...                      ...                   ...   \n",
       "6415     Zimbabwe  ZWE  2015                 0.201042              3.407624   \n",
       "6416     Zimbabwe  ZWE  2016                 0.201319              3.410755   \n",
       "6417     Zimbabwe  ZWE  2017                 0.201639              3.411965   \n",
       "6418     Zimbabwe  ZWE  2018                 0.201976              3.406929   \n",
       "6419     Zimbabwe  ZWE  2019                 0.202482              3.395476   \n",
       "\n",
       "      Anxiety_disorders  Bipolar_disorders  Eating_disorders  \n",
       "0              4.713314           0.703023          0.127700  \n",
       "1              4.702100           0.702069          0.123256  \n",
       "2              4.683743           0.700792          0.118844  \n",
       "3              4.673549           0.700087          0.115089  \n",
       "4              4.670810           0.699898          0.111815  \n",
       "...                 ...                ...               ...  \n",
       "6415           3.184012           0.538596          0.095652  \n",
       "6416           3.187148           0.538593          0.096662  \n",
       "6417           3.188418           0.538589          0.097330  \n",
       "6418           3.172111           0.538585          0.097909  \n",
       "6419           3.137017           0.538580          0.098295  \n",
       "\n",
       "[6420 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mental\n",
    "\n",
    "# 참고 : 데이터명"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81e9d8f3",
   "metadata": {},
   "source": [
    "### 4. 데이터 저장하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc0a8770",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_path = r'D:\\NHIS\\mental_2025_0324_1404.xlsx'\n",
    "mental.to_excel(save_path, index = False)\n",
    "\n",
    "# save_path  : 저장위치/파일명.확장자\n",
    "# to_excel() : 함수\n",
    "# index      : 파라미터\n",
    "# index = False : 행의 이름은 저장하지 않음"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "204e9aad",
   "metadata": {},
   "source": [
    "### 5. 데이터의 정보 보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ed4a9c24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6420 entries, 0 to 6419\n",
      "Data columns (total 8 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Entity                   6420 non-null   object \n",
      " 1   Code                     6240 non-null   object \n",
      " 2   Year                     6420 non-null   int64  \n",
      " 3   Schizophrenia_disorders  6420 non-null   float64\n",
      " 4   Depressive_disorders     6420 non-null   float64\n",
      " 5   Anxiety_disorders        6420 non-null   float64\n",
      " 6   Bipolar_disorders        6420 non-null   float64\n",
      " 7   Eating_disorders         6420 non-null   float64\n",
      "dtypes: float64(5), int64(1), object(2)\n",
      "memory usage: 401.4+ KB\n"
     ]
    }
   ],
   "source": [
    "mental.info()\n",
    "\n",
    "# mental : 데이터\n",
    "# info() : 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bafde82",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 56527 entries, 0 to 56526\n",
      "Data columns (total 22 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   RN_INDI             56527 non-null  int64  \n",
      " 1   MDCARE_STRT_DT      56527 non-null  int64  \n",
      " 2   RN_KEY              56527 non-null  int64  \n",
      " 3   RN_INST             56527 non-null  int64  \n",
      " 4   FORM_CD             56527 non-null  int64  \n",
      " 5   MCARE_SUBJ_CD       56527 non-null  int64  \n",
      " 6   SICK_SYM1           56527 non-null  object \n",
      " 7   SICK_SYM2           44119 non-null  object \n",
      " 8   HSPTZ_PATH_TYPE     1807 non-null   float64\n",
      " 9   OFIJ_TYPE           56527 non-null  object \n",
      " 10  OPRTN_YN            56527 non-null  int64  \n",
      " 11  MDCARE_DD_CNT       56527 non-null  int64  \n",
      " 12  VSHSP_DD_CNT        56527 non-null  int64  \n",
      " 13  TOT_PRSC_DD_CNT     56527 non-null  int64  \n",
      " 14  MCARE_RSLT_TYPE     55473 non-null  float64\n",
      " 15  FST_HSPTZ_DT        1244 non-null   float64\n",
      " 16  EDC_ADD_RT          56527 non-null  float64\n",
      " 17  SPCF_SYM_TYPE       3681 non-null   object \n",
      " 18  ED_RC_TOT_AMT       56527 non-null  int64  \n",
      " 19  EDC_SBA             56527 non-null  int64  \n",
      " 20  EDC_INSUR_BRDN_AMT  56527 non-null  int64  \n",
      " 21  STD_YYYY            56527 non-null  int64  \n",
      "dtypes: float64(4), int64(14), object(4)\n",
      "memory usage: 9.5+ MB\n"
     ]
    }
   ],
   "source": [
    "m20.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7a25882b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataFrame : 파이썬의 데이터 형태, 행과 열로 구성되어 있음, 2차원 구조\n",
    "# RangeIndex : 6420 : 행의 개수, index(행, 행의 이름)\n",
    "# total 8 columns : 열의 개수\n",
    "# #               : number\n",
    "\n",
    "# Null, NaN, NA   : 결측치=결측값(Missing Value)\n",
    "# Non-Null        : 값이 있는 것\n",
    "# DType           : Data Type : 데이터의 유형\n",
    "#                               문자 또는 숫자(정수, 실수)\n",
    "# object : 문자\n",
    "# int    : integer의 약자, 정수\n",
    "# float  : 실수(real number)\n",
    "\n",
    "# memory usage : 401.4+ KB : mental 데이터의 메모리 크기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c15d372",
   "metadata": {},
   "source": [
    "### 6. 데이터 전처리(핸들링)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a56b500",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 6240 entries, 0 to 6419\n",
      "Data columns (total 8 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   Entity                   6240 non-null   object \n",
      " 1   Code                     6240 non-null   object \n",
      " 2   Year                     6240 non-null   int64  \n",
      " 3   Schizophrenia_disorders  6240 non-null   float64\n",
      " 4   Depressive_disorders     6240 non-null   float64\n",
      " 5   Anxiety_disorders        6240 non-null   float64\n",
      " 6   Bipolar_disorders        6240 non-null   float64\n",
      " 7   Eating_disorders         6240 non-null   float64\n",
      "dtypes: float64(5), int64(1), object(2)\n",
      "memory usage: 438.8+ KB\n"
     ]
    }
   ],
   "source": [
    "mental = mental.dropna(axis = 0)\n",
    "mental.info()\n",
    "\n",
    "# 참고\n",
    "# mental   : 데이터\n",
    "# dropna() : 함수, 결측치를 행 기준으로 제거하는 기능\n",
    "# axis     : 축\n",
    "# axis = 0 : 행, default\n",
    "# axis = 1 : 열"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "46f24e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "save_path = r'D:\\NHIS\\mental_2025_0324_1442.xlsx'\n",
    "mental.to_excel(save_path, index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b9b387cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 참고 : 결측치 처리\n",
    "\n",
    "# (1) 삭제하기 : 행 기준\n",
    "# (2) 대체하기 : imputation, impute\n",
    "#    - 평균, 절사평균, 중위수(중앙값), ..."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb92d56",
   "metadata": {},
   "source": [
    "### 7. 탐색적 데이터 분석(EDA, Exploratory Data Analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "782ee9e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 현황 파악, 주요 특징 또는 패턴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "33f61fbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터의 종류\n",
    "# (1) 범주형 데이터(Categorical Data) : 문자, 숫자(숫자의 의미가 없음)\n",
    "# (2) 수치형 데이터(Numerical Data)   : 숫자(숫자의 의미가 있음)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c00b93a7",
   "metadata": {},
   "source": [
    "#### 7.1 범주형 데이터 분석 : 1개의 열\n",
    "- 표 = 빈도표   : 빈도, 백분율\n",
    "- 데이터 시각화 : 막대그래프"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e4d35c63",
   "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>Frequency</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IHME GBD</th>\n",
       "      <td>90</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LCA</th>\n",
       "      <td>30</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NGA</th>\n",
       "      <td>30</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NIU</th>\n",
       "      <td>30</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRK</th>\n",
       "      <td>30</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRC</th>\n",
       "      <td>30</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRL</th>\n",
       "      <td>30</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GRD</th>\n",
       "      <td>30</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GUM</th>\n",
       "      <td>30</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ZWE</th>\n",
       "      <td>30</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Frequency  Percentage\n",
       "Code                           \n",
       "IHME GBD         90        1.44\n",
       "LCA              30        0.48\n",
       "NGA              30        0.48\n",
       "NIU              30        0.48\n",
       "PRK              30        0.48\n",
       "...             ...         ...\n",
       "GRC              30        0.48\n",
       "GRL              30        0.48\n",
       "GRD              30        0.48\n",
       "GUM              30        0.48\n",
       "ZWE              30        0.48\n",
       "\n",
       "[206 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 표(Table) = 빈도표(Frequency Table)\n",
    "\n",
    "freq    = mental['Code'].value_counts()\n",
    "percent = mental['Code'].value_counts(normalize = True) * 100\n",
    "result  = pd.DataFrame({'Frequency' : freq, 'Percentage' : percent})\n",
    "result.round(decimals = 2)\n",
    "\n",
    "# 참고\n",
    "# 데이터의 슬라이싱(Slicing) : 데이터명['열의이름']\n",
    "# mental['Code'] : mental 데이터에서 Code라는 열을 잘라내기\n",
    "# value_counts() : 함수\n",
    "# freq           : 데이터 이름, 빈도를 저장하고 있음, 이름은 사용자 변경가능함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aab5b524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (2) 데이터 시각화 : 막대그래프\n",
    "\n",
    "top_10 = mental['Code'].value_counts().head(n = 10)\n",
    "\n",
    "plt.figure(figsize = (10, 6))\n",
    "plt.bar(top_10.index, top_10.values)\n",
    "plt.xlabel('Code')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Top 10 Code Categories by Frequency')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 참고\n",
    "# plt.figure(figsize = (10, 6)) : 캔바스의 크기(가로 10, 세로 6)\n",
    "# plt.bar(top_10.index, top_10.values) : 막대그래프, X축, Y축\n",
    "# plt.xlabel('Code')                   : X축 제목\n",
    "# plt.ylabel('Frequency')              : Y축 제목\n",
    "# plt.title('Top 10 Code Categories by Frequency') : 차트 제목\n",
    "# plt.xticks(rotation = 45) : X축의 값을 45도 회전시킴\n",
    "# plt.tight_layout()        : 글씨들의 여백 조정\n",
    "# plt.show()                : 그래프 출력, 그래프 작업 종료"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d962bfa",
   "metadata": {},
   "source": [
    "#### 7.2 수치형 데이터 분석 : 1개의 열\n",
    "- 표 = 빈도표   : 구간의 빈도, 백분율\n",
    "- 데이터 시각화 : 히스토그램, 상자그림, 바이올린\n",
    "- 데이터 요약   : 기술통계량 = 요약통계량"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e6b31a6",
   "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>ED_RC_TOT_AMT</th>\n",
       "      <th>AMT_GROUP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14160</td>\n",
       "      <td>기본</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14280</td>\n",
       "      <td>기본</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13980</td>\n",
       "      <td>기본</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13980</td>\n",
       "      <td>기본</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14160</td>\n",
       "      <td>기본</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ED_RC_TOT_AMT AMT_GROUP\n",
       "0          14160        기본\n",
       "1          14280        기본\n",
       "2          13980        기본\n",
       "3          13980        기본\n",
       "4          14160        기본"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 표 = 빈도표 : 구간의 빈도, 백분율\n",
    "# 핵심 : 구간을 어떻게 만들 것인가?\n",
    "# ED_RC_TOT_AMT -> 새로운 열을 만들기(AMT_GROUP)\n",
    "# 0원 이상 ~ 100만원 미만       : 기본\n",
    "# 100원 이상 ~ 1000만원 미만    : 비싸다\n",
    "# 1000만원 이상 ~ 2000만원 미만 : 매우 비싸다\n",
    "# 2000만원 이상 ~ 30000만원 미만 : 매무 매우 비싸다\n",
    "\n",
    "conditions = [\n",
    "    (m20['ED_RC_TOT_AMT'] >= 0) & (m20['ED_RC_TOT_AMT'] < 1000000),       # 0원 이상 ~ 100만원 미만\n",
    "    (m20['ED_RC_TOT_AMT'] >= 1000000) & (m20['ED_RC_TOT_AMT'] < 10000000),  # 100만원 이상 ~ 1000만원 미만\n",
    "    (m20['ED_RC_TOT_AMT'] >= 10000000) & (m20['ED_RC_TOT_AMT'] < 20000000), # 1000만원 이상 ~ 2000만원 미만\n",
    "    (m20['ED_RC_TOT_AMT'] >= 20000000) & (m20['ED_RC_TOT_AMT'] < 30000000) # 2000만원 이상 ~ 30000만원 미만\n",
    "]\n",
    "\n",
    "choices          = ['기본', '비싸다', '매우 비싸다', '매무 매우 비싸다']\n",
    "m20['AMT_GROUP'] = np.select(conditions, choices, default='기타')\n",
    "m20[['ED_RC_TOT_AMT', 'AMT_GROUP']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6cf4feec",
   "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>Frequency</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMT_GROUP</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>기본</th>\n",
       "      <td>55810</td>\n",
       "      <td>98.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>비싸다</th>\n",
       "      <td>699</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>매우 비싸다</th>\n",
       "      <td>16</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>매무 매우 비싸다</th>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Frequency  Percentage\n",
       "AMT_GROUP                       \n",
       "기본             55810       98.73\n",
       "비싸다              699        1.24\n",
       "매우 비싸다            16        0.03\n",
       "매무 매우 비싸다          2        0.00"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq    = m20['AMT_GROUP'].value_counts()\n",
    "percent = m20['AMT_GROUP'].value_counts(normalize = True) * 100\n",
    "result  = pd.DataFrame({'Frequency' : freq, 'Percentage' : percent})\n",
    "result.round(decimals = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c174be3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9d0dcd23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (2) 데이터 시각화 : 히스토그램(Histogram)\n",
    "plt.figure(figsize = (10, 6))\n",
    "plt.hist(mental['Depressive_disorders'], bins = 100)\n",
    "plt.xlabel('Depressive_disorders')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Depressive_disorders')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# hist() : 함수, 히스토그램을 그려주는 함수\n",
    "# bins   : 파라미터, 구간의 개수를 조정하는 파라미터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "065c2d02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (2) 데이터 시각화 : 상자그림(Boxplot)\n",
    "plt.figure(figsize = (10, 6))\n",
    "plt.boxplot(m20['VSHSP_DD_CNT'])\n",
    "plt.title('내원일수의 현황')\n",
    "plt.ylabel('내원일수')\n",
    "plt.show()\n",
    "\n",
    "# 참고\n",
    "# 이상치(Outlier) 유무를 파악\n",
    "# 아주 큰 값이나 아주 작은 값\n",
    "\n",
    "# IQR(Inter Quartile Range) : 사분위범위(Q3 - Q1)\n",
    "# Q1 : 제1 사분위수\n",
    "# Q3 : 제3 사분위수\n",
    "# Min, Max, Range(Max - Min)\n",
    "\n",
    "# boxplot() 함수의 중요한 파라미터 : whis = 1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dc87c25a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (2) 데이터 시각화 : 바이올린(Violin)\n",
    "plt.figure(figsize = (10, 6))\n",
    "plt.violinplot(m20['VSHSP_DD_CNT'])\n",
    "plt.title('내원일수의 현황')\n",
    "plt.ylabel('내원일수')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5b44cbfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    56527.000000\n",
       "mean         1.211757\n",
       "std          2.291078\n",
       "min          0.000000\n",
       "25%          1.000000\n",
       "50%          1.000000\n",
       "75%          1.000000\n",
       "max         42.000000\n",
       "Name: VSHSP_DD_CNT, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (3) 기술통계량(Descriptive Statistic) = 요약통계량(Summary Statistic)\n",
    "# - 중심 = 대표값      : 평균, 절사평균, 중위수, 최빈수, 최소값, 최대값\n",
    "# - 다름 = 산포 = 퍼짐 : 범위, 사분위범위, 표준편차, 중위수절대편차\n",
    "# - 분포의 모양        : 왜도, 첨도\n",
    "\n",
    "m20['VSHSP_DD_CNT'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "05050545",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 참고\n",
    "# count : 데이터의 개수, \n",
    "#         모집단 : N\n",
    "#         표본   : n, 표본 크기(Sample Size)\n",
    "\n",
    "# mean : (표본)평균\n",
    "# std  : (표본)표준편차 : 중심(대표값, 평균)과 얼마나 다를까?\n",
    "# min  : 최소값\n",
    "# 25%  : 제1 사분위수(First Quartile), Q1\n",
    "# 50%  : 중위수(중앙값)(Median), Q2\n",
    "# 75%  : 제3 사분위수(Third Quartile), Q3\n",
    "# max  : 최대값"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "80e74817",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "중위수 절대 편차: 0.0\n",
      "왜도: 11.882714621378824\n",
      "첨도: 145.11176940457204\n"
     ]
    }
   ],
   "source": [
    "median_value = m20['VSHSP_DD_CNT'].median()\n",
    "mad          = np.median(np.abs(m20['VSHSP_DD_CNT'] - median_value))\n",
    "print(\"\\n중위수 절대 편차:\", mad)\n",
    "\n",
    "# 왜도 계산 (분포의 비대칭 정도)\n",
    "skewness = m20['VSHSP_DD_CNT'].skew()\n",
    "print(\"왜도:\", skewness)\n",
    "\n",
    "# 첨도 계산 (분포의 뾰족한 정도, pandas는 Fisher의 첨도(즉, 정규분포=0)를 반환)\n",
    "kurtosis = m20['VSHSP_DD_CNT'].kurt()\n",
    "print(\"첨도:\", kurtosis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63135f81",
   "metadata": {},
   "source": [
    "#### 7.3 범주형 데이터 분석 : 2개의 열\n",
    "- 표 = 교차표\n",
    "- 데이터 시각화 : 누적 막대그래프"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7968f69a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# y : 범주형 데이터 : 입원외래구분(FORM_CD)   : 열\n",
    "# X : 범주형 데이터 : 진료과목(MCARE_SUBJ_CD) : 행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "955e25d6",
   "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>FORM_CD</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCARE_SUBJ_CD</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>1054</td>\n",
       "      <td>38</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>231</td>\n",
       "      <td>17739</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>872</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78</td>\n",
       "      <td>1561</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>63</td>\n",
       "      <td>1646</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>174</td>\n",
       "      <td>9943</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>46</td>\n",
       "      <td>954</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>147</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>901</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>36</td>\n",
       "      <td>1766</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>45</td>\n",
       "      <td>2378</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>44</td>\n",
       "      <td>3221</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>24</td>\n",
       "      <td>4777</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>2872</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13</td>\n",
       "      <td>1257</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>58</td>\n",
       "      <td>381</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>74</td>\n",
       "      <td>1902</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>63</td>\n",
       "      <td>465</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "FORM_CD         2      3     8   10  11    15\n",
       "MCARE_SUBJ_CD                                \n",
       "0                4     45  1054  38  65     0\n",
       "1              231  17739     0   0   0  1230\n",
       "2               26    872     0   0   0     0\n",
       "3               78   1561     0   0   0     0\n",
       "4               63   1646     0   0   0     0\n",
       "5              174   9943     0   0   0     0\n",
       "6               46    954     0   0   0     0\n",
       "7                8     61     0   0   0     0\n",
       "8                8    147     0   0   0     0\n",
       "9                4    901     0   0   0     0\n",
       "10              36   1766     0   0   0     0\n",
       "11              45   2378     0   0   0     0\n",
       "12              44   3221     0   0   0     0\n",
       "13              24   4777     0   0   0     0\n",
       "14               1   2872     0   0   0     0\n",
       "15              13   1257     0   0   0     0\n",
       "16               0     74     0   0   0     0\n",
       "17               0    141     0   0   0     0\n",
       "21              58    381     0   0   0     0\n",
       "22               1     15     0   0   0     0\n",
       "23              74   1902     0   0   0     0\n",
       "24              63    465     0   0   0     0\n",
       "25               0      2     0   0   0     0\n",
       "26               0     18     0   0   0     0\n",
       "55               0      1     0   0   0     0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (1) 교차표(Cross Table) : 빈도, 전체 백분율, 행 백분율, 열 백분율\n",
    "# i. 빈도\n",
    "pd.crosstab(columns = m20['FORM_CD'], index = m20['MCARE_SUBJ_CD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f9830e4a",
   "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>FORM_CD</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCARE_SUBJ_CD</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.007076</td>\n",
       "      <td>0.079608</td>\n",
       "      <td>1.864596</td>\n",
       "      <td>0.067225</td>\n",
       "      <td>0.114989</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.408654</td>\n",
       "      <td>31.381464</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.175951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.045996</td>\n",
       "      <td>1.542626</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.137987</td>\n",
       "      <td>2.761512</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.111451</td>\n",
       "      <td>2.911883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.307818</td>\n",
       "      <td>17.589824</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.081377</td>\n",
       "      <td>1.687689</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.014153</td>\n",
       "      <td>0.107913</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.014153</td>\n",
       "      <td>0.260053</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.007076</td>\n",
       "      <td>1.593929</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.063686</td>\n",
       "      <td>3.124171</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.079608</td>\n",
       "      <td>4.206839</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.077839</td>\n",
       "      <td>5.698162</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.042458</td>\n",
       "      <td>8.450829</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.001769</td>\n",
       "      <td>5.080758</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.022998</td>\n",
       "      <td>2.223716</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.130911</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.249438</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.102606</td>\n",
       "      <td>0.674014</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.001769</td>\n",
       "      <td>0.026536</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.130911</td>\n",
       "      <td>3.364764</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.111451</td>\n",
       "      <td>0.822616</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003538</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.031843</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "FORM_CD              2          3         8         10        11        15\n",
       "MCARE_SUBJ_CD                                                             \n",
       "0              0.007076   0.079608  1.864596  0.067225  0.114989  0.000000\n",
       "1              0.408654  31.381464  0.000000  0.000000  0.000000  2.175951\n",
       "2              0.045996   1.542626  0.000000  0.000000  0.000000  0.000000\n",
       "3              0.137987   2.761512  0.000000  0.000000  0.000000  0.000000\n",
       "4              0.111451   2.911883  0.000000  0.000000  0.000000  0.000000\n",
       "5              0.307818  17.589824  0.000000  0.000000  0.000000  0.000000\n",
       "6              0.081377   1.687689  0.000000  0.000000  0.000000  0.000000\n",
       "7              0.014153   0.107913  0.000000  0.000000  0.000000  0.000000\n",
       "8              0.014153   0.260053  0.000000  0.000000  0.000000  0.000000\n",
       "9              0.007076   1.593929  0.000000  0.000000  0.000000  0.000000\n",
       "10             0.063686   3.124171  0.000000  0.000000  0.000000  0.000000\n",
       "11             0.079608   4.206839  0.000000  0.000000  0.000000  0.000000\n",
       "12             0.077839   5.698162  0.000000  0.000000  0.000000  0.000000\n",
       "13             0.042458   8.450829  0.000000  0.000000  0.000000  0.000000\n",
       "14             0.001769   5.080758  0.000000  0.000000  0.000000  0.000000\n",
       "15             0.022998   2.223716  0.000000  0.000000  0.000000  0.000000\n",
       "16             0.000000   0.130911  0.000000  0.000000  0.000000  0.000000\n",
       "17             0.000000   0.249438  0.000000  0.000000  0.000000  0.000000\n",
       "21             0.102606   0.674014  0.000000  0.000000  0.000000  0.000000\n",
       "22             0.001769   0.026536  0.000000  0.000000  0.000000  0.000000\n",
       "23             0.130911   3.364764  0.000000  0.000000  0.000000  0.000000\n",
       "24             0.111451   0.822616  0.000000  0.000000  0.000000  0.000000\n",
       "25             0.000000   0.003538  0.000000  0.000000  0.000000  0.000000\n",
       "26             0.000000   0.031843  0.000000  0.000000  0.000000  0.000000\n",
       "55             0.000000   0.001769  0.000000  0.000000  0.000000  0.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ii. 전체 백분율\n",
    "pd.crosstab(columns   = m20['FORM_CD'], \n",
    "            index     = m20['MCARE_SUBJ_CD'],\n",
    "            normalize = 'all')*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9c946758",
   "metadata": {
    "scrolled": true
   },
   "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>FORM_CD</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCARE_SUBJ_CD</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.331675</td>\n",
       "      <td>3.731343</td>\n",
       "      <td>87.396352</td>\n",
       "      <td>3.150912</td>\n",
       "      <td>5.389718</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.203125</td>\n",
       "      <td>92.390625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.40625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.895323</td>\n",
       "      <td>97.104677</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.758999</td>\n",
       "      <td>95.241001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.686366</td>\n",
       "      <td>96.313634</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.719877</td>\n",
       "      <td>98.280123</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>95.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.594203</td>\n",
       "      <td>88.405797</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.161290</td>\n",
       "      <td>94.838710</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.441989</td>\n",
       "      <td>99.558011</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.997780</td>\n",
       "      <td>98.002220</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.857202</td>\n",
       "      <td>98.142798</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.347626</td>\n",
       "      <td>98.652374</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.499896</td>\n",
       "      <td>99.500104</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.034807</td>\n",
       "      <td>99.965193</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.023622</td>\n",
       "      <td>98.976378</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>13.211845</td>\n",
       "      <td>86.788155</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>6.250000</td>\n",
       "      <td>93.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.744939</td>\n",
       "      <td>96.255061</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11.931818</td>\n",
       "      <td>88.068182</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "FORM_CD               2           3          8         10        11       15\n",
       "MCARE_SUBJ_CD                                                               \n",
       "0               0.331675    3.731343  87.396352  3.150912  5.389718  0.00000\n",
       "1               1.203125   92.390625   0.000000  0.000000  0.000000  6.40625\n",
       "2               2.895323   97.104677   0.000000  0.000000  0.000000  0.00000\n",
       "3               4.758999   95.241001   0.000000  0.000000  0.000000  0.00000\n",
       "4               3.686366   96.313634   0.000000  0.000000  0.000000  0.00000\n",
       "5               1.719877   98.280123   0.000000  0.000000  0.000000  0.00000\n",
       "6               4.600000   95.400000   0.000000  0.000000  0.000000  0.00000\n",
       "7              11.594203   88.405797   0.000000  0.000000  0.000000  0.00000\n",
       "8               5.161290   94.838710   0.000000  0.000000  0.000000  0.00000\n",
       "9               0.441989   99.558011   0.000000  0.000000  0.000000  0.00000\n",
       "10              1.997780   98.002220   0.000000  0.000000  0.000000  0.00000\n",
       "11              1.857202   98.142798   0.000000  0.000000  0.000000  0.00000\n",
       "12              1.347626   98.652374   0.000000  0.000000  0.000000  0.00000\n",
       "13              0.499896   99.500104   0.000000  0.000000  0.000000  0.00000\n",
       "14              0.034807   99.965193   0.000000  0.000000  0.000000  0.00000\n",
       "15              1.023622   98.976378   0.000000  0.000000  0.000000  0.00000\n",
       "16              0.000000  100.000000   0.000000  0.000000  0.000000  0.00000\n",
       "17              0.000000  100.000000   0.000000  0.000000  0.000000  0.00000\n",
       "21             13.211845   86.788155   0.000000  0.000000  0.000000  0.00000\n",
       "22              6.250000   93.750000   0.000000  0.000000  0.000000  0.00000\n",
       "23              3.744939   96.255061   0.000000  0.000000  0.000000  0.00000\n",
       "24             11.931818   88.068182   0.000000  0.000000  0.000000  0.00000\n",
       "25              0.000000  100.000000   0.000000  0.000000  0.000000  0.00000\n",
       "26              0.000000  100.000000   0.000000  0.000000  0.000000  0.00000\n",
       "55              0.000000  100.000000   0.000000  0.000000  0.000000  0.00000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iii. 행 백분율\n",
    "pd.crosstab(columns   = m20['FORM_CD'], \n",
    "            index     = m20['MCARE_SUBJ_CD'],\n",
    "            normalize = 'index')*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0decf4b",
   "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>FORM_CD</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>8</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCARE_SUBJ_CD</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.399600</td>\n",
       "      <td>0.084684</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.076923</td>\n",
       "      <td>33.382262</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.597403</td>\n",
       "      <td>1.640979</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.792208</td>\n",
       "      <td>2.937579</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.293706</td>\n",
       "      <td>3.097537</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17.382617</td>\n",
       "      <td>18.711304</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.595405</td>\n",
       "      <td>1.795292</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.799201</td>\n",
       "      <td>0.114793</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.799201</td>\n",
       "      <td>0.276633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.399600</td>\n",
       "      <td>1.695553</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.596404</td>\n",
       "      <td>3.323359</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.495504</td>\n",
       "      <td>4.475056</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.395604</td>\n",
       "      <td>6.061461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.397602</td>\n",
       "      <td>8.989631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.099900</td>\n",
       "      <td>5.404693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.298701</td>\n",
       "      <td>2.365494</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.139257</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.265342</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.794206</td>\n",
       "      <td>0.716988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.099900</td>\n",
       "      <td>0.028228</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7.392607</td>\n",
       "      <td>3.579292</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6.293706</td>\n",
       "      <td>0.875064</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003764</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033873</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001882</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "FORM_CD               2          3      8      10     11     15\n",
       "MCARE_SUBJ_CD                                                  \n",
       "0               0.399600   0.084684  100.0  100.0  100.0    0.0\n",
       "1              23.076923  33.382262    0.0    0.0    0.0  100.0\n",
       "2               2.597403   1.640979    0.0    0.0    0.0    0.0\n",
       "3               7.792208   2.937579    0.0    0.0    0.0    0.0\n",
       "4               6.293706   3.097537    0.0    0.0    0.0    0.0\n",
       "5              17.382617  18.711304    0.0    0.0    0.0    0.0\n",
       "6               4.595405   1.795292    0.0    0.0    0.0    0.0\n",
       "7               0.799201   0.114793    0.0    0.0    0.0    0.0\n",
       "8               0.799201   0.276633    0.0    0.0    0.0    0.0\n",
       "9               0.399600   1.695553    0.0    0.0    0.0    0.0\n",
       "10              3.596404   3.323359    0.0    0.0    0.0    0.0\n",
       "11              4.495504   4.475056    0.0    0.0    0.0    0.0\n",
       "12              4.395604   6.061461    0.0    0.0    0.0    0.0\n",
       "13              2.397602   8.989631    0.0    0.0    0.0    0.0\n",
       "14              0.099900   5.404693    0.0    0.0    0.0    0.0\n",
       "15              1.298701   2.365494    0.0    0.0    0.0    0.0\n",
       "16              0.000000   0.139257    0.0    0.0    0.0    0.0\n",
       "17              0.000000   0.265342    0.0    0.0    0.0    0.0\n",
       "21              5.794206   0.716988    0.0    0.0    0.0    0.0\n",
       "22              0.099900   0.028228    0.0    0.0    0.0    0.0\n",
       "23              7.392607   3.579292    0.0    0.0    0.0    0.0\n",
       "24              6.293706   0.875064    0.0    0.0    0.0    0.0\n",
       "25              0.000000   0.003764    0.0    0.0    0.0    0.0\n",
       "26              0.000000   0.033873    0.0    0.0    0.0    0.0\n",
       "55              0.000000   0.001882    0.0    0.0    0.0    0.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iv. 열 백분율\n",
    "pd.crosstab(columns   = m20['FORM_CD'], \n",
    "            index     = m20['MCARE_SUBJ_CD'],\n",
    "            normalize = 'columns')*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3b0c16a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (2) 데이터 시각화 : 누적막대그래프\n",
    "freq_table = pd.crosstab(m20['MCARE_SUBJ_CD'], m20['FORM_CD'])\n",
    "row_pct    = freq_table.div(freq_table.sum(axis = 1), axis = 0) * 100\n",
    "\n",
    "plt.figure(figsize = (10, 6))\n",
    "row_pct.plot(kind = 'bar', stacked = True, ax = plt.gca())\n",
    "plt.title(\"행 백분율 누적막대그래프\")\n",
    "plt.xlabel(\"FORM_CD\")\n",
    "plt.ylabel(\"백분율 (%)\")\n",
    "plt.legend(title = \"MCARE_SUBJ_CD\", bbox_to_anchor = (1.05, 1), loc = 'upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "312702b4",
   "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>FORM_CD</th>\n",
       "      <th>FORM_CD2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>외래</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>외래</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>외래</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>외래</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>외래</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FORM_CD FORM_CD2\n",
       "0        3       외래\n",
       "1        3       외래\n",
       "2        3       외래\n",
       "3        3       외래\n",
       "4        3       외래"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 전처리\n",
    "# 1차 : FORM_CD : 2, 3, 7, 8, 9, 10, 11, 15\n",
    "#       FORM_CD2 : 2, 7, 10        : 입원\n",
    "#                  3, 8, 9, 11, 15 : 외래\n",
    "\n",
    "conditions = [\n",
    "    m20['FORM_CD'].isin([2, 7, 10]),   \n",
    "    m20['FORM_CD'].isin([3, 8, 9, 11, 15])\n",
    "]\n",
    "\n",
    "choices         = ['입원', '외래']\n",
    "m20['FORM_CD2'] = np.select(conditions, choices, default='기타')\n",
    "m20[['FORM_CD', 'FORM_CD2']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1a8b5fce",
   "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>MCARE_SUBJ_CD</th>\n",
       "      <th>MCARE_SUBJ_CD2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>피부과</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>정형외과</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>정형외과</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>정형외과</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>피부과</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MCARE_SUBJ_CD MCARE_SUBJ_CD2\n",
       "0             14            피부과\n",
       "1              5           정형외과\n",
       "2              5           정형외과\n",
       "3              5           정형외과\n",
       "4             14            피부과"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2차 : MCARE_SUBJ_CD\n",
    "mapping = {\n",
    "    '00': '일반의',\n",
    "    '01': '내과',\n",
    "    '02': '신경과',\n",
    "    '03': '정신과',\n",
    "    '04': '외과',\n",
    "    '05': '정형외과',\n",
    "    '06': '신경외과',\n",
    "    '07': '흉부외과',\n",
    "    '08': '성형외과',\n",
    "    '09': '마취통증의학과',\n",
    "    '10': '산부인과',\n",
    "    '11': '소아청소년과',\n",
    "    '12': '안과',\n",
    "    '13': '이비인후과',\n",
    "    '14': '피부과',\n",
    "    '15': '비뇨기과',\n",
    "    '16': '영상의학과',\n",
    "    '17': '방사선 종양학과',\n",
    "    '18': '병리과',\n",
    "    '19': '진단검사의학과',\n",
    "    '20': '결핵과',\n",
    "    '21': '재활의학과',\n",
    "    '22': '핵의학과',\n",
    "    '23': '가정의학과',\n",
    "    '24': '응급의학과',\n",
    "    '25': '산업의학과',\n",
    "    '26': '예방의학과',\n",
    "    '49': '치과',\n",
    "    '50': '구강악안면외과',\n",
    "    '51': '치과보철과',\n",
    "    '52': '치과교정과',\n",
    "    '53': '소아치과',\n",
    "    '54': '치주과',\n",
    "    '55': '치과보존과',\n",
    "    '56': '구강내과',\n",
    "    '57': '구강악안면방사선과',\n",
    "    '58': '구강병리과',\n",
    "    '59': '예방치과',\n",
    "    '60': '치과소계',\n",
    "    '80': '한방내과',\n",
    "    '81': '한방부인과',\n",
    "    '82': '한방소아과',\n",
    "    '83': '한방안이비인후피부과',\n",
    "    '84': '한방신경정신과',\n",
    "    '85': '침구과',\n",
    "    '86': '한방재활의학과',\n",
    "    '87': '사상체질과',\n",
    "    '88': '한방응급'\n",
    "}\n",
    "\n",
    "m20['MCARE_SUBJ_CD2'] = m20['MCARE_SUBJ_CD'].astype(str).str.zfill(2).map(mapping)\n",
    "m20[['MCARE_SUBJ_CD', 'MCARE_SUBJ_CD2']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "097d08e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "freq_table = pd.crosstab(m20['MCARE_SUBJ_CD2'], m20['FORM_CD2'])\n",
    "row_pct    = freq_table.div(freq_table.sum(axis = 1), axis = 0) * 100\n",
    "\n",
    "plt.figure(figsize = (10, 6))\n",
    "row_pct.plot(kind = 'bar', stacked = True, ax = plt.gca())\n",
    "plt.title(\"행 백분율 누적막대그래프\")\n",
    "plt.xlabel(\"FORM_CD\")\n",
    "plt.ylabel(\"백분율 (%)\")\n",
    "plt.legend(title = \"MCARE_SUBJ_CD\", bbox_to_anchor = (1.05, 1), loc = 'upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8ac06ad",
   "metadata": {},
   "source": [
    "#### 7.4 범주별 수치형 데이터 분석 : 2개의 열\n",
    "- 범주별 데이터 시각화\n",
    "- 범주별 기술통계량"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e53a5239",
   "metadata": {},
   "outputs": [],
   "source": [
    "# y : 범주형 데이터\n",
    "# X : 수치형 데이터\n",
    "\n",
    "# y : 수치형 데이터\n",
    "# X : 범주형 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "de74fc0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# y : 수치형 데이터 : ED_RC_TOT_AMT\n",
    "# X : 범주형 데이터 : FORM_CD2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "52d8d684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (1) 범주별 데이터 시각화 : 히스토그램\n",
    "g = sns.FacetGrid(m20, \n",
    "                  col      = \"FORM_CD2\", \n",
    "                  col_wrap = 3, \n",
    "                  sharex   = False, \n",
    "                  sharey   = False, \n",
    "                  height   = 4)\n",
    "\n",
    "g.map_dataframe(plt.hist, \n",
    "                x         = \"ED_RC_TOT_AMT\", \n",
    "                bins      = 30, \n",
    "                color     = 'skyblue', \n",
    "                edgecolor = 'black')\n",
    "\n",
    "g.fig.subplots_adjust(top = 0.9)\n",
    "g.fig.suptitle(\"FORM_CD2별 ED_RC_TOT_AMT 히스토그램\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8027f2d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (2) 범주별 데이터 시각화 : 상자그림\n",
    "g = sns.FacetGrid(m20, \n",
    "                  col      = \"FORM_CD2\", \n",
    "                  col_wrap = 3, \n",
    "                  sharex   = False, \n",
    "                  sharey   = False, \n",
    "                  height   = 4)\n",
    "\n",
    "g.map_dataframe(sns.boxplot, \n",
    "                y = \"ED_RC_TOT_AMT\")\n",
    "\n",
    "g.fig.subplots_adjust(top = 0.9)\n",
    "g.fig.suptitle(\"FORM_CD2별 ED_RC_TOT_AMT 상자그림\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0cd853e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (3) 범주별 데이터 시각화 : 바이올린\n",
    "g = sns.FacetGrid(m20, \n",
    "                  col      = \"FORM_CD2\", \n",
    "                  col_wrap = 3, \n",
    "                  sharex   = False, \n",
    "                  sharey   = False, \n",
    "                  height   = 4)\n",
    "\n",
    "g.map_dataframe(sns.violinplot, \n",
    "                y = \"ED_RC_TOT_AMT\")\n",
    "\n",
    "g.fig.subplots_adjust(top = 0.9)\n",
    "g.fig.suptitle(\"FORM_CD2별 ED_RC_TOT_AMT 바이올린\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "853ac1f6",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FORM_CD2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>외래</th>\n",
       "      <td>55488.0</td>\n",
       "      <td>3.888247e+04</td>\n",
       "      <td>1.304456e+05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12740.0</td>\n",
       "      <td>15920.0</td>\n",
       "      <td>26670.0</td>\n",
       "      <td>5069020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>입원</th>\n",
       "      <td>1039.0</td>\n",
       "      <td>1.847285e+06</td>\n",
       "      <td>2.299582e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>578700.0</td>\n",
       "      <td>1311300.0</td>\n",
       "      <td>2273995.0</td>\n",
       "      <td>28217200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count          mean           std  min       25%        50%  \\\n",
       "FORM_CD2                                                                  \n",
       "외래        55488.0  3.888247e+04  1.304456e+05  0.0   12740.0    15920.0   \n",
       "입원         1039.0  1.847285e+06  2.299582e+06  0.0  578700.0  1311300.0   \n",
       "\n",
       "                75%         max  \n",
       "FORM_CD2                         \n",
       "외래          26670.0   5069020.0  \n",
       "입원        2273995.0  28217200.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2) 범주별 기술통계량\n",
    "m20.groupby('FORM_CD2')['ED_RC_TOT_AMT'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e0e67b79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3549200144025515\n",
      "0.12448431075876218\n"
     ]
    }
   ],
   "source": [
    "# 참고 : 집단 비교\n",
    "# 기준 : 중심(대표값) : 평균 -> 중위수\n",
    "# 기준 : 다름 : 변동계수(CV : Coefficient of Variation) = 표준편차/평균\n",
    "print(130446/38882)\n",
    "print(229958/1847285)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07c02ff7",
   "metadata": {},
   "source": [
    "#### 7.5 수치형 데이터 분석 : 2개의 열\n",
    "- 데이터 시각화 : 산점도\n",
    "- 수치화        : 상관계수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e83d5680",
   "metadata": {},
   "outputs": [],
   "source": [
    "# y : ED_RC_TOT_AMT : 진료비   : 수치형 데이터\n",
    "# X : MDCARE_DD_CNT : 요양일수 : 수치형 데이터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d8367680",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 관계 : 상관관계 = 직선의 관계 = 선형의 관계"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "05f6ead2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# (1) 상관관계 시각화 : 산점도(Scatter Plot)\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.scatterplot(data = m20, x = 'MDCARE_DD_CNT', y = 'ED_RC_TOT_AMT', alpha = 0.6)\n",
    "plt.xlabel(\"MDCARE_DD_CNT\")\n",
    "plt.ylabel(\"ED_RC_TOT_AMT\")\n",
    "plt.title(\"MDCARE_DD_CNT vs ED_RC_TOT_AMT 산점도\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42fa8475",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.3690194139725266, pvalue=0.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (2) 상관관계 수치화 : 상관계수(Pearson) = r\n",
    "# Pearson, Spearman, Kendall\n",
    "\n",
    "pearsonr(m20['MDCARE_DD_CNT'], m20['ED_RC_TOT_AMT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "72383d1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# statistic=0.3690194139725266 : Pearson의 상관계수\n",
    "# r = 0.369"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e341a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 상관계수의 해석의 일반적인 가이드 : 절대값 기준\n",
    "\n",
    "# 0.0 이상 ~ 0.2 미만 : 상관관계가 없다.\n",
    "# 0.2 이상 ~ 0.4 미만 : 약한(낮은) 상관관계가 있다.\n",
    "# 0.4 이상 ~ 0.6 미만 : 보통의 상관관계가 있다.\n",
    "# 0.6 이상 ~ 0.8 미만 : 강한(높은) 상관관계가 있다.\n",
    "# 0.8 이상 ~ 1.0 이하 : 매우 강한(높은) 상관관계가 있다.\n",
    "\n",
    "# 부호(Sign)\n",
    "# + : 양의 상관관계\n",
    "# - : 음의 상관관계"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b267110a",
   "metadata": {},
   "source": [
    "### 8. 데이터 결합(Merge, Join)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ff7f1dba",
   "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>STD_YYYY</th>\n",
       "      <th>RN_INDI</th>\n",
       "      <th>SEX</th>\n",
       "      <th>SGG</th>\n",
       "      <th>GAIBJA_TYPE</th>\n",
       "      <th>CTRB_Q10</th>\n",
       "      <th>DSB_TYPE_CD</th>\n",
       "      <th>G1E_OBJ_YN</th>\n",
       "      <th>SMPL_TYPE_CD</th>\n",
       "      <th>DSB_SVRT_CD_V2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>414780</td>\n",
       "      <td>2</td>\n",
       "      <td>41590</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018</td>\n",
       "      <td>414780</td>\n",
       "      <td>2</td>\n",
       "      <td>41590</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Y</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>414780</td>\n",
       "      <td>2</td>\n",
       "      <td>41590</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>866221</td>\n",
       "      <td>2</td>\n",
       "      <td>41220</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>194269</td>\n",
       "      <td>2</td>\n",
       "      <td>41281</td>\n",
       "      <td>6</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   STD_YYYY  RN_INDI  SEX    SGG  GAIBJA_TYPE  CTRB_Q10  DSB_TYPE_CD  \\\n",
       "0      2019   414780    2  41590            6      10.0          NaN   \n",
       "1      2018   414780    2  41590            6      10.0          NaN   \n",
       "2      2017   414780    2  41590            6      10.0          NaN   \n",
       "3      2016   866221    2  41220            6      10.0          NaN   \n",
       "4      2016   194269    2  41281            6      10.0          NaN   \n",
       "\n",
       "  G1E_OBJ_YN  SMPL_TYPE_CD  DSB_SVRT_CD_V2  \n",
       "0        NaN             1             NaN  \n",
       "1          Y             1             NaN  \n",
       "2        NaN             1             NaN  \n",
       "3        NaN             1             NaN  \n",
       "4        NaN             1             NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = \"D:/NHIS/NSC2_BNC_1619.CSV\"\n",
    "bnc       = pd.read_csv(file_path, header = 0)\n",
    "bnc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f79907c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['RN_INDI', 'MDCARE_STRT_DT', 'RN_KEY', 'RN_INST', 'FORM_CD',\n",
      "       'MCARE_SUBJ_CD', 'SICK_SYM1', 'SICK_SYM2', 'HSPTZ_PATH_TYPE',\n",
      "       'OFIJ_TYPE', 'OPRTN_YN', 'MDCARE_DD_CNT', 'VSHSP_DD_CNT',\n",
      "       'TOT_PRSC_DD_CNT', 'MCARE_RSLT_TYPE', 'FST_HSPTZ_DT', 'EDC_ADD_RT',\n",
      "       'SPCF_SYM_TYPE', 'ED_RC_TOT_AMT', 'EDC_SBA', 'EDC_INSUR_BRDN_AMT',\n",
      "       'STD_YYYY'],\n",
      "      dtype='object')\n",
      "Index(['STD_YYYY', 'RN_INDI', 'SEX', 'SGG', 'GAIBJA_TYPE', 'CTRB_Q10',\n",
      "       'DSB_TYPE_CD', 'G1E_OBJ_YN', 'SMPL_TYPE_CD', 'DSB_SVRT_CD_V2'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(m20.columns)\n",
    "print(bnc.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "65393aed",
   "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>RN_INDI</th>\n",
       "      <th>MDCARE_STRT_DT</th>\n",
       "      <th>RN_KEY</th>\n",
       "      <th>RN_INST</th>\n",
       "      <th>FORM_CD</th>\n",
       "      <th>MCARE_SUBJ_CD</th>\n",
       "      <th>SICK_SYM1</th>\n",
       "      <th>SICK_SYM2</th>\n",
       "      <th>HSPTZ_PATH_TYPE</th>\n",
       "      <th>OFIJ_TYPE</th>\n",
       "      <th>...</th>\n",
       "      <th>EDC_INSUR_BRDN_AMT</th>\n",
       "      <th>STD_YYYY</th>\n",
       "      <th>SEX</th>\n",
       "      <th>SGG</th>\n",
       "      <th>GAIBJA_TYPE</th>\n",
       "      <th>CTRB_Q10</th>\n",
       "      <th>DSB_TYPE_CD</th>\n",
       "      <th>G1E_OBJ_YN</th>\n",
       "      <th>SMPL_TYPE_CD</th>\n",
       "      <th>DSB_SVRT_CD_V2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1001347</td>\n",
       "      <td>20160128</td>\n",
       "      <td>2016010777797</td>\n",
       "      <td>53079</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>L239</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12660</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>45750</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001347</td>\n",
       "      <td>20160114</td>\n",
       "      <td>2016012377871</td>\n",
       "      <td>55889</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>S134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12780</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>45750</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001347</td>\n",
       "      <td>20160119</td>\n",
       "      <td>2016010890509</td>\n",
       "      <td>55889</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>S134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12480</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>45750</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001347</td>\n",
       "      <td>20160127</td>\n",
       "      <td>2016010262074</td>\n",
       "      <td>55889</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>S134</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12480</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>45750</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1001347</td>\n",
       "      <td>20160102</td>\n",
       "      <td>2016011681112</td>\n",
       "      <td>53079</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>L239</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12660</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>45750</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RN_INDI  MDCARE_STRT_DT         RN_KEY  RN_INST  FORM_CD  MCARE_SUBJ_CD  \\\n",
       "0  1001347        20160128  2016010777797    53079        3             14   \n",
       "1  1001347        20160114  2016012377871    55889        3              5   \n",
       "2  1001347        20160119  2016010890509    55889        3              5   \n",
       "3  1001347        20160127  2016010262074    55889        3              5   \n",
       "4  1001347        20160102  2016011681112    53079        3             14   \n",
       "\n",
       "  SICK_SYM1 SICK_SYM2  HSPTZ_PATH_TYPE OFIJ_TYPE  ...  EDC_INSUR_BRDN_AMT  \\\n",
       "0      L239       NaN              NaN         0  ...               12660   \n",
       "1      S134       NaN              NaN         0  ...               12780   \n",
       "2      S134       NaN              NaN         0  ...               12480   \n",
       "3      S134       NaN              NaN         0  ...               12480   \n",
       "4      L239       NaN              NaN         0  ...               12660   \n",
       "\n",
       "   STD_YYYY  SEX    SGG  GAIBJA_TYPE  CTRB_Q10  DSB_TYPE_CD G1E_OBJ_YN  \\\n",
       "0      2016    1  45750            6       NaN          4.0          Y   \n",
       "1      2016    1  45750            6       NaN          4.0          Y   \n",
       "2      2016    1  45750            6       NaN          4.0          Y   \n",
       "3      2016    1  45750            6       NaN          4.0          Y   \n",
       "4      2016    1  45750            6       NaN          4.0          Y   \n",
       "\n",
       "   SMPL_TYPE_CD  DSB_SVRT_CD_V2  \n",
       "0             2             1.0  \n",
       "1             2             1.0  \n",
       "2             2             1.0  \n",
       "3             2             1.0  \n",
       "4             2             1.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# m20 : 진료 명세서\n",
    "# bnc : 자격 정보\n",
    "\n",
    "# 2개의 데이터를 하나의 데이터로 합치고 싶음 : m20_bnc\n",
    "# primary key : 주요키, 기본키, RN_INDI, STD_YYYY\n",
    "\n",
    "m20_bnc = pd.merge(left  = m20, \n",
    "                   right = bnc, \n",
    "                   on    = ['RN_INDI', 'STD_YYYY'], \n",
    "                   how   = 'inner')\n",
    "m20_bnc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fc3dd95f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "중복된 행:\n",
      "       RN_INDI  MDCARE_STRT_DT         RN_KEY  RN_INST  FORM_CD  \\\n",
      "0      1001347        20160128  2016010777797    53079        3   \n",
      "1      1001347        20160114  2016012377871    55889        3   \n",
      "2      1001347        20160119  2016010890509    55889        3   \n",
      "3      1001347        20160127  2016010262074    55889        3   \n",
      "4      1001347        20160102  2016011681112    53079        3   \n",
      "...        ...             ...            ...      ...      ...   \n",
      "56522  1001347        20170804  2017080603813    36371        2   \n",
      "56523   394016        20170805  2017082034745   112785        2   \n",
      "56524   413591        20180621  2018060138563    81393        3   \n",
      "56525   700176        20170403  2017041737334    80758        2   \n",
      "56526     6088        20181218  2018120404360    23654        3   \n",
      "\n",
      "       MCARE_SUBJ_CD SICK_SYM1 SICK_SYM2  HSPTZ_PATH_TYPE OFIJ_TYPE  ...  \\\n",
      "0                 14      L239       NaN              NaN         0  ...   \n",
      "1                  5      S134       NaN              NaN         0  ...   \n",
      "2                  5      S134       NaN              NaN         0  ...   \n",
      "3                  5      S134       NaN              NaN         0  ...   \n",
      "4                 14      L239       NaN              NaN         0  ...   \n",
      "...              ...       ...       ...              ...       ...  ...   \n",
      "56522              5     S8250     M6597             32.0         0  ...   \n",
      "56523              1      K659      C482             32.0         0  ...   \n",
      "56524              1      S011       NaN              NaN         0  ...   \n",
      "56525              6      M513      K297             32.0         0  ...   \n",
      "56526             13       T16       NaN              NaN         0  ...   \n",
      "\n",
      "       EDC_INSUR_BRDN_AMT  STD_YYYY  SEX    SGG  GAIBJA_TYPE  CTRB_Q10  \\\n",
      "0                   12660      2016    1  45750            6       NaN   \n",
      "1                   12780      2016    1  45750            6       NaN   \n",
      "2                   12480      2016    1  45750            6       NaN   \n",
      "3                   12480      2016    1  45750            6       NaN   \n",
      "4                   12660      2016    1  45750            6       NaN   \n",
      "...                   ...       ...  ...    ...          ...       ...   \n",
      "56522              550720      2017    1  45750            6       NaN   \n",
      "56523             3691610      2017    1  50130            6       7.0   \n",
      "56524               57040      2018    1  48270            1      10.0   \n",
      "56525              123370      2017    1  47840            1      10.0   \n",
      "56526               20850      2018    1  27230            5       4.0   \n",
      "\n",
      "       DSB_TYPE_CD G1E_OBJ_YN  SMPL_TYPE_CD  DSB_SVRT_CD_V2  \n",
      "0              4.0          Y             2             1.0  \n",
      "1              4.0          Y             2             1.0  \n",
      "2              4.0          Y             2             1.0  \n",
      "3              4.0          Y             2             1.0  \n",
      "4              4.0          Y             2             1.0  \n",
      "...            ...        ...           ...             ...  \n",
      "56522          4.0        NaN             2             1.0  \n",
      "56523          NaN        NaN             1             NaN  \n",
      "56524          NaN        NaN             1             NaN  \n",
      "56525          4.0        NaN             1             2.0  \n",
      "56526          NaN          Y             1             NaN  \n",
      "\n",
      "[56304 rows x 30 columns]\n",
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 56527 entries, 0 to 56526\n",
      "Series name: None\n",
      "Non-Null Count  Dtype\n",
      "--------------  -----\n",
      "56527 non-null  bool \n",
      "dtypes: bool(1)\n",
      "memory usage: 55.3 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# 중복 확인\n",
    "duplicates = m20_bnc.duplicated(subset = ['RN_INDI', 'STD_YYYY'], \n",
    "                                keep   = False)\n",
    "duplicate_rows = m20_bnc[duplicates]\n",
    "print(\"중복된 행:\")\n",
    "print(duplicate_rows)\n",
    "print(duplicates.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5337905f",
   "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>GAIBJA_TYPE</th>\n",
       "      <th>GAIBJA_TYPE2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>직장가입자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>직장가입자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>직장가입자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>직장가입자</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>직장가입자</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   GAIBJA_TYPE GAIBJA_TYPE2\n",
       "0            6        직장가입자\n",
       "1            6        직장가입자\n",
       "2            6        직장가입자\n",
       "3            6        직장가입자\n",
       "4            6        직장가입자"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 전처리 : GAIBJA_TYPE(건강보험 가입자 자격 구분)\n",
    "\n",
    "# GAIBJA_TYPE   -> GAIBJA_TYPE2\n",
    "# 1, 2             지역가입자\n",
    "# 5, 6             직장가입자\n",
    "# 7, 8             의료급여\n",
    "\n",
    "conditions = [\n",
    "    m20_bnc['GAIBJA_TYPE'].isin([1, 2]),\n",
    "    m20_bnc['GAIBJA_TYPE'].isin([5, 6]),\n",
    "    m20_bnc['GAIBJA_TYPE'].isin([7, 8])\n",
    "]\n",
    "\n",
    "choices                 = ['지역가입자', '직장가입자', '의료급여']\n",
    "m20_bnc['GAIBJA_TYPE2'] = np.select(conditions, choices, default = np.nan)\n",
    "m20_bnc[['GAIBJA_TYPE', 'GAIBJA_TYPE2']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bf18b48",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GAIBJA_TYPE2</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>의료급여</th>\n",
       "      <td>4873.0</td>\n",
       "      <td>155726.492920</td>\n",
       "      <td>478098.493870</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13100.0</td>\n",
       "      <td>25860.0</td>\n",
       "      <td>146120.0</td>\n",
       "      <td>11468400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>지역가입자</th>\n",
       "      <td>12933.0</td>\n",
       "      <td>74447.335498</td>\n",
       "      <td>504040.508397</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12720.0</td>\n",
       "      <td>16410.0</td>\n",
       "      <td>29980.0</td>\n",
       "      <td>28217200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>직장가입자</th>\n",
       "      <td>38721.0</td>\n",
       "      <td>60823.752486</td>\n",
       "      <td>371033.889637</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12750.0</td>\n",
       "      <td>15740.0</td>\n",
       "      <td>24600.0</td>\n",
       "      <td>24701280.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count           mean            std  min      25%      50%  \\\n",
       "GAIBJA_TYPE2                                                                 \n",
       "의료급여           4873.0  155726.492920  478098.493870  0.0  13100.0  25860.0   \n",
       "지역가입자         12933.0   74447.335498  504040.508397  0.0  12720.0  16410.0   \n",
       "직장가입자         38721.0   60823.752486  371033.889637  0.0  12750.0  15740.0   \n",
       "\n",
       "                   75%         max  \n",
       "GAIBJA_TYPE2                        \n",
       "의료급여          146120.0  11468400.0  \n",
       "지역가입자          29980.0  28217200.0  \n",
       "직장가입자          24600.0  24701280.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# GAIBJA_TYPE2 : 건강보험가입자 자격 구분(지역, 직장, 의료급여)\n",
    "# 진료비\n",
    "\n",
    "# 건강보험가입자 자격 구분에 따른 진료비의 기술통계량\n",
    "m20_bnc.groupby('GAIBJA_TYPE2')['ED_RC_TOT_AMT'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "19214d47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 건강보험가입자 자격 구분에 따른 진료비의 바이올린\n",
    "plt.figure(figsize = (10, 8))\n",
    "sns.violinplot(x = 'GAIBJA_TYPE2', y = 'ED_RC_TOT_AMT', data = m20_bnc)\n",
    "plt.title('ED_RC_TOT_AMT에 대한 바이올린 플롯 (GAIBJA_TYPE2 별)')\n",
    "plt.xlabel('GAIBJA_TYPE2')\n",
    "plt.ylabel('ED_RC_TOT_AMT')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1ef6160",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}