06-Visualization/01-pandas-visualization-tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<td><a target=\"_blank\" href=\"https://colab.research.google.com/github/teddylee777/machine-learning/blob/master/06-Visualization/01-pandas-visualization-tutorial.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a></td>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Uvfqtn2gkdHq"
   },
   "source": [
    "## 실습에 필요한 데이터 파일 다운로드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 259
    },
    "colab_type": "code",
    "id": "YWQN_AwokdHr",
    "outputId": "eb33a1bd-9f0e-421a-e69c-194b754bd080",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "directory = 'data/house_price.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FexIsKHlkdHv"
   },
   "source": [
    "## 기본 import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EhBeOJ9o1Stx"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lkohJDB1kdHz"
   },
   "source": [
    "## 구글 코랩 (Colab) 한글 폰트 깨짐현상"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lsmK--NdkdHz"
   },
   "source": [
    "- 구글 코랩 (Google Colab) 에서 한글 폰트 깨짐 현상이 발생합니다.\n",
    "- 이는, 코랩에서 **한글 폰트**가 설치가 안되어 깨지는 현상이 발행하는 것입니다.\n",
    "- 따라서, 한글 폰트를 설치 해주면, 깨짐 현상이 해결됩니다.\n",
    "- 네이버가 제공하는 *나눔 고딕* 폰트를 설치하도록 하겠습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PsK55vWPkdH0"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(directory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 679
    },
    "colab_type": "code",
    "id": "LNRYRPqnkdH3",
    "outputId": "289b3f31-85de-4b37-acd2-85420ffa2b8a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1888904a20>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50672 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 46020 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50900 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 48516 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 50577 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 44032 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50672 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 46020 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 50900 missing from current font.\n",
      "  font.load_char(ord(s), flags=flags)\n",
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 48516 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 50577 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 44032 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 674
    },
    "colab_type": "code",
    "id": "aGy1qftLkdH5",
    "outputId": "c3bc2465-574e-4274-c2e1-36da5b44b7c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading package lists... Done\n",
      "Building dependency tree       \n",
      "Reading state information... Done\n",
      "fonts-nanum is already the newest version (20140930-1).\n",
      "The following packages were automatically installed and are no longer required:\n",
      "  linux-headers-4.4.0-31 linux-headers-4.4.0-31-generic\n",
      "  linux-image-4.4.0-31-generic linux-image-4.4.0-59-generic\n",
      "  linux-image-extra-4.4.0-31-generic linux-image-extra-4.4.0-59-generic\n",
      "Use 'sudo apt autoremove' to remove them.\n",
      "0 upgraded, 0 newly installed, 0 to remove and 158 not upgraded.\n",
      "/usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs\n",
      "/usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 2 dirs\n",
      "/usr/share/fonts/truetype/dejavu: caching, new cache contents: 21 fonts, 0 dirs\n",
      "/usr/share/fonts/truetype/nanum: caching, new cache contents: 17 fonts, 0 dirs\n",
      "/usr/local/share/fonts: caching, new cache contents: 0 fonts, 0 dirs\n",
      "/home/ubuntu/.local/share/fonts: skipping, no such directory\n",
      "/home/ubuntu/.fonts: skipping, no such directory\n",
      "Re-scanning /usr/share/fonts: caching, new cache contents: 0 fonts, 1 dirs\n",
      "Re-scanning /usr/share/fonts/truetype: caching, new cache contents: 0 fonts, 2 dirs\n",
      "/var/cache/fontconfig: cleaning cache directory\n",
      "/home/ubuntu/.cache/fontconfig: not cleaning non-existent cache directory\n",
      "/home/ubuntu/.fontconfig: not cleaning non-existent cache directory\n",
      "fc-cache: succeeded\n"
     ]
    }
   ],
   "source": [
    "!sudo apt-get install -y fonts-nanum\n",
    "!sudo fc-cache -fv\n",
    "!rm ~/.cache/matplotlib -rf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bXzU92tYkdH8"
   },
   "source": [
    "## 상단 메뉴 - 런타임 - 런타임 다시 시작 클릭"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NPEuqZ5lkdIA"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 7)\n",
    "plt.rc('font', family='NanumBarunGothic') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 860
    },
    "colab_type": "code",
    "id": "5NYFtxQPkdIC",
    "outputId": "c71c812e-88bd-4bca-afc4-09f832fff6ee"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f18887c12b0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(directory)\n",
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "41qSMVsQkdIJ"
   },
   "source": [
    "# 본격 시각화 뽀개기!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "voQeWKsAYsjp"
   },
   "source": [
    "## DataFrame을 활용한 시각화"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "74ZWVRYuYv54"
   },
   "source": [
    "kind 옵션:\n",
    "* line: 선그래프\n",
    "* bar: 바 그래프\n",
    "* barh: 수평 바 그래프\n",
    "* hist: 히스토그램\n",
    "* kde: 커널 밀도 그래프\n",
    "* hexbin: 고밀도 산점도 그래프\n",
    "* box: 박스 플롯\n",
    "* area: 면적 그래프\n",
    "* pie: 파이 그래프\n",
    "* scatter: 산점도 그래프\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1Ai1VMJYZU8q"
   },
   "source": [
    "### line 그래프"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "g1jdc0g-ZbVl"
   },
   "source": [
    "* line 그래프는 데이터가 연속적인 경우 사용하기 적절합니다. (예를 들면, 주가 데이터)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "colab_type": "code",
    "id": "5UMbEVHrY9nE",
    "outputId": "2aca11a0-c727-4d9b-f98c-aee130ff20ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1887ef0dd8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QQgipQiqsevrMhJotO99T1t7GECQhhBBCmgRpCFLq2eodyCmzY+cLtTQBVoIIJMtQEEIIIa3Onv292NHdk2rXKwxB9noesHTBJGuv31DQpeWANToUYIQQQkgT8PZrn8Kbrxqfaif1RPVIPWWWPWq+oMt7FKRUINqCAowQQggpMeu3d2NF1+5Uu+17071fgFwwmeeAJbc76ClLthvwgKWEIE3rmQHJoxa10K66PeMuDNCRfVVCCCGEuOZt1z4JAFhx3futtGeeNJ8mrOwm4Q/mgCX7iHbv7xO1V1boASOEEEJakmRBJC1XIQ8t2g1V7hTku5mQd10xCjBCCCGkAMZ+53F8/q6ZuW9Xmlol1SN+KDA1WV9oJ2X3PnMPmHTbSXa2hBoFGCGEEGKRe6avxtpte1Ptunbtxx9mrc2hR9WYT7Kd/HtRMwZlyQGzs93Bv3UdhSgowBqInr5+rNq8p+huEEIajA07urFue7ogIPH092v8+rmV6O5J9rrs6O7Bl++bg4/fNi2nnmXHln5xpYPqETdV7ZShYFgEFGANxFUPvYR33vA0unbtK7orhJAG4szvPom3X/tU0d1oaB6esxb/8+A83PjkkkQ7f4Thlt378+hWJlx5rKQyx5YckodIZdvWMX/XbrfAEKRSarhS6rdKqSlKqelKqau95dcopaYqpZ5VSp3nLetUSo1TSk1WSk1SSp3sLR+hlLrXWz5eKXWMlT1qYqYs7QIA7OzuLbgnhBDSWvjP3W0ppR78l3NbUXE5AcYhyJTfS7yrzimiDMVlALZqrT+qlGoHMFUptR3AKVrrs5RSRwF4yhNbnwDQq7U+Ryl1CoBxAM4C8EUA07XW1yulPgjgBgCXZt+V1qGs7lRCCGlW/KdumtbwvS2NMI1h2YWVvRCpDv1djpOTNQS5HsAhnvg6AEA7gNMA3AsAWuu1AFYCOBHA+QDu8ZbPAnCYUurA4HIAD6MiyiJRSl2ulJqhlJqxadOmjF1ufFTRdwMhhLQo/ks87TE8aFfe57XtUZCm9taElbVgptdeMFQpLNhaD5kEmNb6AQBdAJYBWALgJwB2ect8ugCMBjAqbbnWuh9Au1Iqsj9a63Fa67Fa67GjR4/O0uWmgJ4vQggpBv/xq1K8J43gAXMlrGzT7G+8rDlgn0HFh/daAGMA/DWA0wGMDJiNBLDV+0+yvN8TYiSFMn9ZEeICyZB+QrLQ29ePl9ZuT7WTesD80ghpQq0MlLW8hG3BJ02uz5usIcgTAazSWvdprbtRCUn+AsDFAKCUGuXZLALwTGD5iQB6tNbbQ8vfA2BWHftBCGlSnlq4AWdd9xQen7+h6K6QJuSG8Yvw/hufwdKNOxPt5DlgfhJ+/X1zhbMQpFDe2CsvYaWZwfYC/UoeBWlne1kF2A0ALvBGQU4DcCiAXwHYoJSaCuCPAD7vibPbAByjlJoM4HYAl3ttXAfg/UqpSQCuBPCFOvajpWAokrQSc9ZUvBNzX0n3UjQLq7fsEU2+vHD9DmzaybI09TBz1TYAwKaddspG+LP3FBmp6BdO5SPFdq6VfMPlf9flPgpSa70OwEURP10RYbsXwMcilncB+ECW7bcqDD2SVmQglNMAD2NbnHP90wDSJ1++6IeTcfDQDsz91oV5dKs5EeZs+Zfflj3JZSj86XZS5pG2LpKCTHm5C+e8Pj1fOk1YFRaCdCj48h4okAQLsRJCSo3/Emgd+WXGzn2sC1gP/cJRi/719/Ds5KmD+jwXWHtKe30O3/B+Mdh6KfsoSCll7RcFWJOyZfd+3D19lch25ebd+Mr9c9Dblz4GYvWWPfjv389Bj8CWEELKjv/OTfeAyd7O/qMxrRBrn0MPmO38f46CrMaWh44CrEn5/F0z8d/3zU1NLAWAK+6ahd89vxpzBDk2X/r9bNw9YzWmr9hio5uEEFIo/cLRjVIRMiCsCswYkW66rKMgXSov2wMFOBk3qcFPzN3fm35xMLOMlBn/+myhFDCSIwP1vSypjcFRkPafrPOEA1Fsbdv2lEWmdqntSEOLJfWlUYA1OWW98AiRMpgDxmu51dnZ3YPP/voFdO1KH/n5zJIuvLxpV6rdQH2vNDvh9dcvVAVZPiikI15ta7+i7ryyfnQVXYaClByOmCSEmDJ79Tb8csryVLt12/fi+kcXOh3JF8Xd01fj0ZfW4ydPv5xq+/HbpuH8709MtRusXJ+ShF8CMSB9rEuLwJZ1LkiXh9p2Qn4Rk3ETQkiulOEF2Ox88OYpAIDLzj4+0e7zd83C88u34L0nHYlTXnNIHl1zhu/Z6u7pS7EzbNfBBSv9sC6ugr3U+2dplKY0T4ujIIkt+B4irQS9ueVjX29lqF8zFIXes68ivK64a2aine3QYpaQurS6vu07pqjz3OxpBxRgDUQZXkNN8LwlDUoRl57WGk8v3Jgaauvu6cOUpV2p7d3x7Apc8pMplnpHbLBnf0WAbdiRnF/lKiHdBPH8kuKphsr5QC9rv6Kop6cUYIQQEsNDs9fiU7+cjjueXZFo97UH5uFjt05LTfr+xh9eGpj6hpSDXkd5bC40hO0csEangXRaJBRgZACTi9n2Db6zuwfjJr0sTurdxerfLUcRD9uNnldkzda9iXZLPeG1Y2/yNDWkfEhDi1JcXqZiAdYk+iv3ivmWc8rSoABrIFxdi2W4Wb/9x/n47p8WYsLijam2f5j1Ck7+5mNYsG5HDj0jRVPk9elvO+27oE1o12wUtbubBGUopMhzu8z2Ns0+y0tcWt/LRQ2yPHEpvOyPgmQhVpIztpMjd+yteLS6e9KnOHpmSSXXZs4ahnJaiSIScv0BAGkv6cFisa2hwGavLvbeS5uP0QRXp8xNDpjQrrBRkHbtUtuR2pX0tqQAayDK8E1Thg+rIR2Vy3Z/L+ejbAVc5LP09PXjlokvp5YekI468z0OZXzOZ5lzUCok834cFDkiVrpt/9it3LynsD7YHwVpuUHpdvPeXs6V9SnAiDP29/ZjqmBkmClDO9oBDA6FJy2C4Jk3Z802zFy1NdXu7umrcd2fF+KWickFPdukHjA/BFnCGOTqLeZCoKweAxceRts5YFKybNV2Dlhpz7PTtt3P8SiFAqwBKetNE+Z7jy7ER2+dJgpXmHzYDuusXLYUYK2BybVx8U1TcMlPpqba+Z4vP/Qdx2BuV5oA84WaoJPIN1SZJR+or1EeMjYQ7moZvP9Sj2xR8ZK8xY248KtDMcUyFC2H/JTbfo6atLd0Y2Vk2Jbd+6223+E9hXr6KMBaCZuXstSzBaGw8l+M8vkCRWZWyCIcxInp5k03LLam96mrDwVVwi+qIGr+oyCFdhwF2Tz85rmVuHv6qnRD/yEvOPmuv38kDyOTSZRNHhh+a40+0ofIcJHgLvVstQnvuYEcMGEXk/Kytu+RlbJ4auEGkV2W2yTvF9/vnl+FS8c9Z609k2tFamld1GQ4yOIkfGkfWkpCVyjTtEScC7IEfP3BeQCAj5x+rMi+UW4ZV/LIf2lSfrmnp68fW3bvxxEjhonsX9m2F0cfMtxqH1zo7LY2mQdswFOW4rISe9Q8kuy27pF5jBes2ymyy/Khknde1Ffun2u1PRMPYyONXJV7wJLtXH272hI38mR4O9urh3rabloP2Prt3akjnBoO58+JxngQ+Rc8HWDu+foD83Dmd5/E3v3p99JDs9fi7OuecjLwwjZthqFFcRK+8BZKaq9dmOjjsiaUPJct+ffP/HoGPn7rNOPtxyEVIFlGfqZuW2jn8mUvH5Wb/LvxtEqFjYLMd8PynDI7NK0Ae9u1T+Jzd75YdDecYPtmcK1jTPorueF8C07S7J4nFlTCXLv3p888MMubYme+oEBuT18/fvPcSqMXpc3rXurZ8q+xtE1L64X52NAH0stfnrg9iPS8pL2wHntpA54pQJCbePAKC0FmWEeahybOV2uMb26riL1mTntRoWkFGAA8uTC9qnoj0ihxe2WQF2NS68mlB2z73h5xbg0APDF/A3Z2N+/0MyY116T5UgBw+zPL8fUH5+G3z6fnPvrXhs2rvt178olDkMJcMWknkwROm1AxyYtymt8oUk+AC0+TDVyEUMswv6KtMhSlD0FanhLI6VXKEGQ1jRTTN8LgBVcGXD2u/JvTxQPxP343E5/+5Qxs3NGdartm6x788x0z8F93z7Lej7LQ2W4gwIR5VQCwzZszUTJ3oouXhS9K0gbSzlixBYA8Cd/GtDbtlke6ZRsFadcub8xywGR28uPo7qDYuhcaJQSZt69BnlNmp2NNKsCK7oFbmn3/0vD3P0toJY0VXbsBAHsEOU9+HbKXN+2235GSMOABE5T8MMmDKnrqnvYB72zy9u+avhqAfCoi6Ys/yXNUhsi6q/kR60Wco2MUgrTrbXGJOATZ4IVYXeLiGspKcwqwojvgmMYJQVb+b9JbyTXv5+04GR3ntSkpRNk+4EVpjPORBb/m2j7BHJ2mXiBTbDa7dFOlRp204Gh6uTCZoPNJ2q7Ys+UwJCat6J/3pd/rYIN5Tz9jut0gtq4NZyFIS3byY1MeMZWF5hRgJT3Ytmic3TN7KUkZSMJ38ALyw2iSPrcbhNyASshy4uJN2TtXIJKXz2AOmCMBZvEF+NMJlSmIbIxaBIBJSzaZtZegZ6WjFl16OaTCNO+K+UUWiC1D/ajCQpB2NmuMk/NouY16nktNWQesiR0SBG6T8NuFuUHBfki9Bed/fyL29fZjxXXvz9q93DF5UEtLOwAB76jItnhPTxqcGD4fXIibRnpd2PbWNdK+50Hw+HIUZEYaJUSXB43jLZMzUIjVwYvZ92pJwor+17/UC9CIc1f6x9pk9gWJl6IMI8pMKGoKmORtFXcMh3iDM/KONkhrpJlw6AFDAKSfO+vbLvDZ7G4UZL4hwVKMgqyD5hRgZT3alrBdVytLuyaIms3wQHDxDPEfsr1JMSIPX2g085SUJpeE6YTUpu2nXZ+bd+0zaM2jIA2TdF+KR+VZ2Fa9fcibQ4Z3AgDecOTBiXYm/b/o5CMBAJ8773WJdgcOrQSMxhx2QPK25Zt2hq1K84P2xeyVi+3aCCVXecrq6CIFWANiMreiLBzk5i1kEmbykZj6wkf6Qbpm6x5RWQlgUIBJQkp++KpPINYalQEPmMC2zSAR3eTauHfGasHWKzXcTLE9t15j+fXyo9fSV4p/uYz0hJhN0jxc/rV6iOcxKwLb7zZX+bm27HIn5441pwAr7+m1gpGgsVwR2sgjYmBrwmAOmGwL7/je0zjju0+KbDfuqHhRJPvphx5dCdhtwjkBAWDTzn3Y4aAgrMm15hc3tT0qdOF62ZyHLkNy9idirr8NlxFI2y/Snr6cX/SNkAhf4HvK9rVjv0K/3Ry2sjplmlOAlfRg14thsW1zW0ehTdtPQ3/bEg+YaXL0eqGnDBgUGlnm2ktj5qqtOOXqx/HHOWtF9qdf8wTeef3TItvevn6xuMtSUqKo1KQsmy3jdFbSe6sEdUHF2BqkUIZnexn6kDdpuyxP+jc7eE6OtY2vC0vJ+k0pwFzVISoLkq8DXxy48pbZfqibvAYfnOmJEsHLc8bKLZn6IzkUM725DyXXm+nE8HPWbAcAPL9c3v9te2QesCvvn4tTrn5c5KkaTMKX2Fb+LxGkgx8TJjmKybYuhLApUkFn4/ZxKR7FzwKhmaSQrwku9tyayMhwcuUen3w9icaDZRo8Bpm3V7IpBVhJz23d+BXXJfs3GLaxG4L0c6kktplywATGu/ZVJoaWPBqGdrTLN27I1x+cBwDYsjvdm2San9TjvbA62uzfove9uAaATDianLuB0akSY4N5QgdXsV9csnjJFoE0Cb+UnY9mX6/ZB0gcrjwtJhRVi6sI5N5YyyFIkZXBMSzpsW5OAVbSg20LM0Fj13bF5j0AZC/vx16qTGotE2vmTzWJx2NoR7ZL3HZyqmk/du+rvLA6O9L30bSvWa4JUX6gQW5eA2kHAPaT8PMcVeZyS9IXdJlLsLi8f2xTWDHYlPachSCNrKVt1t/X4G/13MtNKsDMDshvp63Ckg3pib79/Ro/mbAUXVmGu8fg+kEsaf2FlVs9W5NwkLwPklyth2fLcp2CSDRbVgEmwUVNIp8fPLEYwGC9pSTuEY4SzPout7wAACAASURBVIJJON+/lk20tM2rP5MHrNGUYAAX3lGf1NNueNxMQ/CFkLLTcvFjflUXJupStmsagiyDh7Ie8j4PTSrA5La79/Xiqw/MxSdvfz7VdsLijbj+0UW4/tGFqbZdu/aJRMX8dTtE/azGjVAyuSfMrlP5TWziCZPonw6BgMlKZ7v7t7fkJetyeiOTQqxrtu0FIMwBczKPp3mjtrthY7+k99ZrDh1e/8ayYvii6hbMJQoAD6U8M5tqOiA7zcS0baf1wkKQlsVuWQVfcwowA9uNOyveLMmX/pqtlRdMp+Cl/oV7ZuM/fjcTa7buSbTLUkKqyDIUWdo1aVlye554RKUIo9MkZIHNCUckF4Osai/j/d8hEHnDMua5SfpkUlHi/hdfAVBceDGTALOcNO9yfsasuNyWtG1pDtgsb2BLnpgenlacDilNwDTSKMgyiHifphRgJmETfySYxNPg20oE2ELPs7V+e3JZgyxRBEdOLbNwk0G7thna2VZ8JwD8zSlHAwAuOulIZ9uQeNmGGIRZTcMjm3aah9uNdJDFp1wWPV5Uhe9WocOgsDFgIGDr/N1lm410RaUJIuMQZInETRayJP/XsytNKcAyjdwSXGffeni+2Nb3rKUNv86SR1RkEv6gsZt2JcfW7P2e7faw3eesN+nhBw9LtclagNVVLTdZEr4ybFWw3Qzr2B5tZePFYntbLqciyvtFWob3diPkDdo6L8WNgpR61GSUVfA1pwAzuE19r5aJEGo3uAPTbDsyCTATT5WjEKSjR6Fk10p6LzlhWGd6ePHtfzEKgGzAQR4PItkHirzgrUm7plgud9U6OCoP5WjziQyM9M1ZdAaf4/JJrO1s25S07bobBWl/h8vkyWxOAZbBA2aSP9JmIJrSbLPkrZjlapnYphv74S5Ju0eNrHhvJF147agDBVbVSG7OrDdR0ZOYjz54qNj2oKEVkXa84TF05R3tFUw7c+e0VcbtusD25svqOXJF7rshFirm4sbetu1sLg/S+mo+CtKuXd5kmQKpnvPdlAJs5qqtYtvBF4H8KJqIpjRLl6UMXDBc4JGph7LemFG4G7RgtpUCJzCI5OBhHU7adZOQa/5Cd00Zwi/WPQ+NdGOn4MQTW9L2yj8KUmgnM8udphRgtz2zXGz7W0+AmUwWa6KZ0lrN5AFzlQPmqA/NTiPkhOTBO15XCYW6LP2RhIvE63rabgXShGkj3Bu+yHA51VD0drOsU8yVmD5AobiUFPM2y/PR1ZQCLIuoMRkBaNNr1QgPqHpxdR2L8sXcOZ6clfiotO3KNuOgBCNjNzmKTuZ4tazA5M3FW7os+CnFdtO2xE2W05W3YCpDzaksobQoWm0UpJSqEHcd57spBVgWgWTycDcReG7eGW5ecGZ9cAPLAlRj/0XoDtcvntSXs8NQmxPxlxON2/PWoYEvLwAZJu1OwbbYtSVIbdOUAiybB0xua9MDVqobT9CXTLWWCn0FuPf4uDiHeXhGbXvZshwHkwe3kxFRtgVukyRq2w7N5pwHn6nN3EtqZPlgsH692rErg9dPSpnSDppTgDmurWWUA1Zw2WTJ5od1mhTylG/bZaV6oLG+7LNeBpLVihaLNdswsDW5REw+kmyTa+V6y3atiK1CmXFtJhtmaNvy2bQdWpRvV+ppauyrV8f+w4zmFGCZri2DEGSDjVyUYlZ6Id22DOHEstS9KiPWH/qOj3Xa9eS04KjtkYlCOxuUIVesBI8CEsD2vW89BDnglbSTFJi3p1ZKXQJMKXWcUupJpdRUpdQzSqlhSqlrvH8/q5Q6z7PrVEqNU0pNVkpNUkqd7C0foZS611s+Xil1jIV9MiqU6tNn8HldtmH/eZJtuhe7trl4cSz3I+sDT9YP9zmBRtuwfL6z2IrbzHnkW6OQOrpR2I79F7O5hyVduFf/Py8yfTAU1Adbc0GWgTJ9NGQu2KOUagdwN4BPaa0XeP8+F8ApWuuzlFJHAXjKE1ufANCrtT5HKXUKgHEAzgLwRQDTtdbXK6U+COAGAJfWuU+ZQl+Nc/kYhpxMbC0LS6Pz4FjYkVqKGukWpM+isCtDTo18u/EbFovdUlz/5UxudoHLRO68c7uchSBLJG5cYqv/9XjA/grAIgDXKKWmAPhXAOcDuBcAtNZrAawEcKK3/B5v+SwAhymlDgwuB/AwKqKsbrKUIZIcUL/A5IjhnVbbbXZEh8Aot8zytiPXE4RYc3it2K727zIXbXAbcut+A8+zi5GItkOGZbzfs40OLScu+1XGc+eKsocg/TOd1kvb92XeXsl6Sla/AcAbURFR/QAmAdgB4NmATReA0QBGeX/HLtda9yul2pVSbVrrqhmslVKXA7gcAI499tjUjrkqQ3HSUSPw3LItTuYhc7VOGUJHJpShxIYTGqireWIU+new/Vb5YndFGWqWRW8w8s9o0wY6t7aPYyte/2UaKFCPB6wPwENa651a690AngBwLICRAZuRALZ6/0mW94fFFwBorcdprcdqrceOHj06tWNnexW5JXxk7GsAAO87+dXidcxCgOW/cl25o32KTMbP7PEpgRiVdyDX1eLby9DgAUPl34Bp31UZs9syrVVve0lWLr/WpRR+TZcQ615Qp8c4LYfP1ShIu3Zlxda9V48AewbAeZ7XqgPA2QB+AeBiAFBKjUIl/LjIs/WXnwigR2u9PbT8PQBmpW1U8sV8kPdQP/bQA1Jtj/QmjD5gqNs5DuPI5vK06zUok0gsSxK+hDwGYxRV96ee9SSmZxx/KADg0APSw/lvOabyjXb8qIPknbBOSS66BsXW0XN575fpOdjqSGuzZfpYSfz6kbWXpQ9RZA5Baq2nK6UeBzADwD4AdwG4EcAPlVJTURF3n9dadyulbgNwq1Jqsrf8cq+Z6wD8Uil1KYAeAJ9J2+78dTuydjl6Pwb2x2SlHN68BWDWVcs71sBJ+A1bhsJ2OMPAdohBouaog4bKtm+wP2ccfyieX74FJx01Mt0Y8uLOeYZ0ynD9l6ALkQT7lX6cit8L2+MurLcntGsqctjpenLAoLX+HoDvhRZfEWG3F8DHIpZ3AfhAPX3IE9ehOilmYjHdpBT75SwJ311gqizJ73nkzZnViDNp100f0njNqw7A88u3pAqrd54wGpMWb8KbjhphbdsmaK2dFzSO2bLd1iw1V6QQKKpmXEPRKklllrrflIVYnXmz/FUs2pbpOnSVsG/Wh+K23WpYP4xGwqscJzGtH6MOGiJqx2Q2CX/LeeE0bJfWtqF2tHVdVFfCT25zwLbAS5L16KoZCEHaOinuIpB19bEpBViz02iDAEQPjUxzTFraduR66SvmUxDWbj9y6bOJrURwW2zLNTa6UCUeEhrMNoLazove3ClXgpPTQoiPdoN7rJwORHHW8iBNLcBsT60zaGvQh4Ku22yTZruxddWHRqIMQrguRELJrQe1yHdASd8/hZP3dV3oiGqpXWNrmrpolZwyW/1vSgFme5SgSzK5PEsQKiTlIatwLsMIS5kX0+FXbkHHIM9i99XT8uS33ar2cn4OmezzYLirOOTHR2bY3dMnKnLc16+NiiHnhX/+XOQOWr9H6+hjUwqwMtFIng9Xnj1XnkhJJ0qT/N44l0EkZmHFcoRvbZKe6mQvUb6lk7iJFT5x2/P42oNzU+2ueng+PvDjZ1LtynqtleEjsh6aUoBleYfbfsG4bMt2jo0JJRgv2fAUW5hWmI2aE3bvpQzr1G0QNi+feCo0Cd+3c9eF3LA9F6Trj/PfPb9aZGeztFOrhF9t9b/hBNiQjobrclORLWRqvRvybWd9yElynnJJwrfbj7I89zIVILYp1oyVVTFHrqhabbY2a/rBZi3klMFD30z5ZUDj1icE7IeFTUbFmtrVQ+OpGUfHxFntIhf9dfbCNcmdc3MiyjAQwAVl7KvtEKqrfXR67FJ2zNk+JTQs9yIUf1XlLehKsMsNQ9FzFueB9VzFLB+GdWyv8QSYAFf5SS4o6XXdNGTPAStH4rekH2XLRXNVyy01mdrdl4m8Wds5KZbbkIfRGvvJ5DLp2hbV5Ubsb9xmAd9vPvQSPvKzZ1Pt5GVOGv36stP/phRgWShDpW95m26S2p0l4Vt2a9keIZMHZXzgWBeCWfIZy3dYIsnzS7tPeFB6haPXJPPnAmbnb39vv9E6ZRZ09hO5HX68GdjajkBOW77Fcovx+PtpbxTkYEPSNjfv2m9n4wk0pQArPERYAlyF/xs4raA02L4+bQvcrLjqhksPRtoqpi9TG4f3X+6YEdh+vN2/3fmiqL3Lfz0j3QjALROXibb7/PItKKpyQZmFXNlo5BwwKVnE7viX1seuF1z8gR8/gzlrtjnpg09TCrAsuMsBS8kxcRCKqbI1abcEthLymLtSJnzKFforC67mprR5CE3byvP8Ldu021l7Sbvx4MxXRO09v3yzcR/yD+8FPB6WRqY6HU3qoE0bIUjTJsQ5gcY9scdVD8/HUws3imwXxIwQDV4L539/IrbuzuYtowAzoFm/KMq0X85qhpUI28LNTAwHX0wG6xXoiZOaLtm4S9zmA57YSOvzg7PWpra1fW8P9vb0ee3FN7h0486Bv8t+6Za1fy76ZT+87G67JvvfZuG5bnq8Z6zYikXrd4rsFq6PL38xuF07Zye8Hxt37hOt19OXvv19vf14fP6GLN1qTgFm9IIL/d9V+/VY1G7frq3rh22xda/crVfWl1TRFJVL+G93viBvzCK/mrpCZPe5O2cat2075FbUNVvkrdII96m0j3Nf2Y4d3T0i2zwiBWEembsOF/5wksjuoh9Ornt7wcN27Z8WoKevX7ReXP5keGlvne2l0XACrAHupYbF7GXo5kzIPD68CrKSdeRVnknoeVJUTaAyigKpJzwY2krbj6K864WPsnXEt/84H/94+/MiWxvHvujoyN79fdgiDO/9bNIyTFi0SWTbZ1lYSQfOhGk4AWaCs5F6Rp0ousl066JvsqwU/TjMun3rkbqSvc3d5RLaz6csO8V5rGwLTkuhJCuthNosKAaZ9ZjMXJWeGA7YGTBV9PX34Ky1uEjgUfMZObwzpr1q6h1BHF4qFXRhmlKA2c5tcUnR2w/SjMVVs+6T9X6U5DzrmL9tt2213RIcvKQuSF90wQ+dorwp1oVVSYVaVZvWW3SAg07arANWJIn5WqHjNqxTJmliQ5Ch608q1FomBNlouHjQcvRdY2M71Osqf9F4vRLVAZMXhLS93fy25ZKy9tXtSMTG8LDKQ8X5bavRkNbG6xUk4Zu0F6Y5BZjJCy5DGn5ZbsQksnz9FFlcNQsuE+VFwqcBroMikByXHXt7xe3t6O4VtevydDTLqc5TcLrEpjAoyitYlY9p0Ic24c43cggy6ylZsmEXtu+tHaQQfp5Lk/XjcruyesrCNKcAKxEuXhol0D7OaFZR4zIUmrUunO16ctLmxr+0Hos27PTWSV7r6UUbMXu1LOelqi/SPBzjltPai2+xKgTZpNd5HHkLv6yDTYrizmmrsGe/7KNEKqzabNShKDnh++3/3TtbNGXS88u3RD5XwpdKT18/9vX2pba3Z3+vyC4MBZiH2cuo/Dd0FmwmRLcC2UVVcx+7tP17bpl8SpPnA9OfpB21RnjRmlL2XUodBWnoh0k9xw7vnSI9rDc8tgjX/mmhyFYcgrTRRgNquIWCOmRTX96MD948JdXupxNextnXPZVqd/PTMrswTSnAyjAp70DzBl9qttqst/0kMoU2TcK7RrZ2bGyuF9teSV6kWcMepm27XEfUru32kpLwG/AlFYX03s6yv0V+dFx5/9zkkFNJwrJb9sjKLUhFbdL5FHuICzptZXr+dkXMCRnVXpRdGo0nwArMJ8pi22w0updh177egYmE0+jp68fOhKKH4UOxTfgA1drAZa0rCZ5JxRdzOSdluD9cjJArycs3tr2iRksWdJu/7vCDrLQTPG6PzFmHyUtk9aFkbctZ0SWfVkqa2yV1KrZABLLh38UNJ8AkDyTbuS314KZ9Rx4lR6Mri7xJwvt08jcfw8dvmyZYD7j8jhn4y6vGi7Yza/U2nHL143h4dvTUNeFD8KZvPCauBP21B+bizVeNF1dlTqLqeijBw2vmqq1i2xdSbKvz24pJrBa3V5RnwbpAtLPdg4Z2VP0/azu216u3jfP+d4J4hJxUMMmFVbxhM4cgG4mGE2CuKG5S4BK8BUtEHtX4g3lFSTwtrKoMAPNe2Q6gklsgZbng61hD474X1wBIGJEj3mJ2RB8+wp4EH+pPLNiItdv2xtsG/p6ydDNWbrY7UXXZhVWeQi14rJvtuZSkgUz3NE2TZPWMy0c31h8qLrvnqNEjLVKaWoA16zksg/epTF5GN9uWCA7v/zntoJW53crlAAMA7OyWl6SIGmLuo0u4b3E0i8CxXW/N1r0Ubqdf0PBzyzbj0XnrU+1mrd6G/3lwnrgvW/dEX7PhLtn2SjEEWX6aUoAZjebzzqArUdNsSt51deVGPVpphyV7AVQ7NgO2mXohbLsRT544hla/WRkqk2epURUmy4fA+h3dib/7LXZ7eZF79vcmiu1+rbG/tx9aa6zfntw2UNmfzbv2JeZ/Lly/E5/9zQuVbUeIpuCx+/VzK9HT149NSVXaA32V9FGcAyZEQWHOmm2RqQsluBQBAAvW7Si6C1Xk/QxrSgHmChcXbZYT3mNQ9M1MjBrYGrQroSTPAzMCB6wMXkmXlOWjo+yjt2xT9t2Q9u+GxxbhvhfWpNp97YF5eOyl9Tjvhgl4y7fi8y+nr9iKE77+Z/zu+dV427VPYu6a7Yn90lrjrd95Ap/77Ys1bYWv1/f+YBLecnV67udX7p+L0695omYwTXjb972wBm+79km8sDI59UFc30v4Ilq/oxsX3zQF1z+2qOY31/eHtKbZX/1ocqRtmUZBumyvKQVYlpehkVApKF/M54rfzUy1MRE0rsWPWdV8y4MsckjaDV4Pcc/GrNdBVWJ5TCtZD6/th9LqLXsyreciHPfVB+aK7H42aRm27E7P0UmyC3q2vnjvbNF2L74pvQYRUHnJS7hz2kqR3ZX3xbfnIjE7aPvcMll+5JMLNsTO/xcWS9OWV9p8edOuxDb9b9bH529I3f6arfE5iUEembMOANCTMl3Nc17O6eIN1X0MX/euKtz7+ak2+OETi0V2b/rGY+LCpJJBCu/+3wnYvKv2mij7B0oaTSnAGgnXXyK25xIsE43q5ZAIjqqEaOF+ugxxiaYXEk4ZlAe/F3hbfJZuTH55m9rZJG5UbZgfP7lUZPeQsL2kU5j1A0g6/Yu0TIxJX5JywLKO5JTklUWuGEOb9G1c4ByPP3xiSWJJnCD7hOdRIjyXde3GYy+li+d6yTs3kwLMgDIVmnSBs5IVBgntEsweHhlzrwxsXJzjqCbTi/qajVQsKhHcyDsbMnbRY3kieXlvZrEYsI3BZtM8RRK7uN2suU5Chi6Oj99k+HoOb9sfvZx23YsL4Yqsgv0xXCGFPOaijOpze4nUiq1nZ4l2yR5ZQoTNnsOTRJnyr0TCpwGPfz2h0LRVww/88S+tR3ePzP2/YN0OLNmQPnWHBrB2217MWBGfx9IeGHa1bvve2HIf4f1Zv70b04ThqUbG5X2WcS7gWJLEZtYQ5H6pB8xCvbswLp4ZvqhLOx793u7UisTqf0tHLRY9mEO6dWk/peI4SviJP55KGudpSgHmimzXffKJL9OF0WhJ+EUfu6xlD1wKyLlrtuPyX7+Ab/7hpdRta11Jgn3PDybF2FZ39Nwbnsbf3ZI+0a0G8J7/m4QPCybFBYDLfjEdHxn3nMi2SC9Uee7UWmwcl0wefoOjIg0tmoQgpST1M/PHkWU7qWfJtLyEbb1muz3p8WnPoa4GR0GmYNtDMpiEX+bHa3bK4C0q0ruYTxK+PavatfSAOJXlYVXyM1ZvzZYQX7Xt0PbSQkhBEbBrX/woqDJ5XAH7wkqa65TkTQzSL3Bv2Q6xJbUWPH//fd9ccWFjG4Il/FucbXi5A6fawDEPH/qaPgrPjW3PkivkqW/S0H7Esgg722U66oGjIBuEMgggKWajFZ11o2GoGv1otJ7pdgxsU4z/7qdTxW1979GFgXaTG564eNNAGKyRrnkTpPsVd4zDq3/m1y+I2rvtmeWpNrZDkCb87vlVIrsiX599/e7CmmmHfiBUGToC4fWkwioPT1AStj1/UsOowyPuS0mfSRRgPkZeGntnszok5CBRtARKySSh3ahdwUpZ995sQII7PVod5ozeStRxiHuWrw0UhEzr888mLUuxGERSQdwGxV/N6cxeIxv2L33hrhF4M7fv7cHGnenFPpP7M/j35l2y6XMAJE5YbxsXHm35Szzu/kvzDMva7xAKq04HAkzqtQXq82xF2gnPQC4hSOdbqKYpBZiZI8ftIS/qhZHFW1uG41bmkWYS4g676W5Vl6Eo/pgUdW1YmX4phaIOr7zulsxwRZcs7Dx79bbIOTWDx+Ff7piBVZtl7e3ZLxvwIcXGGU8LC7oktlxFyo51dshexx0OhgN+f7ysvhdg37Mlvf/ayxSCtNRO4wkwR3dSGZKonZQzKP7dLaI8t1YFkXdtwMbsIJuV8IjaXpK9iefOolAKnMAfPrHEWrvh/Snyerb90SH9oLednP3Bm6fg3BsmpNrZyCN0jW1vTKY+CLeddno629vwp7nrsDdF0K7asgdnXfukaHojoLYAbBQzVmzBUwtldbaeWrBRZPfEAll7C9fXjsKOOq/z1tZ6luPOa7hWWdw52pgyRZYrO5/GE2ASTF5wjh/ojSKAnJJRcNiwLcvxr6cbqQ/4CIs8PEekQtYPc+kXvdTh0WY5RBMXlgp320oFfWEbcYK81ksYskscBVmfiEtbPW6ARHi789fuwL/d+SKueih6BHOQtdu78dc3PZNqBwBdERXkw8xYuRWf/uUMbNuTHnr+z7tnYatg9oj/d+9s0SwTl/78ucR5P31ufvplPCkUddJc1zO++2TVv+OuhbCdtL00mlOAZSCrV8JqH1y06Si3rSzCpkiy55dls7F9yIsanWpUR6pMQrLOYxC+v6ShRbkHzO6xWrN1r+iFnHSOsvRo4uJNYltJfhzg+HlV03ZY/Mnwa/et3S6bCkkyEbgp8mK5spyxqInAo9gnrFu4UhgWl3j96sFW9KDhBFijvffTwhatIGRsh3ddicqq9UxsNVI7lfk862AZivQk/MIKoucm5prnhrGdA2Y7R+brD87D2699ymqbtpEOeHCrv5Jbl5YI6Wgv0cdG3mSe8ULqvSznc6PhBJiETCPqTGyd5YDZb9h2SM/E1nXOZNH3VF7bz7IZ+8c+PRDqghLl3VrfQ9u5XS6O1V6BZyLPkdbyGlTyFaW9j7NLDUEOVMJPLkNB0inT88AGTSnAXJElHFK0SLBNpor1lo9B1E04Z822yOl34ja9OGX6naAYnrFiS2IxTB3o1MrNe1LLAQSPx/y1OxJt9/X2D9jPXLUt0vUf1bM1W/di7TZZKAMAlm5Mn47IZ/bqbWLbWRG2cQ/RF1dtrVlWM5pNV8IaUbZZqVdAZA2T2k/Cz+ftFN7fPmERMpfPwrQ9L/Ix7D9L0vo4YVEl9Fp0odUiKEuqQb1i3JSmFGDO8p4c3MbVkyPbx8irVlBOUMYuDBy7ddv34uKbpuCrD8wVr/vemOl3ovi7W57FL6auSOlMpS/PLO3CGdfUJmIGz3MwkfR9N06OFFX+4+g7jywYWPbJ25/H9/68sMa2ejsVlnftxlnXycNHF/yf/Hh88OYp4hIFf3PzFCzdKMvH+NBPpqYKUqAyyvJDP5lqJARtYPuaL5sHLOvuSYvA2qjWn7WFBevkHximpDnb1gpHK8YxZWmXVbtF63eKcqmkdpt37cOqLfXZBa/d3r5+PDJnXWp7APDwbJmd9BmUN00pwLJgplOKT9hPw+SLwtUXV17ev53dlWlv5grzQbIQdQNnFeRfuGd21b9NXkzz18ULlIXrd+JbghFUPtv2yAtohrsYHrWUtAvhRO6k623z7urE4ijTBd4xcJGEnCfS28726EbrCC/fXmGCtw3CW0qs1p81rDmwPGOeqXC1j906zardhT+UfWxJ7a68P/7DN7iL0g/kh2avFdkBwCNzZQLscuGsE3lDAVYwrhOos3iUbLU3sI7R6EpBHzyT3ft6U2vmBJv77TTZlCkAMHXpZrHtK1v3YtKS5K9Pp3WIAm0v66otsBmHdBi7aR/SbbMfDCeHUfoCtl4HzG5osah0h76EDQfFdpJdIyPdK/Ggi8w9KYa0Z7CPtGCvSVV+22TOMcxIUwqwLCUlypCE74KZq9yEaSQvI9epDDc//TLOuOYJsb1JiPLuGavTjbxDsGjDTjw+X1afplFJT8E3Edn19aVZsB2CtD0htxTpdqUlCRKJ2Vb4UOZ5KIqsul8YgeNtezRvkl2jidM0mlKAuSJbEn6xOWaSiXJN+pgpCT/DOol9CHRi577elG3b2rq7x2q9L4si5vusZ5v19LaM4i3rh4btF1deZBU70mT9vJFey7FzsQrXrz2NMjGZJ+L7OmAmr2cX/5uK+bvZaUoBlsWbJQp9eS3bfIxkbctJyQqDJmev3oYVBuEum5TlJezSa5p1yHtRFFXU1QbS7vxJmG8i3q5ww1JPWek9YBYEWMkunQqWO1WkAJFeQsFTmSSsdJVdvGFws3mN5o0m33GQTSnAGpXiKpObX/D3zFiD8/53gr1OeOzr7U/MAeju6UtMRA/z4kp5uYLNgik7gjxskCw675X4AQJ1e8ByeCulbSPpZ1fdy/tl/LvnBWHpBGpDVbI9kIcgDTtkCakAa89xMEERXuGBbcdsuiylFpIQ50AFjm/SXkntgpTM4euUugSYqvC4UuqX3r+vUUpNVUo9q5Q6z1vWqZQap5SarJSapJQ62Vs+Qil1r7d8vFLqGMk2Xd1YRq2W4BPeRQ+uvH8uFnyRfgAAIABJREFUvmaQJyVlV3dvZI2uKG54bBHe/f0Jsb9f+6cFsb9F8b/jF4tt/+lXM8S2SzfuwjqD4eVfvm9O7G/RczmWizunrbTWVj338PfHL8KTC2WTAY+58hH8+jl7/baNbQ9YvV5x16P53nzMyEztS6hH3NSbeJ09ihG9vMiQs1RMiz1bwlCltL1mo14P2L8BmAcASql3AzhFa30WgL8FcItSqgPAJwD0aq3PAXAFgHHeul8EMN1bfjOAG0RbFFwfzkpKZLjTUj0HmR968vWSioiGudNgpKCU7z++GO8y8Jat3hJfQHSjw9IDa7bKC5dKBaWE8Kl8Yv4G8X729vXjhscWWetLHFNfTh4V6sp7G7adtnyLfGUA16fUTQPkQ9nrpSZ3SrietAxFUR6wl9buwDu+l15z7jfPrRLZJVGCb98aytinrEh3JSjUkoRV0C4xVBn0lCXlijkWZ9JzOW7SMqHdyxhz5SOxv2cWYEqpMQDeD+DH3qLzAdwLAFrrtQBWAjjRW36Pt3wWgMOUUgcGlwN4GMBZWftiBSPRVjwmfSjD8G8Tb1ESJdgV64R36acTXxavu8eiEDTB3XRcdttrj5hfL/zxYlKeJArpK6G2qr/dEGS9T6Z6Xm7SjxeTj5x6yHUUZOi4x33UN0IZCuk1We2xktkl7n+GpP4iuWfGGpHd9x5N/jjOJMBU5QjdCOA/APgJO6MABIshdQEYLVmute4H0K6UiuyPUupypdQMpdQMyeXhKjk6kwfMkYfNxDZqOpiyUc9N1+iirKwTxZpgdp0b2NYpKjoi3g5lOdxiD5g4CT97X1xh+1XqZDYS84F/mdaXUugoyAz7YqOeXXUSvnkfysrI4Z2Jv2f1gH0WwGNa6+Cn+lYAwSD/SG+ZdHm/J8Rq0FqP01qP1VqPdXVuiqpjlDl/wGDNv7/l2YxbyQ/pS9nlwympbaeFVMP9MFi3qGdVzeFw5EGuV1TUk/htWxjXeErEOWDC9nMSYGX0UBRaByzn9YJIa6tt3DEYgUi6J+6eLhtoctf0Qa9xW4KKCNolXTb/9Kvpg+0lGH7zoZewv1e2z88tq02b+NTZY2qWBT3g/jn57iV/mWjn88/vOL5m2a2TB8OTwzvbE/uYVYCdDuCdSqm7ANwC4FwAewBcDABKqVGohB8XAXgmsPxEAD1a6+2h5e8BMCtjX2Ipg2ehsFBNTs9I21Xus9iW8H1gRAku07px5XWul/aIiyNq812GI2DzJEteDimO+k+D/IG2ba9sOrFgnmOSoL/p6aWi9m5+etD3kjQAosou4UE975XBke1pe79xpyyd5TuPzK9ZdvDQjpplwQLdL3ij5qPSdr76wFzs7K4+3sMiBNZ3HlmAyUsqE6sniVMgowDTWn9aa/0RrfU/ALgOwEQA3wGwQSk1FcAfAXxea90N4DYAxyilJgO4HcDlXjPXAXi/UmoSgCsBfCFLX2L6J7f1/r9kwy5MWJQ8usr/gp2xcgtmrpKXN0hsM9DVIodOZ8VklNntU1aIbW+ZKEtyBOy9eIw8TzZFXx2DRsrojUgjT29zVA5YFJ/6xfSaZVHb/r/HZaNqn44YqRl+US3v2o2129JzoqSDaOq9D8rwwZqGm+na6hsGKffeSyvBy7oDyMN/Qa+X7WdGuBj2t//m5Ei7Xd0yu7T+dbbXypa3v/awmmUdEeonrRbdz7z82xdWRA/2+fnk5VX/jsuvnu4NFor6AAxSdx0wrfUErfVlWut+rfUVWuuztNZv01r/yft9r9b6Y1rrc7TWZ2utp3vLu7TWH9Bav1Nrfb7WWia9HbFk4y5cFvEQjuK5ZVtwyU+mYndKFXagEkJ5dN56o5GIcQTPZVmelVFu3jieTRlJF2TK0ui5FaOu5xufXCJuN4miDmldVeVLciGYeTcN2q07B6z2ERfV19Vb94ja+8WU5TXLoq7J8fPX1243Yl+iJnkPc9XDtV/yUXz057LJmMtIkbli0kndN+/eH7k8fDnt6JZ5pSTvjzTSXvA+QVEjDWlLdVrYKxT3LJDaBft3y8dPw9+9tbpCVVQI9cKTjqhZFpX/mTYbgz/iWFo0OLY97+ClCeSWL8RaT+L7l34/O3Wd+15Yg8/+5gX8RlBHKc8Xky36DaZ3MxlebPKRllYiIY56xIvNoorhbpjsezmuAofUuYNRL5uoJrftqX1pRtnt7Ja9NKW5Z1K7sgjtook7ClnvxueWbcaLgvlyd3b3iEv0fO2BeSK7S34yNXJ52r5cdNKRcmOP9ioBVr3SiUccHLnO0A6ZPDji4GFV/44TJYeH7OKcEsF74qKTX43PnvsXVb9H7fK+iLywjgjvd5qw8o9NfDHdUHt9MSNe/f+nnJ+GE2Cmj6G12/aKY8YAsF0YUwdkX68bvG3Hl2HI9mAty/PYRAgmjkKuSwxlo0b4ZO5BfdRzKstzHRRvG0WUB0y87ToOrnRVqdi2MY2PLRov6B3PS2tls2psifF+AQbnOuk3Ff13KhkGcoS9ZhefclTkOmMOO7Dq36MOGhppd9pxh1T9O+5SfetxrxLZhT9Kwt8oUat190QIsIh7P9UDpvy+xQirUF/S7FraA7Z2ezfOuu4pnHHNk+J1PvSTKWJbyTPRP/yS/Iz/e3wxTrl6vGjbJ33zMcxdEz+9jQnhS2Tpxp3xtgajjcK2Jg8WkzwF39Y0taGeV5rNNIrwi97Iu1aS97KZJzm/Tkd5mPLYfNQLIQppCEk68otUCJ7jMYcdEGsnDcftDdXb+9WnzxjcVuAmDHt1/iYgbpJO9Xdi8qHCnDHm0Kp/x30Af/rs4/GGIwc9W21VOWCV/x99yHDMv/rC2G0FxcP8qy+MHEEIVB/r+VdfGHt/B9+B86++MPadGF4afs9GrfaW14ysOc/RHrDk+8jf5zihFn429/T148eXnopfXHZ6pF1TCrAfPrE4cfit2QO22vjlTfETTIeblbxIXvGSbCV9uu2Z5ZGhkDgenPWK2NaEn06IT4AP70eSCK31MBmIKrFl9roxZQnr1OUBK4kCc9WLes9R1ENYeszq2bL0mpQWSW5YAWbZXRY8XFE5PlGs2Byf3yft3p79soLHYaEmJSqxPIphQ6pH3UVdPsePOhBfed8bqpZVhSC949bRrnDAkNpRgYN23rptFbs4URJcfMCQDuyPeTcH1060C+1UeNaRqPt3WGc7JnzpXXjNocMHlsXlgB08tAMfHhs986EvmPo1Iu0GPVuD7f31W47CcSHx59s1XQgSAH74xBL8eV5tkmveSJ6d/pdwXLy7nveLq2HnZi89gxCkkQfMxDY5bh9H2NykDphdD1i4I/J1i4pMha8RZ0n4deeAyQ+mZB8+duaxorbe/JpDapZFNR+XQxIm7mXVjJx6bO2xi+K/3nPCwN9Z78ewp/3nnxwbaRcWH8HnefC8JoWKkz5AgwIpya5NhQZjRdi8901H1Ai6YDTOvyfSrj2/H/57Jt5jFRJMMWI1vLrUrkb8JtxHn3nnYL5YlPe7t09j7rcuxL+d97rIbfvHSetoO7/FsKcs/JwJ28XRkAIMSBYfzoa513h/DLZjsBlxm0nhP5N2Qv9O2i9XBQ9rkvDj7CJ+ye4By7aebRpxFKTJVsMfH0ldDl97SduR3H8mlfBrpwqStRdFn1Aw9QjtpB4waXv14LryyfGh3KM4OoUlRoBKuC2K8Ok8eFi0R6hGgAUujuAv4Ws9+FxLcnJJUxXD11/U/R/lqQoKgXCeU9wzxF/F/zlOW9YIq5hrNXy/xtmF2wtP4h7Vj56I0OL6HbWjW+MEk48vhH3PdI2wCuV2xQow759pg2waVoDVk1xrCxPvgzTebUJaQmHmdi3sVxSucsBMvBxBampqGYVIbbrA3K1qMtTdZMqqHz5RXfojqR/hycKTBOd3/7Sg2jah4aseeilhqxVMKuHXir/ajUcNpom6FtLmgPORCqaoUV5BTh9TSXBOC4G97bWHxv4mveey3m9ikjzRgXNi0o840/A+x10v4Wsj+OgNCphwSFn2WVm9L0m7Fd7nqNvD70N16Ylg+54HrN8XYNHbCncjPopTWe7nnIVDhmH8UZdxduFjHS52GnVf+qI9eHhmr942UGLkpKNGAADOPXF0jR0AjH+pElF7k2d33gnRdv6xe2NKe75d2uOneBWTkajcjrxx5WmLu9BrbgiDRpNy5uppN0kD1lyUNnLAIn7IeiWUxwNmYCvw0gQxqfD+5IINYttJizeFOhJvO2NlddHipD4v3lA9sjjpHpNM8G7ynJCch/HzZcdImgs0aUl0vbswC9cnj9bzQ05xYR2f55ZFF5gE5J5k13P1SYVVkmCUpgyEl8ftW9jBIvWAVT+c4q+w4D53JLjK2pSq3nZEk1Hvj6CwHPCAeXaxeYihgxPrRPAWP/i5swEAV5z/+kg7f3t/+PdkO38r9//bWYnbAype0H98+3EYM6oiwMKDX/yabCcccTAOO3AIPnjK0ZFtLlxfGXj2+sMr4vCSU6NzxPxDcsLhB2Hk8M7Y9ny741K8uQ0rwBavjx+p5yrHJPwyMKmBZSJqpIm5d05bhev+vFBk+7qv/Rnf+aOsoOMjc9aJvAsAMHHxJnwtMJVDkPBuPDJ3Ha68b46o3SzesgYsCg+gvnIYaR8BRsK/nrILJh8jmbdih7jdrPGA5dDRqPnlovj3385M/N1/aWdNAgfk909Y+Hx47DE4csSwGGtzu8RuBM5JtdBI7nzcx194eZyoCz+TdYwIMhmUFCQokJLC3O1tCsFv6ajw4aBnK9pbGA6zxfU53I2495K/1P8IiCtX4dv5+xdr5xkO6xj0fJ0WyAvUIduDAmHjU15THa7s9CJlWusqu3Co2T/+/jPAD7CF7Qbz4oCDhsa359t1trfFhr+BBhZg3xdOCeISG4nHUctNQou3THw53cjj1meWi21/OXWF2FZaoBAA7hJO9moiQ7IKr3oS660m4TuMQRoJ/zrSh1yJlXrb3SscvWZjWz7DOtsiXy55fCDUUy9M6nmqTThWsdewEtpVrSM8TtJ9PXBIe2ybYZERdwzCXqXDY4RkWKScfNSgIEjqbZVAShBgSgF9gS//qDYHc7sGlw3pqA1B+u8ZacQl7cMl7bT566ddZ/57NWj2o384teZ3rTV6+3WViH7rcdUhdn8qsm17e6r6d8gBQ6oK0PqicIdXB9TvY9jO79O2Pfur8vYOOWBIVdmPwfw5nZjf17ACLAmjkE4dYUQbOWBRlKnoYj3U88IxWddWDpjRuhZPUT1CMK0bZuHNfI5HnqHfqMly4867tF81ddtC5+vdbzgcrzqgs2a94HV64JDaftWD36e0Kc8+dGp0yASQf/KE9YFS8ccuXH9KZCfsSV9CCCJ4jn/0D6fGD+oRhiDDH8VvevWIqq35hI9/sHZW0vUV3G6aByz4fohqs6evNrQYzJn2BV5vhF2QsDcwzjHgr5723OqPEFaR7Q1sP9iX2u3d8exKAMnTiLUrhTlrtmHCok015UiC7+SO9jbs7+3HjU9VZkQM3qtBOwVg7prteHLhRqzeUj2Pa9jOX5b0fmpKAZYXJi9wE03VZ5IF36SYSKrwaB0pJqG/8Lm2WX+rnpbS9tnkmLga1BHG7KMnW5/e95eV6Vpef/hBNXPzhZv8yl+9IbJfYbsveGUPwoepdgSUivzgCr7QhifUX8qC/+WedAovPOkI/OcFJ8T+njUJXykVe0YPCAjgJLsDA8cjyWMQXF/6oXrmaw+N3bfw8riXZVCkzPvWhbHz8gbvoZs+eqr4mAbDY0kesFEHDa0qHxF1L/n5vsG+BK38vEh/AEi9OceDginFsyW1i/CURa3zpDfhfVKdNw0dO9tB8Ph0tKmqUi/BazBopxTw0troAui9ITugcj9SgDnC5H0V9yKJHEZcluzwBiFzEn4d27TrAavD85SaA2YQgszrujO5b0L//sYH3iRa76SjRuLgYR341bMr8earxiceB//5eOY1T2LOmviRoHHTlNROnaIir4+gmdTL+a/nVeoafeDNr060O82b5iVJRJ95/GGJ4kbseY30gEVvd21goESSnV+wOnIDAYITZ4dfjEH80PNt/zgWBw/rjC2MGfynL8SjCPb7oKEdVS/V4B4F76GTjqrOR4q7V9/+2sPw9tceNvDvJA/Yly48sVpYJXjAgiNsq7w9oQmng8Lh9YcfNPB3UsmhoJ3WOvbakdoFWd5VGYgTNA0eEr8f/viapHtb6/irqcpD2K5C21ORdpUwejRBIRusoZY4qjX+p8bFXRJ+eN3s4csk4qZLMJueR769MhL3FRi11OS4BKlH+NgsgltPU2nrmn0k1NEPR0n4WcOzwzvbq66VqrIBIVv/YbtzXy9u8kIQFbtwqLE6UXdg/Yi566Kuj+BDfYig8vn/fOBN+PKFJ6KjTdVU2g4y5cp346y/GBXZtyCp4ZCM95FC9LXz9KKN2ewSuvHAzMHZP4Jm4XY/efvzNduOsgvu82fO/Yt4D1jokVwlwAJtVr+Eq4k7NX/71mNC9cKir413nTgawzrbq94PUU32RHjAFq4bHLQWLtIavGY2BgRuUggyaJckctLs/PIpQGXCcwD43/GLve0P2gVDqGu8kGNbzP0YRPqsCYveuOtAxdzbUXZAZZ/pAXOEqzpg8fNQybFap6oATOo32fKAmQg5l84ik3OX1g2zkiJ1eOJycp5JRUJ7m6o6n9VD99O9YWFedUDnwLZrX+KDf5/z+lFeqYDaNoJ2t/7jWJx01Ahc8MbDY/tSqXquYtsL2vnv1CQPWNrLIOt9pFT0dbg0VFIkzu7ljSG7jH0IEq6bFjdSurMjvmJ8Un2vKo8M4u2CBH8Jth2uBRdXOsX3VFV7wGq357cX9Gx979HB0fLhMhdB0bh9bw827Kh4LZNCkEE7jVphf8EbjxDZffLtYwb+/q+7Z1X9pmI+WD5667Sq35MGD0m9bm2h8HjS60fyrAt+rCVtvikFmLuv8WprW/WywsRNETGkQ366pLaXjnsOOw2KdeaFiQDzMf2ADz/4k7hnxhqjtk28ayYFUKctr67jlLYdk7yusO3ZrzssxrKWPPTXLR9/q/gcX3La0VW2ccfhR/9wSnWuSeBx6R/aL7znBDz7lfPjQ5CB9W+/7PTYr2TfU3bqsYfgja8ekSqs/N/Svrr7deBllOYBSxqRJT24Ya8kVOR1OKyzTWQ3NDRQQtoPyTXnzyDgtxhuOez5iDr/QHKF+7gcsDBVgi5gF85li/OO+sJqV+B5nRSCjMvtCu9z+JrZ59XSCp+G9550ZKRd5fqrtr31H8eK7IL/XrCuurRU0DRKlPq7kRaClPwWztuMO79KRV/DQPX1GPS40gPmiKSTG54mwygXJ+bGiRrRFYdUgD27bLO4zTxpj7loI71U3qJlCROpR/HzyfGTjqeRdjrTKpcHuf6x6lpuRqMgU/oRdy2dEjFXYfgl/5pXHYBDDxyCvznlqNR+jD54sOzCj/7hFLx2dHwBwvDUIkmc4A3t/v1n346LTj5S5B254t2vw4hhnbEhquBefvCUaqFWlVzt/X9YZxuGdbYPPEinLt1clYgbXKezvW0gB+zPc9dV5TYNhEwGpi+pnJ+7nl+FPftrP4IG7RT27u/Db6etinyO9PfrgfslaRRkX0oIMmvaQluMZ6tWWFXslm7cVSUihoWeVcF+BHO+wgQPRVxdSH8eQf952K+ri1KHr/mo8wDIP2SqX9bxdkHRk1QkO4j/Yb6jO/mD2Re+cYMUagRYaPO+AyPsiT/3hNFYcd37a+y0jnkuC+yC29gZGixTXZw2of0UkSWJKCgAWnAa4q71mva8Tfa1Yg5YXiR9lYa/YuIso5qI+4o18Qq5rlbtmnBeTRK+pWn5jh17s3v+0rys4ZF3Sezel714ZqoAizG485/PrFkWTj3UuvIh8cNADZ44/GTb6V+7AB885Wicd0JSaK363P7Hu6MnxgUGz+2xfg6UQCX4exwfgkzuT20fqsNX/3zHDLz/xmdi7dtUxVvxr3e+iA/f8mzV8kpfBvs3cfEmXHn/3MiCygNFIVVlyP1XH5iLCeEZCLz98Z8NdYUgAz/9yznHx7cTuPYvOfXoivcgYrtDA8LKt+vr17jg/ybiM7+eMWgXEGqXnHp01evy9GueEPXj+48vjpwvc49XmPawQF22GwN5fuFuV5V4CCxPzDMSesDi1glHPOKeLb4H7L8CI1mD7fjX1+XehNRfvPDEyHb84/2ti08CAPzdW6tLk/ht+uHZq/46euCLb6dTwmy+3Svb9tYcn+A1F55wvurDJuS6vfbPCwZE/IFD4x0Tn/3NC9iyZ39C7yr8931zMC9mdGOQbz08Hy+9Ej2qMnguvvXwfMx7ZTt6+/oTnSFNKcDyS8KPt62Js1vwgEk5//sTsCFiIlI75KPspF+FQO2NK2V3zNeuhLTTuTPlKzXIrjpCwGlCMO5aOiCiDlWUWPPFR1I1ZyDw0G6PzrWptq0Y//jSUzH5y+9K/oIN/dvkwyJomxSai/WA1eT9RG88StD5ycdBD1i4AGawf1HXS9SEwFElaoLJ9R8Z9xw+cdu0yH729+uqbZ5y9fiq3/1z/YYjD8bn3jUoiqtHKFbzg4+cAqByDMJ2wQ9G387fzxcC01N1hOykuZjhSzvqGes/R4LlMIKpBzc9VT2n6djjXoUjRlTEWnBWgai2L35LxTO8cefgSM+o6+yaS04GUPH8DdgFOu9PJP3nz58DAJi5KjolwReHHz3zWHz/798CoPr+f/XI4fjQaUfjjOMrxUg/8bbj8Mf/eEdNO1prnHrsIfjHs8YAqBQvrfZsDdqdduwhuOzsaDHu2/X265rE/jC79/Xi4dlrawVY1d/V5z34Dg1/kP9s4rIBge/P3xjF/HU7RMXKe/s1/ulX01PtAODuGbJi4v9yxwzs7+tPPDZNKcDywkRUGY2CrLMO2MuGoTgz6uublLiHftSXdrggnu1tRpF2FOImWY4UPqF92r5X7j0LXlcnHHFQze/ha+nko0fg3W84PPIlJ5mMOrYf3v/9h6hEVL3jdaPwmkMPSL6PdHU4RBJO8EsyBB/vVeGF0OaCxyKqff/nOPEX7n2cgAjnkFWPpIsOLQa3D0RfP/1aV4mdyUu6sGBd7Vd6fygcs21PD+6ePjiLhe9kqAydH7Q7+7qnquzCXVWqMoI0bFeTXxX4Z3DOvrBnP05/peXgRudD1eYzBaeQCc89qpQaqF5/zSODU7f1RDyT3++VBrnsF4Mv7uD95t/W/gjVHwc8b0E7/+83esVdp74cnRYSXMc/V2HvW1IJi8F96a/xKEWxvzdZPPjno6evvyblpsoOg6HgqN/iSBsp/E5vwuzPnx9f2w5IL07sY7sOYr/W6OnVifvR8gKsvkr4SW7p5IdF0vb39fZFvsC1rgxFlnJehG09pRf27u9D1650dy5QuXmlwqivX1d9HQLA9BVbI18i3XXMdRemnpF7accxTkR/7MxjU9sKTp9z6RmvSe5H4O83H5Oe11XJiYgm/KCq5Gwkbj5g68fVorcbhRqwTW8/ro5TkCNGDMUbXz0C7zrx8BrbKM/El7wQTVWTETlgg32QHYy4d2B42HxQgEV5cf3jEvz6HxYpwAZHQfos3lCbE9WvNVTILjioIzjPXXhXg3Y1xyVw0MKDROLsgs+x8OGKE9nh60TyPPaFU3B/3vya5BzErt2VZ9yarYPPr6jncdSzKOi59/+O2pv9EXZRBM9jsA/BOlMAsHTjTqzf0V1zjKIu2a27e9DZkSCYvDa37tmfGD7zN7V1T09s6Yxge9G/xf6UKsD8dcN2hx04JLKfYfyRmj5xKSxhuzjCdv0a2NfXXzPSNkjDCTB/tnKg8uKWhqqWbtyJrbtrxcOmnftw9CHD8U/vGHSzzl2zPfZFH0wu7uvXuHfG6tg+vOdNgydkZ3cv/jDrlUg7oDrn4pKfTMXrv/bnqgu3t68fXbv24ciRtXOQPTR7bWSbr46wvX3Kitg+hAn397nl8Qn794bcsiYj+/b1Rh/rv/rR5Jpl3TG2afzistNrltXzvRNc9x2vG1Xze5wAOeP42pGFYctgSPLaD70ZP/jIW+L7EdhOlLgLP1SSRFVYM2rIA877Q56GJAEWztVJtA11KukDf1hne5UXMGhalQPmHfHhXlhKHloc/Ds412NSTtnfv/WYmuUDX9qB9s49ofZjybcL9i7Kc6FTkuuDdmGrkcMHp0wKnrskuzDB4zJi2KBdbe2swb+PftVgSFsiGoDa+ojhD4ao6ygqLyztWA33ktiHdrRhR3cPHp+/IfKDKvihtHrLHvxh1itVg2/C9wQATFq8CY/P34CtgbyknpgX/7Rlm/HxW6cF7AICzGvTX/Or988DAMx7pTqPKSxmZ67aikUbdmLK0uTBVzNXbcXiDbsweUlXot3e/X14ePZadO1KGCyB+HMadEBo6KpnX1gk/ucFr6/696Pz1gNAzUfF4184t+rfcekgX31fdeHduMdQ2C6OsN2mnfswe/W25vKAhRM03/G9p2ts5q6pXIT/HshjuOD/JuH9N1a/0Oev3YHJS7rwyra9WLl5MGz31zc9g/++b06VbXdPH2at3laVC9PTp/Gl38/Bzycvj+xr0Hbi4k34/F2zaitteyc9ynsRHKHoT6T9+xdqyyFMXFSbmBvHzFVb0408Hg4Ju0/9Ij5GHhaBH/7ZszGWtdz3YrwwDRMMXRw0tKNmqHvcKKaTovIEYu64KxMqYketGjU4Iu5rKqq0Q7gbr/aum6s/eFJ6PwJ/n3rsq7Dsu++r+j16vrw4D0OUWJNJsOsfXVTVcpKoutZLOB/4ik9wgX37j/ND7cb3IezdC56DqCT8KK9a1MfUYA2pQcOTj47POwleDq8KfI3714n//6BdlCioEWyIPq4atddg5PFXquZ8Du0Y9Kj5bXS0JdtFhSB9giO1w17jqyssAAAgAElEQVTHKrtAe+G+Brf8ttcOTq4cDhHV5oChhijPVZp39qvveyMA4MQjD8Z/3jUL/3LHDCzfXJvWcX7A4/GpX07H5++ahV89u6Kmv0cF3gNf+v1s/MsdM/C5O18cWBZ1zS3esBMfGfdcVSHTKBG4xPOQ+Z6qpCR3AJgXMy1PEC210/Efz2HijnlVgWQNfOX+uQP/DguX4KTYAPD8ioq3NXzvHBrygMURztN2ZTckwdvYcAIseHdu2b0f63d0V/3c29ePR1+qKOOwKg9OiwEAM1cPipEnFlRXYw57cG54rPKCifoiiFL/cbe4yYi34A3ne++ichHiqT3xJmvbGszwzhNG44rASLf1ofPwPw/OG/j76EOGD0y/AgBrQ3lZ/sNqzGEH4NmvvLsm/v/yxuj8t8NHDKsZ+Rd+YPtFMf/+rcfg1k+OjSzVMEhl5V9cdjqu/7s34zIvodUn6kF4xIihOGBIR6rAe4cn0j7xtuMq6yb4ofzj/uWLThzYTnU/onpd4bRjq/cvKVTwt6fFT+IchSidwigEWTHeG5NLAlS+oIPCYUvA4x3Uof7mfMugdgl6MF5cWf2xElf9/PcvVLy/N3/0tKq+VuwGDcd4Izlv/eTpte0FtuNfd//secXj5vTzef3hB9WEioL7e6p3nj/zztdWjUwM9++4Qyv9+/knxyba+dz00cro2Lj9TaqdFfwpaWaBYAX08EdN7QdDVAgyPrQbx18ePbJSBBcKKzzhtSMiLzNYesV/BwSfV/6zurO9beBj3P+AXBJIuQg+0/2w+Ht/MCliXwbt/GP52d9UhJx/vpJEb9AuCa1ldoDGxTdNEbV38Y+j7YLnTANYFzh+0txAqZe+KJoqCT8tUW534AH9129Jrl+UlOwcvpeXd8Untse9t6IumLicsyhXeXVj8T/d92J8kdBwPPyROeuStxPAn+w0ioOHdgyEcADgmaXxrupbPn5a1WSzb7v2yVjbBz93dlV456zrnqr6/Twvv+euy9+Og4d11gxBjjq+vpfs7FCoMGx75MhhOPTAITjsoKG44E1H4EMJosM/5+1tCkeMGIYvvLdaCPb2afy/e2bjO54H56iRwwe2/9lz/wJhnl64Ee+8/mns6+2D1tFeCJ/unj6cc/1TmLh4E/xX8jGvip6qpq+/H9/+43x8+fezvX4P1qW55zNvD9lq3DN9NT54U6XEQvD4/OcFJ+DCk6pzHGav3oYzrnkC2/cM3kcDFaBD9+m8V7bj9GueqBJFPuEX6YJ1OzD2O49H1oAK11dbsmEn3vrtx/HInHVYvWVv7ES5/Vrji/fOxrf/OB+nfftxAMAhB1TujaDADaYefOqX073fKwTfB36fV3TtxoveqDV/WpW2GKFR2WbnQEmNuCT84Z3tOGPMoTjQSxYPvlSDx+rQA4fg4287FkopvHpE9SjVoN2wjnacPuZVOHBoBzrb26qmfwkf+5HDO3HcYQcmTlfjXxenj6l4p+JCvf6z+ovevZFm9/nzX59oF35G1ubZVv4fLAHzT+94rddm9LH2Q9afe9fgPamUQmd7G3r6+gfOUdrz2W89aBf0bPkCOSqEHgytJoWrgnbBZrTWA8/q2lGG1RuUPP/7tRYJsH4NrNoSPxF2sH9hR0m0YXhEcm12YBRZp9DKi6YKQaZdGMEQlD8cN47g/Xvjpcm1jnZ5ceRhnW1V+WJRPDJn3UDc+fH/emei7dc970/wayqK4EX2l0fLClkqVZkn7vhR8UUxfT489hg8/9XzE+ecCzL1K+/G+QnTqAQ5YEiH+CYZffDQxJE8/m+HHVR5eYbbDb/wDhzSjo+deVxkW1oDExZtxOV3zBj4twr9HmTP/l58+JZnsWTDzkCtqeh+9muN+15cMxA6BpI9Wd94aB5WbdmD9du7oUP7Fd7Gmq17sXrLXnzroZcGw2kDttXGvf0atz2zvKqSv29RWyoF+PJ9czDbC+EHH4htbaoqDAUAP35qCTbu3IdpgdzAuBDkLRNfxqad+zB5yWC4PC5f7NbJy9G1a3/NHIH/v73zjrOqOPv4b+42dpfdZWGX3rsU6QiIgoiCYgEJtmiMPbH3El8LYHh536iJKSYx9h5NNLG9hKioICiWqNhQ0QULFjpSd/fO+8c9c++cOc/MmcsuCwvP9/Phw+65z547d+6cmWeeeQoQXQjvXVSF1Zu2p8uYmFFtmc8m8bc3v8Sd2veh/Clt0Xn63wL0RE/XfMz8rL/8/tcbsC6krNJyiz5bHYrI1dt0l9Z+ndLC3NDvkWNCbezplhTXcaLtfpsCPx0l+sCry9Ov6c+fUoSOHtguIheyfAQ/Th6Ukgs5/Gv3Wxf4TalEvrY0FDOeSm16RnevIH1m9c1Bj1Yl6FZZjMsnhK3SOYlUzrK0YhUoU71bh4/BFOq5W6t9v/tobg9q3qI2VSeNyMxProSjM4/up71f5rp+shN3BPkSkUfOJCll+ijZNRf7ZgGIq86g45ojrRaw3Vv/2rOc8HMSAueM7WYNezWP+A7r1zpUkd3GUQPa4qQRGSfmyNFmsPvISyRw7RF9Qo7X64xEb+c+lDILCwj0aFWCttokYDr3fxZY1prk5eCJc0aFXtMHlv4c3H/68NjPo2iSlxMKu3bRsrQJOrWIV9YAoKRJXlalkXzCoxWuJKzqcVWLoXlfc1e8aXut9ZGWMhVCPveDb1GblFizaXvIYdOcHF79bDUWV63BrGc/RHW6xEkwsRr3No9Lvlq3xTlhpTOZy9R4cuU2SyfdlDI9uVkd64l22FJklGjjpDaZ2rXqVmKz9VQx3HQGaOMtVJtDssH/kdI+Kh2C1naleJkWT9VvcbngKFc41X5dIWlJbITuWVgFIBwAotocshQFH+h7zSVB/2xvGEea72t+NuqjqvFrS4nytGbBSDnL0wu76dyso7tXuIqU6yi5NZu2Y9az4aSx+qmD1OSmB4qQep5DcqF7KwWXal9GcsptCwEAJYE13ZwmlKyaj79eb0llo735M++uJNP25AgRUhxWrNmMliUFmHMRvaFWTVHPy9vXHRIKSKD8/oDUMW7v1pqiZrGWjO5eETrR0RUV/TmJKt40lx1qT91Qm5R4NfA/foBI2KzLKZQLBAXphhogQ+MvY51vVmQP+jBhC1gD46qhpu+QBWAtAAtEF2vd38Bqcg6+a/18+vH/uJ3IczRl8eK/vmOVy3WE8uqDrFlRPtqXuxNjApnB7aP7qP7My0JR8vMTSJFNFn9bGSIAWPZ9ysKRPhaKOB9nflapLXTHWJ2UpSn1c3VtEv/33jehhdxUXpT1pCYpcf2T7wPITLjmAqj7Dyn/kCcc46Rq9eb0ez682J3o76HXlqdlfx/kFrKVYQo5cCclNm6twTwtaOOE4ZlNh+68ur0miYXLVocU0tP275z+eWTXFppSlXk/tTAcMaBN+tqwzuXp71TXk9TEfMzgTKTgoI7N0vd9UWun2nDpkcV925ZmkbQzOguoeVEPTKHKJKl0BLpiQOnSqq//+XY0Kvk1ouSXfhz7iyeWQEqJ21/2K4+1ZXst1m2uTicNNdG/k1U/bLfWgLzrlc9RU5vEe1+tx32LlocsdJScHr1HPdN3vfI5Nm6tDvl1Us/zfYuWY+PWatwydykeD4JwqIX0tc/XYNn3P+AP8z5Nj8WTR3TCySM64ZJDwou+qiZgWmoBhDSRGYFbAHUcrti4rQZ3v1KV3qx89v2mdJ4uCnMcquNthVLUTTkzdYFt/jXnOd8Nl01ujKNSRTIJ3B1Eyxfn2zfvH2m1G1uWRC2NitWb7BGSenOra2XaQEGNBdv+dXev+rLHZcJPiPCEqu8AzIl2a3UykmPKft94y4v6wUehUA+vPgG5/M4iTofarGG+m4/W/5lSVjxkzWgaH+LytOhkU1rIdVu1uKmPZE7u+vf/ReCfYAtcaF9emO5zOg9T+O9U6HZtUqadaM36ZYpbn89k2I5LrKofP1OKgvn9qajbZFJicXBcYwYrKHRLHBWZeblWrkR/nYpuGtQx4zuk1xXUJ31lNVC5uNTr6vtPErKDtfumsrUHCtjHmSNIVUqmU4vidDTU+i3V3scP1DGIeh/9HvcuWh7xX1PWbn2sqe9Jvy3lbKvkFjvyYym2VNc6lXSddVtSyoPuL6mjR5N9u2ErOjss21uqazHP4e+p2FoTTuDZwvLeN/1rKZ5ZkrHUFVlKxdz0r6X47Qufpv1HbXPEHfM/TwdBASnlZubkfigzrCSPG9HUwvKzwien4A/aiYqrvqk+hg7oEU1LY1svzPq+NgtYoRHtrY+7sN9UpGXk/Vx5WPU5yCV36WMZY4LrhOPkOxdbXzONIMr9gb4dPY/7bsJ2FXucBQxChAagvniaFc1fcEws5pzsOn9P/03wv48Cpo4tfWSFcL+/Och89BkqD03c/X2PK4FosV0X1C7YpphQyqUpq9pr9m1NrcS2mlpsra6NVfqaF+Wn/76asHh+vY52HA2nNwja43gfl0XP/FtKAft2Pd2OWinJoz0d3WeIktEzq9+zsCptaYpzOl78+Zr0+NbvS1lF31qxLr0ZqZUSAwIrExUu/s6X62Pf+/HgqP7LtVuwyJI13OSGwGKpo8aZfjT2/cZtkaNCVVNvc3X0qE1X7ChlSL1cSCRQpWTV99HHYW0BgL8H6Whcc8vVj7+Lzlc9g41ba9IO/RQPvLoCN//749j2Pfjqchz4q3mxcuuMZ1U/itPxrfhgppaJ2yTqxZ9dmAEdFLYUGxE57WdqwVUKSlwCZ5siY+Zh05+5bzV3mXtODbun2KYe12mLPqZdcjrZnHDo2Htjz7GA7VFRkEC0w7fVJDFv6Xf49LuNsVGSrgnbfDhUPrHUa6n/1cA3LR96PTCFOmL5Ym3YOqGsC3G15vQaVuZr5j0plPPulx6yajE1F4oNW6txzRNLyPB/c9xvDGQ3EbUNTWVo4bJVGDB9LulobSqir1etwYDpc/HvD76Nve/m7TU48H/nofe1c2IVn83VtekdPWUlM337FHF5mUziJid9IaDG70ZLrcjapJ4rir73R99s1OSj9zYVJjXh+ixO7wbPR1JKjOlZiQEdmlknmzeq1qbbUNG0wJlHyxVRCyCkTOifzwWVPkZ9L8cPC1cbMB3d1YKqy5nzgc6p2lGtep1SgMy+r6nN1NSL2wjdNDelMLlqjupH2VWOKO6b5i61vqbz30TBcCCcqwuwpwoZ2TWcA2+D0Xb1d4f1ax26bvr1up7rrdW1aWXlkL72DOZbq2vTFjBX4JEeyehyudCfLeoZUHNaXBoh20c7XnMVAMLj7r5Fy9M/9zKCBGwnJa45SV+XfBWrHVXAbBpYNkYtXwsYFX1eF2451p4gW0cFjFE0UgUs3OHbampx6t2vY/wtL8fmeDnhL69aX9MHMpBxptdRA//Vz8JHCn96Meq7oWTNHb0qRKs/tALR3Y++EEXzO4U/KJWLTE26Zjg/pSyu2phSNr41gg9++9wnePC1FaEabwozr9qfXlqGB19bgftfXR6RNdunjjzerFobKfS8YUt4Ylah04uJTPw/GJP46k3b04XI4zZv32/cmlZuKMd022SpW8DUZFUXK3jI0Z30LaKVIT2lhI8iSB1BmpOXKkBsSySro94zmUz97OdrKEPHlxTqo/gEWbkWxbkxEciqDQf0CGehV3kEFcoqqMtVrd6EZFKSSWT1BNDKj42q4Xjr8eHI65pkMm3d0bOeq5qDFHpqgrZExJ9CzzB+9oFdQ6/ZNq16agYX54ztHvp9LrFRAoBzjPuZpxPKr/W8ceH7Pfdh+H6uxb73tXPSn+eKCfZ8e72vnYM/Bxvc647oY5XToxpJ37KA1Zo/GRX1ppSyOKuf7bPpx/RA+NmgfA4V+t3OuPeN2PcBgNM95XR2VAGzJU3dGUYt3X/UxXFD3eXfFK7nUsdc33QapQL27JJwLpPDfpPJcE9FWdkwpx1z169PmkrWtijoea4UtvDbkoI88nXXIDY/s8nNwY5YV6C2WHwcZj3zIYCwYqaiPs0mfxvIuHbaihVB7UfKAmYqSkpJykmISAoOM+mhsuDlJBIY0bV56Hjmh21h2TvmZxThOAuYrkT/ZX5UgbZF1q3WlMlMuRj7ex2XRVUAqqyUzbq1tbo23Tc+hWTPefBN73Y8QCjRJkpBXfLVesz/ZBU+NVJA6EcxSlH9cOUGvLj0+5ADL5BKF6LIZjJ3WYq6VzYlyzNl+z6U3Hcbt+GPLy0jN3z6AvzVui146ePvLZbB8B/XJjMWsNCGwPHVUsk5KTZZKkTUB979GPM8qiO+uOAelzM8ACxfvRnlRXmhdlF9849AcfF1u2hq8WUzcR1BxhHXRwrfFBD67XRF1tUePRDDt92+cjdNC1uNxlhqG5cQ66nfJ7bj22eep67e35VrPDdKBcw8dlgdynjt/zXFfR+hRY1wutVpRtRKs8mqGlfZ+KDFHbVsr0nisTe+CO1yrLK1STz6xhf42QPxC7IKYPDxEVFO778LIvN0TAuTsogJkQkWsMl+uTZ134QAqlZtDk2YppVKzwN11v3+Codp/aTakWlP5kjXx1JkWgpdqCg4fWdos4DpvkttmtHWD91fJq7+m86dlnxTFOpo2DwqffPa8XjkrBGha/M+SlmETOX2tWvG4+8/DyeGdfWsqu3pemYSCYFfTumPY4e2J+ui6mvGvy46EP8ztT95H5tis/jzNaTiay7AazdtT8vpu2HzT6uTMn0cdq5mVaIWjvLAAd23UPBULdLUtTP9kVa70jU/6mmAXNZMPZ2AbyBOfo5d0enduiRW4Xvt8zUosfidUfRoRef2Mune0k9u/Zao64KrWLWObx956hLWjWF9W7Z85cw0L3k5CUzs2zoiV17kV04oG3w2qSnq9zO7fBb9Pa4bCbpVqa7BEYf1j5oYbZnsWxLh4DaNWw2E0BGk8NeoKRZ8+r0zI77Of1asw7NLvokXBLDMM4IUyChKFKaipKxvdy74PGJdMwswq3pod73yObZWJ0P5mFwO28rcT0Ul+UDVXjMpDRTv+g7E0X1PfJQ88xhI0SQ3EemjC8bRshSuIswKm4N5SZO8yERqi4hrWpAbkc1LCNgSBZi+Li4SQpCTr77Y9WpdErJs6tgUYCDzjOuJnE0FLCch0nJ3n5opCm9OD7W1EoV5OchNiNDcY8qlijlXp9vtg1kqy4ZPHVQAuPe0jLO3ayG6/7T9vOR0XAuWr8+fT4Z2hZlbzoavpYyydtryVprsTAuYTqFnAFV9y1FHjtSaaqbxALIrjUfhr4D59m3dx3OjtID9zpG13pX0DXA7XLpQX4nNL4j6cj+wFDRVbdSVxepaWaeDb3Wkp3PKyE6k7ErCInPxeDoxH3UMd05Qq9Fs7qofosvlWYG/iXkflawynGeqC4Co5UkpECqoQDeR26xU4fb6Kxy6uTguuScA/CiwLPhMDjccafc1MdEdlH2sujYz91ZCQW1r+CRQ1iGFeTw8eWAmGaTKo+SK8lEvKSugKyRbLdAqCSk1CZuy1Lg3SSQEeSxuLnY+1ofFvzg4/bMQmYWwSFuAEgmB+VcclP49L0eklXm9CLW58NQkk6hNyoiiYsq9XhWf0sLEd7Hwze0XLuJtl9MXH5el7OEzM5ZSX2UlG1x39E2nU5DnJ0cFXPgWdPY9/qqrMuITlZuNXJEjX5gOVZmAooJwXLcZP3yJO7pWTPP0AfNlj1PAzFI8upk7bmfQrbIp1m+pRm1Sxn6hyaRMR8LFDfhvN2zF9KfeDykERZYd0wsffYdHFq/AitWZXVq7ZoUR5bFNWRP8/oVP8N5XdH07F6O7V2C6VrrCxcAOzXDh+FQdtmMtg08tCvm5CVwxMbVLnqodV9j4xeH7AACOMKyJVKTUdYGCMtGIgnIpQqYsBXVMZSs9pRfg7tc2Nc6ev3SMdfeuFm21tozr3dJaQ7IN4YzpU9VAWcBmTu4Xqe2ZbgexuJ15QBfSQlhtKHQLrxqHXx9HR/SY+cB+fdzAiHO7a3EzF36Xwmx+hmxkXeQIETqutd3D/F35tXSrzFTSaFkaXkBUV5rjo0PzzEYvN5FI+2DpC6w5p9QE1RhMX1RTbmdm/tYtGa4pryjks2dfRvTFx2UBqyzJjOtschH64uoyX+XU1zWRUmJ9dUrf77auFjB/RdtPrjDfT86Wt87kyolRSyx1nDrNYw1S2Bz+TcyAh7ri+qoapQLW2aht2FqbFMNHkNEvTEqJAdPnYvpT7+M3z30Ses000y/5aj0Gzvg37pj/GZYESpCtDuOsZz/C3a9U4SYtYeChlqiLXz/3Ma56fAmO+N2C9LWWpQURrT8pJW6a+3FITnHJIfZSEkD4wXOViTBlx/dphecuOTBiPVMTrr6gj+lZiRcuHRPpN2oCHdW9Ai9eNjYdoq+UqibErnJY5+ah+pWuhXjf9s1QNXsS5l02FsM707U/9UntrWsPwVvXHmI19evJGa86rDeePn80ulU29XIgnnPRAfjDiYO9jhFev2Y85l9xUGTC7dA8qqQp62puQlitNNTiZpvMNxvWICGEVdasjZiSDcu4vh+zL1zpLczP8B1RjNsm68Imam7AdCPFR99sQE1SYtqQ9tYF+sWl36dzVLm+8mffW4nr/pnKQ6bnVTIX0ZpaicfejLoRmAbQLPIfZ01uKHDCLqcraq7xHlLAHHK6Rc2lgFELc13wPToDwoq4C6r9vr5dvuO63FOZsOXx8lU6feVcOdIUNgWIii6lPh+l5I3t5VePmKpw0VDY6ocCjVQBM9GP/8zcWnqmbyATGUg5XZuKhHJA1/PkqAfpmsCyY3L3wqqMbDB494spCg6kakyaZmrXmfUFB/dw3k/vhmyO4ICUs+k5B4X/xub/0LWyKc41ZKmQeyClOF8wrkeofTZnWV15VbKlRGSMoktFMcqL6XvpY6J5cT6aF+dbF2W9Llx+bgL9AoXb52igd+tSFObnWCdRXbmrLClAh+ZF0fxua6I525QFLIdQfhTUddvk6WcNirbZJutTt1JhpjnRsUVSUZOnrR/G9Y5OyLbFz3y89P6aGERW62WbMnKZn98M8pu5Fk49Q7suZ+60j/jdfFCYecr+RihpAF29gJQjCo5TuDLF68/49lq7nH685xon+gLuOhI80YhqHb8PvQCbx/16uSudbFxSfJURSlGjlM9ehPO/rwXMN/iCqm1qY7jHOmXDdFWgoJJdA3TEI0VhXlRupaXep8mfTx5CXqcMAD7YLIMnjwgbLv588hDnuNnjFDDzGTeTr7mcts1JVPmi6M7YSsSWYkL311GyPuZitUgM0I7AfJJh2vA1UQO0omdOBDalCoj2m2tXaS6GxZb7XjGxN165alzoWlxkk20R/IHw/7E9FLbUHdkc+1gVn22UMhN/PzW5lRfnW60I5ns2L863WkpMC1iqHfRxHDUGzX52Lejm9+1KRGlTlHRH7jjZ24mJ1vbdmeOe6lsqv55+PxUd7Ds+9LxdHZoXYemNE9O/2/Zb4/u0wtPnj07/bvN769vWb5ff1hIxm42cbqFwRS2Wa+4hruMs3fovhEDV7EmknBkUcscpwzDj6L4ROdMyMqZnJXlP0x+yPqBSn1Dj49YTBnrJ/fcx0ehcaq6bSiiZiYQIrSmA3RJ1w5Hhfiy3FMS+lsibRlmxph8Vvp9NAR/Tk05FYUIZAWxrpOlS0aaM/p6fveCA0O+29chMDvyy5uOpc9H4sGFkAhHhqbNnKGB6XchIdvnMz10rip27MP0oRdfKdR+iTFFheraknE59otgUvzluYLqtcQ7mxwyifY0AYKGR8f/00V2ssu9+GfUxMx9wVxJCc+FylT0x7+s6UjN3b3FWKPsxWlTxsSlrtu81mwhVq+JD5GLyOXK4aHwP/Oa4gRi/T0uvXfh9pw3HMxeMtvYHZQEz26zGLGWNMO+rcsgd3j862dj67anzRkeu2WQp52ebLOXsbOtjUyHwPf6hxHw3PKYPjK/i5iP2+xMH4ZkLov1qctr+XfCPc/ePXDctbTa54vycUOBF//ZleCIoD6VTmJcT+j76tbPL1aWeH/W3PmXlAP/8VdlAtYea5yiFghqDtoLrJpP2pRf71sbfn6ZVatAx51cVFGWyf/dwRQOVQNfEtKjZ1rNDY5QUBVU9w6aAtSjOfOb3p0+w3lPf1M8+pj/mXT6WlJuh+VM/evZItCqlNyf6eNcDS2w0egVs0r5tsFxzZn/3y3Wh1/WHoVlRHqpr7BOlsjTcPG1A6EHQ/WDUg6SOOkwn6s3a4qbeWg3ku346NPbzdKkoRtXsSRjTqzLif2Pyq2kDsNCwEtm49og+eOf6Q71kgegC98HKVEQntXsyH1xX8XPzvp99by+RYjueoyJkKHnFuN5Rc322824264OtHVRJCp+FpyA3B5MHtYMQwktJOLBnJdqUFVrvfSCRlsMm+zcjNxdgV5p/c1w0sMHW3O4to8c01H27t2xK+pdQfWxLmUHJti1rEnKUB+jvmLon5Qzso39NHdw+EjFKdY/ahMW95/OXjgn9XtIkD71bRxcpM+AkNycRCjZJv4cZhGCRo/pzEOG4TCk3lNwOl7EJoL4j38hGVwSvTl3bSL0N1Y/diILfq4nocgrbvGNanW1j1XeTacrZazSG5bKwQ5AIISIRsrZjd30j7TII6E0c1a0CLUtoxUqXcynEupuMq3i7otErYKbJ8H7Ct0uRkxBOC1hJkzxUzZ6EqUPaWwejeg73aVOKqtmTsG/76ASVkU0JHzmgLapmT8oqudwn38bn38pJCBRbwn+pMiK2OURPLZCWtYyMORcdELlmm5zu0XIexd13wZVRk66tvc9fMpa8TrXjpBEdaadYy/dr84Og7v2Lw2mHYNu9Jw+MWixtfacfOYXvHb128zQ6gtHWf1R+O1ubqQXd9mxk43xMhbdTfWHz0aDG0YGWowyqCb+YFPXhpN7/gdOjx5+U1smMQgQAABTZSURBVOSTa446KiOVQ+JYjJq3KoqjCwH1WSs9I898nft9Hcq95epohBrcMToH2xQrsxxNXS1lXSviF1kA6NwiKke5O7QgvqsvYnKaKQXUroClxo7ypbTpQaYC7itnS2NjfgWddzAFlI55QmFT/nxzfumfpKOjfbpcJ+K7TMsJgecvHYN5l421Wsl0Gr0CZoZAmwVew7LC6QNmyvpcd+2MzJey2UV9uJLOIdbR2LXbFJo+baL+ILb3P8lwHHTJUjsE24NPRajYFu/25dHBb7PKlBbSSifVDtsDatvsUUqj7d7Du7QgJGnZHi2bkp/H1h/9LNG21KL2uaXQcn0cm5JtIG5rS49BtcHmsEvJ2vydKNntlt0w1Q2TPJXQipLo56LmEB9LJrUTp/6MSgxKHV9TR7NUO3wdjZs43Ax26H4WOXNusUXQzSQUVgpq7rAl/DUd5F2+rUDG4m+zqN3+E9q524Q6zuviWMhDf+twHwEyY8NmAVbjVSklNl8883uxWXp0uZNGdMQlh9IR+fpYnDakPWYRvmwms4/pbw1wAzJWtIvH98RPR3WOBIApqHqcFPWdDw1IjbEunop5o1fA8rNI2peXk0g71vvIUpiTtCtpoDmgfc3dAH1efkifVnjyvLBPhk35ofxxbLJDifQN2TidZ5WTqR58LmyLHdW91sXe0g5bQkEq6GKAJbSZasfffhb1fQHoBdhmyQHovh7Smc5bQylrd55CH4NTfXrLsRbLGnFfvRxYnKxZHD4jG71mk6W+v3+9TxeBppQ46vNSwQQ+lQB6EMepvlDtoMYgFbzgE/oPxC8eKo2P7X6qq5UFIy51g0oHY7vfi5eN9WrfySM7O99HZ0in8DNg81lVyoiKQKQiEYGUHyWQGWc2S1nH5p4LLZUmxnMudCUkBjJjw+aLpdYSZXGzH9WHf7c5riu5ds0KcePk/hjVjbb+qk1S04Jc/GraAKscAPRslXqGjh/eEWcaxeJ1VFDd2WO64oaj+lo/y+COzVCYl2Od7xRF+bk4ZnA7/P5Ee3L3bOSypdGWIirKz0FF04LYwakY26vSe4cH2MtTnD8uHOXg8jUYb5i7Sy2DxTf896BeLSOf16ZIkI6gO0lRyka2Ls62O9IOW+I/qi/MEHcdPfs+kApft30WyupWZokootp84nB7JmbKv+4gz1w4ALBfV9pqR1mUbLu4bCxr2ejbtpxFFNmMIzNtg+2ZNW85oW8rrwzffz076idXF6hN3ejuFfj52G5Yv6UaD722wvn31x/ZB/u2L8PUP6aKwNsUpluOHYCmBbl4a8U6/OmlZVZFaP6V41Bdk8Sm7TWY9NsFVsXqnesOxZbqWqzetA2TfrvA+r4dmhfh3RsOxbLvfsCU2xY6Fbr3p09AXk4CPf/r/1wfGY+cNQI1tRJ9r5/j9DU6aURHzHlvJe4+dRjeXL6WtIQCKYWua0UxLhzfAxc+8rZ185yfm8Do7hWYMqgdLn3sHWcbFf88d3+voI2mBblkBLdJy5ICfLdxmzW6UTmqp1xgNqGbZcNgzom2IzTVv3HHt+p+PuvDk+eN9vqslx3aE+cc1C1281HSJA8fzpzolFHccmzU57IuctnQaBWwJTekIhvuX1RFvq5H/b03fQIKchO46u9LSNlZU6KmUeq44OnzR0eOhlRiSvOh/3DGxMiEZstj9SgxgV84vidmPv1B6JqvI6eNbBbDnbXI7kyovrCZ26mPd/SAqC+cDdf8SZV6skG12bdwbxyqFqaOzWJLtcOWEHN3UOSzYVL/Nnhmycr070cRPo9A9NiX6j8KnxqBLiXjgdP3w0l3vpb+vTWx8OUkBK6c2Bsznvog8prJqcFx11/PGoFbn/8kVABcR+XG6teuDAuXrcK0oXSuLPX322pqMbxL83R1C5OyojyUIQ/lxXkY3rk5rrb4SAJAaZM89GlbGiun5uGjB7Z1bjTychLIywHmXTYWX661n3J0rWyKhVenykm5UlAUF+TihcvGpv2vXKcXD5yR8hP0VcDMtBA25l58oFdNyyfO3R8rVm+2bkqUBezqw3pj1Q/bMcySsFrJVZYU4MbJ/ax1RlVt1LgIUtUcnyPrJnk5Xhbd3JwESndmJuIGptF+kpyEPSKsRXF+KFdJ04Jc5OUk8M2G6IM5dXB70vLxnxXrItdsSe2oHRe1eFF5rEyfLgW1M2vu6XRrIytlbTexamXDOmLBtCkRlIKZjQLg2sH67OQy7xm9Vl+h8VT6jTyLcke9pU15pW5BRW+l7hu9sU0hAFLllnRsx7yUrC0YYdK+4WdJlRejOEpTws0kzgo9QOL1a8Zbx9hzl2RKNr12zcGkDBAedzOP7uusGzi6R8qCSaWHMNmvaws8dOaI2DqEbZsV4snzRlsjwBQFuTl49OyRZGRkRO5nI8mIxx2RA4Bbjx+EyY60O4pOLYqxf/f4gAhfVPDDziiPFEfbZoUYYbFY67RrVoiR3exy6ti1RdMCZ+k2dZRZXpTnzF9Vk67M4e6TjM+Z/8nT3kajtYApqALQVDZsAHjl09WRazdb/FwozDpwNsbv08p7Mbe9PzW2DyY+F/U+54+jHRMppersMfbz9sbGM++ujFw7oIclMo7oi2wUMJcFzJVBPNKOnai8Us7iNsWaum6maVDoi1H/dmV47+v1uP5I2mFaV+L6tSvFe19twI1T7DVKlU9n+/JCfLl2C/7nR/taZZsYi6KpaCnMj+bK2q2OVX71o30xpBNtKdAtZa57VTbNzBeljiTCw7s0x8/HdsOJwzta/XgU43q3wgczJngXP2bqhtp0n+XwS9Ixc6npnDG6i5cz942T+2HRZ9G1ipJbuGxVrNysKf0xe85Hzo0PkBnL04a4i1ErRf1HMXUYtwZBMb71JPdGGv1TTC0cvySOFIGUZSGbpKg6vlENQHbhtjYzOLW7oD6rECLyuVypMUwGeZrDAeAnI6PRkrs7Nr8ISg/JxgfJZQHbmkUFA0oB8w2hjsM34hcIV3A4oEcFLp/Qy7pzLchNlVuqTUoM6VSOx88ZZT2iEULg9NFdcOeCz9G/XRmePHe007qqvoNhnZtjwZVunwt1ZLFv+zL816Q+DifyzPvd9dOhTquCshDu6Dyh4xs5pY4XfWHlq+FQqYl8iJP7LyKDPMVJIzqRkek7KjeqewWeJBIfmzQvzsfHNx7mDCzLRq5LRTGGdCq3puth6nAEKYQoFkL8QQjxkhDidSHErOD6L4UQC4UQi4QQY4NreUKI24UQ84UQLwsh+gXXS4UQjwXX5woh/EubB1DnyzZzcTOLI7QPZo3J+iLPM92FCzN/iesIK7rD9n+fUQ4zt8n/OiwXJj8fG81ZZuM6xyRmloFwsZ1IyOsyRpk151w+IScM64D83ITVwVeHKjprC9ageOgMIk9VQCstP8/0o/pivqV8BoCQv8cVE3rHKvHXB7X2apLJ2Ohede/qWhl7tK3Cx135+hRqZ92ypIkzkEVXwMb1djvWq/tQiWJN4ny/1DxEWa4ZZnckPzfh5VLiI1eQm4O//3yU1ZLM1M0HrAzAw1LKMQD2AzBVCHEigIFSylEApgL4kxAiF8DJAGqklAcAuADA7cE9LgPwenD9DwB+lW0jTh3lzpGiY2aEdnGBcYznUr9M37BsVDXffGMuIrqh40/j8t7otDUS7Ll0ULPguGt3NMXw5bD5wQHRAuku2lrCpimoGn+uzMV3nDIstMO925IvDEiFUi+dOTFt0reFugOpkPA//nhw6BqVWFLx74sPDP0+yuHvoh9FnzKqs/VIEUj5h6j6ae1ijsGATIJWH2trz+Dzx/kOARmL7BQiaa3Jfl1aoGVJAZl0WCebmIapQ9pjwZUHWR2VFW9fdwgWXR1fhWLxNQfjtpMGx8oxDLP3scMKmJTyaynlguDXYgDbAQwB8Jh6HcByAL0AHAzg0eD62wBaCCGK9esAngJAJ0tykE2CNFcGW5N92oQzgLuOhUYbi2A2R0i2Y69sHLH1Ar9xZHMsNc6w+rg+1cNnjgiVRnEpazdPG4CXLz/IS/b6I/uEinK7jv6mDmmPGUf3tWZm1lGWjlODumidWxR5He2oygtmQkcTIQR6B5afq2JM8If1bxNS7lxO0z00ZS6uDFVRfi6WzTocH994mFNOMWVQe1TNnmQ9ttUZ3qU5Xr78IEyL8QMBUorX/CsOIosUm3RoXoSq2ZMiKVwoyorysPia8bFO3L0CZfHW4/3CyKnEnibNivJji8MDKescOyEzDENRZ2cCIUQOgPsAXA5gCgDdK3AVgEoAFXHXpZRJIUSOECIhpQxpCUKIswCcBQAdO8ZP4jbizqx1zEg2V11GM/u+rT4VhS2XSjbO2Zu3hd9vm8MJfLNRiNnlMH79kX0xdXB7TLltIQD3YWUiIUIFzF3HUomEQFNN1qVrChG+r0vhzkkI/GRkZ/xl/mcAMqU3KFqVNkHV7ElY+s1G3P1KlXck54Irx3lHOR43rAP6tSuzZrU3+fF+Hb1K2vRpU4pjBrdzhtErXNHCdcVVusPEZX3b2bRrVujtx8MwDNNQ1Ck8QQiRB+ABAH+VUs4BsBapo0lFWXDN93rSVL4AQEp5u5RyqJRyaGWlPUt4fWJawLYQpUAUVx/eO2QF27TNrtTMNsox2BbHbCxgm4y2UTXGFKbfisvXJi8nEYryiqtaryuNh8f4P+mKp8pHZENPnHncUHeEDgAM7JCyiNz24/ijH5VJ2TeZaXlxvrcyIYTwVr6AVPDIxH7xfmPPXngAzjhgz4leZRiG2RvZYQuYECIfwMMA/ialfCS4vAApf68HhRAVSB0/Lg2uHwXgFSFELwDVUsr1Qgh1/Y9CiEMAvL0jbblofA/s274MtUngh2325IlmUVpXQrx+7crw4YyJeOGj73DuQ285Q267VTbF/acPR5ernwUAjHGUkjl+eEe0aFqAM+97AxP7tnbkWhKYNaU/nvjPl3i9aq21LAwA/PrYgbjmH+9hTVAOxqX83HvacPzz7a9w09yP0alFUcQfy0Q5hN84uV+sJaVFcT4uHt8TUwa1i5UtbZKHW48fiJFdW8Tm2GmSl4N7TxuODuWFsTmNAOCmafvihGEdvI6SWpc1wdPnj0aneigUyzAMwzC+iB2N7hNCnANgJgA9vfylAE4BMBQp69oMKeWzQohCAHcA6Bhcv0hK+XqgpN0DoBRANYCzpZSfut536NCh8o033tihNgNA56ueAQB8NHMichPCa0Hfsr0WBbmJ2AguKSU2b69FUX5O7JGWlLLeE5i+88U69GtXVu9HTjujrQzDMAyzNyCEeFNKGSlMucMK2K6irgrY+i3VkFJ615BkGIZhGIbZUWwK2F6X0c9WPZ1hGIZhGKah4BoBDMMwDMMwDQwrYAzDMAzDMA0MK2AMwzAMwzANDCtgDMMwDMMwDQwrYAzDMAzDMA0MK2AMwzAMwzANDCtgDMMwDMMwDQwrYAzDMAzDMA0MK2AMwzAMwzANDCtgDMMwDMMwDQwrYAzDMAzDMA0MK2AMwzAMwzANDCtgDMMwDMMwDQwrYAzDMAzDMA0MK2AMwzAMwzANjJBS7uo2ZIUQYiOApbu6HXsQFQBW7epG7GFwn9Y/3Kf1D/dp/cL9Wf/sKX3aSUpZaV7M3RUtqSNLpZRDd3Uj9hSEEG9wf9Yv3Kf1D/dp/cN9Wr9wf9Y/e3qf8hEkwzAMwzBMA8MKGMMwDMMwTAPTGBWw23d1A/YwuD/rH+7T+of7tP7hPq1fuD/rnz26TxudEz7DMAzDMExjpzFawBiGYRiGYRo1rIAxDMMwDMM0MI1GARNCnCeEWCSEeFUIcdyubk9jQQiREEKsFkK8GPx7Prj+SyHEwqBPxwbX8oQQtwsh5gshXhZC9Nuljd/NEEL0CvrsEe2adz8KIUqFEI8F1+cKIdrvoo+yW2D2pxCiixBipTZWHwyuc396IIQoFkL8QQjxkhDidSHErOA6j9EdhOpTHqd1QwjRTAjxqLaeXxJc3/vGqZRyt/8HoBuAtwDkAygB8CGA8l3drsbwD0A5gL8b18YBeCb4uS2Aj5DKCXcagNuC6wMBLNzV7d+d/gH4CYDjATyyI/0IYAaAK4Kfjwbw8K7+TLtZfw4CcDMhx/3p159tAYwOfk4glbD6RB6j9d6nI3mc1qlPWwHoE/ycC+ATAMfujeO0sVjAxgF4Ukq5XUq5EcDLAEbt4jY1FsoBDAt2Ci8IIY4BcDCAxwBASvk1gOUAegXXHw2uvw2ghRCieNc0e/dDSnkfgG+0S9n2Y/o6gKewl49hoj/LARwphHhFCDFH7YLB/emFlPJrKeWC4NdiANsBDAGP0R3G0qc8TuuAlPJbKeUHwa+VAGoA7Ie9cJw2lkz4ZjmCVUh9cUw8VVLKjgAQmGn/BeA7AIs0GdWftn7e1DBNbXRUILt+TF+XUiaFEDlCiISUMtlA7d3deVFK2RMAhBB9ADwjhBgO7s+sEELkALgPwOUApiCm72zXuU8zGH06l8dp3RFCzAZwFoArAQzFXjhOG4sFbC2AMu33suAaE4M+IKWUXwKYA6Ad6P7kfs4OW3/5Xk82tgljZ2KM1Q+QcjvoAe5Pb4QQeQAeAPBXKeUc8BitM2af8jitH6SUVwHogJQrQg/sheO0sShgCwAcHmi5hQDGAli8a5vUOBBCdFfHiEKIUqSOc38P4KjgWgVSpt6lSPWzut4LQLWUcv2uaHcjQe8vn37Urx8C4O1d0ObdFiHEPsFiByFEWwB9ALwH7k8vhBD5AB5Byl1DBYrwGK0DVJ/yOK0bIhV8o06wNgNYD+BW7IXjtFEcQUop3xNCPA1gIQAJ4BYp5cpd3KzGQiWAu4QQAJADYCaAfwDoLoRYiJQSfqGUcqsQ4k4Adwgh5gfXz9pFbW4sPAvg0Cz6cTaAe4QQJwCoBnD2rmj0bkx3AHcKIaoBCABnSyk3cH96cwZSm9MWQgjVF5cC+JbH6A5D9ek8ABN4nO4w2wD8LlDCipBSpp4GcPDeNk45Ez7DMAzDMEwD01iOIBmGYRiGYfYYWAFjGIZhGIZpYFgBYxiGYRiGaWBYAWMYhmEYhmlgWAFjGIZhGIZpYFgBYxiGYRiGaWBYAWMYhmEYhmlgWAFjGIZhGIZpYP4fOkhGLuXq/pYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['분양가'].plot(kind='line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_vwK75gyY9he"
   },
   "outputs": [],
   "source": [
    "df_seoul = df.loc[df['지역'] == '서울']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "colab_type": "code",
    "id": "A1wFuGk_Y9cF",
    "outputId": "3ecd35a1-aeb8-4caf-ad05-76a89cbe9c8e"
   },
   "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>지역</th>\n",
       "      <th>규모</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>6320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>5964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>7092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>6551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>6321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>5966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>6513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>6551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>6427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>6036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>6513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>6551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>6438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>6039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>6513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>6458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>6550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>6127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>7179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2016</td>\n",
       "      <td>3</td>\n",
       "      <td>6371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>6618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>6196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>8096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>6353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>6638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>6205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2819</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>12728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2820</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>8430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2876</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>8223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2877</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>8562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2878</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>12728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2879</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2019</td>\n",
       "      <td>8</td>\n",
       "      <td>8866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2935</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>8205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2936</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>8572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>12728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2938</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>8972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>8230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>8574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>12728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2019</td>\n",
       "      <td>10</td>\n",
       "      <td>8903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3056</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>8196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3057</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>8300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3058</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>12728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3059</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2019</td>\n",
       "      <td>11</td>\n",
       "      <td>8952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3116</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>8115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3117</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>8171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3118</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>12728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3119</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2019</td>\n",
       "      <td>12</td>\n",
       "      <td>8989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3175</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>8330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3176</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>8135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3177</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>12728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3178</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>8779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3234</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>8193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3235</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>8140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3236</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>13835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3237</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>9039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>212 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      지역             규모    연도   월    분양가\n",
       "0     서울          60㎡이하  2015  10   5652\n",
       "1     서울    60㎡초과 85㎡이하  2015  10   5882\n",
       "2     서울   85㎡초과 102㎡이하  2015  10   5721\n",
       "3     서울         102㎡초과  2015  10   5879\n",
       "64    서울          60㎡이하  2015  11   6320\n",
       "65    서울    60㎡초과 85㎡이하  2015  11   5964\n",
       "66    서울   85㎡초과 102㎡이하  2015  11   7092\n",
       "67    서울         102㎡초과  2015  11   6551\n",
       "128   서울          60㎡이하  2015  12   6321\n",
       "129   서울    60㎡초과 85㎡이하  2015  12   5966\n",
       "130   서울   85㎡초과 102㎡이하  2015  12   6513\n",
       "131   서울         102㎡초과  2015  12   6551\n",
       "192   서울          60㎡이하  2016   1   6427\n",
       "193   서울    60㎡초과 85㎡이하  2016   1   6036\n",
       "194   서울   85㎡초과 102㎡이하  2016   1   6513\n",
       "195   서울         102㎡초과  2016   1   6551\n",
       "256   서울          60㎡이하  2016   2   6438\n",
       "257   서울    60㎡초과 85㎡이하  2016   2   6039\n",
       "258   서울   85㎡초과 102㎡이하  2016   2   6513\n",
       "259   서울         102㎡초과  2016   2   6458\n",
       "319   서울          60㎡이하  2016   3   6550\n",
       "320   서울    60㎡초과 85㎡이하  2016   3   6127\n",
       "321   서울   85㎡초과 102㎡이하  2016   3   7179\n",
       "322   서울         102㎡초과  2016   3   6371\n",
       "380   서울          60㎡이하  2016   4   6618\n",
       "381   서울    60㎡초과 85㎡이하  2016   4   6196\n",
       "382   서울   85㎡초과 102㎡이하  2016   4   8096\n",
       "383   서울         102㎡초과  2016   4   6353\n",
       "445   서울          60㎡이하  2016   5   6638\n",
       "446   서울    60㎡초과 85㎡이하  2016   5   6205\n",
       "...   ..            ...   ...  ..    ...\n",
       "2819  서울   85㎡초과 102㎡이하  2019   7  12728\n",
       "2820  서울         102㎡초과  2019   7   8430\n",
       "2876  서울          60㎡이하  2019   8   8223\n",
       "2877  서울    60㎡초과 85㎡이하  2019   8   8562\n",
       "2878  서울   85㎡초과 102㎡이하  2019   8  12728\n",
       "2879  서울         102㎡초과  2019   8   8866\n",
       "2935  서울          60㎡이하  2019   9   8205\n",
       "2936  서울    60㎡초과 85㎡이하  2019   9   8572\n",
       "2937  서울   85㎡초과 102㎡이하  2019   9  12728\n",
       "2938  서울         102㎡초과  2019   9   8972\n",
       "2995  서울          60㎡이하  2019  10   8230\n",
       "2996  서울    60㎡초과 85㎡이하  2019  10   8574\n",
       "2997  서울   85㎡초과 102㎡이하  2019  10  12728\n",
       "2998  서울         102㎡초과  2019  10   8903\n",
       "3056  서울          60㎡이하  2019  11   8196\n",
       "3057  서울    60㎡초과 85㎡이하  2019  11   8300\n",
       "3058  서울   85㎡초과 102㎡이하  2019  11  12728\n",
       "3059  서울         102㎡초과  2019  11   8952\n",
       "3116  서울          60㎡이하  2019  12   8115\n",
       "3117  서울    60㎡초과 85㎡이하  2019  12   8171\n",
       "3118  서울   85㎡초과 102㎡이하  2019  12  12728\n",
       "3119  서울         102㎡초과  2019  12   8989\n",
       "3175  서울          60㎡이하  2020   1   8330\n",
       "3176  서울    60㎡초과 85㎡이하  2020   1   8135\n",
       "3177  서울   85㎡초과 102㎡이하  2020   1  12728\n",
       "3178  서울         102㎡초과  2020   1   8779\n",
       "3234  서울          60㎡이하  2020   2   8193\n",
       "3235  서울    60㎡초과 85㎡이하  2020   2   8140\n",
       "3236  서울   85㎡초과 102㎡이하  2020   2  13835\n",
       "3237  서울         102㎡초과  2020   2   9039\n",
       "\n",
       "[212 rows x 5 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seoul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3r97f3fBZLuz"
   },
   "outputs": [],
   "source": [
    "df_seoul_year = df_seoul.groupby('연도').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 258
    },
    "colab_type": "code",
    "id": "gH6pM_hFZLyK",
    "outputId": "b2ab6363-6d77-4e90-aeae-4e5c26c66bbe"
   },
   "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",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>연도</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>11.0</td>\n",
       "      <td>6201.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>6.5</td>\n",
       "      <td>6674.520833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>6.5</td>\n",
       "      <td>6658.729167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>6.5</td>\n",
       "      <td>7054.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>6.5</td>\n",
       "      <td>8735.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1.5</td>\n",
       "      <td>9647.375000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         월          분양가\n",
       "연도                     \n",
       "2015  11.0  6201.000000\n",
       "2016   6.5  6674.520833\n",
       "2017   6.5  6658.729167\n",
       "2018   6.5  7054.687500\n",
       "2019   6.5  8735.083333\n",
       "2020   1.5  9647.375000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_seoul_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 580
    },
    "colab_type": "code",
    "id": "GYNfydOOY9Tf",
    "outputId": "7d97fae3-3ce9-46c3-8cd2-460968001c09"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1887ecb400>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xc939b5KdFfjTleM5amDnpCOpjlnMJEnKMDFG7n6tkB8/vYAh3dtx56W59OnUOulYqgcWM0mSMkhpRSW3PD6fh2et5LSR3fnFZ8fSpoU/rpsKz7QkSRli3bYSrrl3Fm8u38z1Jw3myycNJivLm8Y2JRYzSZIywPyiLVw1JZ9NO8u44/OHcebonklHUgIsZpIkJexvC9Zy3QNv0ql1cx65Jo9RvTskHUkJsZhJkpSgNVtK+MpDBQzq1pY/Xn4kXdu1SDqSEuTd6SRJSkiMkW89Po+yihT/d9FhljJZzCRJSsrUgiL+vmgdN502lAFd2iQdRxnAYiZJUgLWbS3he08u4LC+HfniJwYkHUcZwmImSVI9izFyy9T57Cqv5LYLxpDtLTGUZjGTJKmeTZu7mucWrOWrpwxhULe2ScdRBrGYSZJUj9ZvK+W7T8xnTJ+OXHnswKTjKMNYzCRJqkfffXI+O0or+fkFo13C1IdYzCRJqidPz13NX+at4YaTBzO4e7uk4ygDWcwkSaoHG7aX8p0n5nNo7w5cfZxLmNoz7/wvSVI9+N60BWwtKee+z4ynWbbzItozvzIkSapjz8xfw7Q5q/h/Jw5mWI/2ScdRBrOYSZJUhzbtKOOWqfMZ0bM9/3H8IUnHUYZzKVOSpDr0g6cWsHlnGZOvOIIclzC1F36FSJJUR/62YC2Pzy7i2hMGMbJXh6TjqAGwmEmSVAe27Cznm4/PY1iPdlx3wqCk46iBcClTkqQ68IOnFrBhRxl3X34EzZs5D6Ka8StFkqRa9o/F63j0zZVc88mBjOrtEqZqzmImSVIt2lpSzjcencfgbm25/qTBScdRA2MxkySpFv34qYWs21bCzz8zhhbNspOOowbGYiZJUi15+e31PJi/gquOG8iYPh2TjqMGaK8X/4cQWgF3Af2A5sBfgT8C04HF6c2KYowXhxBygDuA4UAEro0xzg8htE/vowewC7gixriytg9GkqSkbCsp5+uPzuWQrm34yslDko6jBqomM2aXA5tijJ8AjgJOAzoC98cYj0+/XZze9hKgIsZ4LHA9MCk9fiMwMz1+B3B7LR6DJEmJ++lfF7F6awm3XTCGljkuYWr/1KSYrQE6hhCygdZANjAOODuE8FoI4ZkQwvHpbU8CHgKIMRYAnUMIbaqPA9OAvNo7BEmSkvXakmLuf2M5Vx4zgMP7HZR0HDVgey1mMcbHgWJgKfAO8BvgnhjjkPQs2n8BfwwhdAW6pLd9XzHwgfEYYwrIDiF86LlDCBNDCPkhhPz169cf2JFJklQPdpRWcPOjcxnQpQ1fPXVo0nHUwO21mIUQrgYCMBDoD5wNnPz+52OMC4A3gcHAJqD6DVs6pMd2H0+lC9oHxBgnxRhzY4y5Xbt23eeDkSSpvv3smUUUbd7FbReMdglTB6wmS5lDgeUxxsoYYwlVS5tD0xf6E0LoBYwA5gOvAuekx4cC5THGLbuNnwIU1PaBSJJU315fuoEpM5ZxeV5/jujfKek4agRq8pJMt1O1VHleevvC9NtLIYRyqmbTro4xbg0h3AX8IYTwClWlb2J6H7cC94QQLgLKgatr9SgkSapnO8sq+Nojc+nbqTU3neYSpmrHXotZjHE1cPoePjVtD9vuAi7ew3gxcNb+BJQkKRPd/uxilm/cyZ8nHkXr5r70tGqHN5iVJGkfzSzcyD3TC7n06H4cNbBz0nHUiFjMJEnaB7vKKvnaI3Pp3bEVN58+LOk4amSce5UkaR/84vnFvFe8g/uvHE+bFv4YVe1yxkySpBqatWwTf3j1PT4/vi95g7okHUeNkMVMkqQaKCmv5KZH5tCzfUu+cYZLmKobzsFKklQD//O3t1m6fgdTrjiSdi1zko6jRsoZM0mS9qJgxWbufHkpF+b24bghvjKN6o7FTJKkj1FaUclND8+he/uWfOus4UnHUSPnUqYkSR/jf//+Du+s284fv3gE7V3CVB1zxkySpI8wb+UWfvfSUj592MGcMLRb0nHUBFjMJEnag7KKFDc9MofObZrznbNGJB1HTYRLmZIk7cGv/7GERWu28YdLc+nQ2iVM1Q9nzCRJ2s1bq7bwm38s4bxxvTl5RPek46gJsZhJklRNeWWKmx6eS8fWzfnu2S5hqn65lClJUjW/ffFdFqzeyu++cDgdWzdPOo6aGGfMJElKW7RmK//3wjucPaYXp4/qkXQcNUEWM0mSgIr0Emb7ljl8/5yRScdRE+VSpiRJwO9fXsq8oi3c8fnD6NTGJUwlwxkzSVKT9/babfzqb+/wqUN7cObonknHURNmMZMkNWkVlSluemQubVpk84NzRyUdR02cS5mSpCbtrlffY86KzfzvRePo0rZF0nHUxDljJklqspas285/P/82p47oztkuYSoDWMwkSU1SZSrytUfm0Conmx+dN4oQQtKRJJcyJUlN0x9fe483l2/mfy4cQ7d2LZOOIwHOmEmSmqD3indw+7OLOWlYNyaM7Z10HOlfLGaSpCYllV7CbNEsi5+cf6hLmMooFjNJUpMyeUYhMws38e2zRtC9vUuYyiwWM0lSk7Fsww5ue2Yxxw/tygWHH5x0HOlDLGaSpCYhlYrc/OhcmmUFfnKeS5jKTBYzSVKTcN8by3h96Ua+deZwenVslXQcaY8sZpKkRm/Fxp389K+LOHZwFy48ok/ScaSPZDGTJDVqMUa+/thcAvBT/xemMpzFTJLUqD3wzxW8tmQD3/jUcA4+qHXScaSPZTGTJDVaRZt38ZO/LCTvkM58/si+SceR9spiJklqlGKMfP3RuaRi5GefHk1WlkuYynwWM0lSo/Rw/kpeeaeYm08fRp9OLmGqYbCYSZIandVbdvHDpxdw5IBOXHJUv6TjSDVmMZMkNSoxRr752DzKK1Pc5hKmGhiLmSSpUXnszSL+sXg9XzttGP27tEk6jrRPLGaSpEZj7dYSvj/tLXL7HcTlef2TjiPtM4uZJKlRiDHyrcfnU1qR4rYLXMJUw2QxkyQ1Ck/OWcXfFq7lxlOHMrBr26TjSPtlr8UshNAqhHB/COG1EMLMEMIP0uM/DiFMDyHMCCEcnx7LCSFMCiG8EkJ4OYQwKj3ePoTwcHr8uRDCwXV6VJKkJmXdthK+++RbjOvbkSuOGZB0HGm/NavBNpcDm2KMnw8hZAPTQwhbgLExxrwQQi/ghXQJuwSoiDEeG0IYC0wC8oAbgZkxxttCCOcCtwMX1cUBSZKalhgj3546n51lldx+wWiyXcJUA1aTpcw1QMd0KWsNZAOHAQ8DxBhXAcuAocBJwEPp8QKgcwihTfVxYBpVZU2SpAP21NzVPPvWWr5y8hAGdWuXdBzpgOy1mMUYHweKgaXAO8BvgO3psfcVA12BLnsbjzGmgOwQwoeeO4QwMYSQH0LIX79+/X4dkCSp6diwvZTvPvkWYw7uwFXHuoSphq8m15hdDQRgINAfOBs4AuhQbbMOwKb0W03GU+mC9gExxkkxxtwYY27Xrl337UgkSU3Od558i+0lFdx2wRiaZfv/2dTw1eSreCiwPMZYGWMsoWpp84/AOQAhhC7pbRYDr1YbHwqUxxi37DZ+ClBQy8chSWpi/jpvNU/PXc31Jw1iaA+XMNU41OTi/9uBP4YQzktvXwhMBgaHEKZTVe5uiDGWhBDuAv4QQnglPT4xvY9bgXtCCBcB5cDVtXsYkqSmZOOOMr79xHxG9mrP1Z88JOk4Uq3ZazGLMa4GTt/Dp67fw7a7gIv3MF4MnLU/ASVJ2t33p73F5p3l3Pul8eS4hKlGxK9mSVKD8txba3iiYBXXnTiI4T3bJx1HqlUWM0lSg7F5Zxnfmjqf4T3bc+3xg5KOI9W6mlxjJklSRvjBtAVs2lHGHy8/gubNnFtQ4+NXtSSpQXhh0Voem13Efxx/CKN6d9j7A6QGyGImScp4W3aV843H5jG0ezuuO9ElTDVeLmVKkjLej55aQPH2Mu68NJcWzbKTjiPVGWfMJEkZ7cXF63h41komHjeQ0Qd3TDqOVKcsZpKkjLW1pGoJc1C3ttxw0uCk40h1zqVMSVLG+ulfFrJ2awmP/kceLXNcwlTj54yZJCkjvfpOMQ/8cwVXHTuQcX0PSjqOVC8sZpKkjLO9tIKbH53LwC5t+MopQ5KOI9UblzIlSRnn1r8uZNWWXTxyzdEuYapJccZMkpRRpr9bzJ9eX84VnxjA4f06JR1HqlcWM0lSxtiRXsLs37k1N546NOk4Ur1zKVOSlDFuf3YxKzft4sGJR9OquUuYanqcMZMkZYQ3lm7gnumFXHZ0f44c4BKmmiaLmSQpcbvKKvnao3Pp06kVXzvdJUw1XS5lSpIS9/PnFrNsw07uv2o8rZv7o0lNlzNmkqRE5Rdu5O7X3uMLR/Ul75AuSceREmUxkyQlpqS8kq89MpdeHVrx9TOGJx1HSpzzxZKkxPzi+bdZWryDP31pPG1b+CNJcsZMkpSI2cs38YdXlnLRkX04ZrBLmBJYzCRJCSgpr+SmR+bSo31LvvkplzCl9zlvLEmqd7/6+zssWbedyVccSbuWOUnHkTKGM2aSpHo1d+VmJr28lM8cfjCfHNI16ThSRrGYSZLqTWlFJTc9PJcubZtzy1kjko4jZRyXMiVJ9ebXLyxh8dpt3H15Lh1auYQp7c4ZM0lSvZhftIXfvPgu5x/WmxOHdU86jpSRLGaSpDpXVpHixofn0KlNc77jEqb0kVzKlCTVud+8uIRFa7Yx6ZLD6di6edJxpIzljJkkqU4tWLWVX7+whHPH9uLUkT2SjiNlNIuZJKnOlFemuOmROXRsncP3zh6ZdBwp47mUKUmqM79/6V3eWrWV3158GAe1cQlT2htnzCRJdWLxmm386u/vcObonpxxaM+k40gNgsVMklTrKtJLmO1a5vCDc1zClGrKpUxJUq2785X3mLtyC7/+/Dg6t22RdBypwXDGTJJUq5as28b/PP82p4/swZkuYUr7xGImSao1lanITY/MpXWLbH44YRQhhKQjSQ2KS5mSpFpz96vvMXv5Zn71ubF0becSprSvnDGTJNWKpeu38/PnFnPy8O6cM6ZX0nGkBsliJkk6YJWpyNcemUuLZln85DyXMKX9ZTGTJB2we6YXkr9sE989eyTd2rdMOo7UYFnMJEkHpLB4B7c/u4gThnbl/MN6Jx1HatD2evF/COEG4LxqQ0OAi4A/A4vTY0UxxotDCDnAHcBwIALXxhjnhxDaA3cBPYBdwBUxxpW1dxiSpCSkUpGvPTqXnOwsfnr+aJcwpQO012IWY/wV8CuAEEJzYDpQBtwfY/zqbptfAlTEGI8NIYwFJgF5wI3AzBjjbSGEc4HbqSp3kqQG7N7Xl/HP9zZy26dH06ODS5jSgdrXpcxLgUeBVsDZIYTXQgjPhBCOT3/+JOAhgBhjAdA5hNCm+jgwjaqy9iEhhIkhhPwQQv769ev3MZokqT4t37CTnz2ziOOGdOUzuQcnHUdqFGp8H7MQQjZwDXAisD3GOCQ9PgJ4OoRwJNAFKK72sGKga/XxGGMqhJAdQsiKMaaqP0eMcRJVs2zk5ubG/T4qSVKdSqUiNz86l6wQuPX8Q13ClGrJvsyYfQ54Nsa4tXqhijEuAN4EBgObgA7VHtMhPbb7eGr3UiZJajju/+dyZizdwDc/NZxeHVslHUdqNGpUzELVr0I3AL9Mfzw8faE/IYRewAhgPvAqcE56fChQHmPcstv4KUBB7R6GJKm+rNy0k5/+ZSHHDOrCRUf2STqO1KjUdCnzPGBGjPH9C78GAXeFEMqBAFwdY9waQrgL+EMI4RWqSt/E9Pa3AveEEC4CyoGra+0IJEn1JsbINx6bRwR+6hKmVOtqVMxijI8Bj1X7eBpVF/Hvvt0u4OI9jBcDZ+1/TElSJnhw5gpeeaeYH547kj6dWicdR2p0vMGsJKlGVm3exY+fXshRAztx8fh+SceRGiWLmSRpr95fwqxIRW779BiyslzClOqCxUyStFePzFrJS2+v5+bTh9K3s0uYUl2xmEmSPtaaLSX84KkFHNm/E5ce3T/pOFKjZjGTJH2kGCPfenweZRUpfnbBaJcwpTpmMZMkfaSpBUX8fdE6bjptKAO6tEk6jtToWcwkSXu0bmsJ3+IFGSgAABrpSURBVHtyAYf17cgXPzEg6ThSk2AxkyR9SIyRb02dz67ySm7/zBiyXcKU6oXFTJL0IU/OWcXzC9by1VOGcEjXtknHkZoMi5kk6QPWbyvle0++xZg+Hbny2IFJx5GaFIuZJOkDvvvkfHaUVvLzC0a7hCnVM4uZJOlfnp67mr/MW8MNJw9mcPd2SceRmhyLmSQJgA3bS/nOE/M5tHcHrj7OJUwpCc2SDiBJygzfm7aArSXl3PeZ8TTL9vd2KQn+y5Mk8cz8NUybs4r/d+JghvVon3QcqcmymElSE7dpRxm3TJ3PiJ7t+Y/jD0k6jtSkuZQpSU3c96e9xeadZUy54khyXMKUEuW/QElqwv62YC1TC1Zx7QmDGNHLJUwpaRYzSWqituws55uPz2NYj3Zcd8KgpONIwqVMSWqyfvDUAjbsKOPuy4+geTN/T5cygf8SJakJ+seidTz65kr+45OHMKp3h6TjSEqzmElSE7O1pJxvPDaPId3b8v9OcglTyiQWM0lqYn781ELWbSvh9gvG0KJZdtJxJFVjMZOkJuTlt9fzYP4KJh53CGP6dEw6jqTdWMwkqYnYVlLO1x+dyyFd2/DlkwcnHUfSHvi/MiWpifjpXxexemsJj1yTR8sclzClTOSMmSQ1Aa8tKeb+N5Zz5TEDOLzfQUnHkfQRLGaS1MjtKK3g5kfnMqBLG7566tCk40j6GC5lSlIj97NnFlG0eRcPXX20S5hShnPGTJIasRnvbmDKjGVcntefI/p3SjqOpL2wmElSI7WzrGoJs2+n1tx0mkuYUkPgUqYkNVK3P7uY5Rt38ueJR9G6ud/upYbAGTNJaoRmFm7knumFXHp0P44a2DnpOJJqyGImSY3MrrJKvvbIXHp3bMXNpw9LOo6kfeDctiQ1Mv/93GLeK97B/VeOp00Lv81LDYkzZpLUiMxatom7XnuPz4/vS96gLknHkbSPLGaS1EiUlFdy0yNz6NWhFd84wyVMqSFyjluSGon/+dvbLF2/gylXHEm7ljlJx5G0HyxmktSAVVSmmP7uBqYWFDF1dhEX5vbhuCFdk44laT9ZzCSpgYkxMnflFqYWFDFtzmqKt5fSrkUzLjyiD9/81PCk40k6ABYzSWoglm3YwdTZq3iioIilxTtonp3FCcO6MmFsb04Y1s3XwZQaAYuZJGWw4u2lPDVnFVMLVlGwYjMARw3sxMTjBnLGqJ50aO21ZFJjstdiFkK4ATiv2tAQ4CLgVOAEIADfiDG+GELIAe4AhgMRuDbGOD+E0B64C+gB7AKuiDGurNUjkaRGYkdpBc8vWMvjs4t4dUkxlanIsB7t+PoZwzhnTC96dWyVdERJdWSvxSzG+CvgVwAhhObAdKAtMDbGmBdC6AW8EEIYBVwCVMQYjw0hjAUmAXnAjcDMGONtIYRzgdupKneSJKC8MsWr7xQztaCI595ay67ySnp3bMXE4wYyYWxvhvZol3RESfVgX5cyLwUepapsPQwQY1wVQlgGDAVOAu5MjxeEEDqHENqkxy9O72Ma8L972nkIYSIwEaBv3777GE2SGpYYI7NXbOaJ2UU8NXc1G3aU0aFVDhPG9WbC2F4c0b8TWVkh6ZiS6lGNi1kIIRu4BjiRqhmvGdU+XQx0Bbqk3//I8RhjKoSQHULIijGmqj9HjHESVbNs5Obmxn0+GklqAN5dv50nZhfxxJxVLNuwk+bNsjhleHfOHduLTw7tSotmXsQvNVX7MmP2OeDZGOPWEMImoEO1z3UANqXfPm58e3o8tXspk6TGbN22EqbNWc3U2UXMK9pCCJB3SGf+84RBnD6qB+29IawkaljMQggBuAE4Mz30KlXXk90XQuhC1TLm4vT4OcBrIYShQHmMcUsI4f3x34YQTgEKavcwJCnzbCsp59m31vJEQRGvLSkmFWFU7/bccuZwzh7Ti+7tWyYdUVKGqemM2XnAjBjj+vTHfwFODSFMp+r1Nm+IMZaEEO4C/hBCeCU9PjG9/a3APSGEi4By4OpaOwJJyiBlFSlefns9UwuKeH7BWkorUvTp1Iprjx/EhHG9GNTNi/glfbQQY2ZeypWbmxvz8/OTjiFJe5VKRWYt38TU2UU8PW81m3eWc1DrHM4a3YsJ43pxWN+DqFp4kNRYhRBmxRhzD3Q/3mBWkvbTO2u3pV+jchVFm3fRMieLU0b04LxxvTh2cFdysrOSjiipgbGYSdI+WLOlhCfnVJWxBau3khXgmMFd+eqpQzh1ZA/atvDbqqT953cQSdqLrSXlPDNvDY/PLuL19zYQI4w5uAPfOWsEZ43pSbd2XsQvqXZYzCRpD0orKvnHovU8UVDE3xeto6wiRf/Orbn+xMGcO7YXA7u2TTqipEbIYiZJaalU5J+FG3mioIin565ma0kFXdo25/NH9mXCuN6MObiDF/FLqlMWM0lN3sLVW5laUMS0glWs2lJC6+bZnDayB+eO7cUxg7rQzIv4JdUTi5mkJqlo8y6eLFjF1NlFLF67jeyswCeHdOXmM4ZxyojutG7ut0dJ9c/vPJKajC07y3l63mqmFhTxz/c2AnBY34784NyRnHloTzq3bZFwQklNncVMUqNWUl7JC4vWMXV2Ef9YvI7yysjArm346ilDOHdsb/p2bp10REn6F4uZpEanMhV5fekGps4u4pn5a9hWWkHXdi249Oj+TBjbm1G923sRv6SMZDGT1CjEGHlr1VaeKCjiyTmrWLu1lLYtmnH6qB5MGNubow/pTHaWZUxSZrOYSWrQVmzcyRMFRUwtWMWSddvJyQ58ckg3vn1WL04e3p2WOdlJR5SkGrOYSWpwNu4oq7qIf3YRs5ZtAuDI/p348Xmj+NSonhzUpnnCCSVp/1jMJDUIu8oqeX7hWp6YXcRLb6+nIhUZ0r0tN502lHPH9uLgg7yIX1LDZzGTlLEqKlNMf7fqIv5n31rDjrJKerRvyZeOGcC5Y3szvGc7L+KX1KhYzCRllBgjc1duqboT/5zVFG8vpV3LZpw1uhcTxvVm/IBOZHkRv6RGymImKSMUFu9gakERTxasYmnxDppnZ3HisG5MGNeL44d28yJ+SU2CxUxSYoq3l/LUnFVMLVhFwYrNhADjB3Ri4nEDOWNUTzq0zkk6oiTVK4uZpHq1o7SC5xes5fHZRby6pJjKVGR4z/Z844xhnDO2Fz07tEo6oiQlxmImqc6VV6Z49Z1iphYU8dxba9lVXknvjq2YeNxAJoztzdAe7ZKOKEkZwWImqU7EGJm9YjNTZxfx9NzVbNhRRodWOZx3WG8mjO1Nbr+DvIhfknZjMZNUq95dv50nZhfxxJxVLNuwkxbNsjh5RHcmjO3NJ4d0pXmzrKQjSlLGsphJOmDrtpUwbU7VnfjnFW0hK0DeIV247oRBnD6qB+1aehG/JNWExUzSftlWUs6zb63liYIiXltSTCrCqN7tueXM4Zw9phfd27dMOqIkNTgWM0k1VlaR4uW31/N4QRF/W7CW0ooUfTq14j9PGMS5Y3szqFvbpCNKUoNmMZP0sVKpyKzlm6ou4p+3ms07y+nUpjkXHtGHc8f25rC+HX1ZJEmqJRYz1Zp1W0v40xvLmb6kmBAgKwSaZQeys7LIDpCdlUWzrED2bm/NsgJZ6T+zswLZIZCdnf44pB+f9e/Hf2Dbavv4wP7Sz50VAs2ysj5+2z3sL/v9x2W/n+Hf2zYV76zdxtSCIqbOXkXR5l20zMni1BE9mDCuF8cO7kpOthfxS1Jts5jpgMQYeXP5Ju6Zvoy/zltNZYyM69ORFtnZVMZISXmKylQllalIRSqSSkUqUikqU5HKGKmsTP+Z/nxlKu62bUz6ED/k40rhv0tlFlmBPZbC3Ytg9SK5e4nd07a7F8j3S2xVCQ1kZ2eli+VHF+Ddi/D7zx2AN97bwNTZq1iweivZWYFjBnXhxtOGcOqIHrRp4bcMSapLfpfVfikpr2TanFVMnlHI/KKttGvZjMvy+nPJUf3o36VNrT1PjJFU5F+F7f0yV5FK/bvQVUZS8YPFrnrB++DHqaptK6vt7yO3/Xc5/Pdjqp63IvXhUpna7c8P7jdFZYTKVIqKykh5ZYpd5R/edo8FNUYqKlOkIv8utamqv5e6MqZPR7579gjOGt2Lru1a1N0TSZI+wGKmfbJ6yy7+9PoyHvjnCjbuKGNwt7b8aMIozhvXu05mU0II6RmkprOEWFOp3Yrlv0trtaL6r/KY+sjiuns5PKRbWwbUYrmWJNWcxUx7FWPkjfc2MmVGIc++tZYYIycP787lef05+pDOXvidkKysQBaBnOykk0iSaovFTB9pV1klUwuKmDy9kEVrttGhVQ5XHjOALxzVjz6dWicdT5KkRsdipg9ZsXEn976+jAdnrmDLrnKG9WjHrecfyrlje9OqudMzkiTVFYuZgKrlyunvbuCe6YX8beFaskLgtJHduezo/hw5oJPLlZIk1QOLWRO3o7SCx2YXMWV6Ie+s206nNs259vhDuHh8P3p1bJV0PEmSmhSLWRNVWLyDKTOW8fCsFWwrqeDQ3h34+WfGcNbonrT0anJJkhJhMWtCUqnIy++sZ/L0Ql58ez3ZIfCpQ3tyWV5/X1ZHkqQMYDFrAraVlPPIrJVMmbGM94p30LVdC64/cTAXj+9Lt/Ytk44nSZLSLGaN2JJ125kyo5BHZ61kR1kl4/p25FefG8sZo3rSvJmvcyhJUqaxmDUylanIPxatY/KMQl55p5jm2VmcNaYnl+f1Z/TBHZOOJ0mSPobFrJHYsrOch/JXcO/ry1i+cSc92rfkxlOH8Lkj+9Klra91KElSQ2Axa+AWr9nGPdMLmTq7iF3llRzZvxM3nz6MU0d2Jyfb5UpJkhqSGhWzEEI/4G6gFZACrgJeABanNymKMV4cQsgB7gCGAxG4NsY4P4TQHrgL6AHsAq6IMa6s1SNpQioqU/xt4VrumV7I60s30qJZFhPG9ubSvH6M7NUh6XiSJGk/7bWYhRCygQeBL8YYF6Y/Hg3cH2P86m6bXwJUxBiPDSGMBSYBecCNwMwY420hhHOB24GLavNAmoKNO8r488zl3Pf6coo276J3x1Z8/YxhXJjbh4PaNE86niRJOkA1mTE7g6qZsR+HELoDDwALgLNDCEcB24BbY4wvAicBdwLEGAtCCJ1DCG3S4xen9zcN+N89PVEIYSIwEaBv3777e0yNzvyiLUyeXsiTc1ZRWpEi75DOfOfsEZw8vDvZWd57TJKkxqImxWwYVUuTJ1G1jPkyVbNnQwBCCCOAp0MIRwJdgOJqjy0GulYfjzGmQgjZIYSsGGOq+hPFGCdRNctGbm5uPJADa+jKK1M8M38Nk6cXkr9sE61ysrng8IO5LK8/Q7q3SzqeJEmqAzUpZpXAkzHGbQAhhL8BY4C5ADHGBSGEN4HBwCag+kVOHdJj749vT4+ndi9lqrJ+WykP/HM5972xjLVbS+nbqTW3nDmcz+T2oUOrnKTjSZKkOlSTYvYq8NMQwk+BAHwCmBpCyIkxlocQegEjgPnpbc8BXgshDAXKY4xbQgjvj/82hHAKUFAXB9OQzVmxmcnTC3lq7mrKKlMcN6QrPz2/H8cP6UaWy5WSJDUJey1mMcaZIYTngXygFPgzVUuTL4UQyqkqa1fHGLeGEO4C/hBCeAXIIn29GHArcE8I4SKgHLi69g+l4SmrSPGXeau5Z3ohBSs206Z5Nhcd2YdL8/pzSNe2SceTJEn1LMSYmZdy5ebmxvz8/KRj1Im1W0u4743l3P/Gcoq3lzKwSxsuPbofnz78YNq1dLlSkqSGJoQwK8aYe6D78Qaz9STGyJvLN3HP9GX8dd5qKmPkhKHduCyvP8cO6uJypSRJspjVtZLySqbNWcXkGYXML9pKu5bNuCyvP5ce3Y9+ndskHU+SJGUQi1kdWbV5F396fRl/nrmCjTvKGNytLT+aMIrzxvWmTQv/2iVJ0ofZEGpRjJE33tvI5OmFPLdgLTFGTh7encvz+nP0IZ0JweVKSZL00SxmtWBXWSVTC4qYPL2QRWu20bF1DlceO4AvjO9Hn06tk44nSZIaCIvZAVixcSf3vr6MB2euYMuucob3bM/PPn0o54zpTavm2UnHkyRJDYzFbB/FGHltyQbumV7I3xetJSsETh/Zg8vy+nNE/4NcrpQkSfvNYlZDO0oreOzNlUyesYwl67bTuU1z/vP4QVx8VF96dmiVdDxJktQIWMz2orB4B5NnFPJI/kq2lVYw+uAO/PdnxnDm6J60zHG5UpIk1R6L2R6kUpGX3lnP5OmFvLh4PTnZgU8d2pPL8vozrk9HlyslSVKdsJhVs62knEdmrWTKjGW8V7yDru1a8OWTB/P5I/vSrX3LpONJkqRGzmIGLFm3nSkzCnl01kp2lFVyWN+OfPlzYzljVE+aN8tKOp4kSWoimmwxq0xF/rFoHZNnFPLKO8U0z87irDE9uTyvP6MP7ph0PEmS1AQ1uWK2ZWc5D+WvYMrrhazYuIse7Vty46lD+NyRfenStkXS8SRJUhPWZIrZojVbmTx9GY/PXklJeYoj+3fi66cP59SR3cnJdrlSkiQlr1EXs4rKFH9buJZ7phfy+tKNtGiWxYSxvbksrz8jerVPOp4kSdIHNMpitnFHGX+euZw/zVjGqi0l9O7Yiq+fMYwLc/twUJvmSceTJEnao0ZVzOYXbWHy9EKemLOKsooUeYd05rvnjOTk4d3JzvLeY5IkKbM1+GJWXpnimflrmDy9kPxlm2iVk81nDj+Yy/L6M6R7u6TjSZIk1ViDLWbrt5XywD+Xc98by1i7tZR+nVtzy5nD+UxuHzq0ykk6niRJ0j5rcMWsYMVmJk8v5Om5qymrTHHckK789Px+HD+kG1kuV0qSpAasQRSz0opK/jJvNfdMX8acFZtp26IZnx/fl0uO7schXdsmHU+SJKlWZHQxW7u1hPteX8b9/1xB8fZSBnZtw/fPGcn5h/WmXUuXKyVJUuOSscVs+cadfOLWF6iMkROHduOyvP4cM6iLy5WSJKnRythitq2kguvz+nPJ0f3o17lN0nEkSZLqXMYWs+E923HLWSOSjiFJklRvMvZFIrOCS5aSJKlpydhiJkmS1NRYzCRJkjKExUySJClDWMwkSZIyhMVMkiQpQ1jMJEmSMoTFTJIkKUNYzCRJkjKExUySJClDWMwkSZIyhMVMkiQpQ1jMJEmSMoTFTJIkKUNYzCRJkjKExUySJClDhBhj0hn2KISwDVicdA7tly5AcdIhtN88fw2b56/h8tw1bENjjO0OdCfNaiNJHVkcY8xNOoT2XQgh33PXcHn+GjbPX8PluWvYQgj5tbEflzIlSZIyhMVMkiQpQ2RyMZuUdADtN89dw+b5a9g8fw2X565hq5Xzl7EX/0uSJDU1mTxjJkmS1KRYzCRJkjJEvRazEEKbEMIdIYSXQggzQwg/SY//OIQwPYQwI4RwfLXtTwshFIUQrqk29sUQwsIQwovpt5vq8xiaqto4d+nxL4QQZoUQXg4h/LCeD6PJqqV/e3+t9u/uxRDCuwkcSpNTS+dufAjh1fQ+ZoQQjk3gUJqkWjp/vUMIT6fP4SshhD4JHEqTtC/nL4QwMITwWPr7Y34I4TPp8fYhhIfT5+65EMLBH/ec9X0fsw7AAzHGV0MIWcDCEMJ8YGyMMS+E0At4IYQwKsZYAQwDpuy2j47A12KM0+o3epN3wOcu/cV7HpAXYywNIWTyffQamwM+fzHGM95/P4RwBvCpeszflNXG983/Ba6LMc4MIRwK/AkYU58H0YTVxvm7Hbg7xvho+vvoHcA59XgMTVmNzx/QDfhKjHFZCKE38HfgYeBGYGaM8bYQwrlUnc+LPuoJ63XGLMa4Ksb4avrDNkAZcDhVwYkxrgKWAUPTH/8KKN1tNx2Bb6db6n0hhAH1Er6Jq6Vzdx0wC3gmhPAcMLweootaO3/VfY2qby6qY7V07tZQdVd50n+uqePYSqul8zeWqh/yAC8DR9dxbKXty/mLMb4eY1yW3rYX8E76/ZOAh9LvTwPyPu45E7nGLISQTdVvBDcBbfngS1AUA10/5uHfjzEeGWM8GniMfx+s6sEBnrthQCrGeALwfeCPdZVTe3aA5+/9fRwPFMYYl9dFRu3ZAZ67q4FfhBDmAr9Pf6x6dIDnbyFwevr9i4Ccusioj7Yv5y+E0AP4JXBteuhfL7UVY0wB2enZtz2q92IWQsihahr9wRjjM8AmqqYK39chPbZH6YN6//1HgYNDCKGO4qqaAz13QGX68cQYXwN6eu7qTy2cv/d9A7i19hPqo9TCuXsU+GKMcTRwNvCElxLUn1o4f/8FXBRCeBHoCbxdR1G1B/ty/kIIPYE/A1fFGFekP7/79qnqXWZ39X3xf3OqAj8ZY/xzevhV0mvlIYQuVE3nfuSLl4cQxlR7/2TgrejN2OpcbZy79PYnpbcfCazx3NWPWjp/hBDGA1tijB+7nWpPLZ27gcD7PyTWUPXbfZs6CawPqKXzVxRjPDfGeDxV5+7uukus6vbl/KUv6n8E+M8Y44Jqu6m+/SlAwcc9Z33/xnQlcDzQOYTw/lT6V4G1IYTpVBXFG2KMJR+zj3NCCL8HSoCtwBfrMK/+rTbO3S3A/SGEiUA5cEUd5tUH1cb5A/gm8L26Cqk9qo1zdy3wZAhhG1W/uX8/xrilDjPr32rj/H0xhHAp0BKYGmP8XV0G1gfU+PyFEH4B9ADuqLYYdBJVKwz3hBAuoupn38deSuCd/yVJkjKEN5iVJEnKEBYzSZKkDGExkyRJyhAWM0mSpAxhMZMkScoQFjNJjV4I4eD0zTklKaN552dJjUb6bva/BsYDBwGbge1Ac2BnepsvA18B1u/28KtjjLPqL60kfZjFTFJj8gUgO8Y4Ln3H7hnARGAjVXfvft/tMcZfJxFQkj6OxUxSY7KnyzPOpupu25KU8SxmkhqTe4G8EEIBUAb8HlgKdNltu5tCCJfvNvbNGONzdR9Rkj6aL8kkqdFLv7jwn9IvAl19vDDG2D+RUJK0B/6vTEmNTghhxm5D24Enk8giSfvCGTNJjc5HzYSFEB4HelYbGgfMrvbx0zHGH9ZxPEn6SF5jJqlRCiHk7za0M8Z4XCJhJKmGnDGTJEnKEF5jJkmSlCEsZpIkSRnCYib9/3brWAAAAABgkL/1NHYURQAwIWYAABNiBgAwIWYAABNiBgAwEYWwdVAO2tPUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_seoul_year['분양가'].plot(kind='line')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VbGNHcTuZOnN"
   },
   "source": [
    "## bar 그래프"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4ZfK31ENZfKs"
   },
   "source": [
    "bar 그래프는 그룹별로 비교할 때 유용합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 345
    },
    "colab_type": "code",
    "id": "bR_oHnCwZOqs",
    "outputId": "6481698a-5520-4972-83ce-1ff2369f05c3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "지역\n",
       "강원    2448.156863\n",
       "경기    4133.952830\n",
       "경남    2858.932367\n",
       "경북    2570.465000\n",
       "광주    3055.043750\n",
       "대구    3679.620690\n",
       "대전    3176.127389\n",
       "부산    3691.981132\n",
       "서울    7308.943396\n",
       "세종    2983.543147\n",
       "울산    2990.373913\n",
       "인천    3684.302885\n",
       "전남    2326.250000\n",
       "전북    2381.416268\n",
       "제주    3472.677966\n",
       "충남    2534.950000\n",
       "충북    2348.183962\n",
       "Name: 분양가, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('지역')['분양가'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "EJP5ZRMAZOtw",
    "outputId": "9f121f3a-6ae7-4f3b-a2b4-1a68c2f22445"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1887dd99e8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('지역')['분양가'].mean().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "3z3MdeqSZOw7",
    "outputId": "27be1b30-b10d-48a6-d546-5aa0ad93d26f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1887db1c88>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('지역')['분양가'].mean().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oQwtDhhXZOyI"
   },
   "source": [
    "### 히스토그램 (hist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VX0j9nlgZO1X"
   },
   "source": [
    "히스토그램은 **분포-빈도 를 시각화**하여 보여줍니다\n",
    "\n",
    "가로축에는 분포를, 세로축에는 빈도가 시각화되어 보여집니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 445
    },
    "colab_type": "code",
    "id": "9AcmLgnUZqUz",
    "outputId": "840cfe04-ac60-4d37-f262-66f4add75137"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1887ca8710>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['분양가'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K45AdW2JZqRH"
   },
   "source": [
    "### 커널 밀도 그래프"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_i5y3U1OZqNf"
   },
   "source": [
    "* 히스토그램과 유사하게 밀도를 보여주는 그래프입니다.\n",
    "* 히스토그램과 유사한 모양새를 갖추고 있습니다. \n",
    "* 부드러운 라인을 가지고 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "colab_type": "code",
    "id": "CC1WzmRPZqKH",
    "outputId": "096da7f0-d569-4325-d91c-3f0d6dce193d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1887c770b8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 8722 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['분양가'].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o7kog7FdZy_H"
   },
   "source": [
    "### Hexbin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WfWGI0cWZy5O"
   },
   "source": [
    "* hexbin은 고밀도 산점도 그래프입니다.\n",
    "* x와 y 키 값을 넣어 주어야 합니다.\n",
    "* x, y 값 모두 numeric 한 값을 넣어 주어야합니다.\n",
    "* 데이터의 밀도를 추정합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 555
    },
    "colab_type": "code",
    "id": "Da2Mxu45Zy0v",
    "outputId": "d7c8cf15-a430-493c-e18c-0b29bac3d738"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f18385bcbe0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='hexbin', x='분양가', y='연도', gridsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1hyq8zfYZ3hM"
   },
   "source": [
    "### 박스 플롯(box)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t9ccLmrFZ3ck"
   },
   "outputs": [],
   "source": [
    "df_seoul = df.loc[df['지역'] == '서울']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 635
    },
    "colab_type": "code",
    "id": "3azbD7U8Z3Xj",
    "outputId": "13fc26fa-8353-4b8f-85bd-f51f0ef22c93"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f18384fc208>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ArG9v23133vmlu2e9jHXhbbuT5KZZLwM4BwQYsKr+8MV9+e4+0XAu7Lz7a7NeAnCOuAUJANBMgAEANBNgAADNBBgAQDMBBgDQTIABADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAMwEGANBs86wXAKx/O+/+2qyXsC5su2jLrJcAnCMCDFhV391306yXcFp23v21NbNWYO1zCxIAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZmcVYFX19qr67ap6pqoOVtXWqrpn2n+2qq6fjttSVfur6kBVPV1VV03zS6rqkWn+eFVdeQ7OCQDgvLZ5pW+sqk1J/nWSnxljvDjtX5fk6jHGNVV1RZLfmWLr1iSvjzGuraqrk+xPck2Su5IcGmP8SlV9MMm9SW45y3MCADivnc0VsL+Z5DtJ7qmqbyX5+0luSPJIkowxvpfkpSS7pvnD0/z5JJdW1cVL50key2KU/QlVtaeq5qtq/vDhw2exZACA2VvxFbAk70iyO4sRdTzJ00n+d5JnlxxzJMmOJNun7becjzGOV9WmqrpgjHF86S8aY+zP4lWzzM3NjbNYMwDAzJ3NFbA3kvzGGOMPxxh/nOS3kvxwkm1LjtmW5NXp53Tmx5fHFwDAenM2AXYwyfXTVavNSf56ki8muTlJqmp7Fm8/fmc69sR8V5KFMcbRZfMbkzx/FusBAFgTVnwLcoxxqKq+mWQ+yWtJvpLkviT/vKqeyWLcfXSMcayqHkhyf1UdmOZ7po/Zl+TBqrolyUKS21d+KgAAa8PZPAOWMcanknxq2fjOkxz3/SQfPsn8SJIPnM0aAADWGl/ECgDQTIABADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAMwEGANBMgAEANBNgAADNBBgAQDMBBgDQTIABADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAMwEGANBMgAEANBNgAADNBBgAQDMBBgDQTIABADQTYAAAzQQYAEAzAQYA0EyAAQA0E2AAAM0EGABAs82zXgDAmaqq1fncT537zxxjnPsPBdY8AQasOaIGWOvcggQAaCbAAACaCTAAgGYCDACgmQADAGgmwAAAmgkwAIBmAgwAoJkAAwBoJsAAAJoJMACAZgIMAKCZAAMAaCbAAACaCTAAgGY1xpj1Gs5IVR1O8tKs1wGsO9uTHJn1IoB15+1jjB3Lh2suwABWQ1XNjzHmZr0OYGNwCxIAoJkAAwBoJsAAFu2f9QKAjcMzYAAAzVwBAwBoJsAAAJoJMGDdqaqfq6qfXzb7ZFX9VFVtq6qvVtV8VX29qq6YXv98Vb33LT6vpn+frKorV/8MgPVu86wXALBKbq+qDyzZvzLJ3Un2JvntMcbnptc/neTHp2Puq6pfSzKS/K3p34uS/Kckt7atHFj3BBiwXn12jLHvxE5VfXLa/ItJPjdtfyPJLy95zz8YYzw5bf/T6X1/L8m21V0qsNG4BQmsR/8nyc9W1X848ZPkJ5MsJHk6yY9Nx/3EtH/Cr1bVP1v2WT+e5JHVXjCwsfgaCmBDqaotSf5Jkh9J8rtJfmGM8cdV9fkkXx1j/NaSYz+c5H1jjL877T+Z5IeSfHOMcVf74oF1wy1IYF2pqt9IcsWS0TuS/LcsXv1Kkt8bY/xEVV2bZE+Sx6rq95P8+yTPLfmcDyb5R0luWPYrbhpj/MFqrR/YGAQYsK6MMW6uqvcleW2M8dR0+/HHlkZTVf3VLD58/zNJfi/J1Um+kGQ+yatV9S+S/HAWr3692n4SwLonwID16C8l+aMkTyV5MYvPhC31niRfGmP8l2n/2ar6epK/luS/J/nkGON/di0W2Hg8hA+sVx+vqvkk70zym9P3fn16eu2pJH+nqnZX1YVVNZfFr534j0kivoDV5iF8YEOqqhuS3JbkzyZ5KYtfW3FotqsCNgoBBgDQzC1IAIBmAgwAoJkAAwBoJsAAAJoJMACAZv8X7D5+Ji2I8ogAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_seoul['분양가'].plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "colab_type": "code",
    "id": "3qm5ezfIZyvo",
    "outputId": "649c3ba7-5045-4db5-c9a7-5a01712ed5a5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "# image source : https://justinsighting.com/how-to-interpret-box-plots/\n",
    "Image('https://justinsighting.com/wp-content/uploads/2016/12/boxplot-description.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Gm2efnKFZ-Dj"
   },
   "source": [
    "IQR은 Inter Quantile Range의 약어로써, (3Q - 1Q) * 1.5 값입니다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5S0OiJOaaIka"
   },
   "source": [
    "### Pie 그래프"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 618
    },
    "colab_type": "code",
    "id": "Btnl0KBuZ9-K",
    "outputId": "87421588-6890-4c38-ea4a-d2f723f7dc7d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f18384c0ac8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('연도')['분양가'].count().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4-qKkUW-aO1B"
   },
   "source": [
    "### scatter plot (산점도 그래프)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uMMXnIJzZqFi"
   },
   "source": [
    "* 점으로 데이터를 표기해 줍니다\n",
    "* x, y 값을 넣어주어야합니다 (hexbin과 유사)\n",
    "* x축과 y축을 지정해주면 그에 맞는 데이터 분포도를 볼 수 있습니다.\n",
    "* 역시 numeric 한 column 만 지정할 수 있습니다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "colab_type": "code",
    "id": "ycrsSifEazf9",
    "outputId": "15c9266d-b5ab-4905-81af-04b33b23dc79"
   },
   "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>지역</th>\n",
       "      <th>규모</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>인천</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>인천</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>경기</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>경기</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>경기</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>경기</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>부산</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>부산</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>부산</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>부산</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>대구</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>대구</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>대구</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>대구</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>광주</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>광주</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>대전</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>대전</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>대전</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>울산</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>울산</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>울산</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>울산</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>세종</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3263</th>\n",
       "      <td>세종</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3264</th>\n",
       "      <td>세종</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3265</th>\n",
       "      <td>강원</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3266</th>\n",
       "      <td>강원</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3267</th>\n",
       "      <td>강원</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3268</th>\n",
       "      <td>강원</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3269</th>\n",
       "      <td>충북</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3270</th>\n",
       "      <td>충북</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3271</th>\n",
       "      <td>충북</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3272</th>\n",
       "      <td>충북</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3273</th>\n",
       "      <td>충남</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3274</th>\n",
       "      <td>충남</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3275</th>\n",
       "      <td>충남</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3276</th>\n",
       "      <td>충남</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3277</th>\n",
       "      <td>전북</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3278</th>\n",
       "      <td>전북</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3279</th>\n",
       "      <td>전북</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3280</th>\n",
       "      <td>전북</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3281</th>\n",
       "      <td>전남</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3282</th>\n",
       "      <td>전남</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3283</th>\n",
       "      <td>전남</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3284</th>\n",
       "      <td>경북</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3285</th>\n",
       "      <td>경북</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3286</th>\n",
       "      <td>경북</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3287</th>\n",
       "      <td>경남</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>2780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3288</th>\n",
       "      <td>경남</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3289</th>\n",
       "      <td>경남</td>\n",
       "      <td>85㎡초과 102㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3290</th>\n",
       "      <td>제주</td>\n",
       "      <td>60㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>4039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3291</th>\n",
       "      <td>제주</td>\n",
       "      <td>60㎡초과 85㎡이하</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3292</th>\n",
       "      <td>제주</td>\n",
       "      <td>102㎡초과</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3601</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3293 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      지역             규모    연도   월   분양가\n",
       "0     서울          60㎡이하  2015  10  5652\n",
       "1     서울    60㎡초과 85㎡이하  2015  10  5882\n",
       "2     서울   85㎡초과 102㎡이하  2015  10  5721\n",
       "3     서울         102㎡초과  2015  10  5879\n",
       "4     인천          60㎡이하  2015  10  3488\n",
       "5     인천    60㎡초과 85㎡이하  2015  10  3119\n",
       "6     인천   85㎡초과 102㎡이하  2015  10  3545\n",
       "7     인천         102㎡초과  2015  10  3408\n",
       "8     경기          60㎡이하  2015  10  3126\n",
       "9     경기    60㎡초과 85㎡이하  2015  10  3239\n",
       "10    경기   85㎡초과 102㎡이하  2015  10  3496\n",
       "11    경기         102㎡초과  2015  10  3680\n",
       "12    부산          60㎡이하  2015  10  2950\n",
       "13    부산    60㎡초과 85㎡이하  2015  10  2999\n",
       "14    부산   85㎡초과 102㎡이하  2015  10  2957\n",
       "15    부산         102㎡초과  2015  10  3500\n",
       "16    대구          60㎡이하  2015  10  2614\n",
       "17    대구    60㎡초과 85㎡이하  2015  10  2696\n",
       "18    대구   85㎡초과 102㎡이하  2015  10  2557\n",
       "19    대구         102㎡초과  2015  10  2598\n",
       "20    광주          60㎡이하  2015  10  2253\n",
       "21    광주    60㎡초과 85㎡이하  2015  10  2439\n",
       "22    대전          60㎡이하  2015  10  2585\n",
       "23    대전    60㎡초과 85㎡이하  2015  10  2428\n",
       "24    대전   85㎡초과 102㎡이하  2015  10  2461\n",
       "25    울산          60㎡이하  2015  10  2422\n",
       "26    울산    60㎡초과 85㎡이하  2015  10  3040\n",
       "27    울산   85㎡초과 102㎡이하  2015  10  2951\n",
       "28    울산         102㎡초과  2015  10  2690\n",
       "29    세종          60㎡이하  2015  10  2572\n",
       "...   ..            ...   ...  ..   ...\n",
       "3263  세종   85㎡초과 102㎡이하  2020   2  3510\n",
       "3264  세종         102㎡초과  2020   2  3931\n",
       "3265  강원          60㎡이하  2020   2  2560\n",
       "3266  강원    60㎡초과 85㎡이하  2020   2  2501\n",
       "3267  강원   85㎡초과 102㎡이하  2020   2  3329\n",
       "3268  강원         102㎡초과  2020   2  3906\n",
       "3269  충북          60㎡이하  2020   2  2420\n",
       "3270  충북    60㎡초과 85㎡이하  2020   2  2384\n",
       "3271  충북   85㎡초과 102㎡이하  2020   2  2634\n",
       "3272  충북         102㎡초과  2020   2  2532\n",
       "3273  충남          60㎡이하  2020   2  2389\n",
       "3274  충남    60㎡초과 85㎡이하  2020   2  2622\n",
       "3275  충남   85㎡초과 102㎡이하  2020   2  3201\n",
       "3276  충남         102㎡초과  2020   2  2835\n",
       "3277  전북          60㎡이하  2020   2  2526\n",
       "3278  전북    60㎡초과 85㎡이하  2020   2  2458\n",
       "3279  전북   85㎡초과 102㎡이하  2020   2  2764\n",
       "3280  전북         102㎡초과  2020   2  2667\n",
       "3281  전남          60㎡이하  2020   2  2529\n",
       "3282  전남    60㎡초과 85㎡이하  2020   2  2507\n",
       "3283  전남         102㎡초과  2020   2  3053\n",
       "3284  경북          60㎡이하  2020   2  2578\n",
       "3285  경북    60㎡초과 85㎡이하  2020   2  2549\n",
       "3286  경북         102㎡초과  2020   2  2756\n",
       "3287  경남          60㎡이하  2020   2  2780\n",
       "3288  경남    60㎡초과 85㎡이하  2020   2  3065\n",
       "3289  경남   85㎡초과 102㎡이하  2020   2  3247\n",
       "3290  제주          60㎡이하  2020   2  4039\n",
       "3291  제주    60㎡초과 85㎡이하  2020   2  3962\n",
       "3292  제주         102㎡초과  2020   2  3601\n",
       "\n",
       "[3293 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 718
    },
    "colab_type": "code",
    "id": "dpAzJs0waSa2",
    "outputId": "377de838-368e-403a-b2ef-32a34201873c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f183801c550>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x='월', y='분양가', kind='scatter')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "day 5 수업중자료(시각화)의 사본",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}