대여소명과 고장의 연관성 분석.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "29146bb5",
   "metadata": {},
   "source": [
    "# 대여소명과 고장의 연관성 분석\n",
    "\n",
    "대여소명과 고장이 어떤 연관성이 있을지 분석해보았습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "dcdf285c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from math import sqrt, ceil\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rcParams['font.family'] ='Malgun Gothic'\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3c5ad85a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"training_data7~12.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3c52b6f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def 대여소명에_따른_분할(string = '학교'):\n",
    "    df_contain = df[(df['대여 대여소명'].str.contains(string)) | (df['반납대여소명'].str.contains(string))]\n",
    "\n",
    "    df_contain_broken = df_contain[df_contain['고장'] == 1]\n",
    "    df_contain_normal = df_contain[df_contain['고장'] == 0]\n",
    "\n",
    "    print(f\"'{string}'의 고장데이터 : {len(df_contain_broken)}개 / '{string}'의 정상데이터 : {len(df_contain_normal)}개\")\n",
    "    \n",
    "    if len(df_contain_broken) == 0 or len(df_contain_normal) == 0:\n",
    "        print('데이터가 없습니다')\n",
    "        return [0, 0, \"\"]\n",
    "    \n",
    "    if len(df_contain_broken) >= len(df_contain_normal):\n",
    "        print(\"고장데이터가 정상데이터에 대해 {:.2f}% 더 많습니다.\".format((len(df_contain_broken) / len(df_contain_normal) - 1) * 100))\n",
    "    else:\n",
    "        print(\"정상데이터가 고장데이터에 대해 {:.2f}% 더 많습니다.\".format((len(df_contain_normal) / len(df_contain_broken) - 1) * 100))\n",
    "    return [len(df_contain_broken), len(df_contain_normal), string]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "611e13d3",
   "metadata": {},
   "source": [
    "**분석 결과의 신뢰성과 타당성을 높이기 위해, 데이터가 100개 이상인 항목들에 대해서만 분석했습니다.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a91c9ba",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "## 1. 학교 관련\n",
    "\n",
    "학교는 아무래도 젊은 연령대, 특히 아이들이 많이 타서 고장과 연관성이 있을 거라고 예상했습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "14250b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "1827c196",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'학교'의 고장데이터 : 1150개 / '학교'의 정상데이터 : 994개\n",
      "고장데이터가 정상데이터에 대해 15.69% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('학교'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c8d97193",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'초등학교'의 고장데이터 : 394개 / '초등학교'의 정상데이터 : 289개\n",
      "고장데이터가 정상데이터에 대해 36.33% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('초등학교'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "230f524c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'중학교'의 고장데이터 : 304개 / '중학교'의 정상데이터 : 294개\n",
      "고장데이터가 정상데이터에 대해 3.40% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('중학교'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "543f4e21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'고등학교'의 고장데이터 : 181개 / '고등학교'의 정상데이터 : 169개\n",
      "고장데이터가 정상데이터에 대해 7.10% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('고등학교'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "029a8126",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'대학교'의 고장데이터 : 243개 / '대학교'의 정상데이터 : 227개\n",
      "고장데이터가 정상데이터에 대해 7.05% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('대학교'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a7df0afd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1150, 994, '학교'], [394, 289, '초등학교'], [304, 294, '중학교'], [181, 169, '고등학교'], [243, 227, '대학교']]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ad336381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eGD58ODQaTYO8TyKqGfYYEpHcsK1CcsArGVVIS0vD7t27S383Go3Q6XQVfmjfeustpKamYsGCBZg+fToGDRqE+Pj4Mo9JTU2t8kMLAIcPH4ZGo8Hy5csxf/58hIeHAwD8/f0xZ84cbNiwARERETh+/Dg/tEQyxx5DImpobKuQXPFKRhVWrFiBefPmYdWqVQAcE6PatWtXZpiCyWTC6NGj4eXlhfnz58PLywsAsG7dOjz66KPYsGEDmjZtWq/JVEQkX+wxJCKR2FYhueK/olpIT08vrdYvMxgMmD17Nlq3bl3m9ltuuQUnT54s/cIfO3ZstRM8Z8yYgRkzZpS73WazVdhwuOuuuzBr1qzavg0iqqO0tDScP38evXr1AlCzHsM1a9Zg0aJFGDRoEH799Ve0bdu29DGpqanVfplf2WMYExNTevvlHsO0tDQsX74cx48fx8iRI530TolIqdhWIbnglYwqXN07kJWVhR07dmDEiBEVPj4pKQkzZ87E2rVrYTKZADgmaI4YMQLPPvssIiIi6pTDx8cHycnJCAgIqNPzicg52GNIRHLDtgrJFedk1EJwcHClH9r8/Hz07dsXERERiIuLQ2JiIhITE7F161Z4e3ujX79+KCkpaeTERNSQquoxXLZsWWmBAfzbY9i0aVMANe8xjIiIKPcTGhpa4e1PPfWU898kESkK2yokF7yScUlSUhK6d+9eq+dc2Xu5ceNGTJw4EWfOnKnwseHh4fjjjz/Qs2fPWmdj7wCRPLDHkIhEYluFlITX6S+JiYmp17rzPXr0gNlsxowZM/DII4/A19cXgGNXzdmzZ8PT07PcEpZEpGw16TF88sknERcXB39/fwBAdnY2PvvsM/Tr1w/Hjh2Dp6dnY0YmIgVjW4WUhEWGk/j5+SEuLg4ff/wxevfuXabXcujQodi+fTsbE0QKUlWP4ZWb6l3pyi//ffv2Qa/X46WXXirzmKCgIEybNg1ffPEFjh07VqceQyKiumBbhRoTh0spwI4dO9C7d28uTUmkIPn5+Wjfvj2eeeaZCnsMv/vuOxw/frxOX+gclkBEcsO2Cl2NE78VoF+/fvzQEinM5R7D5ORk9O7dGy1atECLFi0wYMAA5OXlsceQiFwK2yp0NV7JICJSGPYYEhGR3LHIULB8owXns4qRnFOC9AIj0vNNSC8wIqPAhCKzDZIkwS4B9sv/tUul/y9JEjx0GoT5GhDqa0CYrwfC/AwI9TEgzM/xe4iPHloNL3YRubJisxVJ2SVIyi5GUk4xkrJLcDGvBEaLDVa7BJtdKv2vrczvdqhVKoT4GBznkUvnjfBL/w3zdZxLvPSc+kfkymx2CRdyS5CU42iPJGcXIyXXiHyjBSVmG4rNVhSbbTDb7JfaIY52iVqlgr+nDgFeOgR66RHopYP/pf8GeukR4KVDk0BPNA/xgUZdfsNTkj8WGQpgtdlxIrUA+87nYP/5XJxOL8T57GLklVga9HVVKiDK3xNdmvijcxN/dIkOQOcm/vD31DXo6xKR8yVmFGLf+VwkZBReKigcjYGsInODvq6PQYuYIC90jPJDpyg/dIr2R4coPxYfRApjtdlx9EI+9pzLwYmL+aVFRWqeEVZ7wzUl9Vo1WoX6oF2EL9pe+mkX4YcIf48Ge01yDhYZMpRdZMa+cznYdz4He8/l4HBKHorNNtGxADgKj2ZBXujcJABdov3RpYk/usYEwEPHYRtEcmG12XEwOQ87z2RdOpfkIruBi4naUKuANuG+6NksEL1ig3BNbCCaBHpV/0QiajR5JRbsO+doh+w5l42DSXkoscijLQIA/p46tI/0Rf+WIbihTSi6NPGHSsUrHnLCIkMGbHYJcYlZWH34IrafzsTZrGLRkWrFoFWjf8tg3Ng+HDe1C0NUACezEjW20+mF2HoqA1tPZ2JnYjYKTFbRkWolyt8DN7YPw/DOUejTPAhqDo8galR2u4S4M1lYeyQVcYlZOJVeCCW1EIO99bi2dQiubx2K69uEItTXIDqS22ORIcjlD/PqQxex7mgqMgvl08tYXx0i/TCscwRGdIlCbIi36DhELis934gVB1KwbG8K4tMKRMdxmjBfA27tFIHhXaLQKzaQvZNEDUSSJOw+m4NVhy5gzZFUZBSYREdyCpXK0Ra5oU0ohnWORKdof9GR3BKLjEZkt0vYfTYbqw9fdKkPc1U6RPphRNdI3NGzCcJ8OX6SqL6MFhv+PJaGZXuTsfV0JmwNOBZaDiL8PHBr5wiM6BKJHk1ZcBDVlyRJ2Hc+B6sOXcQfhy8iLd/12yLtInwxtkcTjOoexbZII2KR0Qhyisz4ccc5LNp1zi0+zBXRa9QY3iUSk65tzh4FojrYczYby/YlY9WhiygwKmsolLNEB3hibI9oPNA/FsE+HApBVBs5RWYs2nUei3aeR0puieg4QmjUKtzYLgzj+zbD9a1D2GnRwFhkNKCk7GLM25KIJXuSZTVZSrResYF4cEBz3NwxgsvSEVXBaLFh0c7z+HHHWcXN1WpIHjo1xl0Tg4eva4GYIE4YJ6pKfGoBvtt2BisOpMBosYuOIxvNgr1wb++muLt3U66a2UBYZDSAw8l5+HJzAtYeSXX5oQz10STQEw/0i8VdvWPg58EPONFlJqsNP+08j7mbEtz26mdNaNUqjOgSiakDW6FthK/oOESysudsNuZsOI0N8Rmio8ian4cWD13XAg9e2xw+Bi6t7UwsMpxoY3w6vtqciO0JWaKjKIq3XoPxfZvhsRtbsdggt2a22vHz7vP4YkMCUvONouMohkoFDGobhqkDW6JXbJDoOERCbT6Zgc//OY1dZ7NFR1GUQC8dJl/fEg/0b8Z9fJyERYYT7D2Xg/+uOoYDSbmioyhasLceTw9pg3t7N+UwKnIrFpsdi3cn4YsNp3Ehj8VFfVzTLBDP39IWfVsEi45C1KhOpxfiv6uOYdNJXrmojxAfPabc0BLj+zbjHmD1xCKjHlJyS/D+mhNYefCC6CgupW24L/4zoj2uax0qOgpRg7LZJfyyJwmf/XPabSdiNpTbe0TjtWHtOUGcXF6+0YJZf53CjzvOwmJjk85ZwnwNePzGVrivTzN2fNYRi4w6MFltmLsxAV9uSuAkqgZ0Y7sw/Gd4e7QI9REdhcjpTqUV4IWlh3gFtAH5e+rw0tB2uKd3DFeRIZdjt0v4eXcSPv4zHllFrrPXltx0ivbDB2O7oGMUV8asLRYZtbTtdCZeX3EEiZlFoqO4BZ1GhfF9m+HpwW24+gO5BKvNjrkbE/DZP6dhtrGTojF0bxqA6aM7o0OUn+goRE6x60w23lp5FEcv5IuO4ha0ahUevr4FnrqpNYdQ1QKLjBrKLjLjrZVH8dsBDo0SIdLfAzPHdUO/lhxnTcp19EIeXvjlEI5dZMOgsWnUKkzoH4tnh7SBN1eQIYUqMdvw9qpj+GnXedFR3FKLEG+8d3tn9OGcrxphkVEDcYlZeOrn/VxKUjC1CphyQ0s8O6QNtBq16DhENWa22vHZP6fw5aYEjpkWLMLPA2+N6ohbOkaIjkJUK0cv5OHJn/YjIYMjKURSqYB7ejfFK7e2gy9XxKwSi4wq2O0SPt9wGrP+PsX9LmSka0wAZt/dDc2CvUVHIarWgaRcvLj0IE6mFYqOQle4v18z/Gd4B+i17LAgeZMkCd9sPYMP18XDbOUQS7mI8PPAzHFd0b9ViOgossUioxIZBSY8s/gAtp7OFB2FKuBj0OKtkR0xtmcT0VGIKiRJEj7/5zQ+ZSeFbHWO9sece3ugaTB3DSd5yiw04flfDmIjN9STJY1ahZeHtsPD17cQHUWWWGRUYPvpTDy1+AAyCjg8Su5Gdo3CO2M6cRM/kpUikxXPLjmAdUfTREehavh6aDHjzq4cPkWys+lkBp5bchCZhWyLyN3IrlH4YGwXeOo5KfxKLDKuYLNLmPXXSXy+4TTY8agcMUGe+PaBXmgd7is6ChHOZBZh8o97cCqdw6OUQqUCnrixNZ4Z3JpL3ZIsfPrXScz6+xTYQlOO9pF++Or/eiImiFdGL2ORcUmRyYopC/ZiyykOj1IiPw8t5o7viQEcG0kCbT2ViUcX7kW+0So6CtXBLR3DMXNcN64+RcJYbXa8tvwIFu9JEh2F6iDAS4fP7unOzYQvYZEBILfYjAe+242D3BRL0XQaFaaP6Yxx18SIjkJuaMmeJLy2/DBXj1K4tuG+mPfANeyNpEZXbLbi0YX7OP9C4TRqFV64pS2m3NBSdBTh3L7ISM0z4v++2cmhDS7kqZta45khbUTHIDcyY108Pt9wWnQMcpJIfw8sergvmodwBTtqHJmFJjz4/W4cSs4THYWc5P5+zfDWyI5uPQTTrYuMM5lFGD9vJ1JyS0RHISd7oF8zvOnmH25qeDa7hBd+OYhf96eIjkJOFu5nwKKH+6JlqI/oKOTiEjMKMeG73TifXSw6CjnZ7T2i8dEdXaFRu2dbxG2LjCMpeZjw3S5kFppFR6EGMqpbFGbc2RU6btxHDUCSJDz3y0H8uo8FhqsK9TXgp4f7oFUYF5WghrHvfA4e+mEPsovYFnFVt3QMx2f39HDLPXncssjYmZiFh37YgwITJ2e6usHtw/Dl+J7cIZyc7j8rDmNB3HnRMaiBhfjosejhvmjD1evIyfaey8H93+xEkdkmOgo1sJvahWHu+J5uV2i417uFY/WXB77bxQLDTfx1PB0vLTsMN6ylqQFNX32MBYabyCw0456v4nAiNV90FHIhl0dTsMBwD3+fSMejC/e63Y7tblVkHLuQjykL9sJoca+/ZHe3bF8y3l9zQnQMchGfrD+Jr7ecER2DGlFWkaPQOHqBk3Kp/k6mFeD+b3ehgEtdu5W/jqfj0YX7YLG5TxvUbYqMi3klePD73SjkFQy39L/NiZi3JVF0DFK4/21KwKy/T4mOQQLkFFtw37ydLDSoXpJzivF/3+zkHAw39dfxNLz662HRMRqNW8zJyDdacOfcHYhPKxAdpVrZ6+ei8MgGqD3+XdEk4t73oTJ4IWvVx7BkJUOymqCPaI2gmx+F1je4wuPYCnOQ/c/XMCUfByQbvDsMROCgByHZrMha9zmM5w9DrTUgaOjj8GjSAQBgLchExrL/Ivy+D6HWGRrl/TYmlQqYOa4rxnRvIjoKKdD8HWfx+m9HRccgwSL9PfD749ci1Nf1zpHUsHKLzRg7dzsSMopER6m1ytomWv8wmNMSkLXuC9iKsqHWeSDwpsnwbN693DEkmxU5G75B8ckdgEoNj5iOCBr8CNQePm7XNnnl1nZ4xA320XD5IsNis2PCd7uw7XSW6Cg1kr1+LtQefgi47r4yt9uMhbDmXIQhsjUkuw3Z6+fCXpyP0DGvljuGZDXj4g9Pw7vTYPj1GgWVWgNrfia0fiEo2P8HLJnnEDRkKszpichc/QmiJn4GAEj/9R349hgBz9hujfFWhdBpVPj6/mswsG2Y6CikIL/uS8ZzvxyEnM6WlX3p5277CcZzB8s81l6SB5/OQxA0ZErpbTZjIS5+9+RVR5Vgy89AxAOfQh8a61Zf+rXRKzYQix7uy5XrqMaMFhvum7cTe8/liI5SJ5W1TeymYlyYNxXBw5+BZ2w3GM8fRvqv7yD6oS+h8Qks89jcbT/BeP4wwsa+DpVWj6y1nwGShJDhz7hd20StAr4c3xM3d4wQHaVBaUUHaGgvLTukmALjMrVH+Q2gNB4+0ES2BgCo1Bp4tuyFvO1LKnx+wcF10PgEw7/P7aW3af1CAADmjLPwatkbAKAPawHJ5FiXuyh+G9QGL5f6EFfEYpPw6MJ9WPhQH3RvGlj9E8jtHUnJw8u/HpZVgXGZ3zWjyn3phwx7qszvdnMJLnw1Gb49RpS5XePhgyZTvy1zW9GxTSjYvxqGiFYo2P8H1DoDmkz5ptyXfvb6LxEwcKJbFhgAsPtsDqb9fhTvjuksOgopxDOLDyi2wLisorZJ0fHN0Ee2Lm07eDTtDI+Yjig6sRl+14wq81hzeiK82vSDWu8JAPDuMBA5f3/luM/N2iZ2CXh68QEsndIfHaL8RMdpMC7dDfPxn/GKXMNebah6l1lrfjoK9q2Gb88RFd5fHL8NPp0HV3ifLjgGxafiIEl2GJOOQOMfDrupCHnbfkLgjQ/VO7sSFJtteOiHPUjPN4qOQjKXb7Tg0YX7ZLsiSEVf+lfL370CHi16Qhdc9TBByW5D7rZFCLj+fgCOL33PFr0AuMeXfm0t2nkeC3eeEx2DFOCbrWew5kiq6Bj1VlHbxJRyAoboDmVuM0S2hTmt/OIY3m0HoOjoRtiKcmE3G1G4/w94dxgIwD3bJo62yG6kF7huW8Rli4wV+1Pw2T+nRceok5zNPyD5i4lI/ekVlJzZV3p7XtxSJM26BylfPgR9WAt4t7++wudbMs5CslmQuuBFJM99EOm/vAlLtqPY8u06FJLdhgv/exh5235C8NAnkLPhO/j1vh0aT9etpq+WVWTGM0sOwG6XYfc0ycYLvxyU9S681XVI2M0lKNi3Cv797672WMUntkDjEwyPmE4A3PNLv7be/P0o9pzNFh2DZGz/+Ry8v+a46BhOUVHbxFaUDY13QJnHqb38YTeWX/LZq/310Hj7I3nO/UiafQ+sBZnw6zUagPu2TS7kGTH5x70wWlxzKWOXnJNxJrMII2ZvUeT605Jkh0qlhmS3wXhmHzJWzkD43dNhiGhV+hhrQRZyN/8Ie3Eewu58s9wxzn00Cp4teiL4lieg9vJD/q7lKDz0J6ImfQGVpuwIOWPyUeRtX4yw2/+DnM0/wpJ5HmoPHwQOmlTppHJX8sItbfHYoFbVP5DczrwtiXhntXwbB9nr56IofhtUah20gRHw73snPJv3KPOY/L0rYTx/CGFjXqv2eBe/fwr+A+6FV+s+AADJakHWn1/AdP4QtAERCLrlceTvXAZDkw7w6XRjg7wnJQrxMWDlEwMQ6e8pOgrJTF6xBcNmb0FKbonoKPVWWdskd+N38O50I3w63VT62IL9f6AkcS/Cxr5e5hjZf38NW3Eugm9+DFBrkLvpe1jzMxB2+3/KvZ67tU1GdYvCrLvLT5ZXOpe7kmG22vHET/sUWWAAgErl+Cu5PO/Cu/31KDm5o8xjtL7BCB76BIznD8GSc6HcMTSefo7q3ycQKrUGfn3Gwl5SAEt2cpnHSVYLcv76CkE3P4r8Xcuh8Q5C+Li34d1xEHI2fNNwb1JGPll/UvHjZMn59p7LwQdr5b23SuDgRxDz+AJET5kH/963I+O3D2BKLXv1tvDgOvj1vK3aY5lST8NuLIRnq16lt6m0OoQMewrRU75B+N3TYSvKhjU/Hd7trkX2P/OQtuQNZPz+IawFyprz5myZhSY8Mt91eyKpbiRJwnO/HHCJAgOovG2i9vCFvbjsVQtbcR403mXnPNotRhTsW42gIVOhNnhBrTMg8MaHYDp/uHSkxWXu2Db57cAF/HZAecP7q+NyRcYHa0/gSIoL7cxqtwGaCubnq9WAWgOVVl/uLl1IDOzmf09sKpUKUKmg0ujKPC5vxxJ4tb8euoAImFKOw6ulo4Hh0bQLLOlnnfo25Mpql/DkT/uRV2IRHYVkIrvIjMcX7YPFJu+LvNV1SJgunoK9pACGmOonJxcdXg+vDjeUHvNq7vilXxuHkvPw1koub0z/+npLIv46ni46RsO51DbRR7SCKaXsFV/HPI12Vz3eDly6GlJKpQZUaki2svuXuWvb5I3fjiLNxeaKulSRseVUBr7dpuydeEsS90KSHJNMS87sQ/HJ7fBqOwDFp3bCnOGYZCjZLMjd+D0MUe2g9Q0pdwyfbsOQt3URbCWOYitv56/QBkZCGxhV+hhz5nmUnNlXOh5SGxCO4oTdjtdN2A1daLOGfJuykpJbgpeXHRIdg2RAkiQ8vfgALuYp8ER/VYdE0dEN8Grb39HJUAXJbkPRsc3watO/0se465d+bfy0KwnbEzJFxyAZ2HsuGx+ujRcdw6kqa5t4dxgI47mDKLm0bHZJwm5YspLg1fbaMs9XG7zg2bwHcjf/AMlugyRJyNv+MzQ+QWUWpXDntkleiQUvuVhbxGWWsM03WvDS0kOyXGayNvL3/IbM1TOh0hqg9QtF6JjXoA9pCmt2CjJWvAfJXAyVzgDP5j0RMuql0udlrHgPPt2HwbNZV3i3uxbW7BRc/PZxQKODIaIVQse8VtrYkCQJ2X9+gaAhU6BSawAA/v3uRsbKj1CwbxW0AREIGf6MkPcvypojqVgQdw7j+7ruCYyq9+WmRGw+mSE6Ro2UJO6FR/PuUKnUpV/64fd9+O/9Z/YhcOCEao9jvngKgFRm3leZ+y996UeMdxz78pe+f0iMy3/p19Zry49gzVPXwUOnER2FBCk2W/HkTwdgdbFFRSprmwBAyMgXkf3nXNiNBdAFRCLsjjeg1nsAKNs2CR7xHHI2fIuUryYDkh36iFYIu+ON0nYI2ybAxvgMLNp5Hvf2aSo6ilO4zMTv5385iKV7k6t/IFElDFo1Vj95HVqF+VT/YHI5yTnFGDxzE4wWeS5Xe7W0JW/AnJZQ+qUfcN14eDR1DI2yGwuRNOtuRE/9Dlq/0DLPu/JLH3A0HkpO70T43e+Wew1JkpD20ysIHDQJhkv79NgKc5Cx8iNYc1NLv/Svfg139tiglnjhlnbVP5Bc0nt/HMf/NieKjkEK5q3XYO3T1yMmyEt0lHpziSLj7+NpmPTDHtExyAVc1zoE8yf1ER2DBHhk/h6sO5omOgYpnE6jwsonrkW7CNdddpMqdjKtAMNnb5H9fC6Sv97Ng/Dzw32hVlc93FXuFD8nw2S1YdrvnHBHzrHlVCbWH2ND091sPpnBAoOcwmKT8PKyw9yDxw39Z8URFhjkFLvOZCt+jjHgAkXG99vOIjnHNZaII3l4Z/UxmKxcjtJdWGx2vMmVgciJDiTl4scdZ0XHoEa0bG8ydp3hxozkPLP+OoWsQpPoGPWi6CIju8iMzzcoc1dvkq9zWcX4ZqvyexCoZr7ZegaJGUWiY5CL+WhdPC64yB4JVLW8Egvec5FdvUk+CkxWzP77lOgY9aLoImPWXydRYLRW/0CiWprzz2mku9h61VReap4Rnyn8JE7yVGS24Y3fjoiOQY1gxrp4ZBaaRccgF7Ro13mczVRuJ5hii4zEjEIs2nVedAxyUUVmG96X+Y7PVH/T/ziOIjOHxlHD+Ot4Onaf5RAaV3YkJQ8Ld54THYNclMUm4aN1yt1zRbFFxntrTnCCFTWo5ftTsP98jugY1EDiErOw8uAF0THIxX38p3IbCFS9T/86Cc7xp4a0+vBFHEjKFR2jThRZZMQlZnEFIGpwkgS8veqY6BjUQN5fwytV1PDiErOx/TR3AndFxy/m4+8T6aJjkBt47w9lzvlRXJEhSRLeVegfNinP/vO5bCC4oO0JmYrtGSLl+Xj9SdERqAHM2XAayt9pjJRg55ls/KXAznXFFRmbT2XiUHKe6BjkRuZuShAdgZxs7kb+nVLj2XsuB9vYWeFSzmQW4Y/DF0XHIDcyQ4FDLxVXZPyw/azoCORmtpzKxJEUFrau4nByHracYoOPGteX7KxwKXM3nuZcDGpUJ1ILsPlkhugYtaKoIuNcVhE2xnP8IzU+Xs1wHWzskQjsrHAdF3JLsHx/iugY5IaUtoeXooqMH7afY88BCbHuSCo31nIBF3JLsPZoqugY5Kb+tzlRdARygq82J3J1SxJi86kMnE4vEB2jxhRTZBSbrfhlb5LoGOSmrHYJC+K4FrrSzY87Bxt7KkiQPw5fRHJOsegYVA85RWb8vJt7dJEYkgR8t+2s6Bg1ppgiY9m+FO7uTUL9vDsJRgs3blMqo8WGn7mBJwlks0tYwWE2irZ8fwqMFrvoGOTGfjtwAUUmZbSHFVNk/MgJ3yRYdpEZv3PzNsX6/cAF5BRbRMcgN/fbAZ5DlOyXvcmiI5CbKzRZFXMeUUSRse10Jk6lF4qOQYTl+9gLqVRL97FxQOKdSi/E0QucAK5ER1LycPxivugYRFi0SxnDtxVRZCziEAeSiV1ns5FVaBIdg2ops9CEPWezRccgAuC4qkbKs5RXMUgmjqTkK6KzQvZFhtlqx6Z4Za0LTK7LZpfwpwJ33XR3fx5N48p0JBsrD16AxK2iFcVml7DqEDffI/lYd1T+bRHZFxnbEzJRqJAJLuQeuMur8nDZWpKTC3lG7DzDK2tKsiMhC5m8ik0ysl4BHZ6yLzKU8IdI7iUuMQt5nECsGPlGC3YkcIdvkhelTNwkh5Vc9INk5vjFfCRly3tJbFkXGZIk4e/j3OGb5MVik/DnMfaMK8Xfx9O4cRbJzpojF2GxcSlUJbDa7LwaSrL013F5d8TLusg4nJKH1Hyj6BhE5aw5wi8cpVjLvyuSodxiCzZyvqEiHEzORV4Jr16T/Mh9tI+siwy5/+GR+9p6KhMFRn7pyF2J2YbNJzlUiuRp80kWGUqwIyFLdASiCu06ky3r4dssMojqwGyzYwN7IWVv08l0lHCXdpKpPedyREegGohL5CR9kierXcKGePlOK5BtkZGUXYwTqQWiYxBVai/3XZA9zukiOYtPzecVUZkzW+3Yy2KQZGy9jOdlyLbI2HaaQxxI3g4k5YqOQNU4mJwrOgJRpewSsP98rugYVIWDybm8GkqyJueNZmVbZLABR3J3/GIBTFZ++chVidmGhIwi0TGIqsQhU/IWx/kYJHNp+SakF8hzkSQWGUR1ZLbZcfRCvugYVIljF/Ng4zbfJHN7z8m3F5KAHYksMkj+DifniY5QIVkWGUUmK06mcT4Gyd9BFsOyJdeTLtGVDpzPZTEsU2arHfvO80oTyd8hmX7fybLIOJKSB55zSQl4xU2+DqfwKhPJX5HZhmO8IipLJ1LzYbRww0SSvyMpLDJq7PhFnnBJGVhkyJdcT7pEV9vDIVOylMg5XaQQh2X6fSfLIiOeQ6VIIc5lFSOnyCw6Bl3FaLHhdEah6BhENcLOCnlKzGSRQcqQXmBCap78Jn/Lssjg/hikJHLtQXBnxy7mc5w7KUZyTonoCFSBMywySEEOyXDJdtkVGZIk4SSLDFKQlFw2EOSGQ6VISS7wHCJLZzJ5NZSU41S6/P69yq7IyCg0ocjMvQdIOeR4idLdcSw1KUl6gYlX3mTobGax6AhENSbHtojsiozMAo5vJ2VJy5ffB9vdZXGeDCmIzS7xPCIz6flGFJqsomMQ1ZgczyGyKzKyikyiIxDVSqoMP9juLquQ5xFSlot5HDIlJ5z0TUqTXiC/7z3ZFRmZbByQwsjxEqW7y+aVDFKYC7k8j8jJWRYZpDDpMuzwlF2RkVXIxgEpixwvUbq7TJ5HSGF4JUNesot5DiFlySg0QZLkNbdLdkUGGwekNDnFFhgtXKxALiRJQi4bCKQwvJIhL8UmntNJWSw2SXZX8WVYZHC4FClPej7/3cpFXokFVq7UQwrDKxnywknfpERpMmuLyK7I4IRNUqK0AvZCygWvhpIS8d+tvBSbWWSQ8qTLrC0ivyJDZpd6iGqCw6XkQ26Xi4lqwmKzi45AV+B+XaRERTIb5ie7IqPQyN4DUh6rjcNz5CKby2CTAll4DpGVYg6XIgWy2uXVWSG7IkOjVomOQFRrnAMgHyarvE6yRDVh5ZUMWZFbjzBRTcitw5NFBpETsIEgH2oVzyGkPOyokJcizskgBbLJ7DyiFR3gajqN7OoeResdkIdRQUnorTmJcNM5qCCvf4CuQtK+BiBSdAwCOyoayjX+BfgwbB1CTedFR3FJNp9IAANFx6BLeEW0ekE6C8IMFoTqzQjWmRGkNSFQY0KAxgQ/tRG+aiN8UQJvlMBTKoGnVAyDrQhaO4e0NhS77mkAMaJjlJJdkcEGQt3p1BKGh2bgFt8z6CrFIzz3ADRFqcAF0cncAXeHlQteyXCudj7F+DRyPdpeWA5VEifVNxhbK9EJ6Ap6F+3wrLgwMCNAUwJ/jQm+qhL4wFiuMNDZiqGzFkFjLYLGXACYi6CSbIAVjh+SBylfdIIyZFdk6DRsINRUmMGCsWEXcINHAtpZjsE/+yBUeUVAnuhkbkgtu4+S22JHhXM08TBhdsxGdE/9BaqkYtFxXB/PIbLioZNPkRGstyBUX4vCwF4Eg72YhYE7ktl5RF5pAGjV8vlgy01n3yKMDjmPftrTaF58GB7Zx6HK4OQ0WZDZB9udyalxoETBegtmNduOAek/QZUkr14xl6bSiE5AV/DQ1e/v43JhEHb1UCLtpaFElRQGelsRtNZiaC2FUFsKWRhQ7ajldR6RXctIyysZAACVSsLNwdkY5n8WPVTxiMw7AG1BMnBRdDKqkNZDdAK6xNsgu9OaInhrbZgRuwe3ZC+EOilTdBz34+EnOgFdYUBgHtpHJ11RGJTAV2WCL4rhBSO8pGJ4SMUw2FgYkIxoDaITlCG7b2Otmw518NdZMTo0FTd5n0FH6zEEZR+AqjAPKBSdjGrEJ0x0ArrEWy+705qs6dQSpjc/hNvzF0CbnCI6jvvyChadgK7wmOoXIOsX0TGIasdbXm0R2X0be+rldamnobT0KsHY0GQM0J9GK+NheGUdhSrbAmSLTkZ1wiJDNrwN7nEOqS+VSsJ/msXj/4wLoU9JEB2HvENFJ6AreQaKTkBUe77hohOUIbsiI9LfU3SEBnFDUA6GB55HL1U8ogsPQZ+bCKSJTkVOodHzC0lGfDhcqlpPND2DR20/wTP1iOgodJl3iOgEdCWPANEJiGrPJ0J0gjJk923cJFD5RYanxoaRYRkY4p2IzvbjCM05CHVxJsAFWlyTzC5PursALz289RoUmbkowtX+LyoFL2iXwC99t+godDVeyZAXdhyR0nj4Azp5zQ+VYZHhJTpCrUV7mHBHWAquNySgtfkofLMOQ5VTAuSITkaNwoeNA7lpFuyNYxe5MtJlt4Vl4E3vZQi+uFl0FKoM52TIi1+U6AREtSOzqxiADIuM6AD5X8m4xr8Ao4LOoY/2FJoVHYI++yRU6dxJ2235yGsMJAHNQ1hkAMANwTl4L2AlIlPWQZXPc5Ss8UqGvARzc0RSGJnNxwBkWGQ0CZJXkaFTSxgWmoGhvmfRVTqB8LyD0BRe5FKy9C82DmQnNkR5V0SdqYd/IT4O/QOxKSuhKuKwMUXwbyI6AV0puCUAFQAW56QQvJJRPT8PHXw9tCgwillcOlRvwZiwCxjkeQbtLEcRkH2Au2hT1QKaiU5AV2kW7C06ghCtvUvwadRf6HDhV6iSTaLjUE1pPYHA5qJT0JV0no7CLy9JdBKimuGVjJppEuiF44001KGjbxFuD05CP92pf3fRzmTPH9VCRCfRCegqzUPcq8iI9DDjs5hN6Jm6BKqkItFxqLZCWgNq7lQvO8GtWGSQcoS0EZ2gHJkWGZ4NUmSoVBKGBGdjWMA59EA8ovIPQJufBKQ6/aXInUR0Fp2ArhLrJlcyAnVWfBIbh+szFkGdlCs6DtVVWHvRCagiIa2BxA2iUxDVTGQ30QnKkWWR0SzIOeOpfbVWjAlLx03eCY5dtHMOQl2Yy120yXk8AjiWWoZCfQ3wMWhRaBIz7LKheWvs+KD5XtyasxCapHTRcai+QtuJTkAVCW4tOgFRzWgMsuyskGWR0bmJf52e18LLiNtDk3Gd/hRaGo/CO/sIVNlm7qJNDYdXMWQrNsQLR1Jca4UpjcqOd5ofxZ2FC6BN5jAOlyHDxgHh0uRvIgUIaw9odKJTlCPLIqN7TM02wbkuKBcjAs6jlzoeTQoPQZ+bwF20qXGxyJCtVqE+LlVkvNLsJCaYF8Fw4aToKORsvJIhT2EdRCcgqpnIrqITVEiWRUbTYC8EeeuRXWQuvc1TY8OI0Azc7HMGne0nEJa7n7tok3jhnPQtV72bB2PFgQuiY9Tb1JizeEL6GV5ph0RHoYbg4c8V6uTKL9Kx6lfOGdFJiKoW1U10ggrJssgAgCEtPBCWG4/rPRLQxnwUflmHoMotAXJFJyO6Aq9kyNaAVsreQfnuyIt4Rf8L/NPiREehhtTsWq4sJWfNr2ORQfLHKxm180HTXcCpt0XHIKqchz8Q3lF0CqpEs2BvRAd4IiW3RHSUWhkWmom3fX5FyMWNoqNQY2h+negEVJXY64B9P4pOQVQ5tVa2oypkW2QglidekrnmNwBqjegUVIUBrYKxZE+y6Bg1cm1QHt4PXInolDVQFXCXYbfB7zp5498PyV1Ud0BrEJ2iQvK9RhvVHdC5x1r3pFCtBotOQNUY0CpEdIRqdfErxF+tl2G+8Qk0SfkDKrDAcBtewbwaKnd+kUAQV5kiGWszVHSCSsm3yNDogKZ9RKcgqhyLDNnr31K+RUYLLyNWtl6N3+xPolXSMqjsrrmnB1Uh9lpApRKdgqoTe63oBESVY5FRR21uFZ2AqGKh7QD/aNEpqBqhvga0DfcVHaOMCIMZi1tvwN/aJ9E5aSFUVqPoSCQKh+IoQ/PrRScgqph/DBAhz/kYgNyLjA4jAZW8I5Kb4lUMxegvk1Wm/HVWfNN6O7Z7PYs+SV9DZS4UHYlE43lEGZpfz7YIyVObW0QnqJK8PzW+EUBMX9EpiMprdZPoBFRDAwQPmfLU2DCr5V7s83sRNyV9DnVJttA8JBNRPYCg5qJTUE34hAHNBohOQVSezEf8yLvIAICOo0UnICpL78MvHAUZ0CoEvobGX0hPo7Ljv82P4FDI6xiV8jE0RamNnoFkrNNY0QmoNrqME52AqCydt+yXwJZ/kdF+JABOjCMZ6ThatsvFUXmeeg1GdI1q1Nd8odkpHIv8L/7v4rvQ5Z1t1NcmBVCpgU63i05BtdFhFKDheZ9kpOUg2bdF5F9k+EUCTTlkimSk+/+JTkC1dFevmEZ5nYeaJOFozId4LG0aDNnxjfKapEBN+wF+jVv4Uj15+Mt+/Du5mY5jRCeolvyLDADoMFp0AiKH4NYsehWoW0wA2kU03CpTd0ak4kDsZ/hP5kvwzjjQYK9DLoJXMZSJQ6ZILrxCLo30kTdlFBkdRzu2TScSrdu9ohNQHY27xvlXM24OycbOFt/go9xnEZC6w+nHJxek1gId5N8DSRVofbPjigaRaN3vA7R60SmqpYwiwzdCERUbuTiVhkWGgo3pHg29xjmnvD4B+djcahH+V/Qkwi/87ZRjkptoeyvgLY9llamWtAbH3AwioVRAz4miQ9SIMooMAOj7qOgE5O5a3eQoeEmRAr31GNIxvF7H6OhbhD9bL8fP5ifQNHkVVJLdSenIbfR9THQCqo9u94lOQO6u5SDFLH+tnCIjphcQfY3oFOTOuo8XnYDq6a46DpmK9TTit9ZrsEp6Em2SfoHKbnFyMnILUd2BZv1Ep6D6aNoXiOkjOgW5s2seFJ2gxpRTZABA36miE5C7CowF2g4XnYLq6dpWIYgO8Kzx40P1FixqvREb9E+ha9J8qKwlDZiOXB6vYriGa58RnYDclW+U7Dfgu5KyiowOox1/wESN7dpnAQ0XH1A6tVqF8X2bVfs4X60VX7WKw07v59A/6SuoTAWNkI5cmm8UN5d1FW2GAmEdRKcgd9TjfkW1RZRVZGi0QK9JolOQu/GP4YRvF3J/v2YI9NJVeJ9BbcfMlvtxIOBl3Jw8G+qSzEZORy6r90OApuJ/d6QwKhUw4CnRKcjd6H2BPo+ITlEryioyAMdYNJ2X6BTkTgY8xcaBC/E2aPHQdS3K3KZSSXiz+XEcCX0Dt6d8BE3hBUHpyCXpvBWzGgzVUKc7AP+molOQO+n3GOAVJDpFrSivyPAKAvo9LjoFuQvfSMflSXIpE/rHll7NeKZpIo5HTceEi/+FLi9RcDJySf2fUFzjgKqh0QL92RahRuIZpMh/b8orMgBHz7J3qOgU5A4GPOVYG51cirdBi48GB+JI04/xVPp/4JF1THQkclXeYY4ig1xPj/sdOy8TNbRrnwYMvqJT1JoyiwyDDzDwZdEpyNV5hwE9J4hOQQ1k8DUd4VPCYVHUwG540fGdRa5H5wnc8JLoFOTqfCOB3pNFp6gTZRYZANBjAhDcWnQKcmWDXnV8iZBr0nuxs4IaVlBLzsVwdb0mAeGdRKcgV3b984ptiyi3yNBogcFvik5Briq6J9DjAdEpqKF1/z8gpK3oFOSqbnpDUctNUh2oNcCwj0SnIFcVGKvotohyiwwAaD8CaMrdU8nJVGpg+MeAWtkfD6oBtQYYPkN0CnJF0ddwXwx30aw/0PlO0SnIFQ1+U9GrWyq/FXXzdEejkMhZrnkQiOouOgU1lubXA93Gi04he1NXlaDd54UAAItNwtubTOg8txAxnxTguu+KcCDVVuHz9lywoe+8IrSYVYCYTwrwzFojrHap9P6X/zKi5ewCtJpdgBUnLKW3F5oldPuyEGmF9oZ9Yw1BrXV0VJD7GPJfQM+5N+RE7UYAHceITlEvym+dN+kJ9H1UdApZuLIRAACSJGHmDhPafl6Ipp84vsQtNqmKIwCLj1igeisfqZe+2C02CQ/+VoLmswrQ8YtCbDtvLX1sSr4dPf5XiBJL1cdUFO9Q4MbXRaegxnbzf7liXRWS8uz48dC/BcDJLDusdglxk7yR9IwvxnfW4bafiis8v3jrgGXjPJH4lC8OT/XBlvNWzN3tONba01acyLTj5OM+2DTBG0+vNcJ2qQD5zz8mPN5bj3AfBX5N9X8CiOomOgU1Jr9I4PoXRKdodFe3OxYcMqPz3EJEfVyAvvOKcCS94s6HxBw7xiwuRtvPHR0VU1eVlGlLuGTnQ214BADDZ4pOUW8KPHtX4MbXgZA2olMIdXUjAACmbzHj93grtkz0wvlnfLF5ojc0VfyN2+wS3ttqKnPbN/st8NIBZ57yxaLbPfHYH8bS+x5fY8SHQzzgqVM59b0INeS/gGeA6BTU2LyCgKHvi04hW8+sM2Jit38v2XcM0+DtQR7w1js++49co0eRWcKp7PJf/O1DNYj2c5x4AjxUuKGZFikFjscdSrPhlpZaaNQqRPupEe6jQnaJhF0pNhxOt2FSdwUOEwhpAwx8RXQKEqHvo261IM3V7Y41pyx4e5MZf9zrhQvP+eKxXjrcsaQEklS+82HFCQumXqNH/OM+OPaoDxJy7Hhrk6P94bKdD7Vxy7uAb7joFPXmGn9LOg9g9FxApRGdRJirGwEZRXa8v9WE+WM8Eebt+GuO8lVDraq8IJi7x4Jrm5b9MzyUZsPw1o6Ji10jNMg3OT7oy45Z4GdQYXALF5rU2GwA0O0e0SlIlM53AK1vFp1CdlaftCCrRMIdHSpv8BdbJBRbAH9D5ecXSZKwO8WG9YlW3N/VcawOoWqsPmWFySohPtOGYoujEHliTQnmDveAqorzlSyp1MCoOdxbx11p9cBts9xmCPfV7Y75hyx4so8eMf6O9/9/XfXwNQCbzpW/mvFsPwNubuloP/gaVHi8tx7/nHGMlHDJzofaaHkT0P0+0SmcwnU+CU2ucdsNjypqBKw6acW1TTWlH/bqXCiw45M4E94e5FHm9g6hGqw4YYVdkrD5nBWxAWrkGSW8vdmEmTe70Bephz8w5kvRKUi04TMBnbfoFLKRVWzHk2uNmDvco8rHvfa3CQNj/71icbUxi4vh814Bbl5QhJcGGNAh1NGZMaKNDh1D1ej4RSEm/GbEd6M8MWO7GSNa69AmWIGdRn2mAjG9RacgkWIHuMWwqYraHWYbysy3AoAQLxVOZlU/tCmjSIK/h6NTweU6H2pD7wPc9qnoFE7jOkUG4NjXILS96BSNqrJGwOF0O5r5q/HISsd8im5fFuLHg+YKjyFJEib+VoJpNxgQ5Fn2w/twDx2sdqDV7EK8vcmEr2/zxIvrjXi+nx7BXi70z2fk50BAU9EpSLSAGC5HeYkkSZj0uxFP9zGgXUjFDf4is4QHVpRg0zkr5o+pfB335Xd5oeAVX2ya4I1ZO034cNu/wzI/GOKB00/6YsckbwR4qPDrCQteHKDHO5tNGLqgCKN/LsaJzIrHdctKUAvgJs7nIjg26GvaX3SKBlNZu+PODjp8tsuM+EwbJEnCyngLtpyzIaOo6nmbWcV2vL/NjEnd9QBcsPOhNga/6VJtERdqJcJxiXrMXMfKHm6gqkZAgUnCypNW3NlRh8QnffD9aE88/6cJm85ayx3n0zgzfPQq3N9VX+4+g1aFb0Y5Jm3+db83LhbacS7Pjjs76vDcOiOGLijCPcuKcaFAwZOwej0EdBgpOgXJRff7uNoUgPe3mmGxA4/3rnhoQkK2Hb2+LoJODWx90Buh3lV/nahVKnQJ1+Djmz0we2fFHR5TV5dg1lAPLD1mRWqhHWvHe+PtQQZM+t1Y4eNlQ60Dxnyl2A2zyMnUGmDs14BnoOgkTldVu+OuTjo838+AO34pQYvZhVh9yopBzbXwKd+0KHUg1Ya+3xTjro5a3N3p33ONy3Q+1EbLGx3tERfiWkUG4Fh69KZpolM0iqoaASFeKgxtpcXgFlqoVCp0i9BgfBcdfo8vW2RsPmfFZ7vM+GpE1cMhAMBklfDUWiO+GO6Jj7ebEemrwtrx3hjfWYfn/pR5I6AyEZ0dE6yIrjR8htvv4jt7lxlbzlkR+EEBAt7Px4hFxTiVbUfA+/nYnWLDjT8W4Zm+eswb6QmvWiz+YNCqKlws4rv9ZrQJUqN/jBbbk6ylc8G6hGvk34kx5G0gppfoFCQn/k0cV8hdTHWdD1N76XF4qg/OPOWLL0d4IrXQjraVXAn9dr8ZQxcUY/qNBrxzY+VtEMV2PtRGYCww9hvAxYaCuWaX/4AngdTDwOElopM0qNm7zCgySwj8oAAAYLUDJVYg4P18PNfPgAJz2S9mtQowXPVZn7PbjPQiCS1nF5a5ve3nhfj0Fg9M7P5vF8S7W0y4u6MOLQLV2J5sw4whjjkZNzbX4sW/yq5KpQh6H+CO7zlJk8rTeQJ3/gB8NRAwF4hOI8TF53zL/L7xrBVTVhlx4nEffL3XjHYhajzcs4ouyku+3mvG6HZahHqrkVlsx6t/l50sCgDpRXZ8EmfG1gcd82GaB6rxxykrbm2tw64UGyLkvJJM+5FAPy6jThVoP8LRM717nugkTlNVu2P3w95ofcVwpvhMG05l2XFDs/JFxtJjFry9yYStD3qjVVDln+8rOx8WHirBiDYK6nyoKZ0XcNdCxyqHLsY1iwwAGPkZkHUKuLBfdJIGU1UjoMTiKBz+SrRicAstjmfYsOiwBWvHe5V5zuI7yv4OAKq38hH/uE+ZL/ZjGTasS7Bh64OOxzcPUGH1KSvah2qw6qQVncJk3AiozPCPgZBWolOQXIW0AkbOBpZOFJ1Edk5l27EjyYbYT8sWYK9dZ8DDPfV4co0RfaI1uK+LDhcKJHT/XxHUKscqMg/30OHJPmWLk6fWGvHWQAP8Lq1ONfUaPcYvL0GzTwsQ4aPCvNtkOgwppC0w+gvRKUjObp4OnI8D0o6ITuIUVbU7sortSC20I8JHjYsFdkz63fG5rujK5SdxZrx3k0eVBYaiOx9qY9TnQIRrXjlXSRUtYOwq8i84eiIL00QnaRRXftgBYEeSFY/+YURGkYRQbxXevdGAW1s7ehDv/KUYU6/R48bm5etM1Vv5uPjcv0WGJEkY+EMxPr7ZA9dEOXokUgvtuHdZCRJz7GgRqMYPoz1rvJKVLPR6iDvyUs388SKw63+iU5DcGPyByRuA4Jaik5Dc5ZwF5g0BitJFJ3G6K9sd8Zk23LqwGFY74K1X4fFeejzW+98OhSvbHWEfFUCrBvRXXeTY/4gPAi8tQHPPsmKM66DDmPaOdkuRWcL45SXYd9FW2vnQOVzhE8H7P+nYDNZFuXaRAQDndwI/jABsFU80JDfU+hbgnp8ck/OIqmO3AT/fB5xcIzoJyYVKA9zzM9CG+6pQDV04AHw/HDAXVvtQchMtBgHjl7l0W0RBXc911LQPe6zpXxFdgDu+dekPNTmZWgPc+R3QhPsf0CW3fcoCg2onqhsw7gfHSmREAc3coi3i+kUGAPS4H7j2GdEpSLSApsC9SwCDj+gkpDQ6T+DexUBIG9FJSLQh/3V8pxDVVqvBjvmi5N68w4D/W+6SE72v5h5FBuDY4KTvY6JTkCjeocD/rQD8IkUnIaXyCgLG/wr48t+Q27r2GcfqhUR11e0e4EZu2ui2PAIcBYabzOVynyIDAIa+C/SeLDoFNTaDn2Pco5t8qKkBBcQ4/i0Z/EUnocbWc6Kjs4qovq5/3uU2XaMa0PsA9y112ZWkKuJeRQYA3Pqh48uC3IOHv+NDHdlVdBJyFeEdHYWGBwsNt9HxdmD4TNEpyJXc+hHQeZzoFNRYdN6O4dputmmn668uVRFJAn5/HNi/QHQSakjeYcD//erY1ZvI2VIPA/PHAEUZopNQQ+o0FhjzP0DDCbvkZHY7sOppYN8PopNQQ9L7APf9AjTrLzpJo3PPIgNwfLh/exQ4+JPoJNQQ/JsC96/gEClqWJmngR9HAfnJopNQQ+j9CHDrB4Cq/GZiRE6z9hUgjps6uqTLQ6Sa9ROdRAj3LTIAR6Hx1xvAdq724FJC2joKDL8o0UnIHeQmOQqN7ATRSciZbvwPcP0LolOQu9j4AbDxXdEpyJl8oxx7ckV1E51EGPcuMi7b+z2w+jnAbhWdhOorqjtw3zLAO1h0EnInhemOoVNpR0QnofpSaYARM4GeE0QnIXez9wdg1TOAZBOdhOorsptjw043X9GSRcZlCRuAJQ8ApjzRSaiuWgwE7loAGHxFJyF3ZMwDlj0MnFonOgnVlcYAjJ0HdBgpOgm5q/i1wNKJgKVYdBKqq/a3AWO+AvReopMIxyLjShnxwMI7gdxzopNQragc69ff+B+X3z2TZE6SgA3vAps/AsBTq6L4xwB3/gA06Sk6Cbm7i4eAXx4AshNFJ6HauvYZ4KZpnMd1CYuMqxVlAj/dAyTvEp2EasLgD4z5Emg3THQSon8dXwUsnwKYC0QnoZpoNRi4/Wu32IGXFMKY71gF89hvopNQTWj0wIhPge73iU4iKywyKmI1AX++Duz6n+gkVJXwzsBdPwJBLUQnISov4yTw871A1inRSagyKjVww8uOCd5q99s2ihQg7ktg/euAzSw6CVXGL9oxzNINl6itDouMqsSvAVY8CpRki05CV+t6DzDiE0DnKToJUeXYGylfnkGOhkGrm0QnIapa8l7glwlA3nnRSehqnccBwz4CPANEJ5ElFhnVyb8ArJgKJG4UnYQAwOAH3PwO0PMB0UmIau7wUuCPF9hhIRfNrwdGfQEExIhOQlQzJTmOIZgn14pOQgDgGejo6Ow4RnQSWWORUROSBOyeB6x/gys+iNR2ODB8Bve/IGUqTHcsT3lilegk7svD39FJ0eN+0UmIak+SHEvu//UmYMwVHMaNtRoCjPoc8I0QnUT2WGTURlYC8NvjwPntopO4F59w4NYPgY6jRSchqr9DvwBrXnD0TFLjaTcCGP4xGwakfIUZwJ//AQ79LDqJe9F5A7e8A1zzoOgkisEioy6OLAPWTwPykkQncXEqoMf/AUP+y/GO5FoK0hyFBudqNDyfcMeY6Q6jRCchcq4zW4DVzwKZJ0UncX3tRjiuggY1F51EUVhk1JWlBNg2G9j2KYdQNYSQto5ex+bXiU5C1HDOxwHrXgNS9ohO4no0euCaScDAlxzjp4lckdUMbJ8FbP4YsJaITuN6onsCN08HmvUTnUSRWGTUV14K8Nc04PAvopO4hsBYYOArjhUbuKQkuYsjy4C/3uJGoM6gUjvOH4NeBQKbiU5D1DhyzjqW3j++EtwI1An8mwKDpwGdxnJjvXpgkeEsSbscE8PP7xCdRJn8ooHrnwe6/x+g0YlOQ9T4rGbH3jybPwKMeaLTKFPrWxwNg/COopMQiZF2zHEOObYCkOyi0yiPwR+47lmg71RAaxCdRvFYZDjb+Z3AtllA/B9gb0INeIcC1z7rmEil8xCdhki8khxg19fAzi+B4izRaZSh2QDgxv9wMyyiyzJOAltmOJbPlmyi08ifVzDQcyLQ91HAO1h0GpfBIqOhZJ4Cts8GDi4GbCbRaeQnuJWjsOg5AdB7i05DJD/mYmD/AiBujmMoBJWlMQCd7wD6PAJEdhWdhkieshKALTMdK1HZraLTyE9YB6DPFKDLXezobAAsMhpaQRqwcy6w51sOgVBrgXbDHcVF8xs4zpGoJux2xwZcO+cCZzaLTiOeTwTQa5Kj19EnVHQaImXIPQ/smw8c/IkrY0IFtL7ZMSSq5SDRYVwai4zGYikB4tc4Ll2eXg/YzKITNR6/Jo4dunvczzXqieoj4yRweIljoQl3urqh0gCxA4Du9zv2y+G8LaK6kSTgzCbgwCLg2O/utSKVdyjQ8Xag92QgpJXoNG6BRYYIJTnA0RWOguPcNrjk3A3vUKDNLUD7kUCrwYBaIzoRkWtJ2u0oOI4uB4oyRKdxPpUaaNof6DQGaD+KVy2InM2Y7zh/HFgIJO0UnaZh+EQA7W9z7JPTrD/bIo2MRYZoeSnAkaXAyXVA8m5lX+EIaQu0Gwa0HQZEX8MlaIkag80KJG4Ejv/u+K+Sl8FVqYGYPo7exg6jAN9w0YmI3EN2InD6byDhH8cmf+YC0Ynqzi/a0cHZYZTjfMK2iDAsMuTEUuLoTTiz2fEhv7BP3hO1vIKBqB5Ai4FA21uB4JaiExFRzlkgcZNjSMSZzTK/yqECQtsBza93/MReC3gGiA5F5N5sVscGoQkbgMQNQMpeebdFfCKAmF6OgqJpfyC6B+d8ygSLDDkzFTr23Ti7FUg/DmTGOyZviVj7WuftWMEluodjB8zoHo6N84hIviQJSDsKJMU51s9PP+b4r0nEIhQqwD8GCG0DRHQBmvYFYnrXaTfu+Ph4rF27Fk899VSNnyNJEj7++GM8//zztX49IrdmzHe0Q1IPOdoiGSeArNNiCg+1FojoDDTp7Th/xPQGApo2fg6qERYZSmMpcXy4M+IdP5nxjsucxTmAMRcwF9b92B7+gG8k4BcFBDRz7JYbGOsYBhXalmMZiVxFXrKjsZB2FMg65VgFrygdKEx3XPmoa+NB7wN4BgFegY4v/svnjpA2QEjrGi1X/eGHH+LDDz8EABQWFkKn08FgcGyKtXr1avTp0wd//fUX3nnnHWzcuLHCY8TGxiIuLg4REf8uNGG1WqHT6VDXrzwPDw8kJycjJCSkTs8ncilWs6Mtkn7MUXSkHwfyUxxzTktyHIVJXeebegU7zh+Xf/yv+P+g5oDO06lvhRqOVnQAqiWdp6OKj+hc8f02y6UPee6lD3ouYC5yrMai0Tv+q77i/zV6xxe/b0SdPrjsUSRSIP8mjp/WQ8rfJ0mOc0dhmmMzQJvFcfVUki7999IPJMDgB3gFXSosgpyyQ+6LL76IF198EQBw++23Y/DgwXj00UdrdYyioiIUFtajw+Uqa9euhclkwsqVKzFx4kSnHZdIsbR6ILyD46cidtu/7ZDLP6Z8x7wrldrRaanSONodem9A5wXovQCfcO6d5UJYZLgajQ7wCXP81FNNehSTkpKwfPnySouMinoUbTYbXnjhhToXGexRJGpAKpWjYPAKEhrDarVi7969kCQJjz76KNauXYsJEyYAAMxmM7p06VLh8xITE5GZmYnNmzejVav6LVMpSRJ++eUXvPbaa1i9ejVeeOEF2Gw2TJw4ERoNr+wSVUqtceyc7aTds9mhqUycck+VevHFF5GZmYnMzEwMGzYMH3zwQenvffr0qdExGrJHkYhc15w5czB69GiUlJTg119/xdChQ5GamorU1FQsWbKk0ue9+uqrGD16NKZNm4bMzMxy94eEhKBJkyZVvvaOHTvwySefoFevXli5ciW2bt2KYcOGYfv27di/fz86d+6MN998E7///jtsNlu93yuRO/vwww8REhKCkJAQeHh4wNfXt/T3nTsdS+te7tCsTGxsLFJTU8vcdrlDs648PDwqPIdQzbHIoGpd7lFcv349AEdDPyIiAhERERg3blylz7uyR7G+JEnCkiVL8MQTT2D16tWYMWMG5s2bxy94Ihe0ePFiLFq0CO+//z4WLVqE6dOnY/bs2bBaq54r8uqrryIjIwOLFy/G66+/joEDB+LUqVNlHpOZmYnk5OQqj3P48GFoNBosX74c8+fPR3i4Yyldf39/zJkzBxs2bEBERASOHz/OKxpE9cQOTRcmEVXj008/lZ588knplltukZYtW1bmvvXr10s33HBDhc+76667pNGjR0tNmjSRMjIySm+3WCwSACk4OFiKjo6u8rW3b98uzZw5U+rZs6c0fvx4KTU1VZIkScrNzZUeffRRqX379tK0adOk3377TbJarfV7o0Qk3PPPPy/16dNHyszMLL0tNzdXGjdunPT7779LklT+vFNQUCANHDhQGjdunFRUVFR6+6pVq6QWLVpISUlJpecdIpIni8UiNW3aVBo9erQkSZK0Zs0aKTw8XAoPD5cCAwMrbWskJCRIAKRvvvmm3PFq+5m32+3S4sWLpVatWkmrV6+WOnToIH399ddsX9QRV5eiKi1evBgzZ87Exo0bUVJSgiFDhuCBBx7Ao48+Cq1WW+kqL6+++ip27tyJNWvW4Pvvv8fs2bOxfPlytG7dularvHz11VcwGo0YM2YMYmJiyt2flpaG5cuXIy8vDy+99JKz3jYRCXLx4kWEhIRAp9NV+pj9+/djwYIF+Pjjj0tvO3nyJNq0aVPusTabDRqNBna7HePGjcPSpUurfP0ZM2ZgxowZlR7nanfddRdmzZpV5TGJqHqzZs1CYmIi4uPjMXnyZNx+++2l91W1otzdd98Nk8mEPXv2YP/+/aXzNS+3NYKDg0vnclZmx44diIuLw8KFC9G+fXvMmDED4eHhyMvLw6uvvooNGzZg3Lhx6NGjB4YPH84rmDXEid9UqRdeeAFbtmzBH3/8AU9PT3h6euKff/7B5MmT0bx5c9x2223lnlNYWIjbbrsNYWFhWLlyJfR6PSZPnozo6GgMHToUmzZtKjMJvDqTJ0+u8v7w8HBMmTKl1u+NiOQpMjKy9P937tyJTz/9FHv27IHVaoVarUZgYCDuvvtuTJ8+vczzLhcYZ86cwcyZM7F+/XqYzWYAgJeXF2677TZ88cUX1b7+888/X+FEUR8fHyQnJyMgIKAe746IKnJ5iOSVHZrJycmlHZqVuTxE8nKH5sCBA0s7NC+rybyKK4dIXtmheXmI5OUOzePHj2PkyJH1e7PuRPCVFJKxCxcuSGazucrH7Nu3T3r22WfL3BYfH1/hYy9fbrTZbNLYsWOrff2PPvqo9FLplT8hISEV3v7kk0/W8J0Rkdxt375dCg4OlhYvXlzmPHTmzBlp3Lhx0siRI8s9Jz09XQoLC5M++eQTKT8/v/T2zMxM6fXXX5datmwpGY3GOuXx9vaWcnJy6vRcIqoch0i6Lg6Xohqpqkfx8ccfh4eHR7nnVNWj+MwzzyAsrG7L7LJHkcj1vfnmmzh9+jQWLFhQ7r7Tp0+jQ4cOpeeVy1atWoVXXnkFhw8frvCYwcHB+Oeff9C1a9da5+F5h6hhcIik62KRQdXasWNH6VCDMWPGlJ4Izp49i5deeglGoxG//fZbmedkZGSgU6dOeOWVVzBp0iT4+voCALKysjBr1iwsWrQIR48eLd13ozb4ZU/k+uLi4jBixAh88cUXGD16NPR6PQAgISEBL774Iux2e7klLTMzM9GpUyf85z//wcSJE+Ht7djUKzs7G59++ikWLFiA48eP87xDJFPs0HQtXMKWqrVu3ToMHToU48aNK9PTEBsbi+nTp2PNmjXlnrNz506EhYXh6aefLi0wAEdP4ttvv42cnBycOHGiUfITkfL07dsXq1evxq+//or27dujadOmaNasGcaNG4d+/frhp59+KveckJAQ7NixAydOnEDPnj3RokULtGjRAtdeey2Ki4uxbdu2OhUYRNTwduzYgeHDh2PMmDE4duwYzpw5g4SEBCxduhS7d+/GXXfdVe45GRkZ6Nu3L1q2bIndu3cjMTERiYmJ2LRpE3Q6Hfr37w+TySTg3RDAKxlUA+xRJCJ3t2PHDvTu3ZuryhA1EA6RdD28kkHVYo8iEbm7fv36scAgakBDhw7F2rVrsWTJkjLFREJCAl566SUMHz683HP69u2LjIwMfP755ygqKiq9PTs7G2+88Qb8/f3Rrl27RslP5fFKBikOexSJiIhcz86dO/HJJ59g9+7dsFgsUKlUCAkJwT333FPlnIyPP/4Yf/31V2lx4uHhgWHDhuG5554rsyx2bfBKRv2xyCAiIiIiugI7NOuPRQYRERERETkV52QQEREREZFTscggIiIiIiKnYpFBREREREROxSKDiIiIiIicikUGERERERE5FYsMIiIiIiJyKhYZRERERETkVCwyiIiIiIjIqVhkEBERERGRU7HIICIiIiIip2KRQURERERETsUig4iIiIiInIpFBhERERERORWLDCIiIiIicioWGURERERE5FQsMoiIiIiIyKlYZBARERERkVOxyCAiIiIiIqdikUFERERERE7FIoOIiIiIiJyKRQYRERERETkViwwiIiIiInIqFhlERERERORULDKIiIiIiMipWGQQEREREZFTscggIiIiIiKnYpFBREREREROxSKDiIiIiIicikUGERERERE5FYsMIiIiIiJyKhYZRERERETkVCwyiIiIiIjIqVhkuLkXX3wRkyZNqvC+yZMnY/bs2Y2ciIiIiIiUjkWGQgUEBEClUlX5M3jw4GqPU1xcjKKiogrvu3DhArKzs50dnYhkpF27dvj+++8rvO/AgQMICAiAyWQqd9/atWsxcODAhg1HRESKxSJDgCZNmlRbIKhUKpw4caLK48yfPx9JSUmV/vz888/lnjNhwoQyrzFnzhwsXry4zG133313te9h69atNXoPKpUKS5curfOfFRE5V25uLlQqFc6ePVvtY61WK/Ly8iBJUrn7CgsLa3QMIiJyT1rRAdzRnj17YLVaK70/Pz8fHTt2rPY4ISEhaNKkSa1e+5NPPsE777xT5WO8vLyqPU7v3r2RlJRU7eO6dOlS42xE5ByxsbE4d+5cudvPnDmDgICASp+3ceNGDBo0qNztnp6eZX4vKSmpNkOrVq2QkJBQ7eOGDx+OVatWVfs4IiJSFhYZAkRERFR5f25uboO9dmBgIAIDA3Hu3Dl88cUXOH78OGw2G1q3bo0pU6agXbt2NTqOXq+vUYGjVvNiGZEI//vf/zBs2LAyt0VGRqKgoKDS5/Tr169GnQceHh7VPmbr1q1VdqYAwDvvvIPk5ORqj0VERMrDIkPGVCpVlfffeuutVd4fHR1d4Rf4li1bcPPNN8PHxwfXXHMNtFotfvnlF3z++edYsmQJbr/99tLHFhYWIjU1FVqtFiEhIQ3yPojI+YKCgmp9pdNgMKBJkyYwm8347rvvsGnTJmRkZCAqKgojRozAHXfcUePPc3WdKQDg4+NTq3xERKQcLDJkyGw2AwB0Ol2ljzl27BjsdnuVx9FqK/7rfffdd3HDDTfgt99+g8FgAADYbDY8+OCDePPNN8sUGR9//DE+/vhjtGzZEqdPn67tW4HFYil9DSKSP6PRiAEDBuDgwYPo27cvQkNDcfToUfz4448YM2YMfv3119LH2mw2pKamAgCCg4OrPGdVhp0QRESuiWNZBMvMzMSSJUvK3HZ5JZeqhiRERUWhSZMmVf5U1pNYVFSEwYMHl2n8azQa3HrrreVWmpo2bRokSaq0wNi7dy8++OCD0t/j4uLw0Ucflf7OIoNIWX777TfEx8fjwIED2Lp1K5YvX449e/Zg/fr1WL58Ofbt21f62OTkZERGRiIyMhI7d+6s9Wvx/EBE5LpYZAh24sQJ3HvvvWVuu9zQ9/b2LnP7999/X+MVnapaqWrChAn48MMP8fPPP+PChQtIS0vD77//jldeeQUPPfRQrfIfP34c8+fPL/39yJEjWLhwIQBAkiSYTCYOiSCSoYEDB6JVq1ZITEwsc3tRURFiY2PRqVOnMrcPGjQIXl5eZToimjVrBkmSIEkSrr322gpf57nnniud52G32/H0008jJSUFAIsMIiJXxuFSMpSRkQGDwQB/f/8yt48bN65Ge19U5MqrGg8++CD8/f0xc+ZMTJ06FZIkoXXr1njzzTfxwAMP1Cv7lfLy8mC32xEcHOy0YxKRc8yYMQMRERG47777ytw+YsQIvPjii3j22WcxefJkhIWF4fz583jvvffQvHlz9O3bt1avM2vWLNxzzz2IiYmB3W7HrFmzMGHCBERHR6OkpISdEERELopFhgyZTKYKl7D18vKqdHlZk8kEtVpd4zHRY8eOxdixY6t8zLRp08oVOrVhNpvRsWPHOk8YJ6KGc8011yA2Nrbc8rRhYWGIi4vDW2+9hSFDhiAzMxNRUVEYOXIk5syZU6d5F5XJyclBhw4dnHY8IiKSDw6XkqHBgwdj7969VT4mKysLL7/8Mrp06QIPDw94eHhAr9fDz88P/fv3x2effQaLxVLp80ePHl3tEKvevXuXmy9SG2FhYThy5AiCgoLqfAwich6j0Vijx7Vq1ap0s8+SkhIkJCTgk08+KdNhcO211+Lbb7+tV57IyEg0b968XscgIiJ54pWMRlTR7riXV2apaudcf39/BAYGlv5uMpnQr18/REdH44MPPkDPnj0REBAAq9WKzMxMbN26FW+88Qa2bdtW4a7fgGO38MsTzCtz9TAKwLED8JXL4mZmZsJisZTmz8rKgtlsrvD9BAcHw9fXt8rXJCLneOSRR/D000/DZrPBbDajoKAA1113HZYtW1btc1esWIExY8ZU+7i6rjp32dy5c+v8XCIikjcWGY2oqh67qu576aWX8P7775f+Hh8fj1OnTuH3338vs3meXq9H06ZNce+998JkMuHxxx+v9Ji+vr7VNvgrGhaRnJxcYdarb6voMXPnzsWUKVOqfE0iqr+VK1fCYrFAo9HAYDDA09MTvr6+CAwMRF5eXrXPHzZsGDIyMqp9jenTp5e7PTU1tdwVk4sXL+Ls2bOw2WwAgAsXLpTbedxgMCAyMrLabEREpAwsMhqRJElOOU67du3QqVMnPPjgg3jhhRfQrVs3BAQEwGazITMzE9u2bcPbb7+NO+64wymvd6XY2FinvQ8iahidO3eu1/P1en21c6kq66S4++67sWnTpjK3jRw5sszvw4cPL/e8Pn36IC4urpZJiYhIrlhkKJBer8f27dsxe/ZsfPTRR4iPj0deXh5UKhVCQkLQuXNnTJs2DRMmTBAdlYjczMaNG0VHICIiGWCRoVC+vr547bXX8NprrzXYazz22GMIDw9vsOMTERERkWtSSRz7QkTktmw2G9RqNVQqVa2el5iYiJ07d+Kee+5poGRERKRkLDKIiIiIiMipuE8GERERERE5FYsMIiIiIiJyKhYZRERERETkVCwyiIiIiIjIqVhkEBERERGRU7HIICIiIiIip2KRQURERERETsUig4iIiIiInIpFBhERERERORWLDCIiIiIicioWGURERERE5FQsMoiIiIiIyKlYZBARERERkVOxyCAiIiIiIqdikSHY448/DrvdLjoGESkUzyFERCRHKkmSJNEhlGDp0qWYMmVKpfdnZWXBYrFAq9WW3jZq1Chs27YNAJCTkwN/f3+o1Y66LjMzEwCgUqnKPa+mPDw8kJycjJCQkFo/l4gaF88hRETkTlhkOIlWq4XRaKz0iz48PBwbNmxAhw4dytxe1wbC2rVrceutt+Lbb7/FxIkT65ybiOSB5xAiInIlHC7VCM6ePYv09HRs2rSp3seSJAlLlizBE088gdWrV2PGjBmYN28ebDabE5ISkRzxHEJERErDKxlOolarYbPZoFKpyt03fvx4WCwWbNu2Dfv27UNYWFjpfSqVCsHBwdBqtUhNTa30+Dt27EBcXBwWLlyI9u3bY8aMGQgPD0deXh5effVVbNiwAePGjUOPHj0wfPhwaDSaBnmfRNQweA4hIiJXwisZdZCWlobdu3eX/m40GqHT6SpsHLz11ltITU3FggULMH36dAwaNAjx8fFlHpOamlpl4wAADh8+DI1Gg+XLl2P+/PkIDw8HAPj7+2POnDnYsGEDIiIicPz4cTYOiGSO5xAiInJ1vJJRBytWrMC8efOwatUqAI4JmO3atSudiAkAJpMJo0ePhpeXF+bPnw8vLy8AwLp16/Doo49iw4YNaNq0ab0mbRKRMvEcQkREro7fSk6Qnp5e2it4mcFgwOzZs9G6desyt99yyy04efJkaU/h2LFjS1eLqcyMGTMwY8aMcrfbbLYKexzvuusuzJo1q7Zvg4gE4TmEiIhcDYsMJwgPD8cHH3xQ7vbLjYOkpCTMnDkTa9euhclkAuBYOnLEiBH4/PPPq20gPP/883j++efL3e7j44Pk5GQEBATU/00QkTA8hxARkavhnAwnCA4OxogRIyq8Lz8/H3379kVERATi4uKQmJiIxMREbN26Fd7e3ujXrx9KSkoaOTERyQnPIURE5Go4J6MaSUlJ6N69e62ec+W46o0bN2LixIk4c+ZMhY8NDw/HH3/8gZ49e9Y6G3shieSP5xAiInJHHC5VjZiYmDJf+LXVo0cPmM1mzJgxA4888gh8fX0BOHbvnT17Njw9PcttrkVEroPnECIickccLtXA/Pz8EBcXh+TkZPTu3RstWrRAixYtMGDAAOTl5WH79u3w9PQUHZOIZIrnECIiUiIOl1KwHTt2oHfv3lzTnojqhOcQIiJqKCwyiIiIiIjIqThcioiIiIiInIoTv12YzS6hxGKD8dKPSqWCt14Db4MWOg3rSyKqntFig8liR4nFBovNDoNODW+9Fl56DVQqleh4REQkUywyFCo934jTGYVIyChCQnohUnJLkFtsRm6xBTnFFuQbLTBb7ZU+X69Rw8ugKW0seBu08DFoEenvgRahPmge4o2Wod5oFuwNvZYFCZGrMVvtOJNZhISMQiSkF+JMZhEyCk3ILbYgt8RxLik0WVHZgFqVCvDSaeBl0JZ2XnjrtfD30qF5iDeah3ijRYg3mod6I8zXo3HfHBERCcc5GTKXV2zB7rPZOJlegIT0Sw2CjEIUGK2N8voatQrRAZ6OBkOoN1qH+aJ380C0CvNtlNcnovqRJAnHLxbgcEouEjKKcDrdcQ5JzimBzd44p39fD62j4AjxRotQH3SK9kOv2CD4euga5fWJiKjxsciQGaPFhl1nsrEtIRPbT2fh6IU8NFI7oFZCfQ3o1yIY/VoGo3/LYDQL9hYdiYguOZ9VjG0Jmdh6OhNxCVnIKjKLjlSORq1Cp2h/9G8ZjH4tgtErNgieeq5yRUTkKlhkCGazSziQlIvtpx0Ngv3nc2G2VT7MSa6iAzzR91LRcUObUIT6GkRHInIbmYUmbDvt6JjYlpCJ5JwS0ZFqTa9Ro2uMP/q1DMGAlo6iQ63mnA8iIqVikSFIfGoBlu5NwooDF5BRYBIdx6k0ahX6twzGbV2jMLRTBPw4JILI6YwWG9YdTcXSvcnYdjpTllc86yPM14DhXSIxsmsUujcNFB2HiIhqiUVGI8opMuO3AylYti8Fh1PyRMdpFHqtGje1C8O4XjG4oXUoeyaJ6mnvuWws3ZuCVYcuNNrcLNGaBXvh9u5NcOc1TRAVwN3NiYiUgEVGA7Pa7NgYn4Gle5Pxz4l0RQ6FcpZIfw/c0bMJ7uoVgyaBXqLjECnGxbwS/LovBcv2JiMxs0h0HGHUKuC61qG4q1cMbu4QDi2X4iYiki0WGQ2k2GzFop3nMW/LGaTmG0XHkRWtWoWR3aLw+KBWaBHqIzoOkWwdScnD3I0JWHPkossNh6qvmCBPTL2hFe7o2YTLbBMRyRCLDCfLLTbj++1n8cP2s8gptoiOI2tqFTC8SxSeuLEV2oRzSVyiy3YmZmHOxgRsPpkhOorsRfp7YMoNLXFXrxh46Lg6FRGRXLDIcJICowVfbzmD77aeQYHJPcZJO4tKBdzSIQKP39gKnaL9RcchEmbvuWzMWHcSOxKzREdRnFBfAx65vgXu69OMS+ESEckAi4x6KjHb8P32s/jf5gTk8spFvd3YLgzPDmnDYoPcypGUPMz4Mx4b43nlor6CvfWYdF1zPDigOa9sEBEJxCKjHtYfS8O0347gQh7nXDiTWgX8X99meP6WttwRmFxaXokF7685jp93J4FnYueKCfLE26M6YVDbMNFRiIjcEouMOkjLN2Lab0ex9miq6CguLdzPgNdHdMCILlGioxA53W8HUvDfVceRWeha++TIzfDOkZh2WweE+XmIjkJE5FZYZNSC3S5hwc5z+GhtPOddNKKBbUPx31GdEBPEZW9J+ZKyi/GfFUewiZO6G42vQYvnb2mL/+vbjHv1EBE1EhYZNXQiNR+v/HoY+8/nio7iljx0ajxxY2tMvr4FdFwbnxTIarNj3tYzmPXXKZRYbKLjuKWuTfwxfUxnzvkiImoELDKqYbba8clfJ/H15kRYuVC9cG3CfTD7nu5oF+EnOgpRjR1JycMLSw/h+MV80VHcnkatwkPXNccLN7flZn5ERA2IRUYVLuaVYOqCfTiQlCs6Cl3BU6fBO6M7YWzPJqKjEFXr513n8cbvR2G22kVHoSv0jg3C5/d251wNIqIGwiKjEttPZ+KJn/Yjq8gsOgpV4p7eTfHmyA4waLlMJcmP0WLDtN+OYvGeJNFRqBKhvgZ8dk939G0RLDoKEZHLYZFRgbkbEzDjz3jYODxK9jpH++OL+3pwUjjJSnJOMaYu2IfDKXmio1A1NGoVXrilLabc0FJ0FCIil8Ii4wqFJiueX3KQS9MqTICXDp/c1Y3r4ZMsbD6Zgad+3o8cbs6pKDd3CMeMcV3hx715iIicgkXGJafSCvDIgr1IzCgSHYXqQKUCnhjUCs8MaQOViktUUuOTJAmf/3Man/x1ErwIqkyxwV6YO74n2kdyYQkiovpikQFge0ImHv5hD4rMXFZS6UZ3i8JHd3blMrfUqKw2O55dchC/H7wgOgrVk69Bi68fuIbzNIiI6snti4yN8emYsmAvjBau/OIqBrYNxdz7esJTzwnh1PDMVjseX7QPfx5LEx2FnESvVeOze7rjlo4RoqMQESmWWxcZ646m4olF+2G2scBwNd2bBuD7Cb3h78Xx1dRwjBYbJs/fi83cvdvlaNQqvDumE+7q1VR0FCIiRXLbIuO3Ayl4bslBbrDnwjpE+mHBQ30Q5K0XHYVcUJHJige/342dZ7JFR6EGolIBb43siPv7xYqOQkSkOG5ZZCzZnYSXfz3EyZluoF2ELxY+1AfBPgbRUciF5JVY8MC3u7hRp5t4fUQHTLq2uegYRESK4nZFxg/bz+LNlUfhXu/avbUO88HCh/sgzJc7+1L9ZReZ8X/f7MTRC/mio1AjeuXWdniEe2kQEdWYWy3BM3/HWUz7nQWGuzmVXogHv9+NYrNVdBRSuHyjBfd+HccCww29t+YElnD3diKiGnObIuPPo6mY9vtR0TFIkCMp+Xjyp/2wc4wc1ZHZascjP+7FidQC0VFIkNeWH8b205miYxARKYJbDJfafz4H93wdp7hlarPXz0XhkQ1Qe/iU3hZx7/vQ+jt2traVFCB343fQBkbBv+8d5Z5vMxbi4ndPXnWrBFt+BiIe+BT60FhkrfscxvOHodYaEDT0cXg06QAAsBZkImPZfxF+34dQ61xnPsOE/rF4c2RH0TFIYSRJwjOLD2DFAWXtg8FziPP5eWjx66P90SrMV3QUIiJZ04oO0NDOZxXjoR/2KK7AuMzvmlEIuO6+crfnbPgWhYf/gkprgG9gZIXP1Xj4oMnUb8vcVnRsEwr2r4YhohUK9v8Btc6AJlO+gTk9EZmrP0HUxM8AANnrv0TAwIku1TgAgO+3n0WzYC9MHMBJnFRzM/6MV1yBcRnPIc6Vb7Ri4ve7sfzRAQjhghJERJVy6eFShSYrJv2wG1lFZtFR6kzt4V3x7QZvRNw/Ex7NutT4WJLdhtxtixBw/f0AAHPGWXi26AUA0Ie1gGQqBgAUxW+D2uAFz9hu9QsvU/9ddQx/ceM0qqEV+1MwZ0OC6Bh1xnOI8yVll1zqvLKJjkJEJFsuW2TY7RKe+mk/TqUXio5SL2pDxQ0E//53QRdQu91oi09sgcYnGB4xnQAAuuAYFJ+KgyTZYUw6Ao1/OOymIuRt+wmBNz5U7+xyZZeAJ3/ejyMpeaKjkMwdTMrFS8sOiY5RLzyHNIwDSbl4ZvEBuMGIYyKiOnHZIuOjP+Px94l00THqLWfzD0j+YiJSf3oFJWf21etY+buWw++aUaW/+3YdCsluw4X/PYy8bT8heOgTyNnwHfx63w6Np199o8tasdmGST/sRnq+UXQUkqm0fCMmz98Dk1WZQy0v4zmk4aw5kooP18WLjkFEJEsuOSdjQ3w65m5U7vCGywIHP4KgIVMh2W0wntmHjN8+QPjd02GIaFXrY5lST8NuLIRnq16lt6m0OoQMe6r0d2PyUVjz0xHUbjKy/5kHS+Z5qD18EDhoErS+wU55T3KSlm/CC0sP4YcHe4uOQjIjSRKe/vkA0vJNoqPUC88hDe/LTQm4rlUI+rcKER2FiEhWXO5KRm6xGS8tVfbwhstUKsdfj0qtgWfLXvBufz1KTu6o07GKDq+HV4cbSo95NclqQc5fXyHo5keRv2s5NN5BCB/3Nrw7DkLOhm/q/B7kbtPJDPy446zoGCQz3247ix2JWaJj1BvPIQ1PkoDnfjmIvBKL6ChERLLickXGayuOIL1A2b2PlbLbAE3tLz5JdhuKjm2GV5v+lT4mb8cSeLW/HrqACJhSjsOrpaO30qNpF1jSz9Y1sSK8+8dxJGQoe+4OOc/p9AJ8uPaE6BgNg+eQBnExz4jXVxwRHYOISFZcqsj47UAKVh+6KDqG05Qk7oUkOcaDl5zZh+KT2+HVdkCtj2O+eAqAVOkQCXPmeZSc2Qe/XqMBANqAcBQn7Ha8bsJu6EKb1Sm/Uhgtdjyz+AAsNmWPvaf6s9rseGbxQcXPw7iM55DG8/vBC/j9oDKXOSYiagguMycjLd+IN35zrR298/f8hszVM6HSGqD1C0XomNegD2la7fMyVrwHn+7D4NmsKwDAdDEe+vAWFT5WkiRk//kFgoZMgUqtAQD497sbGSs/QsG+VdAGRCBk+DPOe1MydSg5D7P/PoXnbm4rOgoJNPuf0zjsQquO8RzSuF5fcQS9YgMR6e8pOgoRkXAus+P3/d/uwuaTGaJjkIJp1CoseaQfejYLFB2FBDiQlIs75m6H1e4Sp0QSZECrYCyY1AcqlUp0FCIioVxiuNT8uHMsMKjebHYJzyw+gGKzVXQUamRGiw3PLjnAAoPqbdvpLHy37azoGEREwim+yEjNM+K9P46LjkEu4nx2sUssf0y18+lfp5CYUSQ6BrmIj/+MR4arLkBCRFRDii8yPv4zHsVmm+gY5ELmbTmD1Dxu0ucuUnJL8N22M6JjkAspMtvwyV8nRccgIhJK0UXGidR8LNuXLDoGuZgSiw0fcRdftzFjXbzLrCZF8rF4dxJOpRWIjkFEJIyii4z3/jgBDqGmhrB8fzKOXnCdVYaoYkdS8rDiQIroGOSCbHYJ73IoLxG5McUWGVtPZWITJ3tTA7FLwPTVbCC4uvfWHIdrrK9HcrQhPgNbT2WKjkFEJIQiiwxJkvDeGjYAqWFtT8jCPyfSRMegBrIxPh3bTmeJjkEubvofx2HnJXcickOKLDJWHEjB0Qv5omOQG3j3jxOwsYHgcux2Ce+vOSE6BrmB4xfzsZRzB4nIDSmuyDBZbZixjqt2UOM4nV6IJXuSRMcgJ1u6LxknUjkplxrHzD9PwmTlKohE5F4UV2T8ui8FKbklomOQG/lqcyKHO7gQSZIwZ8Np0THIjaTmG7F8HxcYICL3orgi44ftZ0VHIDdzJrMI649zboar2BifgXNZxaJjkJuZt/UMJK4yQERuRFFFRlxiFoc4kBDztiSKjkBO8j07KkiA0+mF2BjPFRGJyH0oqsj4ccdZ0RHITe0+m4MDSbmiY1A9nckswuZTbOiRGF+zs4KI3IhiiozUPCP+PMohKyTO/B3nREegevpxx1nui0HCbE/Iwun0QtExiIgahWKKjIU7z8HKybck0KpDF5BbbBYdg+qo2GzF0r1cSpTEWriTnRVE5B4UUWSYrXb8tIvLiJJYJqudjVQFW7YvBQVGq+gY5OaW7U1GiZnL2RKR61NEkbH68AVkFppExyDCop3nRUegOprPOV0kA/lGK1YeuiA6BhFRg1NEkbF4N69ikDwkZhbh6IU80TGolvafz8HJNI6FJ3lYdeii6AhERA1O9kVGZqEJu85ki45BVIoLECjPH4fZqCP5iEvIQoHRIjoGEVGDkn2Rse5oKjjfm+Rk3dFU0RGoltYc4d8ZyYfZZsc/J9JFxyAialCyLzLWHGbjgOTlRGoBkrK5Y7RSHErORXJOiegYRGX8eYxXRInItcm6yMgrtiAuMUt0DKJyeDVDOfh3RXK0KT4DJitXmSIi1yXrImPTqQzujUGyxHkZyvHPCe7wTfJTaLJi+2l2ohGR65J1kbGRY1ZJpvacy0YWl1WWvdQ8I45fzBcdg6hCfx7jVTYicl2yLTIkScKmk+yBJHmyS8Bfx3k1Q+42xLOjguRr/bF02Hm1nohclGyLjCMp+cgqMouOQVSpzacyRUegamxmRwXJWGahCcdTeaWNiFyTbIuM/Uk5oiMQVelQcq7oCFSN/edzRUcgqtKhZG7uSUSuSbZFxmGeeEnmkrJLkMOrbbKVUWBCar5RdAyiKrGzgohclXyLjBQWGSR/h/jvVLaO8O+GFOBgEv+dEpFrkmWRYbTYcDq9UHQMomodSsoVHYEqwY4KUoKTaQUwWrhfBhG5HlkWGccv5nN/DFKEgxzWJ1sc605KYLVLOMZllonIBcmyyOAwB1KKwym5oiNQJXgeIaXgFVEickWyLDI4zIGUIi3fhDROLpYdTvomJeFVNyJyRTItMnjpmJSDDQT54VUMUhIuIEFErkh2RYbdLuF0eoHoGEQ1di6rSHQEukp8Gs8hpBxJ2cWiIxAROZ3sioysIjMsNk76JuVILzCJjkBX4RA2UhKT1Y7cYu65Q0SuRXZFRnoBGwekLGzQyk8GCz9SmLR8/pslItciuyKDjQNSGhYZ8sOrS6Q0XKiAiFyN7IoMNg5IadLZAyk77KwgpWFnBRG5GtkVGWwckNKwcSA/PI+Q0qTzPEJELoZFBlE9FZltKDRZRcegS4rNVv59kOJwTgYRuRrZFRmc+E1KlJrHf7dywY4KUiJeESUiVyO7IoMNBFKizEL+u5ULzusiJeI5hIhcjeyKjAIjhzmQ8pisdtER6JIiDpUiBeI5hIhcjeyKDJVKJToCUa1ZbWwgyAXPIaREVm5CS0QuRn5FhugARHVgtbOBIBc8h5ASWe3sqCAi1yK7IkMtu0RE1bOxyJANNa9kkALxHEJErkYrOsDV2ECom0iDGR18CtHGKx/NdXmI1uQiDFkItGbAx5wBfXEq1CXZomO6LvUPAEaLTkEAeAqpG4PajvY+xWjnXYCWhnzEaHMRqc5GsC0LfpYMeBrToS1Og8rKVZAahKEFgP2iUxAROY3sigy2D8pSqSS09DSivU8hWnvko5kuF1HqHIRKWQiwZsDLmA5dcSpU5kKgCI4fanxq2X2U3BbPIeUF6qzo5FuINp4FaGFwdEJEIAuBtiz4mtNhKEmDujgDKrMdMItO66Z4DiEiFyO7s5o7TdrUqSW09ylCO69CtPLIQ1NtLiJV2Qi2O3oOvYxp0BSlQWUzAflw/JA8sYEgG+50DgGA2EudEG0889FMl4codQ7CkI0ASwa8zenQF6VCZcoDiuH4IXniOYSIXIzszmqu0j7w1VrRybcIbTwL0NKQhybaXEQgG0G2TPhaMuBRnHqp59DGnkNXwAaCbLjKOUSnltDOuwjtvAvRypCPprpLnRBSFvwvD18qSnUMXyqA44eUS60RnYCIyKlk1zLSKKCFEHFp/kPb6uY/lMDxQ65P7yU6AV2ihHld/jorOvkUoY1nPloY8tFEm4Nw5JTvhLDYgFzRaalR6LxFJyAicirZFRneBnGRVCoJLTyNaO995dCDbIRKWfC3ZsLbmMb5D1Qxn3DRCegSH4HnEACI8TCio28RWnkUIFaXi2hNLkKRhUBrJrxNadAXp0FtzGEnBJXlEyY6ARGRU8muyAjxMTTIcWs1/4FDD6i2WGTIRoivvkGOq1HZ0c6nBO29CtDSowDNdDmIUOUiRMqEvyUTXsY0x+pLlmKeQ6j2fCNEJyAicir5FRl1aCB4a23o7FOItl6OpRebaHM4/4Eaj94HMPiITkGXBHsboFYBtdl24N9zSCFaGPIQo81FOLIQbHesvuRRkg51cTpUFiuQB8cPkTPxSgYRuRjZFRmhV13JuHL+Q6wuH000OY75D7ZMeJvSYbg8/8EIxw9RY2PjQFY0ahWCvPXILHT0KER7mNDRpxCtPf8dvhR2efjSlXOoeA4hkXx4JYOIXIvsiozbgi9ibOwczn8g5WDjQHbWtvwVfmlx0BWlQWUpAgrh+CGSKw65JCIXI7siI9xXB6RuEx2DqOZ82TiQmxDkArmJomMQ1RzPI0TkYtSiA5QT0FR0AqLaYQ+k/PjHiE5AVDs8jxCRi5FfkeETDmgaZoUpogbhFy06AV2NnRWkJBo94B0qOgURkVPJr8hQqYDAWNEpiGouopPoBHS1oBaiExDVXFh77vhNRC5HfkUGAER2EZ2AqOYiuopOQFeL5N8JKUgEv/OIyPXIs8iI6iE6AVHN+DUBvINFp6Cr+UdzjDspB4tiInJB8iwyollkkELwqpt8RXUXnYCoZlhkEJELkmeREdEFUHF8KikAGwfyxSKDlEClBsI5r4uIXI88iwy9FxDaTnQKoupxLLV8cdglKUFwa8d3HhGRi5FnkQEA0eyFJAXgcCn54pUMUgKeQ4jIRcm3yGAvJMmddxjg30R0CqqMTyg35SP543cdEbko+RYZTa4RnYCoaq1uEp2AqhPdU3QCoqrxPEJELkq+RUZEF8AnQnQKosq1vll0AqoO/45IzgKaAaFtRacgImoQ8i0yVCqgzS2iUxBVTK1lD6QStBnKlepIvvgdR0QuTL5FBgC0Gy46AVHFYvoCHv6iU1B1vIOBmN6iUxBVrDWLDCJyXfIuMprfAOi8RacgKq8Nh+EoRtthohMQlafzAmKvFZ2CiKjByLvI0HkALQeJTkFUHnsglYNXREmOmt/g+I4jInJR8i4yADYQSH4CmgJh3CxSMYJbAiFtRKcgKotXQ4nIxcm/yODETZKbdiNEJ6Da4pApkhOVBmhzq+gUREQNSv5FhlcQ0Ky/6BRE/+r+f6ITUG11GCk6AdG/Wg0G/CJFpyAialDyLzIAoOcE0QmIHJr0BsI7iE5BtRXd07H3DpEcXDNRdAIioganjCKjwyjAO0x0CiIWvErWa5LoBESAXzQ3iSQit6CMIkOjA3o+IDoFuTsPf6DT7aJTUF11HgcYuLcJCdZ9PKDmPEMicn3KKDIAoOdETgAnsTqPA3SeolNQXem9gK53i05B7kylBnrcLzoFEVGjUE6R4R8NtOVqHCQQh0opX6+HRCcgd9ZqMODfRHQKIqJGoZwiA+CYahIn+hogopPoFFRfoW2A2OtEpyB3xY4KInIjyioyWgwCgluJTkHuqP8TohOQs/BqBokQ3Nqx7xMRkZtQVpGhUgH9nxSdgtxNeGfHCmfkGtrfxs4KanwDX+aEbyJyK8oqMgDHyhxsIFBjGvSqo8Al16DWAINeE52C3ElYB6DTWNEpiIgalfKKDDYQqDFF9wTaDROdgpyt4xggsqvoFOQuBr7CjgoicjvKKzIANhCo8Qx6VXQCaggqFXDTG6JTkDuI7OoYokdE5GaUWWSwgUCNIaavY8lJck2tBgPNrhWdglzdoNd4FYOI3JIyiwzA0UDgUpTUkG7ksDyXN3ia6ATkypr0AtrcIjoFEZEQyi0yAOAmNhCogbQaDDS/XnQKamgxvYG2nHNDDWTwm6ITEBEJo+wiI6aXY34GkTPpvIHhM0WnoMYy+E1AoxedglxN9/FALIfjEZH7UnaRAQC3fgh4BolOQa5k0KtAYDPRKaixhLYFrn9RdApyJd5hwM3viE5BRCSU8osMnzBg6PuiUwgxdVUJ2n1eWPr7ihMWdPyiEE0/KUDvrwux9by1wuetOWVBty8L0XxWAZrPKsC7W0yl91lsEh78rQTNZxWg4xeF2HbFMVLy7ejxv0KUWKSGe1OiRXUH+k4VnYIa27XPABGdRacQgueRBnDrB4BnoOgURERCKb/IAICudwFthopO0aiS8uz48ZCl9PczOXbcv7wEP4z2xPlnfDH9Rg+M/KkYecbyX+Rh3mr884A3zjzli20PeuPLPWasjHcc65v9FnjpgDNP+WLR7Z547A9j6fMeX2PEh0M84Klz0ZVS1FrgttncldcdabTAqC8c/wbcCM8jDaDNUKDT7aJTEBEJ5xpFBgCM+BQw+ItO0WieWWfExG660t8Pp9vQJliNa6IcDeQhLbXw0qlwKtte7rk9ozQI8nR8wUf5qtErWoOUAkcj4lCaDcNbOxpaXSM0yDc5bl92zAI/gwqDW7hwI6zfY0BkF9EpSJTILsCAp0WnaFQ8jziZ3hcY/rHoFEREsuA6RYZfJHCLe4yBXX3SgqwSCXd0+LdxcF1TLdKLJKxPcAxL+OmwBUGeKnQJr/yv2C45Hh+facft7R1f+h1CNVhxwgq7JGHzOStiA9TIM0p4e7MJM282NOwbEymwuWNXXnJvN7wEhLYTnaJR8DzSAG56A/BvIjoFEZEsuFZ3Uo/7gSO/AokbRCdpMFnFdjy51ojV93ohtfDfIQyBnirMuNkDNy8ohrcOMNuALRO9oNdUPCSh19eFOJxmR4iXCt+N8kSYt6MR8XAPHR5dbUOr2YVoEajG17d54sX1RjzfT49gL9epSctQa4HRXwA6T9FJSDSt3jFs6pshgGQTnabB8DzSAGKvA3o9JDoFEZFsuFaRAQCj5gD/ux4ozhSdxOkkScKk3414uo8B7UI0SC38dzLlrhQbXv3biP2PeKNruBp/n7Fh7JISbH3QG7EB5b/Udz/sA5tdQlyyDRN+M+K/gwy4u5MOBq0K34z6t7G99bwV5/LsmNXRA8+tM+Johg2Bnip8fLMHonxdpLEw+E2gWX/RKUgumvQEbngR2Pie6CQNgueRBuAXDdzxHaB2gfdCROQkrndG9I8Gxv3gkhM4399qhsUOPN5bV+6+WTtNeKyXHt0iNFCpHGOex7TT4uu95kqPp1GrMKCpFm9cr8fnu8o/zmSV8NRaI74Y7omPt5sR6avC2vHeGN9Zh+f+NFZwRAXqMAro/4ToFCQ3N7zkspv08TziZBo9cOcPgE+o6CRERLLiekUG4NgA6ebpolM43exdZmw5Z0XgBwUIeD8fIxYV41S2HQHv56PYAmjVZYc06DQqmGsw4sOgVcGzfHsD724x4e6OOrQIVGN78r8TOW9srsWhtPITQRUnpI1jaAzR1VQqYMz/gODWopM4Hc8jTnbLu46NYYmIqAzXLDIAoO8UoOu9olM41cXnfJH/ih9yX3b8rLrXC62D1Mh92Q/3dNLhs11mnM9zfGkfSLXhx4MWjGlf/orO57vMKLi02su5XDumbzFhQteyOx4fy7BhXYINz/Rz3N48QIXVpxzDKladtKJTmML/6eh9gbsWAgYf0UlIrjz8gLsXAQY/0UmciucRJ+pyN9D7YdEpiIhkyfXGFF1pxCdAxnHgwn7RSRrcuI465JskDF1QjCKLhEAPFb66zQP9Yxx/xXf+Uoyp1+hxY3MtDqfZ0OZzEwwax0TP5/vpcV+Xf7sgJUnC1NVGfD7Mo7RX8z/XG3DvshJ8vsuMFoFq/DBa4ZOkR30OhLYRnYLkLrSN44rGz/cCcOHN4y7heaQWwjsDt30qOgURkWypJEly7W/OvBTgqxuAogzRSUgu+j8B3Oweyx2Tk2x4D9j0vugUJBce/sDkjUBQC9FJiIhkS+HXqmvAPxoY9yOgceG12anmOowGBr8tOgUpzcCXgXYjRKcgOdB6Avf8zAKDiKgarl9kAI7lScf9AKgrmJVI7qPFIOD2r7nMJNWeSgXc8S3Q8ibRSUgktRa483sueU1EVAPu09pqeytwxzcuubQt1UBUD+CuBY7N1ojqQmsA7l7o2HSN3JAKGPk50Hao6CBERIrgPkUG4NgTYcz/AJV7vW23F94ZGL+MK0lR/ek8gXsXAzF9RSehxjbsI6DbPaJTEBEphvu1tjvf4eiNgqrah5ILCOsA3P8b4BUkOgm5Cr03cN8vQHRP0UmosQx9n0vVEhHVkvsVGQDQ/T7H8rbk2kLaAvf/DngHi05CrsbDDxj/KxDRRXQSamhD3gb6ThWdgohIcVx/Cduq7Poa+OMFuMP6924npg9w908sMKhhFWcDP44EUg+LTkLOptYCwz8Gek4QnYSISJHcu8gAgKPLgV8fAWwm0UnIWTqMAsZ8Beg8RCchd2AqAJbcDyT8IzoJOYve17GKVOvBopMQESkWiwwAOLcD+PkeoCRHdBKqr/5PAEP+61hylKix2KzAyqeAAwtEJ6H68o0C7lsCRHQWnYSISNFYZFyWeQpYdBeQnSA6CdWFSgPc+gEnZ5JYmz4CNkwHh2AqVFhHx6R+/2jRSYiIFI9FxpVKcoBfJgCJG0UnodrQeTs2SuP69SQHx34Hlk8BLEWik1BttBgEjPvRMamfiIjqjUXG1WxWYN2rwK7/iU5CNRHY3DF2Oqqb6CRE/0o9Avx0D5B3XnQSqok+U4CbpwMabtZKROQsLDIqc3Q5sOoZztOQs273Abd+yE32SJ6Ks4HVzzrOJSRP3qHAqC+ANjeLTkJE5HJYZFQl/yKwYiqQuEF0ErqSh79jn5NOY0UnIarewcWOpbJNeaKT0JVaDQFGfwH4hIlOQkTkklhkVEeSgF1fAeunAdYS0WmoaX/g9q+AgBjRSYhqLjfJMU/j3FbRSUhjAIa85RgixVXoiIgaDIuMmsqIB359GLh4UHQS96TWAje8BFz3HKDWiE5DVHt2O7Djc+Cf/wI2s+g07im0PTB2HhDRSXQSIiKXxyKjNmwWYOP7wLZZgN0iOo37iOnjWJ42qrvoJET1l3oEWDGFu4Q3Jq0nMOAp4NqnAZ2n6DRERG6BRUZdZJ4G1r8OxP8hOolr841yDGvoMk50EiLnstuB/fOB/2/v3mOsrPM7jn9kuMy4ICiziIooeIlYid1aFV1tzSZaoovFdBdN+kfTpKHGaBP/UKPbKEn3j2rwlkibtCb1D7pGwjKNiOKSEPE2smhpyori6tAKu6JM0EZg5DJM/zgKKEJAvswZ4PX6ZzKX58z3APlx3s/znOdZ+vNkyyfNnubYduH05Lp/SEaNb/YkAMcVkXE4upYlL/4s+dgeyVItw5Irb2+cGjX0e82eBo6cbZ8nrzySvPFPyc4vmj3NseXUi5Kp/5hMuLrZkwAcl0TG4dq1K/mvuY09kps/bvY0R78Lfpxc9/PklAnNngT6z2cfNi4u8faCZk9y9Gs7JfnRz5JL/tr7twCaSGRU2bY5efXR5I1/dqff72LiNY0jFxP+pNmTQPOs+3Xyq79P1i1v9iRHn9ZRyaV/0zgK2nZys6cBOO6JjGo9nyZv/lvy639NPv99s6cZ2E5oSS7888YbMt2xG/b4387GlajWPJ/07Wr2NAPbSeOSK25L/uiv3JgTYAARGUdK747kNwsaLxQ2/HezpxlYBrclP/jL5IrbnRYFB7Kpq3F0dOW/O0L6TWP+IPnh3yUX/SRpGdzsaQD4BpHRH9a+nHTOSd57Mclx/Mc9/NTG3sbL/zb5Xnuzp4GjR8+nyVtPJcv/5fg+QnrCoOTsqxs7KM6/rtnTAHAAIqM/bepKVv0yebsj+eTtZk/TP4aNTCZNSyb/JJnwp8mgQc2eCI5evTuS3y5pvEF8zQvJ9s3Nnqh/nHZxMvmnyUV/kZx0erOnAeAgiIxm2bimERu/WZB0r2n2NLVahjX2Mk6ekZz/Z8ngYc2eCI49O75IfvurRnC892KyY2uzJ6p1ysTGqVCTf5p8//xmTwPAIRIZA8HHqxvB8e6i5JPVOSpPqRo+Njn7quScHyWTfpy0jmz2RHD82L41eW9xsvo/Gvfv+eKzZk906AYNTk77w8Y6MunGZNwlzZ4IgMMgMgaank+TD5cnH76efPhG8vuVSe/2Zk+1rxGnJWf9sPGC4Oyrk/Zzmz0RkCR9fckn7+xZQz58I/m/dc2eal+DBien/6Cxhpx1VTJ+iqtDARxDRMZAt+OL5HdvJR92Jr/7z8b7Oj79n2RnT//8/hMGJSPHJaPPS9rPS8Zc2IgLUQFHj8/WfRkcncnGd5NNa5PPP0q/HTUdOiIZfU4y+tzGOjLuj5MzRQXAsUxkHI36+pLPN3wZHGsbLxg+Xdu4a/C2zY1LXW7fmuzo+fI87f38FZ8wKBk2onHjqtZRjY8nnf7li4HzGi8ITpmYDGntz2cH9IcdPY0dFl+tH5vWNtaULRsb68b2rY2PO7Ye+Ghqy7CkbdSedeTE0Y1LU48+d09UjBjbT08KgIFCZBzr+vr2xMaOrY1TFIa0JUNO9IZs4OD07tyz82LXjsa9boa0NtaRQS3Nng6AAcj1RI91J5yQDD2xcV+KUeMbRyraTj7kwFizZk0ef/zxQ9qmr68vs2fPPqRtgAGoZXDjYg4nndZYR4Z/v3EU9BADwzoCcPwQGeShhx5Ke3t72tvb09ramhEjRuz+fPny5UmSdevWpaOjY7+PcfbZZ2fDhg1f+1pvb2/uuuuu7zxXa2truru7v/P2QP+xjgCwN5FB7r777nR3d6e7uzvXX399Hnzwwd2fX3755Qf1GFu2bMnmzXU3Blu8eHG2bduWhQsXlj0mcORYRwDYm8hgt507d+att97KkiVLkjT+gx47dmzGjh2bGTNm7He7rq6udHd35+WXXz7sGfr6+jJv3rzccccdWbRoUWbPnp0nn3wyvb29h/3YwJFnHQEgERnsZc6cOZk+fXp6enqyYMGCTJ06NRs2bMiGDRsyb968/W533333Zfr06XnggQe+9bSE9vb2jBs37oC/u7OzM48++mguvfTSLFy4MK+++mquv/76vP7661m5cmUmT56cWbNm5dlnn/VCAQYw6wgASTK42QMwMDzzzDP5xS9+kZdeeik9PT259tprs379+tx2220ZPHj//0zuu+++bNy4MS+88EKeeuqpXHPNNeno6Mh55523+2cO5nzoVatWpaWlJR0dHTnzzDN3f33kyJGZM2dOPv7443R0dOSdd97JjTfeeHhPFjgirCMAfEVkkLvuuiuvvPJKnn/++bS1taWtrS1Lly7NzJkzM2HChEybNm2fbTZv3pxp06ZlzJgxWbhwYYYOHZqZM2fmjDPOyNSpU7Ns2bKMHXvw18afOXPmAb9/6qmn5tZbbz3k5wb0D+sIAHtznwzy0Ucfpb29PUOGDNnvz6xcuTJz587Nww8/vPtr7733Xs4///x9fra3tzctLS3ZtWtXZsyYkfnz5x/w98+ePftbL1H51eN8080333zIl8EEjizrCAB7Exl8zfLly/PYY4/lzTffzM6dOzNo0KCcfPLJueWWW3L77bentXXfu3+vXbs2jzzySJYsWZLt2xt3Bj7xxBMzbdq03HnnnRkzZsx3mmX48OFZv359Ro0adThPCehn1hEAvPGb3To7O3PDDTfkpptuyurVq7N27dp88MEHmT9/flasWJGbb755n202btyYKVOm5JxzzsmKFSvS1dWVrq6uLFu2LEOGDMmVV16Zbdu2NeHZAM1gHQEgcSSDvcyaNSvvv/9+5s6du8/33n///Vx44YW79zB+5bnnnsu9996bVatWfetjjh49OkuXLs3FF198yPPYAwlHH+sIAIkjGexl6tSpWbx4cebNm/e1FwEffPBB7rnnntxwww37bDNlypRs3LgxTzzxRLZs2bL765s2bcr999+fkSNH5oILLuiX+YHms44AkIgM9jJlypQsWrQoCxYsyKRJkzJ+/PicddZZmTFjRq644oo8/fTT+2zT3t6ezs7OvPvuu7nkkksyceLETJw4MVdddVW2bt2a1157LcOGDWvCswGawToCQOJ0KQawzs7OXHbZZd96ZRiAg2EdAWgOkQEAAJRyuhQAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApUQGAABQSmQAAAClRAYAAFBKZAAAAKVEBgAAUEpkAAAApf4fdvclamzoNMsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 데이터프레임으로 변환\n",
    "df2 = pd.DataFrame(data, columns=['고장 수', '정상 수', '이름'])\n",
    "\n",
    "# 데이터의 개수를 기반으로 그리드 크기 결정\n",
    "grid_size = ceil(sqrt(len(df2)))\n",
    "\n",
    "# 파이차트 그리기\n",
    "fig, axs = plt.subplots(grid_size, grid_size, figsize=(10, 10), subplot_kw=dict(aspect=\"equal\"))\n",
    "axs = axs.ravel()\n",
    "\n",
    "# 루핑하여 데이터 그리기 및 빈 서브플롯 숨기기\n",
    "for i in range(grid_size * grid_size):\n",
    "    if i < len(df2):\n",
    "        axs[i].pie([df2['고장 수'][i], df2['정상 수'][i]], labels=['고장 수', '정상 수'], autopct='%1.1f%%')\n",
    "        axs[i].set_title(df2['이름'][i])\n",
    "        axs[i].set_xlim([-1.3, 1.3])\n",
    "        axs[i].set_ylim([-1.3, 1.3])\n",
    "    else:\n",
    "        axs[i].axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3c924c0",
   "metadata": {},
   "source": [
    "### 결론 및 해석\n",
    "\n",
    "**위 결과를 보면, 대여소 명에 초등학교가 들어간 대여소의 고장데이터가 정상데이터에 대해 36.33% 더 많습니다.**\n",
    "\n",
    "중학교, 고등학교는 큰 차이는 없어보입니다.\n",
    "\n",
    "아이들이 자신이 빌리거나, 대여소에 있는 자전거들을 아이들이 장난치면서 자전거가 고장이 많은 것 같습니다. \n",
    "\n",
    "초등학교가 주변에 있는 대여소는 고장에 특히 더 신경써야 할 것 같습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e4b94bb",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "## 2. 역 관련"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24ee4703",
   "metadata": {},
   "source": [
    "따릉이 대여이력의 대여소명을 조금 살펴보면 역 주변 대여소들이 많습니다. 지하철역 주변 대여소와 고장의 관계가 있지 않을까 예상했습니다.\n",
    "\n",
    "많이 이용되는 만큼 고장이 많이 날거라고 예상하여 고장데이터가 더 많을 것이라고 생각했습니다.\n",
    "\n",
    "이용자가 많은 10개의 역을 뽑아 분석했습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "bc3f449f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "325b932e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'역'의 고장데이터 : 6810개 / '역'의 정상데이터 : 6954개\n",
      "정상데이터가 고장데이터에 대해 2.11% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('역'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3a209ddd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'출구'의 고장데이터 : 5993개 / '출구'의 정상데이터 : 6117개\n",
      "정상데이터가 고장데이터에 대해 2.07% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('출구'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1b1151a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'홍대입구역'의 고장데이터 : 118개 / '홍대입구역'의 정상데이터 : 70개\n",
      "고장데이터가 정상데이터에 대해 68.57% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('홍대입구역'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "5ef3b717",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'성수역'의 고장데이터 : 78개 / '성수역'의 정상데이터 : 55개\n",
      "고장데이터가 정상데이터에 대해 41.82% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('성수역'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "53c466b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'신도림역'의 고장데이터 : 78개 / '신도림역'의 정상데이터 : 64개\n",
      "고장데이터가 정상데이터에 대해 21.88% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('신도림역'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "c9b5056c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'영등포역'의 고장데이터 : 56개 / '영등포역'의 정상데이터 : 47개\n",
      "고장데이터가 정상데이터에 대해 19.15% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('영등포역'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "8ab27aa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'구로디지털단지역'의 고장데이터 : 81개 / '구로디지털단지역'의 정상데이터 : 68개\n",
      "고장데이터가 정상데이터에 대해 19.12% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('구로디지털단지역'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "53c52690",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'시청역'의 고장데이터 : 48개 / '시청역'의 정상데이터 : 56개\n",
      "정상데이터가 고장데이터에 대해 16.67% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('시청역'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "71ef429b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'이수역'의 고장데이터 : 73개 / '이수역'의 정상데이터 : 90개\n",
      "정상데이터가 고장데이터에 대해 23.29% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('이수역'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "07bdafa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'건대입구역'의 고장데이터 : 43개 / '건대입구역'의 정상데이터 : 62개\n",
      "정상데이터가 고장데이터에 대해 44.19% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('건대입구역'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "de31ab2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'서울역'의 고장데이터 : 91개 / '서울역'의 정상데이터 : 163개\n",
      "정상데이터가 고장데이터에 대해 79.12% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('서울역'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "be9076e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'용산역'의 고장데이터 : 55개 / '용산역'의 정상데이터 : 109개\n",
      "정상데이터가 고장데이터에 대해 98.18% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('용산역'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ccac66fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# 각 데이터 리스트에서 고장 수와 정상 수를 추출\n",
    "values1 = [x[0] for x in data]\n",
    "values2 = [x[1] for x in data]\n",
    "labels = [x[2] for x in data]\n",
    "\n",
    "# '고장 수'와 '정상 수' 합치기\n",
    "total = [i + j for i, j in zip(values1, values2)]\n",
    "\n",
    "# 비율 구하기\n",
    "values1 = [x / y for x, y in zip(values1, total)]\n",
    "values2 = [x / y for x, y in zip(values2, total)]\n",
    "total = [1 for _ in range(len(data))]  # 모든 Total의 합이 1이 되도록 설정\n",
    "\n",
    "# 그래프 그리기\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "bar_width = 0.35\n",
    "index = np.arange(len(data))\n",
    "\n",
    "rects1 = ax.barh(index, values1, bar_width, label='고장')\n",
    "rects2 = ax.barh(index, values2, bar_width, left=values1, label='정상')\n",
    "\n",
    "# y-축의 눈금과 라벨을 설정\n",
    "tick_locations = index\n",
    "plt.yticks(tick_locations, labels)\n",
    "\n",
    "ax.set_xlim([0, 1])  # x-축의 범위를 0~1로 지정\n",
    "ax.axvline(x=0.5, linestyle='--', color='gray') # x 값이 0.5인 위치에 점선 추가\n",
    "ax.legend()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0c922a5",
   "metadata": {},
   "source": [
    "### 결과 및 해석\n",
    "\n",
    "`출구`와 `역`이 들어간 대여소는 고장과 정상이 고르게 있는 편입니다.\n",
    "\n",
    "**하지만, 개별 역에 대해서는 편차가 꽤 있는 것을 볼 수 있습니다.**\n",
    "\n",
    "개별 역마다 고장을 유발하거나 고장을 줄이는 숨은 변수가 있어보입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42ebb76d",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "## 3. 기타\n",
    "\n",
    "학교와 역 외에 대여소명에서 많이 보이는 단어들로 분석해보았습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "f8b13f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "57643df6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'한강'의 고장데이터 : 168개 / '한강'의 정상데이터 : 143개\n",
      "고장데이터가 정상데이터에 대해 17.48% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('한강'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "0d526163",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'병원'의 고장데이터 : 364개 / '병원'의 정상데이터 : 312개\n",
      "고장데이터가 정상데이터에 대해 16.67% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('병원'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b27b016e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'경찰'의 고장데이터 : 200개 / '경찰'의 정상데이터 : 173개\n",
      "고장데이터가 정상데이터에 대해 15.61% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('경찰'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "e450661c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'교회'의 고장데이터 : 162개 / '교회'의 정상데이터 : 146개\n",
      "고장데이터가 정상데이터에 대해 10.96% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('교회'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "4e89f559",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'버스'의 고장데이터 : 859개 / '버스'의 정상데이터 : 778개\n",
      "고장데이터가 정상데이터에 대해 10.41% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('버스'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "88d2049e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'광장'의 고장데이터 : 111개 / '광장'의 정상데이터 : 142개\n",
      "정상데이터가 고장데이터에 대해 27.93% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('광장'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "5fca50ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'공항'의 고장데이터 : 62개 / '공항'의 정상데이터 : 85개\n",
      "정상데이터가 고장데이터에 대해 37.10% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('공항'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "9e51ac63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'광화문'의 고장데이터 : 88개 / '광화문'의 정상데이터 : 198개\n",
      "정상데이터가 고장데이터에 대해 125.00% 더 많습니다.\n"
     ]
    }
   ],
   "source": [
    "data.append(대여소명에_따른_분할('광화문'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "99a114b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAEQFm82mlJQUcxkAKkIAAhAVnE6nhg8fHu4yAFQTdIEBAADLsWwLUNsxS2R3xYe7DKDa2h43ONwlALAST3AHOLVsAAIQXcrk0DQdCWU3a5ac8oa5IgCRrNp0gY0cOVIbNmwot33IkCHy+/1hqAhAJDEk5duSlW9LFhNhADiRkAYgn8+n1NTU4/7ExsZq1apVxzx2zZo1OnDgQLntr732GgEIAACclJB2gcXExCgvL++4r3fp0kUOx5GSdu3apW7dugW8PmTIEHN58ODB+te//lU1hQIAgKgW1nuABg8erP/85z+y2480RJWWlpoz5qalpWnXrl36/vvvNWPGDO3Zs0cOh0Ndu3bV4MGDFRsbG87SAQBANRbWe4Dmz58f0H1VVFSkunXrmuvffvutBgwYoBtvvFHTpk3ThAkTdPDgQd14440B50lOTtZ5550XsroBAED1FlFPgW3bti1gBNdVq1apffv2at26taQjA5394Q9/0MMPPxxw3L59+8yus9/yeDzyeDzmutvtDn7hAACgWglJANq5c6e6dOlSbnvt2rWVlpZ2zGPy8vJ05ZVXaty4cfrLX/6ijh07qqioSLNmzdLIkSMr/d7Z2dnKyso65doBVA82SUlGobkMABWxGYYR0U+MlpaWaunSpdq+fbtq1qyp7t27q2XLlubrPXr00IoVKxQTE3PM44/VApSenq70u+cyECJwGhgIEUAouT2GksYWqbCwUImJiad9vpB3gX3//fd6+umn9dFHH6msrEySVLNmTfXr109///vfA+4BkqTY2Fh17dpVGzdu1DvvvKNnn31WdrtdqampuvTSS/Xee+8dN/xIksvlksvlqtLPBAAAqpeQ3gS9Z88enX/++WrdurXWr1+v3Nxc5ebm6sMPP1RxcbF69eql3zZIFRcXq0uXLiopKdHkyZP16aefKicnR+PHj1d+fr66desW0MIDAABwIiENQKtXr1ZqaqruvPNOJSQkmNvr1aunf/3rX9q4caN+/vnngGPWrVsnm82mrKwsnXXWWYqLi1N8fLxatmyp7Oxsud1ubdy4MZQfA0AEOjoVxjQNVllkPd8BIAKFNAB169ZNu3fv1tSpU3Xo0CFz+8GDB/Xggw+qTZs2atCgQcAx7dq1U1FRkf7973/r8OHD5vaSkhJNmTJFhmGoVatWIfsMACKTIelnW6p+tqUyFQaAEwppAEpNTVVOTo7WrVunjh07qmXLlmrVqpUuuugiORwOffTRR+agiEedccYZ+u9//6vVq1frnHPOUbNmzdS8eXN17NhRGzZsUE5OTkBrEgAAwImEvJ24VatWmjp16kkd07p1a02fPr2KKgIAAFZj2Y7yjVm9g/IYHWBdheEuIFBpqZSdfWR59G6J6XKA6OJ2S2OTgna6sE6FAQAAEA4EIAAAYDmW7QIDEH3i4xndHUDlRPxUGMHmdruVlJQUtKG0AQBA1Qv29zddYAAAwHIIQAAAwHK4BwhAVCgrK9Nrr70mSbrxxhvldDrDXBGASEYAAhAVDMPQjz/+aC4DQEXoAgMAAJZDAAIAAJZDAAIAAJZDAAIAAJZDAAIAAJbDU2AAogaPvgOoLKbCAAAAEY+pMAAAAE4TAQgAAFgO9wABiAper1dz586VJA0cOFAOB3/eABwffyEARAW/36+tW7eaywBQEbrAAACA5RCAAACA5RCAAACA5RCAAACA5RCAAACA5RCAAACA5TAVBgAAiHhMhQEAAHCaCEAAAMByGAkaQFTwer166623JEnXXHMNU2EAqJBl/0K0HbNEdld8uMsAotb2uMEhfT+/HNpku1OSdPU3t0ryhvT9AVQxT3BvWY6oLrBnn31WW7ZsOalj7rjjDub9AQAAJyVkAcjn8yk1NbXcT40aNfTyyy9Lkt58803t3LnTPObqq69WcnKykpOTFRMTo7p165rrR02aNIkABAAATkrIusBiYmKUl5dXbvugQYMUGxt7zGPeeecdc7l+/fpavny5MjIyqqxGAABgDWHpArv//vvNVhu3262aNWtWuP/27du1d+9erVixIhTlAQCAKBeWADRu3DgzABUVFSklJcV8beDAgUpNTdVnn31mbnvwwQc1cOBAPf7449q7d2+58x3tTgMAAKiMsD8F9sknn8jv95uBaO7cucrMzDRfz8rKUl5enhYtWqRZs2bp4osv1vz589WqVStzn7y8PB55BQAAlRaS1LBt2zb17t1bhmGYQadJkyaKiYmRzWaT3W7X3LlzA47xeDzq16+f4uPj9e6778rpdOqmm25SamqqLr/8ci1fvlyNGzc+4Xt7PB55PB5z3e12B/fDAYgITnk1yphgLgNARUISgJo1a6b169crJiZGDodDsbGxstsr7n1zuVyaMGGCWrRoEbC9d+/e+u677xQTEyNJGjBgQIXnys7OVlZW1ul/CAARzSYpluADoJJCPhnq/v379e9//1tLly7V7t27JUkNGjRQ7969NWTIENWrV69coNm5c6eeeeYZLV682GzNiYuL05VXXqkRI0ZUeP/PsVqA0tPTlX73XAZCBKpQqAdCBBDd3B5DSWOLqudkqAUFBercubMOHjyoiRMn6rPPPtNnn32m559/Xvn5+erevbuKi4sDjnG73erWrZtSU1O1Zs0a5ebmKjc3VytXrlTNmjV1/vnn69ChQ8d9T5fLpcTExIAfANHHqxi9rd56W73lVUy4ywEQ4UIagHJychQfH6/s7Gy1atVK8fHxio+P19lnn61x48bJ6XRq9erVAcd8/vnnio2N1T/+8Q8lJSWZ2+vWrasxY8aopKREmzZtCuXHABCB/LLpK1sbfWVrI79s4S4HQIQLaQDq1KmT9u7dqylTpuiXX34xtxcXF2vy5Mk6cOCAzj333IBjOnbsqNLSUj311FMqKioytx88eFBZWVmqUaMGgyMCAICTEtIAlJqaqpycHG3YsEGdOnVSs2bN1KxZM3Xq1EkbNmxQTk6O6tWrF3BMYmKi1qxZo127dum8884zj7ngggtUWFioVatWqUaNGqH8GAAAoJoL+eA5LVu21JQpU07qmPT0dD333HNVUxAAALCciJoNHgAAIBQsO3zyxqzePBEGVKnC0L5daamUnX1kefRu6TiTLAOoptxuaWzSiferJFqAAACA5Vi2BQhAdHE6nRo5cqS5DAAVIQABiAo2m001a9YMdxkAqgm6wAAAgOXQAgQgKni9Xi1ZskTSkUmTHQ7+vAE4PlqAAEQFv9+vdevWad26dfL7/eEuB0CEIwABAADLIQABAADLIQABAADLIQABAADLIQABAADLIQABAADLYaAMAFHB6XTqrrvuMpcBoCIEIABRwWazqXbt2uEuA0A1QRcYAACwHFqAAEQFn8+nDz/8UJLUq1cvxcTEhLkiAJGMFiAAUcHn82n16tVavXq1fD5fuMsBEOEIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIIQAAAwHIYBwhAVHA6nbrtttvMZQCoCAEIQFSw2WyqV69euMsAUE3QBQYAACyHFiAAUcHn8+mTTz6RJF144YVMhQGgQgQgAFHB5/NpxYoVkqTu3bsTgABUiC4wAABgOZZtAWo7ZonsrvhwlwEgSBzy6Q81jixnPLRYXlW+BWh73OAqqgpA0HiMoJ4uolqAZsyYoT/+8Y/hLgMAAES5kAWgjz/+WD169AjY5vV6ZbPZTuo8vXv31htvvBHM0gAAgMVEVAvQiRiGoS1btmjz5s3hLgUAAFRj1SoATZs2TcnJyZo6dar27NkT7nIAAEA1FdKboHfv3q2JEyea636/X5KUl5cnSSosLDzusa+//rrGjh2r5cuXa8mSJbrsssv0xhtvqHnz5lVbNIBqwSe73jvc2lwGgIqENAB5vV4VFBSY60cDUGZmpiSpoKDAXD5q3bp1Gj9+vL799lstX75cTZo00S233KLatWvrkksu0R133KEbb7xRDRs2DNnnABB5DNm0z6gZ7jIAVBMhDUDp6el68MEHzXWv16sxY8Zo48aNko48Bfbxxx+br7/wwguaNGmShg8frhkzZgQMbDZw4ED17NlT48aN08UXX6wVK1YoNTW13Ht6PB55PB5z3e12V8EnAwAA1UlEtxPfeuut+vrrr3Xbbbcdc1TXevXq6emnn9aWLVuOGX4kKTs7W0lJSeZPenp6VZcNIAzs8qutI09tHXmyyx/ucgBEuJC2ABUWFga08Ph8vip/z1GjRmnEiBHmutvtJgQBUcguQ12cuyRJm70pRCAAFQpZAKpXr57atGmjF154IWD7oEGDqvR9XS6XXC5Xlb4HAACoXkIWgDIyMjR79uxK779q1Sr179+/0vunp6dr7dq1p1IaAACwmIidC6x79+7m4/EAAADBFNE3QQMAAFSFiGoBuuqqq3TJJZeEuwwAABDlIioA1a1bV3Xr1g13GQAAIMrZDMMwwl1EKLndbiUlJamwsFCJiYnhLgdAkPj9fu3YsUOS1LhxY9nt9PAD0STY398R1QIEAKfKbreradOm4S4DQDXBP5EAAIDl0AIEICr4fD6tX79ektSpU6djTp8DAEcRgABEBZ/Pp0WLFkmSOnToQAACUCG6wAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOUQgAAAgOXwGDyAqOBwOHTDDTeYywBQEf5KAIgKdrtdLVu2DHcZAKoJusAAAIDl0AIEICr4fD59/fXXkqR27doxEjSAChGAAEQFn8+nd955R5KUkZFBAAJQIbrAAACA5RCAAACA5RCAAACA5RCAAACA5RCAAACA5RCAAACA5fAYPICo4HA4dO2115rLAFAR/koAiAp2u11t2rQJdxkAqgm6wAAAgOXQAgQgKvj9fn377beSpNatW8tu5993AI6PvxAAooLX69W8efM0b948eb3ecJcDIMIRgAAAgOUQgAAAgOVY9h6gtmOWyO6KD3cZAILEIZ/+UOPIcsZDi+VV5WeD3x43uIqqAhA0HiOop6MFCAAAWE61CUD//e9/9fvf/77c9mXLlqlnz56hLwgAAFRbIe0CGzNmjCZNmnTc1wsLC7Vo0SJlZmZKkjZv3qx9+/ZJkr766ivl5+dr5cqV5v6dO3eu2oIBAEBUCmkAysrKUlZW1nFf79atW8D6ggULtHr1anO9efPmeu6558z1iRMnBr1GANWTTzZ9UtrUXAaAikTcTdA22//+cN1zzz2SpA8//FDLli2T2+3WWWedpaFDh+qMM86QJG3cuDEsdQKILIbs+t6XHO4yAFQTEXUPkM/nKzeJ4f3336/77rtPbdq00ZVXXqn9+/erffv2+vHHH8NUJQAAqO7C0gI0depU3XLLLZKkpUuXqnnz5mrWrJnKysrkcrnM/fx+vyZOnKjc3FzVq1dPknTZZZfpwIEDmjt3ru69994TvpfH45HH4zHX3W53kD8NgEhgk6FG9kJJ0k/+JBl0gwGoQFhagIYPH24uv/jii/r8888lSYcOHVJCQoL5mt1uV506dcz5faQjrUTfffed0tPTK/Ve2dnZSkpKMn8qexyA6iVGfl3q+l6Xur5XjPzhLgdAhIuoLrCDBw+a9/Yc9dprr2nYsGHq1auXrr32Wp199tnKyMjQoEGDKnXOUaNGqbCw0PzZuXNnVZQOAACqkYi6CfrZZ59VcnLgTYy/+93v9N1332nOnDl6+umn9cUXXwS0Ep2Iy+UK6FYDAACIqAB04403HnO7zWZTamqqatasaYYfv98fcG8PAABAZYUkAO3YsUPt27c3130+n2rXri1JKikp0aJFizRs2LByx5x77rny+Xzy+XzyeDwqKSlRWlrakcIdDvXq1Us33HBDKD4CAACIIiEJQI0bN1ZBQcFJH7dp0ybZ7XY5HI6A8YF+bdmyZadZHQAAsJqI6gL7Le7dAQAAVSGiA1BldOvWTdOnTw93GQDCzCebVpc2NpcBoCI2wzCMcBcRSm63W0lJSSosLFRiYmK4ywEAAJUQ7O/viBoHCAAAIBSqfRcYAEhHhsbYsWOHpCMPXtjt/PsOwPHxFwJAVPB6vXrllVf0yiuvyOv1hrscABGOAAQAACyHAAQAACyHAAQAACyHAAQAACyHAAQAACyHAAQAACyHcYAARIWYmBhlZmaaywBQEQIQgKgQExOjCy64INxlAKgm6AIDAACWQwsQgKjg9/u1e/duSVKDBg2YCgNAhfgLASAqeL1eTZ8+XdOnT2cqDAAnRAACAACWQwACAACWQwACAACWQwACAACWQwACAACWQwACAACWwzhAAKJCTEyMLrroInMZACpCAAIQFWJiYtSzZ89wlwGgmqALDAAAWA4tQACigmEYys/PlySlpKTIZrOFuSIAkYwWIABRoaysTFOmTNGUKVNUVlYW7nIARDgCEAAAsBwCEAAAsBwCEAAAsBwCEAAAsBzLPgXWdswS2V3x4S4DQJA45NMfahxZznhosbw6vcEQt8cNDkJVAILGYwT1dBHTArRhwwY9+OCDx3ytZ8+eWrZsWcA2n8+npk2bhqAyAAAQbULWApSWlqbDhw+b6x6PR126dNFHH30kSdq7d69Wrlxpvn7DDTfok08+kSTl5+frhhtukMvlkiS98cYb6tKli3788cdQlQ8gwvll09dl9c1lAKhIyALQrl27Atbvu+8+2Ww2dezYUTt27FBZWZnOPfdc8/XXX3/dXD733HP18MMP6+qrrza3eb3eqi8aQLXhl13rvOnhLgNANRGWLrADBw5oxowZGjp0qD7//HPt27dPb7755jH3zc3N1TfffKPZs2cf8/V+/fpp4MCBVVkuAACIMiEPQGVlZRoyZIhuvPFGtWnTpsJ9S0pKNHToUD355JPaunWrnnvuuXL7DBs2TH/605+qqFoA1YehBJtHCTaPpODeLAkg+oQ0AO3atUuZmZlKSEjQk08+KZ/Pp+TkZCUnJ2vAgAEB+27dulWZmZlKS0vTnXfeqQULFujVV1/V4MGDtXXrVnO/K6+8UpdddlkoPwaACOSQX9fFfa3r4r6WQ/5wlwMgwoUkAO3bt09PPPGEzjnnHHXv3l1z5syRw+FQTEyM9u3bV64LrLS0VFdddZV69Oih1157TTabTfXr19eqVavUtm1bHT582Nx2Ih6PR263O+AHAABYW0hugp4wYYLWrFmjJUuWqHPnzsfcx+VyqW7dupKk2NhYffnll+ZTX7/eZ/To0eZ6Xl7eCd87OztbWVlZp1E9AACINjbDMELaWf7WW2/phRde0LZt2+Tz+RQTE6NGjRrpz3/+s4YOHSqbLfDx1bS0tOM+8bVnzx6dqHyPxyOPx2Ouu91upaenK/3uuQyECESRIwMhfiFJevXQuQyECEQZt8dQ0tgiFRYWKjEx8bTPF9KRoCdNmqTnn39e06dP1wUXXGCGnS+//FK33nqrNm/erOzs7IBjfvv4/K/9Niwdi8vlKteSBAAArC2kN0G///77uvvuu9WjR4+A8NKhQwc98MADev/998sd43A41LRp02P+NGnSJJTlAwCAKBHSFqC+fftq/Pjxat++vbp3725u37Bhg5544gn17dv3mMd9//33cjgsO20ZAAAIspCmiuHDhys1NVVZWVnmPUB2u11paWm69dZbNXTo0GMel5aWdtxzTpo0qdwj9ACsxy+bvvWmmMsAUJGQN6v0799f/fv3r/T+THkBoDL8smtNGd3iAConYmaDBwAACJWQPwYfbm63W0lJSUF7jA5AZDAMQyUlJZKk+Pj4Sj0lCqD6CPb3Ny1AAKJCWVmZnnrqKT311FMqKysLdzkAIhwBCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4BCAAAWA4TbAGICna7Xeecc465DAAVIQABiAoOh0P9+vULdxkAqgn+mQQAACyHFiAAUcEwDHMEaKfTyVQYACpECxCAqFBWVqbs7GxlZ2czFQaAEyIAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAy2EcIABRwW63KyMjw1wGgIoQgABEBYfDoeuuuy7cZQCoJvhnEgAAsBwCEAAAsBy6wABEhdLSUmVnZ0uSRo0apdjY2DBXBCCS0QIEAAAshwAEAAAshwAEAAAshwAEAAAshwAEAAAshwAEAAAsx7KPwbcds0R2V3y4ywAQJDHy6+LYJElS64eWyBeEf99tjxt82ucAECQeI6ini5gWoB9//FGzZs06qWMWL16snj17Vk1BAKoVn+xaVtpCy0pbBCX8AIhuIfsr0bNnT3388cfH3bZ161ZNnTq13HETJkxQmzZtdOaZZ+qss87SiBEjVFJSEoKKAQBAtAp7F9iYMWOUkpKivXv3lpvBedq0aZo9e7aWLl2qhg0b6pdfftHdd9+tO++8U9OnTw9TxQAAoLoLaQDq379/wPD0Bw4c0MyZM9WtWzfl5ORo2rRpAft/8803uuaaa9SwYUNJUs2aNfWnP/1J99xzTyjLBlANOOTT9XFfSZJmHz5HXsWEuSIAkSykAWj+/PkB9+z07NlTxcXFKigoUHFxcbn9//rXv+qqq65SbGysOnTooJ07d2rs2LF69NFHQ1g1gOrCafOHuwQA1UTYu8DmzZunlStXavfu3eVea926tdatW6c33nhDy5YtU3JyshYsWKAmTZqEoVIAABAtQhaAEhISNHDgwID7fEpLS/Xuu++qR48eWrZsmR577DHztcmTJ2vXrl3y+/3yer0qLS3Vnj17tHbtWhUVFSk9PV1XXnnlCd/X4/HI4/GY6263O7gfDAAAVDshC0Dvv/9+ha/HxcUpJSXFXO/QoYMaN26s2NhYxcXFadCgQXrppZd01llnqVatWkpKStKKFStO+L7Z2dnKyso67foBAED0CHkX2KZNm/Tiiy9q7dq1ys/PV3x8vJo2barrr79ec+bMMffr3r17wHEul0utW7eWy+VSv379JEmFhYWqX79+he83atQojRgxwlx3u91KT08P3gcCAADVTkhHC1u9erV69uypdu3aaf78+dqyZYtycnJ033336aWXXtKf//zncsd0795dX3/9tbmenJys6dOna/r06br77rtP+J4ul0uJiYkBPwAAwNpC/hTYwIEDNWzYMHNbQkKCLrjgAk2ePFlt2rTRjBkzAo5xu93y+Xzatm2bYmKOPNbatm1bSVJiYqLq1asXsvoBRC5DNu32JZjLAFCRkLYAXXLJJZo/f74++OAD+f3/e1x1586devjhh9WrV6/jHns0/Pxa48aNdc0111RJrQCqF5/sWlx6thaXns1UGABOKKQtQJdddplmzpypiRMnavjw4fL5fLLb7UpKStK1116ru+6665jHZWZmyuE4fqnfffcdXVsAAKDSQn4TdGZmpjIzMyu9/8aNG6uwGgAAYEVhHwgRAILBIZ+uizvywMQbh9sxFQaAClk2AG3M6k23GRBFSktLlZ39hSRp0yN9AuYdPHWFQTgHgKBwu6WxSUE7HXcKAgAAyyEAAQAAyyEAAQAAyyEAAQAAyyEAAQAAy7HsU2AAoovNZlPDhg3NZQCoCAEIQFRwOp26+eabw10GgGqCLjAAAGA5BCAAAGA5dIEBiAplZWWaNGmSJOn222+X0+kMc0UAIhkBCEBUMAxDhYWF5jIAVIQuMAAAYDkEIAAAYDkEIAAAYDkEIAAAYDkEIAAAYDk8BQYgKthsNqWkpJjLAFARAhCAqOB0OjV8+PBwlwGgmqALDAAAWA4BCAAAWA5dYACiQllZmaZNmyZJuvnmm5kKA0CFCEAAooJhGMrPzzeXAaAidIEBAADLIQABAADLIQABAADLIQABAADLIQABAADL4SkwAFHBZrMpKSnJXAaAitgMiz0v6na7lZSUpPS758ruig93OQAi3Pa4weEuAYAkt8dQ0tgiFRYWKjEx8bTPRxcYAACwnLAEoGHDhumVV14ptz0tLU3bt28/qXP16NEjSFUBAACrCNk9QGvWrDGXd+3apS1btpjbEhMTlZGRUe6YZcuWaciQIQHbCgoK9OSTT+pvf/ubJCknJ6cKqwZQXcTIr8tcmyVJizxny0cDN4AKhCQA+f1+jR071lyPi4vTpk2btGnTJklS27Zt9dhjj5U7LjMzU3l5eQHbWrVqpW7dulVtwQCqHZsMpdhLzGUAqEhIApDdbtfbb78tn8+nN954Q59//rni4uLUq1cvXXTRRZU+zwcffCCn06kuXbpUYbUAACDahayN2Ov16tJLL9XChQt13nnnqUWLFho1apQefPDBSh2fl5en2267TRMmTCj3WkJCgmrXrh3kigEAQLQK2T1AK1euVGlpqWbOnGluGzBggBo2bKh//vOfcrlcko7c1OxwOLR27VqlpKRIkr7++mtdf/31GjVqlC655JJy5y4uLj7u+3o8Hnk8HnPd7XYH6yMBAIBqKmQBKCEhQQcPHpTX65XDceRtDxw4IIfDIafTae63cOFCNW7cWElJSTp48KCmTJmiF154QTNmzDhm+DmR7OxsZWVlBe1zAACA6i9kAahz5866+OKL1bVrV1122WU6dOiQ3n33XU2aNEl2+/964hITE1W7dm35/X4NGDBA7du31/r1683WoN/q1atXhe87atQojRgxwlx3u91KT08PzocCAADVUkinwpg4caI2bdqkL7/8UnFxcbr33nuVmpp6zH3tdrs++ugjSdL27dv12GOP6YMPPjC7s1wuly677DLNmDGjwvd0uVxm9xqA6HbYYHYfAJUT8oEyXn31Vf3ud79T//79y4WfNm3aKDY2NmDb/v37df7556tJkyb69NNPlZubq9zcXK1atUp169ZVt27dKrwHCIA1eBWj1w930OuHO8irmHCXAyDChTwAvffeeyooKDjma0uWLFHDhg0Dtq1du1ZnnHGGRowYETD3R506dcwnyL766qsqqxcAAESfiB8q9bzzztP+/fs1fvx4FRUVmdsLCgr0xBNPSJLOOeeccJUHAACqobB0mPfs2dN8Euy3/va3v+mBBx4w1+vWratVq1bpmWeeUZcuXVRaWirpyL09ffr00Zo1a5SQkBCSugFErhj5dWnsd5KkpaUtmQoDQIVCHoA2btx40seceeaZev7556ugGgDRwiZDDWKKzWUAqIhlH5nYmNU74J4iANVbaWmpsrO/kCRteqRPuQcqTl1hkM4D4LS43dLYpKCdjjZiAABgOQQgAABgOQQgAABgOQQgAABgOZa9CRpA9Pn1xMoAUBGbYRiWel7U7XYrKSlJhYWFPAUGAEA1Eezvb7rAAACA5RCAAACA5XAPEICo4PV6NXfuXEnSwIEDjzvdDgBIBCAAUcLv92vr1q3mMgBUhC4wAABgOQQgAABgOQQgAABgOQQgAABgOQQgAABgOZZ7CuzowNdutzvMlQAIptLSUh0+fFjSkd/v2NjYMFcEIJiOfm8HawILy02FkZubq+bNm4e7DAAAcAq2bdumZs2anfZ5LNcCVLduXUnSjh07lJSUFOZq4Ha7lZ6erp07dzI3W5hxLSIH1yJycC0iR2FhoRo3bmx+j58uywUgu/3IbU9JSUn8zxxBEhMTuR4RgmsRObgWkYNrETmOfo+f9nmCchYAAIBqhAAEAAAsx3IByOVyacyYMXK5XOEuBeJ6RBKuReTgWkQOrkXkCPa1sNxTYAAAAJZrAQIAACAAAQAAyyEAAQAAy4naAHTo0CHdcsstatKkidLS0nTfffcdc/jsL774Qt26dVOTJk2UkZGhpUuXhqHa6FaZa1FWVqZHHnlE7dq1U3p6ui688EJ9+eWX4Sk4ilX29+KoX375RSkpKRo7dmwIq7SGyl4LwzD0zDPPqFWrVmrcuLHOOusslZWVhaHi6FbZ6/H222+rTZs2aty4sc477zytXLkyDNVGP8MwNHPmTJ1//vnH3ee0v7+NKHXbbbcZf/nLX4yysjKjoKDA6Ny5szFhwoSAfdxut9GoUSNj6dKlhmEYxscff2wkJSUZu3fvDkfJUasy12Ljxo3GP//5T6O4uNgwDMN44YUXjLS0NKO0tDQcJUetylyLXxs3bpwRExNjZGdnh7BKa6jstXj00UeNiy66yNizZ49hGIbx008/GT6fL9TlRr3KXI/c3FyjVq1axtq1aw3DMIwPPvjAqFOnjlFQUBCOkqPWokWLjLZt2xrNmzc3WrVqdcx9gvH9HZUBqKioyIiPjzf2799vbnvzzTeNDh06BOz34osvGv369QvY1rdvX+O5554LSZ1WUNlrcSx16tQxvvnmm6osz1JO9lr89NNPRsuWLY3+/fsTgIKsstdi7969Rs2aNY0dO3aEukRLqez1eOedd4xOnToFbGvUqJEZiBAc8+bNMxYsWGAsX778uAEoGN/fUdkFtn79ep155pkB84V07dpVGzdulM/nM7etXr1aF1xwQcCxXbt2pesliCp7LX6rpKREJSUlzNcWRCd7Le6++26NHj1atWrVCmWZllDZa/H++++rR48eSk9PD0eZllHZ63HhhRdq7969ZlfL66+/rrp166p9+/YhrzmaDRgwQJdffnmF+wTj+zsqA9Du3btVv379gG316tWT1+tVYWHhCffbv39/SOq0gspei9964IEH1LNnTzVq1KiqS7SMk7kWs2bN0v79+zV06NBQlmgZlb0WX3/9tZo0aaK//vWvOvPMM9WhQwfNnDkz1OVGvcpejzp16uipp57S73//eyUkJOimm27StGnTFBsbG+qSLS8Y399RGYC8Xm+5m9eOpnibzXbC/X69D05PZa/FUb/88otuuukmrVixQq+++mpIarSKyl6LH374QQ888IBmzJjB70IVqey1KCoq0nvvvafrrrtOubm5mjFjhkaOHKkVK1aEtN5oV9nr8dlnn2n06NH64osvVFRUpIULF2rAgAHavn17KMuFgvP9HZUBqG7dutq3b1/Atvz8fMXFxQV0qRxvv9TU1JDUaQWVvRaStG3bNnXp0kVOp1MrV65USkpKKEuNepW5FocOHVL//v01btw4ul2qUGV/L5KTk9WnTx9lZmbKZrOpQ4cOGjJkiN59991QlxzVKns9xo8fr9tvv10dOnSQzWZTZmamrrnmGk2bNi3UJVteML6/ozIAdezYUVu2bNHBgwfNbatWrVLXrl1lt//vI3fq1EmrVq0KOHbVqlUVPnaHk1PZa1FQUKBLLrlEf//73zV9+nTFx8eHo9yoVplr8eGHH2rz5s265ZZbVLt2bdWuXVuzZs1SVlaWLr300nCVHnUq+3uRkZGhoqKigGPtdrvi4uJCVqsVVPZ6lJaWyuFwBBzrdDpVWloaslpxRFC+v0/tHu3Id9VVVxm33nqrUVZWZuTn5xvt2rUz3nrrrYB9du7cadSuXdv48MMPDcMwjAULFhhNmjQxH8VGcFTmWkydOtX4/e9/H54CLaQy1+K3brrpJp4CqwKVuRYlJSVGgwYNzEd9N23aZDRo0MD46quvwlBxdKvM9ZgzZ47RokUL48cffzQMwzC++OIL44wzzjBycnLCUHH0q+gpsGB8f0dtAMrPzzeuuuoqIzk52WjSpInx/PPPG4ZhGK+++qpx5513mvstXrzYaNWqlZGSkmKcf/75xoYNG8JVctSqzLW49957jVq1ahlNmjQJ+Jk6dWo4S486lf29+DUCUNWo7LVYtWqV0aFDB6NRo0ZGhw4djIULF4ar5KhW2esxbdo0o3Xr1kbjxo2Nc845x3jzzTfDVXLU+20ACvb3N7PBAwAAy4nKe4AAAAAqQgACAACWQwACAACWQwACAACWQwACAACWQwACAACWQwACAACWQwACAACWQwACAACWQwACAACWQwACAACWQwACAACW83/m3f5I3uGBKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 각 데이터 리스트에서 고장 수와 정상 수를 추출\n",
    "values1 = [x[0] for x in data]\n",
    "values2 = [x[1] for x in data]\n",
    "labels = [x[2] for x in data]\n",
    "\n",
    "# '고장 수'와 '정상 수' 합치기\n",
    "total = [i + j for i, j in zip(values1, values2)]\n",
    "\n",
    "# 비율 구하기\n",
    "values1 = [x / y for x, y in zip(values1, total)]\n",
    "values2 = [x / y for x, y in zip(values2, total)]\n",
    "total = [1 for _ in range(len(data))]  # 모든 Total의 합이 1이 되도록 설정\n",
    "\n",
    "# 그래프 그리기\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "bar_width = 0.35\n",
    "index = np.arange(len(data))\n",
    "\n",
    "rects1 = ax.barh(index, values1, bar_width, label='고장')\n",
    "rects2 = ax.barh(index, values2, bar_width, left=values1, label='정상')\n",
    "\n",
    "# y-축의 눈금과 라벨을 설정\n",
    "tick_locations = index\n",
    "plt.yticks(tick_locations, labels)\n",
    "\n",
    "ax.set_xlim([0, 1])  # x-축의 범위를 0~1로 지정\n",
    "ax.axvline(x=0.5, linestyle='--', color='gray') # x 값이 0.5인 위치에 점선 추가\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3311b44",
   "metadata": {},
   "source": [
    "### 결과 및 해석\n",
    "\n",
    "교회, 경찰서, 병원등의 기관이나 버스, 한강은 고장이 좀 더 많은 것으로 나타났습니다.\n",
    "\n",
    "**그런데, 광화문, 광장, 공항은 정상데이터가 더 많습니다. 특히 광화문은 정상데이터가 압도적입니다.**\n",
    "\n",
    "이 결과로부터 유추해볼수 있는 사실은 사람들이 많이 몰리는 곳은 의외로 고장이 적다는 것 같습니다. 예상과는 반대입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4940ae2c",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "\n",
    "## 대여소명과 고장의 연관성 분석 결론\n",
    "\n",
    "흥미롭게도 분석하기 전 예상과는 조금 다른 결과들을 볼 수 있었습니다. 고장과 관련된 숨은 변수들이 많은 것 같습니다."
   ]
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}