{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b012d01e-fb1c-4425-b2d6-7d8165e722b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 패키지 로딩하기\n",
    "import pandas            as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn           as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a59eda7d-9180-46b5-891d-0f0f15ffe3c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 그래프의 한글 깨짐 방지\n",
    "plt.rcParams[\"font.family\"] = \"Malgun Gothic\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7c82c39b-094d-4ef0-beb2-5dca64ffb029",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PID</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>SCH_ID</th>\n",
       "      <th>SEX</th>\n",
       "      <th>DQ1M1</th>\n",
       "      <th>DQ1M2</th>\n",
       "      <th>DQ1M3</th>\n",
       "      <th>DQ1M4</th>\n",
       "      <th>DQ1M5</th>\n",
       "      <th>DQ1M6</th>\n",
       "      <th>...</th>\n",
       "      <th>Q36_2M3</th>\n",
       "      <th>Q36_2M4</th>\n",
       "      <th>Q36_2M5</th>\n",
       "      <th>Q36_2M6</th>\n",
       "      <th>Q36_2M7</th>\n",
       "      <th>WT_A</th>\n",
       "      <th>WT_B</th>\n",
       "      <th>CLASS1</th>\n",
       "      <th>CLASS2</th>\n",
       "      <th>AREA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9003</td>\n",
       "      <td>2</td>\n",
       "      <td>9739</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9018</td>\n",
       "      <td>2</td>\n",
       "      <td>9783</td>\n",
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       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9019</td>\n",
       "      <td>2</td>\n",
       "      <td>9793</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9060</td>\n",
       "      <td>2</td>\n",
       "      <td>9725</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9066</td>\n",
       "      <td>2</td>\n",
       "      <td>9727</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 313 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PID  TYPE  SCH_ID  SEX  DQ1M1  DQ1M2  DQ1M3  DQ1M4  DQ1M5  DQ1M6  ...  \\\n",
       "0  9003     2    9739    2      3    NaN    NaN    NaN    NaN    NaN  ...   \n",
       "1  9018     2    9783    2      4    NaN    NaN    NaN    NaN    NaN  ...   \n",
       "2  9019     2    9793    2      2    3.0    4.0    NaN    NaN    NaN  ...   \n",
       "3  9060     2    9725    1      3    4.0    NaN    NaN    NaN    NaN  ...   \n",
       "4  9066     2    9727    1      1    2.0    4.0    NaN    NaN    NaN  ...   \n",
       "\n",
       "   Q36_2M3  Q36_2M4 Q36_2M5  Q36_2M6 Q36_2M7  WT_A  WT_B  CLASS1  CLASS2  AREA  \n",
       "0      NaN      NaN     NaN      NaN     NaN   1.0   1.0     NaN     2.0     4  \n",
       "1      NaN      NaN     NaN      NaN     NaN   1.0   1.0     NaN     2.0     2  \n",
       "2      NaN      NaN     NaN      NaN     NaN   1.0   1.0     NaN     2.0     2  \n",
       "3      NaN      NaN     NaN      NaN     NaN   1.0   1.0     NaN     2.0     1  \n",
       "4      NaN      NaN     NaN      NaN     NaN   1.0   1.0     NaN     2.0     4  \n",
       "\n",
       "[5 rows x 313 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 엑셀 파일 경로를 지정합니다.\n",
    "file_path = 'd:/NCRC/10대 청소년의 정신건강 실태조사.xlsx'\n",
    "\n",
    "# pandas의 read_excel 함수를 사용하여 엑셀 파일을 읽어옵니다.\n",
    "survey = pd.read_excel(file_path)\n",
    "survey.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48f625cc-3fb3-4a11-becd-3fcdca860f4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6689 entries, 0 to 6688\n",
      "Columns: 313 entries, PID to AREA\n",
      "dtypes: float64(61), int64(250), object(2)\n",
      "memory usage: 16.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# 데이터의 정보 보기\n",
    "survey.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4fcbdcea-c042-4201-bda3-07e523ffef64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PID          0\n",
       "TYPE         0\n",
       "SCH_ID       0\n",
       "SEX          0\n",
       "DQ1M1        0\n",
       "          ... \n",
       "WT_A         0\n",
       "WT_B         0\n",
       "CLASS1     752\n",
       "CLASS2    5937\n",
       "AREA         0\n",
       "Length: 313, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 결측치의 현황\n",
    "missing_values = survey.isnull().sum()\n",
    "missing_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9264939-9aed-4ac0-8111-d96416514af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 결측치 현황을 엑셀로 저장하기\n",
    "missing_values.to_excel(\"d:/NCRC/missing.xlsx\", index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "93f5f88f-a061-4577-b0e2-afaaa85b4d3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PID</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>SCH_ID</th>\n",
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       "      <th>DQ1M6</th>\n",
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       "      <th>Q36_2M5</th>\n",
       "      <th>Q36_2M6</th>\n",
       "      <th>Q36_2M7</th>\n",
       "      <th>WT_A</th>\n",
       "      <th>WT_B</th>\n",
       "      <th>CLASS1</th>\n",
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       "      <th>0</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>가족과 함께 살고 있다</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9018</td>\n",
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       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>가족과 함께 살고 있다</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9019</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>가족과 함께 살고 있다</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9060</td>\n",
       "      <td>2</td>\n",
       "      <td>9725</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>가족과 함께 살고 있다</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9066</td>\n",
       "      <td>2</td>\n",
       "      <td>9727</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>가족과 함께 살고 있다</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 314 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PID  TYPE  SCH_ID  SEX  DQ1M1  DQ1M2  DQ1M3  DQ1M4  DQ1M5  DQ1M6  ...  \\\n",
       "0  9003     2    9739    2      3    NaN    NaN    NaN    NaN    NaN  ...   \n",
       "1  9018     2    9783    2      4    NaN    NaN    NaN    NaN    NaN  ...   \n",
       "2  9019     2    9793    2      2    3.0    4.0    NaN    NaN    NaN  ...   \n",
       "3  9060     2    9725    1      3    4.0    NaN    NaN    NaN    NaN  ...   \n",
       "4  9066     2    9727    1      1    2.0    4.0    NaN    NaN    NaN  ...   \n",
       "\n",
       "   Q36_2M4  Q36_2M5 Q36_2M6  Q36_2M7 WT_A  WT_B  CLASS1  CLASS2  AREA  \\\n",
       "0      NaN      NaN     NaN      NaN  1.0   1.0     NaN     2.0     4   \n",
       "1      NaN      NaN     NaN      NaN  1.0   1.0     NaN     2.0     2   \n",
       "2      NaN      NaN     NaN      NaN  1.0   1.0     NaN     2.0     2   \n",
       "3      NaN      NaN     NaN      NaN  1.0   1.0     NaN     2.0     1   \n",
       "4      NaN      NaN     NaN      NaN  1.0   1.0     NaN     2.0     4   \n",
       "\n",
       "           거주형태  \n",
       "0  가족과 함께 살고 있다  \n",
       "1  가족과 함께 살고 있다  \n",
       "2  가족과 함께 살고 있다  \n",
       "3  가족과 함께 살고 있다  \n",
       "4  가족과 함께 살고 있다  \n",
       "\n",
       "[5 rows x 314 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터의 가공\n",
    "residence_mapping = {1: \"가족과 함께 살고 있다\", \n",
    "                     2: \"기숙사/하숙/자취\", \n",
    "                     3: \"기타\"}\n",
    "survey['거주형태'] = survey['DQ2'].map(residence_mapping)\n",
    "survey.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "338f6925-f31a-458b-8a51-917306dfa0b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "거주형태\n",
       "가족과 함께 살고 있다    6423\n",
       "기숙사/하숙/자취        194\n",
       "기타                72\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 빈도(Frequency) 구하기\n",
    "residence_counts = survey['거주형태'].value_counts()\n",
    "residence_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "435dfdae-3970-49b6-a39f-e714833e5ad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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offv22rJli7p06aL9+/fL399fixcv1rx587Rjxw55e3vrpZdeUmRkpJYuXSrp6kQazZo10+LFi3Xq1Ck9+OCDCgkJUefOnYt1/AAAAABgsdlsNmcP4g9Vq1bV1q1btWXLFq1du1bLli2z14WHh6tt27aKiorSgw8+qP/5n/9ReHi4JCk5OVk1atRQYmKikpOT1bJlS506dUqurlcz49SpU7VlyxaH/eUnPT1d3t7eSktLk5eX18050RJg4p7k6zdCqfb6vX7OHgIAAIBTFTYb3Baz/12+fFmxsbEKCwvTXXfdpbi4OLVs2dKhTVhYmPbu3avs7Gzt2rXLod7Pz09BQUHat2+f4uLi1KxZM3ug+nPfvGRmZio9Pd1hAwAAAIDCcmqoOnTokAIDA1WxYkV99tlnmj59uiQpISFB1atXd2hbrVo1nT17VklJSbJarfLz88uzvqC+eZkwYYK8vb3tW2BgYDGeIQAAAIDSzqmhqn79+jp+/LguXryooUOHqkWLFvr9999ltVp17V2JVqtVFotFVqtVkgqsz68uL9HR0UpLS7Nvx48fL8YzBAAAAFDa3Ra3/3l4eKh3797q3Lmz5s6dK19fX4fZ/CQpKSlJ/v7+8vHxkc1m07lz5/KsL6hvXtzd3eXl5eWwAQAAAEBh3Rah6g/u7u6qWLGiQkNDtW3bNoe6rVu3qkWLFvL09FRISIhD/alTp3T69Gk1bdpUoaGh2r59u3JycnL1BQAAAIDi5rRQdfLkSS1YsEDZ2dmSri4svGLFCvXq1Ut9+vTRxo0btWnTJknSmjVrtH//fkVEREiSIiMjNXbsWKWmpiorK0vR0dEaNGiQKlasqLCwMAUEBGjSpEnKycnR4cOHNWPGDA0ZMsRZpwoAAACgFCvSOlXFyd3dXR999JGioqJUuXJl1a9fXytXrlRwcLAkaeHChRo8eLBSUlIUHBysVatWydPTU5IUFRWlkydPqkGDBnJ1dVV4eLgmTpwoSbJYLFq6dKkGDBigKVOmyMfHRzExMQoNDXXWqQIAAAAoxW6rdapuB6xTdRXrVIF1qgAAQFlXotapAgAAAICSilAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABggFAFAAAAAAYIVQAAAABgwKmhatOmTWrZsqWCg4NVv359vffee/a6PXv2qHnz5goKClLDhg21fv16h76xsbEKDg5WzZo11bVrVyUnJ9vrzp49q4iICNWuXVtBQUGKiYm5ZecEAAAAoGxxaqhasGCBZs+erYMHD+rrr7/WxIkTtW7dOmVkZKhz584aN26c4uPjNXPmTPXq1UuJiYmSpMWLF2vevHnasWOHjh07poCAAEVGRtr3269fPzVu3Fjx8fGKi4vT9OnTtWrVKmedJgAAAIBSzGKz2WzOHsQfXnnlFbm6uio4OFhr167VsmXL7HXh4eFq27atoqKi9OCDD+p//ud/FB4eLklKTk5WjRo1lJiYqOTkZLVs2VKnTp2Sq6urJGnq1KnasmWLw/7yk56eLm9vb6WlpcnLy+vmnGgJMHFP8vUboVR7/V4/Zw8BAADAqQqbDW6rZ6qSkpLk7e2tuLg4tWzZ0qEuLCxMe/fuVXZ2tnbt2uVQ7+fnp6CgIO3bt09xcXFq1qyZPVD9uS8AAAAAFLfbJlTt2LFDq1evVu/evZWQkKDq1as71FerVk1nz55VUlKSrFar/Pz88qwvqG9eMjMzlZ6e7rABAAAAQGHdFqHq888/V3h4uObNm6e6devKarXq2rsSrVarLBaLrFarJBVYn19dXiZMmCBvb2/7FhgYWIxnBgAAAKC0c2qoslqtevHFFzVmzBitX79enTt3liT5+vo6zOYnXb010N/fXz4+PrLZbDp37lye9QX1zUt0dLTS0tLs2/Hjx4vxDAEAAACUdk4NVVFRUTp06JB27Nihe+65x14eGhqqbdu2ObTdunWrWrRoIU9PT4WEhDjUnzp1SqdPn1bTpk0VGhqq7du3KycnJ1ffvLi7u8vLy8thAwAAAIDCclqounTpkmbOnKm5c+eqUqVKDnV9+vTRxo0btWnTJknSmjVrtH//fkVEREiSIiMjNXbsWKWmpiorK0vR0dEaNGiQKlasqLCwMAUEBGjSpEnKycnR4cOHNWPGDA0ZMuSWnyMAAACA0s/1+k1ujiNHjignJ0fNmjVzKK9fv742btyohQsXavDgwUpJSVFwcLBWrVolT09PSVevcJ08eVINGjSQq6urwsPDNXHiREmSxWLR0qVLNWDAAE2ZMkU+Pj6KiYlRaGjoLT9HAAAAAKXfbbVO1e2AdaquYp0qsE4VAAAo60rkOlUAAAAAUNIQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAAKEKAAAAAAwQqgAAAADAgFNDlc1m07x589S8eXOH8j179qh58+YKCgpSw4YNtX79eof62NhYBQcHq2bNmuratauSk5PtdWfPnlVERIRq166toKAgxcTE3JJzAQAAAFA2OS1UrVu3Tk2aNNHYsWOVmppqL8/IyFDnzp01btw4xcfHa+bMmerVq5cSExMlSYsXL9a8efO0Y8cOHTt2TAEBAYqMjLT379evnxo3bqz4+HjFxcVp+vTpWrVq1a0+PQAAAABlhNNC1fnz5zV+/Hh99NFHDuULFizQAw88oPbt20uSWrVqpdatW2vRokWSrl6lGjNmjHx9feXi4qK3335bq1evVkpKig4cOKCdO3dq5MiRslgsqlGjhqKiovTxxx/f8vMDAAAAUDa4OuvAPXv2lCRt3rzZoTwuLk4tW7Z0KAsLC9PevXuVnZ2tXbt2OdT7+fkpKChI+/bt09GjR9WsWTO5uro69H333XfzHUdmZqYyMzPtr9PT001OCwAAAEAZc9tNVJGQkKDq1as7lFWrVk1nz55VUlKSrFar/Pz88qwvqG9+JkyYIG9vb/sWGBhYfCcDAAAAoNS77UKV1WqVzWbLVWaxWGS1WiWpwPr86vITHR2ttLQ0+3b8+PFiOhMAAAAAZcFtF6p8fX0dZvOTpKSkJPn7+8vHx0c2m03nzp3Ls76gvvlxd3eXl5eXwwYAAAAAhXXbharQ0FBt27bNoWzr1q1q0aKFPD09FRIS4lB/6tQpnT59Wk2bNlVoaKi2b9+unJycXH0BAAAA4Ga47UJVnz59tHHjRm3atEmStGbNGu3fv18RERGSpMjISPs07FlZWYqOjtagQYNUsWJFhYWFKSAgQJMmTVJOTo4OHz6sGTNmaMiQIc48JQAAAAClmNNm/8tPrVq1tHDhQg0ePFgpKSkKDg7WqlWr5OnpKUmKiorSyZMn1aBBA7m6uio8PFwTJ06UJFksFi1dulQDBgzQlClT5OPjo5iYGIWGhjrzlAAAAACUYhbbtTM7lHHp6eny9vZWWlpamX6+auKe5Os3Qqn2+r1+128EAABQihU2G9x2t/8BAAAAQElCqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA4QqAAAAADBAqAIAAAAAA67OHgAA4Db1mcXZI4Cz9bY5ewQAUCJwpQoAAAAADBCqAAAAAMAAoQoAAAAADBCqAAAAAMAAoQoAAAAADBCqAAAAAMAAoQoAAAAADBCqAAAAAMBAqVz899KlS4qKitJXX30lq9Wqp59+WpMmTVK5cmRIAACAwrKMZRHwss42hkXAC6NUpozhw4crJydHhw4d0s8//6zNmzdr+vTpzh4WAAAAgFKo1IWq8+fPa+7cuZo0aZJcXV3l7e2tN954Q5988omzhwYAAACgFCp1oeq///2v6tatq6pVq9rLwsLC9NNPPyk7O9uJIwMAAABQGpW6Z6oSEhJUvXp1h7Jq1aopOztb6enp8vX1dajLzMxUZmam/XVaWpokKT09/eYP9jZ2+XyGs4cAJ0tPd3P2EOBsF509ADhdGf9fCEmXnT0AOFtZ/0z8x/nbbAU/W1bqQpXVas110larVZJkseR+2HLChAkaO3ZsrvLAwMCbM0CghMj9VwGgzBnk7ewRAHAy74m8D0hSRkaGvL3z/12UulDl6+ur5ORkh7KkpCRVqFAhz19EdHS0XnnlFfvrnJwcpaSkqGrVqnmGMJR+6enpCgwM1PHjx+Xl5eXs4QBwAt4HAEi8F+DqFaqMjAzVqFGjwHalLlTdd999+u2333Tu3Dn5+PhIkrZu3aqwsLA8p1R3d3eXu7u7Q1mVKlVuxVBxm/Py8uINFCjjeB8AIPFeUNYVdIXqD6Vuogp/f3917NhRb7zxhrKzs5WcnKzx48dr6NChzh4aAAAAgFKo1IUqSfroo4+UkJCggIAA3X///YqMjFTXrl2dPSwAAAAApVCpu/1Pkvz8/LRixQpnDwMllLu7u8aMGZPrtlAAZQfvAwAk3gtQeBbb9eYHBAAAAADkq1Te/gcAAAAAtwqhCgAAAPj/5syZo759+xa6fXZ2NsvwgFCFwrFYLMrOzr4lx7p8+bJcXYv2uF98fLweeuihXOWrV6/W6NGjb2gcc+bMUf/+/W+o75/FxcXlWjvtVps+fbr+/ve/F6nPhg0b1KZNm5szIJRZRf2wcqMOHjyoOnXqFLnfm2++qVGjRuUqr1Onjg4ePFgMI7txo0aN0ptvvllgm/79+2v27Nm5yuvUqaP4+PibNDKg5Dh+/Lj8/f0dtmrVqqlSpUr59tmwYYPc3d1Vq1Yth+3hhx++hSPH7Y5QBX3zzTe53mB8fX3VokWL6/Zt1KiR/Pz87Jubm5sqV67sUDZmzBiHPm+99Vau4/n7+8vFxaVI4z5z5ox++ukn/fTTT/rll1905MgR++uffvpJFy9eVHJysg4fPuzQ79ChQ6pVq5aqVq2q559/XnXq1NEdd9yhxx57TJLUpk0bfffdd0UaiyT9/e9/15w5c3KVR0dHa+/evbnKv/jiizx/D39sFotFly9fLtSxf/31Vz3xxBMKDg5WcHCwnn76aZ04caLAPqNGjZKXl1eufxIjRowo1DGBa93Ih5W8dOjQQZ9++mmh29eqVUtHjx4t4mhvjsjISL399ttF7nfixAn732C1atUcPsD9eYH6vCxZskSxsbGKjY3VL7/8og0bNthf//DDD5KufpPOI9SAFBgYqMTERIdt3bp1ql27doH9WrRooRMnTjhs//nPf27RqFESlMrZ/1A0jzzyiBITEx3K5s+fr1WrVl23788//2z/ee3atRo+fLh8fX21aNEi1axZM88+o0ePznX1KDMzs8iL6m3YsEGfffaZ/fW9996r119/3f568uTJefarX7++Tpw4oVmzZun333/X0aNH9eWXX+b57W5RHD16VNWrVy90+x49eqhHjx751hf2at25c+f06KOPaurUqerRo4dsNpveffddderUSXv27CkwrL7yyivX/eYbKKw/Pqz82e7du4t0ZWrVqlU6cuSIYmNj9dhjj8nPz6+4hylJSktLU0hIiPbv36+2bdvq2LFjkqSLFy9KkmbOnClJqlGjhn788cdC7XPHjh3avHmzXF1d1adPH9WrV++6fR566CGNHTtW7dq1s38R8v3332vo0KH6/vvvC3XcS5cu6fz585KkpKQkVaxY0f76ypUrhdoHUJZ9++23evDBB509DJRwhCrkacuWLdd9g0lOTtYvv/yinTt3au3atWrUqJG+++47HTp0SIMGDVKVKlX0+OOPq2HDhmrUqJHc3Nzy3Vd2drbKly9fpDH27t1bvXv31vr167Vy5UolJyerVq1a6tevn5o2bSrp6oec/CQmJqpatWqSpJSUlEKtlp2fuLg4ValSRVu3btWRI0dUt25dh/oePXqofPny+vLLL9WsWbMbPk5evv32W91zzz32gGaxWBQVFaVZs2bp119/VePGjYv1eEBRFPbDyo8//qh33nlHqamp2rp1q/bu3auOHTuqT58+GjhwYIFfumRkZCgtLa1I4/roo4/Us2dPValSRbt37y5S32sdPnxYsbGx+umnn7R27Vqlpqbq6aef1mOPPabBgwfb32eutXv3bqWmpqpdu3YO5ZcuXdKFCxcKffx+/fpJuvolV2xsrE6ePKlBgwYV6UseoKyy2WyaM2eOpk2bVmC7lJQUrVu3zqGsfv36uvPOO2/m8FCCcPsfcklLS9OyZcsUERFRYLvExEStWbNGderU0YoVKzRt2jSNHj1a586d05o1azR27FglJiZqxowZefbPyspSVlaWpKsfIq4NVefPn9f58+cLvGVl2rRpGjVqlB599FGNGDFC9913n7p3765vvvnG3mbz5s164okn9N577zn0/f7773X//fdLkk6ePKn09HQtX768yM8/ff311xo+fLg++OADzZs3T88++6zmzp1rPzfp6q1+ycnJRQpUOTk5hbpa5e3trfT09Fx9z58/rypVqhT6eEBx++PDSkFXqjZu3KimTZtq8uTJev7557VixQpVq1ZNHTp0sP8dt23bVvfdd58yMjJy9T9z5ozS09P122+/FXpcOTk5ev/99zVkyBB7WUJCgvr27as777xTd955p/r06aOEhIQC9/PLL7/oL3/5i1599VV16NBBGzduVP369RUaGqotW7YoMDBQ3bt3V0hISJ7PM02bNk1RUVG5yg8cOKCDBw/KarUW6nxsNpsWLVqkrl276rPPPtOIESPUokULffDBB7fNbZHA7WrOnDny9va+7jPESUlJWrJkicP257t1JNkfe/jvf/97E0eM2xVXqpDLm2++qe7du6tGjRoO5Y8//rgsFotGjBihtm3bqnHjxpo4caJDmxMnTtg/4N95550aPnx4vscZP368srOzNW7cOF26dMnhm2ir1WoPPOvXr8/3XufPP/9c77zzjv1h0fvuu09nz57VF198oUceeUSS1LhxY40aNcrh2+ILFy5o+/bt9ufG4uLilJWVpdWrV+vs2bOF+j1J0muvvaZt27Zp2bJl8vX1lSR9+eWXeu2111SrVq1c30DnJzk5WSdPnrRfYcvMzJTFYilUqGrVqpXKly+vUaNGaeDAgbpy5YomT56sRx99VLVq1Sr0ueTlu+++swez+Ph4o6t5KHsK82GlXbt29ud+rlW5cmUNGzZMw4YNU2ZmZp6Lby5dulQBAQH697//rV69ejnUJScn2yebmTx5sv09YOXKlQoJCVFISIikq1fKO3XqpGeffdb+LNd7772nTp06aefOnfleRW/YsGGez0tKVxcMHThwoAYOHJjn2E+fPq0NGzbYbzP8sxUrVsjNzU0rVqxQ9+7dHepmzZql1atX6y9/+Ytmz56ttLQ0NW/eXE2bNtVXX31lv+Wwbdu2io2N1bFjx3K9TwO46scff9Qbb7yhTZs2XbdtSEjIdR8TcPakVHAuQhUcfP7551q+fHme37KMHz9eLi4uCgoK0tatWxUeHp6rTUZGhjZt2pRrprnrPZdw4cKFXB/Y9+/ff93xtmnTRhMnTpSnp6f8/f114MABzZo1y+FBcT8/PzVv3tyh3/Tp09WzZ095eHgoOztb27Zt0759++Tv71+kGe/eeOMNeXt7q1y5/7voW7lyZYcPSq+//roaNWpU4H6+//57zZw5U6tXr5Z09fdRuXLlQo3BxcVFX331lWbPnq3Ro0erfPny6tChg5588slCn0d+HnroIW3evNl4Pyh7CvNhJb/3kfxc+z6SlZWld999V4sXL1a/fv20a9cu+5cxklSpUiX7VbI//z1NmzZN0dHRDmO1WCwaNmyYvezll1/Wp59+qh9++MG+zxYtWsjFxUUffvih7rvvPt17772FHrt0NUj98YzjzJkz9eyzz6pChQoObX755Rft3r1bs2bN0oQJExQeHu7wXGSvXr00dOhQeXh4SLp6pXrfvn25voCpX7++/ep8Tk5OkcYJlAU7d+5Ut27dNHPmTN19993OHg5KAUIV7BYsWKDhw4dr3bp19qsuf9a0aVP7P+6WLVsW6zcy5cuXt8++VxRjx47V7Nmz9eabbyolJUW1atVSTEyMHn300Xz7HD9+XNOmTdO3334r6erMWQ0bNpS/v3+Rj+/j4yNJeumll7RkyZJ8233++ecKCAgo9H6TkpKK9DxE+fLl9cILL+iZZ57RpUuXHB7uT0lJUbt27fT4448Xen+AicJ+WMnvfaRr167q27evevbsWeBxoqOj9eCDD+qhhx7Shx9+qKeeeko7duywv395eHioffv2Dn1+/PFHnT59Wn/961/tZR4eHrpw4YJsNpvDWjPnz593eBY0Li5OwcHB9td5jX3o0KGqVauWXn311XzHnZWVpdmzZysuLs6hPDs7W/3799fkyZP15JNPau7cuXrrrbc0duxYe5sqVao4TBXftWtX/frrr8rOzrZvOTk5stlsysnJUbly5fT+++/nOxagrLl48aKmTp2q9957T5988kmhP3ucOnVKs2fPls1mk9VqVVZWls6fP6/AwEA9/fTTN3nUKAkIVdDp06c1atQoffvtt9qwYYMaNmx4y8dQv359TZo0qcj9XFxc9Le//U1/+9vf8qzv379/rrWmnn/+eb322mu68847lZ2drcmTJ+e5Lk1RTJ8+XdOnT8+zrn379srMzCzS/u64444bumVn4cKF2rhxo8OsiDExMZKuXmm81urVq5WYmCibzabs7GxlZmbqwoUL6tevX5FnYwRu9MPKjRg/frw2b95sn9K4Q4cOGjx4sJo3b67ly5fnOzHOtGnT9PLLLzuEp7vvvlv16tXTSy+9pNdff10Wi0WTJ09WQEDATZnoZdGiRWrZsqXD7blXrlzRwIED1aBBAz3zzDOSpHnz5ql58+aqVKmSXnvttTz3NW/ePPvzl66urnr55ZfVpEkTvfTSSw7t8np2CyiLNmzYoK1bt+r7778v9Fp2devWVUREhOLj42WxWFSuXDl5eHioatWqCgoKurkDRolBqIIWLFggV1dX7dq1q8gfpCdMmJDvt6BWq1UuLi4O6yUtXbpUJ0+elNVqVVxcnK5cuaK33npLmZmZ9q2oYWLOnDl64YUX8nze5/Lly3riiSc0f/58e9n8+fPtV3JGjRqlypUr53puwdl8fX3VrVu3Ivfbu3evdu3aZf+GWlK+t1326dNH999/v/0fRLly5VShQgX5+PioTp06PGiLIruRDyuS9M9//jPPiWReeuklWa1Wubu7O7yPHDlyRP/5z3+0bt06VaxY0V7+yiuvqEKFCjp37lyeV3qTk5O1bt26XMeyWCxatmyZYmJi7DPptWnTRitXrnS4tTcv8+bNy3Nttz++zDh9+rSuXLnicHveu+++q3fffdeh/dq1a5WWlqZFixbZy/z8/LRx40bFxMTkewvfte/Z1xsvUNZ16dJFXbp0KVKf+vXra9y4cfnWZ2dnmw4LpQChCho6dOgN942OjnZ4NuHPjh49qoceesih7NKlS0pJSVG5cuXUqlUrubq6ys3NTX5+fqpQoYIqVqx4Q/f/P/nkk3kuvDt//vxcU6Decccd9p/Dw8P1wgsvFPl41+rbt6/WrVuX7yKnf35u4vjx4/k+i5Hfmjx/fhYjP/Hx8dqwYYMefPBBRUdHa/z48dq7d69+/vlnubu7a+fOnXrggQfs7e+++27uI0exupEPK5I0cuRIjRw5Ms+6vN5H6tatq7Vr1+bZ/o+/54MHD+aq+/DDD9W3b1+HIPaHChUq6B//+If+8Y9/FGnszzzzjP3KUl7+fEVMuvocmcViybW4en6/u6CgoFwhEABw+yFU4Zbq06fPddtcvnz5Fozkqms/2JiIiYnJdathXgIDA4t9hqADBw6oW7dumjJlitq1a6cRI0Zo+vTpio2N1SeffKLKlSurV69emjFjhjp06FCsxwZKgitXrmjWrFn22wWdZdq0aUZfZAEAbk/cJ4BSYdGiRfL398+1Xftcwc0UFRWV5xj8/f1v+vMMkyZN0rvvvqvHHntMbm5umjx5sr7++mt99NFHatOmjUJDQ/XFF19o3bp1hV77BihNlixZorCwsHyXZ7gVTpw4oe3bt193DUAAzvX000/nudxBflxdXZWUlHQTR4SSwGIraGVV4P9LTk7O99a0/KSmpupf//pXvg9YF/fxiltmZqauXLmS7y19pd2VK1d08eJF1qZCsSrq39XFixe1efNmderUqUjHycnJUVpamn2GTlPnzp3LtXxCYSxZskQ9evTIdRvgjbh48aIk5Xn74vUkJSXJ19f3urcRAwBuDKEKAAAAAAxw+x8AAAAAGCBUAQAAAIABQhUAAAAAGCBUAQBQCDt37tSFCxdylZ85c0ZnzpxxwogAALcLQhUAoNSZM2eO+vbtW6z7fO6553TkyJFc5R988IHefffdIu/vZowRAOAcLP4LAChRPvnkE0VHRzuUXbp0SZ06ddKCBQvy7dekSROHK0rnzp1ThQoV5OHhYS8bPHiwRo8eLenqNOSTJk2y1yUmJuqdd95R1apVJUkdOnTIdzHt/v37a+XKlQ7Tn48YMUIvv/yyLBaLmHgXAEoXQhUAoER57rnn9NxzzzmUvfnmm/Z1nPLz448/2n8+d+6c7rzzTnXv3l2zZs3Ks72Hh4fuv/9+++t69erJy8tLrq5X/3XWqFGjwONNmTJF/fv3L7ANAKB04PY/AECJt2XLFj344IOFanv48GF17NhRb7zxhn7//XeNHTtWV65cydWucuXKeuqpp3TkyBGNGDFCc+fO1ejRo/X555+rQ4cO+vDDD1WnTh3FxsYW89kAAEoaQhUAoEQ7ePCg9u3bp44dO+bb5vTp01q1apWeeeYZhYeHa9y4cXrllVe0fv16Wa1WNW7cWG+99ZY2bdqk8+fP2/utXr1an332mX788Udt375dhw4dUlBQkIYPH6733ntPR48e1dChQ/M8ZnJyso4ePWrfrFZrcZ86AOA2QagCAJRor776qoYNG6Y333xTfn5+8vPz05AhQxzabN++XV999ZWefPJJ/fDDD/rrX/8qSSpfvrzeeustff/996patarmzp2rrKwse7/Dhw/rkUceUZUqVSRJFotF4eHhOnTo0HXH9f7776tnz572LTU11V7Xt2/ffG87BACUPBYbT8sCAEqod955RwsWLNDWrVvl7u5uL58zZ442bNig+fPnG+3/2LFj6tixoz766CM1b95ciYmJGjhwoHr37q24uDitWLFC6enpevnllzVu3Dh7v/79+6tNmzZ5PlNlsVi0bNkybdq0SSkpKcZjBAA4HxNVAABKpKlTp2rGjBn65ptvHALVteLi4hQREZGr/PLlyypfvrxcXFwcygMCArRz505JUu3atbVx40Z99NFH+uSTT+Tl5aXRo0erefPm6tu3r95//31Nnjy5yLf2de3aVampqdqwYUOR+gEAbk+EKgBAiXLkyBENHz5cCQkJ+vbbb1WzZs0C27do0UInTpzIVd61a1f17dtXPXv2LLB/QECA3njjDX3++ef64osvNHToUGVkZMjPz09NmjTRgAEDdO+99+bqd/HiRaWmpio7O1uXLl3ShQsXdNdddxXtZAEAJQKhCgBQokyfPl333HOPFi5cKDc3t1tyzGeffVZnzpzRmDFj1LBhQ3l6eurs2bPatGmTnnjiCc2aNUuPP/64vX316tUVGxur6dOny83NTZ6engoMDNTChQtvyXgBALcWoQoAUKK88847t/yYCxcu1E8//aSQkBB7mb+/v3r37q0DBw5oyZIlDqFq0qRJDgsHAwBKN2b/AwDgOh555BFNnjxZp0+fdijfvn27/v3vf+uRRx5x0sgAALcDrlQBAHAdS5cu1bRp09SpUydduHBBOTk5KleunOrWravJkyerW7duzh4iAMCJmFIdAFDqZGZm6sqVK6pUqVK+bQ4cOCA/Pz/5+vrewpH9n8KMEQBQMhCqAAAAAMAAz1QBAAAAgAFCFQAAAAAYIFQBAAAAgAFCFQAAAAAYIFQBAAAAgAFCFQAAAAAYIFQBAAAAgAFCFQAAAAAYIFQBAAAAgIH/B0I8vZeuXiwyAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 막대그래프를 그립니다.\n",
    "plt.figure(figsize = (10, 6))\n",
    "residence_counts.plot(kind  = 'bar', \n",
    "                      color =['skyblue', 'orange', 'green'])\n",
    "plt.title('거주형태 분포')\n",
    "plt.xlabel('거주형태')\n",
    "plt.ylabel('빈도수')\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eecec579-399e-40f4-9a43-c5be00aa59f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2c5e8c54-de03-4349-83c0-7cf446fdf741",
   "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>PID</th>\n",
       "      <th>TYPE</th>\n",
       "      <th>SCH_ID</th>\n",
       "      <th>SEX</th>\n",
       "      <th>DQ1M1</th>\n",
       "      <th>DQ1M2</th>\n",
       "      <th>DQ1M3</th>\n",
       "      <th>DQ1M4</th>\n",
       "      <th>DQ1M5</th>\n",
       "      <th>DQ1M6</th>\n",
       "      <th>...</th>\n",
       "      <th>Q36_2M5</th>\n",
       "      <th>Q36_2M6</th>\n",
       "      <th>Q36_2M7</th>\n",
       "      <th>WT_A</th>\n",
       "      <th>WT_B</th>\n",
       "      <th>CLASS1</th>\n",
       "      <th>CLASS2</th>\n",
       "      <th>AREA</th>\n",
       "      <th>거주형태</th>\n",
       "      <th>Q15A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9003</td>\n",
       "      <td>2</td>\n",
       "      <td>9739</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>가족과 함께 살고 있다</td>\n",
       "      <td>2.12500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9018</td>\n",
       "      <td>2</td>\n",
       "      <td>9783</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>가족과 함께 살고 있다</td>\n",
       "      <td>2.53125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9019</td>\n",
       "      <td>2</td>\n",
       "      <td>9793</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>가족과 함께 살고 있다</td>\n",
       "      <td>2.12500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9060</td>\n",
       "      <td>2</td>\n",
       "      <td>9725</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>가족과 함께 살고 있다</td>\n",
       "      <td>2.40625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9066</td>\n",
       "      <td>2</td>\n",
       "      <td>9727</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>가족과 함께 살고 있다</td>\n",
       "      <td>2.34375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 315 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PID  TYPE  SCH_ID  SEX  DQ1M1  DQ1M2  DQ1M3  DQ1M4  DQ1M5  DQ1M6  ...  \\\n",
       "0  9003     2    9739    2      3    NaN    NaN    NaN    NaN    NaN  ...   \n",
       "1  9018     2    9783    2      4    NaN    NaN    NaN    NaN    NaN  ...   \n",
       "2  9019     2    9793    2      2    3.0    4.0    NaN    NaN    NaN  ...   \n",
       "3  9060     2    9725    1      3    4.0    NaN    NaN    NaN    NaN  ...   \n",
       "4  9066     2    9727    1      1    2.0    4.0    NaN    NaN    NaN  ...   \n",
       "\n",
       "   Q36_2M5  Q36_2M6 Q36_2M7  WT_A WT_B  CLASS1  CLASS2  AREA          거주형태  \\\n",
       "0      NaN      NaN     NaN   1.0  1.0     NaN     2.0     4  가족과 함께 살고 있다   \n",
       "1      NaN      NaN     NaN   1.0  1.0     NaN     2.0     2  가족과 함께 살고 있다   \n",
       "2      NaN      NaN     NaN   1.0  1.0     NaN     2.0     2  가족과 함께 살고 있다   \n",
       "3      NaN      NaN     NaN   1.0  1.0     NaN     2.0     1  가족과 함께 살고 있다   \n",
       "4      NaN      NaN     NaN   1.0  1.0     NaN     2.0     4  가족과 함께 살고 있다   \n",
       "\n",
       "      Q15A  \n",
       "0  2.12500  \n",
       "1  2.53125  \n",
       "2  2.12500  \n",
       "3  2.40625  \n",
       "4  2.34375  \n",
       "\n",
       "[5 rows x 315 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 가공\n",
    "# 'Q15A'로 시작하는 열들을 선택합니다.\n",
    "q15a_columns = [col for col in survey.columns if col.startswith('Q15A')]\n",
    "\n",
    "# 선택한 열들의 평균을 계산하여 새로운 열 'Q15A'에 저장합니다.\n",
    "survey['Q15A'] = survey[q15a_columns].mean(axis = 1)\n",
    "survey.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "283b4e38-3984-4afb-8e01-2b4e03e62f34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Q15A의 히스토그램\n",
    "# 'Q15A' 열의 현황을 히스토그램으로 시각화합니다.\n",
    "plt.figure(figsize = (10, 6))\n",
    "plt.hist(survey['Q15A'].dropna(), \n",
    "         bins      = 20, \n",
    "         color     = 'skyblue', \n",
    "         edgecolor = 'black')\n",
    "plt.title('Q15A 열의 현황')\n",
    "plt.xlabel('Q15A 값')\n",
    "plt.ylabel('빈도수')\n",
    "plt.grid(axis = 'y', linestyle = '--', alpha = 0.7)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d2b76301-6b8d-44c7-b88b-5a840e86f07f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 상자그림, 바이올린\n",
    "\n",
    "plt.figure(figsize = (12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(y = survey[\"Q15A\"], color = 'skyblue')\n",
    "plt.title('Q15A 열의 상자그림')\n",
    "plt.ylabel('Q15A 값')\n",
    "\n",
    "# 바이올린 그림을 그립니다.\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.violinplot(y = survey[\"Q15A\"], color='skyblue')\n",
    "plt.title('Q15A 열의 바이올린 그림')\n",
    "plt.ylabel('Q15A 값')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ea999d6-ba75-45fc-99e8-70d6db717740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6689.000000\n",
       "mean        2.480163\n",
       "std         0.305580\n",
       "min         1.000000\n",
       "25%         2.343750\n",
       "50%         2.500000\n",
       "75%         2.625000\n",
       "max         4.000000\n",
       "Name: Q15A, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q15A의 기술통계량\n",
    "survey['Q15A'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "80fef306-1203-46a8-b06b-f5dfc6e7b75c",
   "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>SEX</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>1</th>\n",
       "      <td>3104.0</td>\n",
       "      <td>2.484909</td>\n",
       "      <td>0.353686</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.34375</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.625</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3585.0</td>\n",
       "      <td>2.476055</td>\n",
       "      <td>0.256729</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.34375</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.625</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      count      mean       std  min      25%  50%    75%  max\n",
       "SEX                                                           \n",
       "1    3104.0  2.484909  0.353686  1.0  2.34375  2.5  2.625  4.0\n",
       "2    3585.0  2.476055  0.256729  1.0  2.34375  2.5  2.625  4.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 성별에 따른 Q15A의 기술통계량\n",
    "survey.groupby('SEX')['Q15A'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a693e0b1-09b2-4a4c-93ec-9b8c47ae5f95",
   "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",
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