Analysis_model4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a8a22909-bea3-4dca-9f80-2e7d3bc1b2be",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 다변량 데이터 탐색\n",
    "## 상관관계 분석\n",
    "- cov() : 결측값을 제외한 쌍별 공분산을 구하는 메서드\n",
    "- DataFame.cov(min_peirods=None, ddof=1)\n",
    "- corr() : 각 열 간의 상관 계수를 반환하는 메서드\n",
    "- DataFrame.corr(method='pearson', min_periods=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9e453c19-863d-4dec-a88a-3d563f934da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)   # FutureWarning 제거\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "font_family = 'Malgun Gothic'\n",
    "\n",
    "sns.set(font=font_family, rc={'axes.unicode_minus' : False})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f49a812e-a38e-4395-83d6-1eda9c9850b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 데이터셋 준비\n",
    "# iris 붓꽃 데이터 로드\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris = load_iris()\n",
    "\n",
    "columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width']\n",
    "\n",
    "# 데이터 생성 및 준비\n",
    "data = pd.DataFrame(iris.data, columns=columns)\n",
    "sy = pd.Series(iris.target, dtype='category')\n",
    "sy = sy.cat.rename_categories(iris.target_names)\n",
    "data['species'] = iris.target_names\n",
    "\n",
    "# 필터명 정의\n",
    "cols = iris.feature_names\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d6ce2b38-4ad1-4f92-bddb-d449a03ac1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>0.685694</td>\n",
       "      <td>-0.042434</td>\n",
       "      <td>1.274315</td>\n",
       "      <td>0.516271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.042434</td>\n",
       "      <td>0.189979</td>\n",
       "      <td>-0.329656</td>\n",
       "      <td>-0.121639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>1.274315</td>\n",
       "      <td>-0.329656</td>\n",
       "      <td>3.116278</td>\n",
       "      <td>1.295609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.516271</td>\n",
       "      <td>-0.121639</td>\n",
       "      <td>1.295609</td>\n",
       "      <td>0.581006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      0.685694    -0.042434      1.274315     0.516271\n",
       "sepal_width      -0.042434     0.189979     -0.329656    -0.121639\n",
       "petal_length      1.274315    -0.329656      3.116278     1.295609\n",
       "petal_width       0.516271    -0.121639      1.295609     0.581006"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 공분산 산출\n",
    "# 공분산은 수치적인 의미보다 패턴 확인에 중점\n",
    "df_cov = data.cov()\n",
    "df_cov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3794720e-3d3e-4ded-a003-14b02a5b988b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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5xwuoTsQaMRrDOr3vd5q7t8bh7/dI12GeHN12QK9MBecHX0JbdGnNras30ORqqOPmivsQDxa+9YnpJBNA0SpFfhlLoS2UmJgYFixYgJOTE9WrV6dXr17ExcXh7e2Nra0tTZo0ITg4r8ugQYMGjBs3DpVKRbNmzRgwYACZmZmsWbOGYcOG0aRJE2rWzN/98DiBgYEEBwfj4+MDgI+PD25ubo8sP3LkSIKCghg8eDBVqlTRDcgDdOvWjTFjxhAbG8vMmTOLFUdpykzLYOsHEbwV/j8URSH5yk02BX1J20FdSL+t4twvv7M5+GsGzX6brqP7YG5hzraQ1aQnp2FuaUHgpplocnKxsLJk77LtJtFC+adLcfF8s3wNa7eHo2gVTv/+Bxu/2wbA6PFvcXh/FKd//4P3R09lXmgIZmZm3ElRETS+4AHqh7Vp35oq1Soz8m0f3bo5H37OqeiYEjuf0lJjaFeyb6WSvPt36k0bhlUlB7T31Khv3OHceBMYJyiie2kZfPtBOO+HT0GrKNy6coOvg5bRaVA30m6ncuqXaPq/N5i6LeqTrc4mQ5XB0sDPAKj/YiOcqjkx8dsZuvoSLyawavqXxjqdoikjPSiPY6Yohvn5ZWRkJElJSY+9pcwYZsyYQc+ePenYsaNB6/3fc/mbif9Wm++eNXYIZcZXlo2MHUKZEVG+jP+uoxRFXN781HXcW1L0G4Zsx+T/DUppMPrvUMLCwjhwQL+punz5cuzs7J6ovsTERJycnChfvjxJSUlERUUxadIkQ4QqhBDGYwItFKMnlPs/nDGUhIQEAgICsLOzQ1EUQkJCsLe3N1j9QghhFP+mhFKc35aUpHbt2ukG5YUQ4plRApNDGprRWyhCCCGK4N/UQhFCCFGCjHg7cFFJQhFCCFNQjLm8jEUSihBCmIB/Tj1UFklCEUIIUyBdXkIIIQyiBObyMjRJKEIIYQqkhSKEEMIgyvojipGEIoQQpkG6vIQQQhiEdHkJIYQwBLltWAghhGFIC0UIIYRBSEIRQghhEDL1yrNLnlL4QPVyzsYOoczoceuQsUMoMyqrHQovJIrMmM+KLypJKEIIYQokoQghhDCIErjLS61WExISwuXLl8nMzOSdd97hlVde0W2/efMmQUFBZGRk4OTkxNy5c6lYseIj6zM3eIRCCCEMT6sU/VVEGRkZeHt7s2bNGr755hvmzp1Lbm6ubvvChQsZOXIk69evp2vXrixduvSx9UkLRQghTEExEkVaWhppaWn51js4OODg8GBsy9nZGWfnvDHQihUr4ujoSGZmJhUqVAAgOjqa2bNnA9CnTx8GDx782ONKQhFCCBOgaIre5RUREcHixYvzrQ8MDGTs2LEF7nPy5EkcHBx0yUSlUuHo6KjbbmtrS3Z29mOPKwlFCCFMQTFaKL6+vgwYMCDf+odbJw/buXMnq1ev5tNPP9Wty8nJwcLCQq+cpeXjU4YkFCGEMAHFuW244j+6th5n6dKlJCYmEh4eTrly5XTrnZ2dSU5O1i1nZWXpbS+IDMoLIYQpKIFB+ZiYGE6dOsWsWbPyJQtzc3Pq1q1LVFQUANu3b8fDw+Ox9UkLRQghTEEJzA155swZLl68yIgRI3Trxo8fz6lTp/Dz82P69OkEBQXx2WefUa1aNebMmfPY+swURSn7v5Ypg56v1NLYIZQZ8kv5B47dijV2CGVGZVv5pfx9SannnrqO1KFdi1zWce0vT328JyEtFCGEMAVlf/Z6SShCCGEKZC4vIYQQhiEtFCGEEIYgLRQhhBCGYQItlDLzO5TIyMhCJx57FEVR+PDDDynohrWlS5cSGRlZ4H6enp5PdDwhhChtSm7RX8byTLRQzMzM+OCDDwotd+TIEaysrGjTpk0pRPVkevfrwejxb6FWq/nrz3iC/zuLbLX+/DluTRoQMj8Yc3MzMjOzCBo3k2sJ1/XKjJv4Dq/07kbvLkMAaNfhBcb+zx8LC0ts7coze+ZCog4dL7XzehovdGzFmKn+aDQaUm6n8sl/55GWelevTE2XGvhN8OXCmYts+Gqzbn3fYb3pNegVFEUhKeEGcyd/RlZmVmmfgsF5e/dh0sQxqLPUxF68RMCYINRqtV6ZqVPG07nTS9jZ2ZKSkorfqPe5fTsFB4cKrI5YjK1teSo6OrBp0w7mzV9ipDN5en379yTw/VGos9Rc+vMyk96fiVqdf86pxk0bEjxjAt+u2sj//bAHgIoVHZg1dypVqlbGuZITe3ftZ86sL0r7FIpEkRZK2XL8+HGuXbtm7DAeqUq1yoyfPJph/Ufh/aovd9PSeWv08HzlFiyZxQeTZ+P9qi+rv1rPx59N19ter+HzdOvRWW+dfQV7RvtOYFj/t5nw7lQWLv+kRM/FUKxtrJg0532m+s/Ev99YTh49xeigt/XK9Hv9NT5aNoOKTg6YmT1Y36JNM3oM6E7g4Am86zWev2IvM3LssFI+A8OrXr0qM6ZPwLPHYDq590OlSmP8uLfzlVu85Gt69hpKJ/d+/H7yDO+O9gXg3r1MfIa9g+crg+n48mv4vTUMF5dapX0aBlG1WhX+FzQG775v0OeVYaSl3WXUuyPzlZscPJ6PZk/BvoKd3nrnSk6EL1+Nj9fbvNptMG3ataKbR6fSCr94tMV4GUmxE0p2djZBQUEMHToULy8vUlJSiIiIYOTIkfj4+LBhwwYgrwsrJCSEwMBAfHx8GDduHPfu3QNgwYIFDBs2jP79+z+yO+phfn5+/PnnnwBs2rSJcePGAXkPhxk4cCDwoPtKo9Ewe/ZsfHx8ePvtt7l06RIAW7duZcuWLSxfvpwZM2bo6l60aBGvv/463t7e3Lhxo7iXw6B69+vBjsid3E3L+/a9NmIzPXrp/5ipcdOGqFRpnIu5AMDPP+ylcdOGlCufN21CedvyfDQvmJCp8/T22/vzfjLS867/pbh4rK2tCp3orSxo796OM8f+4EbiTQC2f/cD//HsqFfm/zbvwq/3u5w7dUFvfdMXG3PswAlyc/L6AH7a/HO+fU3RIO8+bNiwDZUqb3ry8K/W0K9vz3zl0v7+O7KxsaFuXVeio88AkJubS+bfrTRX19qkqtK4eTM53/6moO+AnmyN/Ik0Vd65frtyI6/2zj89yKLPVuDd903iL1/VW//XpXhOnfwDyPvsiDlznmrVq5R84E9A0Rb9ZSzFTii//vortra2rF27lo0bN3LhwgWuX7/OqlWr+O6779i6datuQrGjR48ye/Zs1q1bR8OGDVm1ahUAffv25bvvvuPbb79l+fLlhR7T3d2dI0eOAHndVjdv3kSr1RIdHU27du30ym7ZsoXs7GzWrVvHkiVLuHz5MgD9+/dnwIABjB49mpCQEACuXbtGx44dWbNmDZ6enmzcuLG4l8OgarvU5Gp8gm75WkIi1WpUzV/mcoLeuqSkm1StVhkLCwsWhc1h2edfcSPp1iOPM2hYf347fELvQTplVY061bl2JVG3fC8jEwtLCywsH8yCqs5SF7QrZ38/T+dX/oO9Q9630i6vdsa5slPJBlwKnnOtw6W/ruiW4+MTqFmzer5yjRs34MCv20i4Es2NpFv8+NMe3bb3xvsTfWIXe3dvIjj4E7KyTLMbsI5LLb0kcfVqItX/8Z4BdAn0capVr4JHD3f27Dpg0BgN5ZlMKG5ubhw9epTVq1ejVqvZv38/R48eZcSIEfj6+nL37l2uX8/rz/fw8NDNrd+rVy9OnjwJ5H1D+uKLLwgODi5Sq6Bz584cOXKEnJwccnJyaNasGefOnePo0aN07qzftXPgwAGGDMkbN7CxscHd3f2R9VauXFk3ntKyZUsSExMfWbYkVK5aibXbwnWvCg4VyM3V6LZrNFqUfzz209raWq8MgFajRatV+Piz6fy69xAH9h0p8HhWVpZMmjEej1fdmTh2RoFlyhorays0/zhfTa4GijBj0KnfzrB55Vbmr/yE+Ss/Jjcnh5Tbd0oq1BJTrVoV9uzaqHs5VKyg92VAo9GiLeDxsOfOXaSTez9qu7yAoigsmD9Tt+3zL8J44UVPOrn3Y+7cGbz4QovSOJWnVqVqZSK/j9C9HBzs9d4PWo2mwJtzCuPetSMR3y3hvTFTuXnj0V/GjEnRmBX5ZSzF7vOoU6cOGzZsYP369QwaNIgWLVoQGBiYbxbK2NhYrKysdMuZmZmUK1eOuLg4QkJCmDp1KqNGjeK1114r9JjPPfccSUlJREVF8eKLL+Lq6srRo0c5ffo0Y8aM0SurVqv1unJycnIeWa+NjY3u35aWlgW+KUvS7ZvJDO33oO874H0/atV+8E2zVp0aJFzRT3LXE29Qs7b+t9Eq1SpToYIdXTxepnnLxgwdORArayvquNRi008ReL/qS6XKziz9ZgFbNnzPvJCyOehYkFvXb9GiXXPdcrny5chWZ6Mp4sOGfty4kx837gSgXec2xJ39s0TiLEk3btyiu+cg3XLQ5LG41Hkw5uHqWov4+KsF7QrkvScWLf6KHdtX5dt25co1IiN/oHPnDpyIPm3YwEvArZu38XrNV7c8boI/tWvX0C3XrlOTq1eKN046OXg8z9d1wcdrFKmpKoPFamjP5KB8SkoKtra2+Pn50aFDB9q3b8+mTZvQaPK+JZw792AStH379unuPNmwYQP/+c9/iIuLo1mzZrRo0YL4+HiSkpKKdNzWrVsTHh5Oly5daNeuHYcPH8bOzg5ra2u9ci1btmT79u0ApKens2fPg2a+jY0NGRkZxT3lUvN/O3bTf/BrlLctD8DwNwezZcP3emVOnjjDc3VdqFvfFYAevbtx7Eg05/6I5aWmnvTuMoTeXYbw5pBALsVdxvvVvDffR/OnsjLsO9atLnzMqiw58ksUL3Vpi9PfXVX9h7/Gz1v3FLLXAxYWeX/iDk4OvDPpLdaGbSiROEtT5JYfef31gdj+/Xfyjv9Ivl2zWa+MjY0N9eo9p1vu368nBw/+BkC9es/pvkyVK1cOT4/OHD58rHSCN7Aftv+M95C+umvh6+fDxnXbi7y/Rw93mjV3Y7Tff8t0MgFQtGZFfhlLsVsoMTExLFiwACcnJ6pXr06vXr2Ii4vD29sbW1tbmjRpQnBwMAANGjRg3LhxqFQqmjVrxoABA8jMzGTNmjUMGzaMJk2aULNmzSIdt1OnThw8eBBX17wP0tzcXDp06JCv3MiRIwkKCmLw4MF6XVoA3bp1Y8yYMcTGxjJz5szinnqJuxQXzzfL17B2eziKVuH073+w8bttAIwe/xaH90dx+vc/eH/0VOaFhmBmZsadFBVB4wu/ZbpN+9ZUqVaZkW/76NbN+fBzTkXHlNj5GMJdVTqfTQ9lwcqP0SoKifHXmT1xAa8NeZWU23c4vOfoI/e1sLRgxZZQcnNzsbKyZNWStVz8w/RaKP8UG/snixaFs2f3JhStlmPHT7EyYj0AkyaOYe/eg5w9F0t42KdYWVmhVqs5dz6OSZPzxg4bNazHurUrSL+bDsCyFRFE/RZttPN5Gn/GXSZs+Soiv49Aq9VyMjqGdWvyvjQFvvc2B389ysnfH/033r7DizRoVJfI7yN06w7uj+KzeU/2m7iSZAotlBKbvj4yMpKkpCQCAgJKonqjk+nrH5Dp6x+Q6esfkOnrHzDE9PXXOnQrctlaR/Y+9fGeRJm7bzQsLIwDB/Tvsli+fDl2dnaP2EMIIZ59ptBCKXMJxd/fH39/f2OHIYQQZYrWiHdvFVWJJRQvL6+SqloIIf51jDnYXlRlroUihBAiP0koQgghDKJkbp8yLEkoQghhAqSFIoQQwiAURRKKEEIIA9CYwF1e/6rnoQghhKlSFLMiv4pep8KuXbsYPjz/c5f27t1Lt27dGDFiBCNGjGDfvn2F1ictFCGEMAElMYbi7++Pq6trgbO+p6WlMXToUEaNGlXk+iShCCGECSjOXV5paWmkpaXlW+/g4ICDw4MpcUJDQylXrhy//vprvrIqlQpHR8dixSgJRQghTEBxWigREREsXrw43/rAwEDGjh2rWy5Xrtwj61Cr1Wzbto3NmzfTpk0bxo0bl292938qsckhn3UyOeQDMjnkAzI55AMyOeQDhpgc8szzfYpc1vXUmiK1UO7z9PRk165dBdaVm5vLvHnzcHR0LHSyX2mhCCGECSjOV/9HJY4nYWlpibe3N59//nmhZeUuLyGEMAFaxazIL0O4c+fB47J37txJ06ZNC91HWihCCGECSuOHjbGxsRw4cAA/Pz/Cw8M5duwYFhYW1K9fn2nTphW6v4yhPCEZQ3lAxlAekDGUB2QM5QFDjKFE1+lX5LIvXN321Md7EtJCEUIIE2CorqySJAnlCX1l2cjYIZQZPW4dMnYIZUZm4oHCC/1LHGo62dghPFM02rI/5C0JRQghTIApjE1IQhFCCBMgXV5CCCEMQqavF0IIYRBaYwdQBJJQhBDCBChIC0UIIYQB5EqXlxBCCEOQFooQQgiDkDEUIYQQBiEtFCGEEAYhLRQhhBAGoZEWihBCCEMoxhOAjUYSihBCmACttFCEEEIYgkwOKYQQwiBMYVDeaBPsR0VFERwc/Mjtnp6eJR5DaRxDCCEMQWtmVuSXsZRIC+X8+fMkJCTg4eFREtU/sbIaV1FUbNuI+h8M58LkcNL/iNfbVnN4d6r264C5tRWKRsP5CSvIvHzDSJGWHG/vPkyaOAZ1lprYi5cIGBOEWq3WKzN1yng6d3oJOztbUlJS8Rv1Prdvp+DgUIHVEYuxtS1PRUcHNm3awbz5S4x0JobxyWdLuRR/lfSMezznUpuZk8dRzsZGr8zVa9dZ9vUaGjeqz4jB/Y0TaClxaNuIeh+M4OLkcNL/uKy3rcbw7lTt1xGzv98jsRNWkHk5yTiBPiGNsQMoghJpoZw9e5bY2LL3bO2yGldhGoeOwSWgDxYVyhe4/ca2w5wcNIvofh+QtPEAz73nVcoRlrzq1asyY/oEPHsMppN7P1SqNMaPeztfucVLvqZnr6F0cu/H7yfP8O5oXwDu3cvEZ9g7eL4ymI4vv4bfW8NwcalV2qdhUGP9fQn/Yjbrwr/AzMyMnXv2623fuO1H/jdjNqlpd0ExhR74J+cWGkidgL5YPuI9cnPbEU4N+oiT/WZwY+N+XEzwPaI1K/rLWIrUQklISGDKlCk0aNCACxcuoNVq+eSTT3B2diYkJITk5GQURWHmzJkkJiYSFhZGdnY2Z86cYdmyZURERPDTTz+Rk5ODp6cno0ePLnKAWq2WhQsXcubMGbKysvD396dbt26EhoaiVquJi4vjypUrDBkyBF/fvA+PZcuWsXfvXhwcHHB1dcXJyYkXXnghX1wAa9asYefOndy5c4cFCxbQqFHZe7TvhUlfos3MpnXkBwVu19zNBMDMwhzbBrVIO/lnaYZXKgZ592HDhm2oVGkAhH+1hi9XfJqvlZGWdhcAGxsb6tZ1Zf36bQDk5uaSm5sLgKtrbVJVady8mVyKZ2B4FeztAEjPyODW7RQa1a+rt/21V7oxqF8vlnz1rTHCK1Wxk8LQZmbTKnJmgds1d+8B998jtbl7Mq4UozOMZ+our9OnTxMcHIybmxtRUVF89NFHVK9eHT8/P5o0acLFixeZP38+S5cuxd/fn6SkJAICAgDo0KEDvr6+aDQaXn31Vd0Hf1Fs3bqVqlWrsnLlSrKyshgyZAhdunQBIDY2lmXLlpGZmUmvXr0YOnQoJ06c4NSpU6xbtw4zMzPGjh2Lk5MTL7/8cr64NBoNFStWZNWqVXz//fesXLmS2bNnF+PylQ5tZvZjt1s62tEiYhK29WuRsv80l+asK6XISs9zrnU48ftp3XJ8fAI1a1bPV65x4waELV+Am1t9Vq5cz48/7dFte2+8PyNHDqKSsxN+b79PVlZWqcReUqJOnOTz5SuJv3qNUSOH4Nawnt728uXKGSmy0lf4e8SeZn+/R+7sP81fc9aWUmSGYwptzCJ3eTVu3Bg3NzcA2rdvT0JCAgcPHmT27NmMGDGCkJAQ7t69W+C+1tbWLFu2jEmTJqFSqUhJSSlygPv27eP7779nxIgRjBo1iuzsbN3+np6eWFhYYG9vj4uLC8nJyRw8eBBvb28sLCwwNzene/fuj6zbzMyMHj16ANCyZUsSExOLHFdZkpuaQXS/DzjYfBRpv8fRLOx9Y4f01KpVq8KeXRt1L4eKFXQtDACNRotWm/++l3PnLtLJvR+1XV5AURQWzJ+p2/b5F2G88KInndz7MXfuDF58oUVpnEqJaf9iK9Z++Tk/b17Judg/Wb1+i7FDKrNyU9M52W8Gh5uP4u7vF2lqgu+RkujyUhSFXbt2MXz48Hzbbt68yVtvvcWQIUMYPXo0KpWq0PqK3EKxsrLSWzYzM8PKyorVq1c/dr87d+4wfvx4pk2bxvDhwwkICEApRn+uRqPhww8/1CWzh1lbW+v+bWlpiUajISsrSy/WnJycR9Ztbm6uq8PKyqrADyiTolVI+PInnp/gbexIntqNG7fo7jlItxw0eSwudR6Mebi61iI+/uoj91er1Sxa/BU7tq/Kt+3KlWtERv5A584dOBF9uoC9TYu9nR1D+vfm6+82MmLIAGOHU7ZptSR8+ROuEwYVXraMKYlPJ39/f1xdXblxI/9NPAsXLmTkyJF06dKF9evXs3TpUqZMmfLY+orcQomJiSEhIQGAH374gZYtW9KoUSN+/PFHIO+DOy4ur1/SxsaGjIwMIG/8pWbNmrRt25aMjAzOnj1b1EMC0LZtW9ate9CFc+7cuceWb9WqFdu3b0dRFLKzs/n+++912x6O61li51ZH9+/KPV5AdcL0bjwoTOSWH3n99YHY2uYNur7jP5Jv12zWK2NjY0O9es/plvv368nBg78BUK/ec9j8fQdUuXLl8PTozOHDx0on+BKQqkrjdnJeS11RFPYeOMKLLZsZOaqy6+H3SKUeL5Jmgu8RjVnRX2lpaSQkJOR7paWl6dUZGhrKtGnTCjxedHS0bnihT58+HDp0qNAYi9xCadSoEaGhoSQmJmJnZ8fHH3+MWq0mODhY10oJCAigfv36vPTSSyxfvpxLly4RGhqKhYUFPj4+uLi4FHvQe9iwYXz44YcMHDgQa2trunbtSuPGjR9Zvnfv3hw/fpyBAwdSqVIlWrZsibl5Xt58OK77g/KmqsbQrmTfSiV59+/UmzYMq0oOaO+pUd+4w7nxS40dnsHFxv7JokXh7Nm9CUWr5djxU6yMWA/ApIlj2Lv3IGfPxRIe9ilWVlao1WrOnY9j0uQQABo1rMe6tStIv5sOwLIVEUT9Fm2083la9zIz+e/02Zibm2Nhbk6b1s3x9fEi8vudVHJ2wr1jO2OHaHTVh3Yl+5aKlN3R1J32OlaVHNDcU5N94w7nx5veLePFaaFERESwePHifOsDAwMZO3asbrncI8bZVCoVjo6OumVbW1uysx8/TgVgphSh/ykhIYFp06axcuXKQissa8LCwnBycmLQIMM2cfdWG2zQ+kxZjzuFf3P5t8hMPGDsEMqMQ00nGzuEMqPLjY1PXceK2vnHOR5l6Nml+VojAA4ODjg4OORb7+npya5du3TLt2/fZty4cXz33Xe6db169dL1SD2K0adeiYyMZMsW/cHEWbNm4erq+kT1JScnY2FhgaOjIyqVih07drB06bP3jV0I8e9SnEfKPypxFJWzszPJyQ9uq8/Kynpka+ZhRk8oXl5eeHkZ7kdGqampTJw4ERsbG7RaLQEBAdSpU6fwHYUQogwrzVuGzM3NqVu3LlFRUbRv357t27cXaYaRIiWU2rVrm0x3V7169YiMjDR2GEIIYVClMfVKbGwsBw4cwM/Pj+nTpxMUFMRnn31GtWrVmDNnTqH7G72FIoQQonAlOaXK/fGThg0b0rBhQwBq1qzJqlX5b7t/HEkoQghhAkzhV3KSUIQQwgRIQhFCCGEQpjCXlyQUIYQwAcaclr6oJKEIIYQJMIUHbElCEUIIE6A1gU4vSShCCGECZFBeCCGEQZT99okkFCGEMAnSQhFCCGEQcpeXEEIIg9CYQKeXJBQhhDAB0uUlhBDCIOS24WdYRHlT+JlR6aisfvIH+Txr5CmFD6wtb+wIyo4uBqij7KcTSShCCGESpMtLCCGEQcigvBBCCIOQFooQQgiDUKSFIoQQwhCkhSKEEMIg5LZhIYQQBlH204kkFCGEMAm5JpBSJKEIIYQJMIVBeXNjByCEEKJw2mK8imPXrl14eXkxePBgwsLC9Lbt3buXbt26MWLECEaMGMG+ffseW5e0UIQQwgSURAslIyODxYsXs2bNGsqXL8+IESPo3Lkzbm5uAKSlpTF06FBGjRpVpPokoQghhAkoTssjLS2NtLS0fOsdHBxwcHgw997Bgwfp3Lkz9vb2APTp04f9+/frEopKpcLR0bHIx5WEIoQQJkCjFL2FEhERweLFi/OtDwwMZOzYsbrl69evU6tWLd1yjRo1uHTpkm5ZrVazbds2Nm/eTJs2bRg3bhzW1taPPK4kFCGEMAHF+R2Kr68vAwYMyLf+4dYJQHZ2NnZ2drplc3NzzM0fDK37+/vj7+9Pbm4u8+bNIzw8nICAgEceVxKKEEKYgOKMofyza+tRqlatSkJCgm45KSmJGjVq5CtnaWmJt7c3n3/++WPrKzN3eUVFRREcHPzE+8+dO5f09PR867dt20ZoaGiB+3h6ej7x8YQQojSVxF1enTp14ueffyYrKwuNRsOOHTv0Phfv3Lmj+/fOnTtp2rTpY+srlRbK+fPnSUhIwMPDo8SOMXly4Q82Ko04DMWtQzOGTB6ORqMhLTmNryYuIUP1IGG2690Rn6kjuXn1BgAXj51n86drsbS2xGeqLzXq1sS2oj1Xz10mYloYmlzTfyBY3/49CXx/FOosNZf+vMyk92eiVmfnK9e4aUOCZ0zg21Ub+b8f9gBQsaIDs+ZOpUrVyjhXcmLvrv3MmfVFaZ+CwTm0bUS9D0ZwcXI46X9c1ttWY3h3qvbriJm1FYpGQ+yEFWReTjJOoCWgYYemeE0ejlaj4W6yioiJy7j30Hvkxd4d8J46klt/v0f+PHaebZ+uA6D1K+3o/lZvtIqCOj2TVZOXcTc5/yB2WVISU69UqlSJt956i+HDh6MoCt7e3mRkZPDVV1/h5+dHeHg4x44dw8LCgvr16zNt2rTH1lcqCeXs2bMkJSUZ/YO8rMRRGCsbK96Y5c+8ESGkJN7mFb/X8J40jIjgB/eI2znas2/dbraHbtLb16Z8OU7uOc63M08B4P/pWNx9PNj77c5SPQdDq1qtCv8LGsNrrwwjTXWXj+ZMYdS7I1n8ebheucnB42nbvhWWlvp/2s6VnAhfvppTJ//AwsKC9VvC6ebRib27D5TmaRiUW2ggFvblsaxQ8KMRb247wvVv8xJq9aFdcXnPiwvvLS3NEEuMpY0Vr8/y5/MRH3En8Tbd/XrTf9JQvgv+UlfGztGeA+t282PoZr19y1WwxSfEjw+6v0dWeiZdRrzCa+8NZu308H8epkwpqR829u/fn/79++uta9iwIQATJ04sVl1P1OWVkJDAiBEjCAkJ4fXXX2fo0KH89ddfqFQq/vvf//LGG2/g6+vLX3/9xaFDhwgLC2PTpk28++67QN4dCD4+PgwcOJDly5cXerwZM2bw66+/AnDkyBG8vLx02/r164dGo9FrpoWFhTF48GDeeustTpw4AVBgHABr1qxh5MiR9OnThwsXLjzJ5TC4Zp1bcfHEeVISbwPw67rdtPZoq1fGrqI96Xfu5ts3Q5VOzIFTuuW/zvxJxSpOJRtwKeg7oCdbI38iTZV3zt+u3MirvfN/MVj02Qq8+75J/OWreuv/uhTPqZN/AKDRaIg5c55q1auUfOAlKHZSGH+8OZ+c5Px/BwCau/cAMLMwx7ZBbe6ejCvN8EpU084t+fPEee78/R45uG4PLT3a6JV51HskV51NVnomdo55t8pWqOxIalJKyQf9lDSKUuSXsTxxC+X06dMEBwfj5uZGVFQUH330EdWrV8fPz48mTZpw8eJF5s+fz9KlS/H39ycpKUl3d0CHDh3w9fVFo9Hw6quv4uvr+9hjubu7c/ToUdzd3fn111/RaDSkpqaSlpaGq6srFhYWurJHjx4lOjqatWvXYmZmxtixY6lSpQovv/xyvjg0Gg0VK1Zk1apVfP/996xcuZLZs2c/6SUxmMq1q3Lryg3dclZGFhYWFlhYWui6rqxsrHH36cDLXu7c+Os6mz5dq0tA95WzL0+nQV0Jn2T630rruNTi9N8JAeDq1USq16iar1xmZlahdVWrXgWPHu4sXfS1QWMsbdrM/N19D7N0tKdZxCRs69fizv7T/DVnbSlFVvIq1a7K7YfeI+qMLMwtLDC3tED793vE0saaTj4v0eHv98i2T9dxJ/E2udm5bJ79LTN2fkZqUjI52TnM83p8V05Z8EzPNty4cWPdj1/at2/P9OnTiYuL4+rVq4XsCdbW1ixbtoy4uDhUKhUpKY//dtChQwdWrFgBwOXLlxkwYAC//fYbqampdOrUSa/s/v37GTRokC7JdO/enWvXrhVYr5mZGT169ACgZcuWbNy4sdDYS4OVtSUajf7QmkajQXnom8eWhevYsjCvP7jjAHfGLZ/IzL4PxpGea16PESFvs+XzDVz546/SCdyAqlStzIqvP9Utx1++Su5D40Daf1yPonLv2pEp09/jvTFTuXnjlkFiLatyU9M52W8GmJtT++2eNA17n5g35hs7LIOwLOA9otVo4KG/iR0L17Nj4XoA2g/ozLvL/8cnfYOoXq8WvQK9mNZ5DHeT02jfvxN+X4xjmX/ZvjbP9PNQrKys9JbNzMywsrJi9erVj93vzp07jB8/nmnTpjF8+HACAgIK/WCwtbXF3t6eU6dO4eLiwssvv8zatWtRqVT5+vjUarVe/3lOTs4j6zU3N9f9SMfKygqttmz8l6VcT6Zh28a6ZevyNuSoc9BqCo7v8JZfGTrNV9eC6TLMkzY9X2JJwAJSrieXVtgGdevmbbxee9ByHTfBn9q1H9zOWLtOTa5eKfiLwqNMDh7P83Vd8PEaRWqqymCxlnlaLQlf/oTrhEHGjsRg7lxPoX5bN91yYe+RqC37GTTNF3NLC5p3e4HTu4/rBuGjth5g0PQ39Fo3ZdEzPTlkTEyM7v7lH374gZYtW9KoUSN+/PFHIO+DPC4ur8/WxsaGjIwMIG/8pWbNmrRt25aMjAzOnj1bpON16tSJhQsX0qVLFxo0aEB8fDypqalUr15dr1zLli3Zvn07iqKQnZ2ti+efcZRlp/f9TnP31jhUrghA12GeHN2mP3hcwfnBPeYturTm1tUbaHI11HFzxX2IBwvf+sRkk0lBftj+M95D+mJrmzcA7evnw8Z124u8v0cPd5o1d2O033//NcnEzq2O7t+VerxI2olYI0ZjWH/s+52m7q2o8Pd7pNMwD45tO6hXxv6h90jTLq24ffUG2lwNV89exu0/LbCwyvvi2aBdY9Jup5bpZAJ5XV5FfRnLE7dQGjVqRGhoKImJidjZ2fHxxx+jVqsJDg7WtVICAgKoX78+L730EsuXL+fSpUuEhoZiYWGBj48PLi4uNGrUqEjH69y5M6GhobRpkzfwVq1aNSpWrJivXO/evTl27BgDBw7E2dmZ5s2b67Y9HMeyZcue9NRL3L20DL79IJz3w6egVRRuXbnB10HL6DSoG2m3Uzn1SzT93xtM3Rb1yVZnk6HKYGngZwDUf7ERTtWcmPjtDF19iRcTWDX9y0cdziT8GXeZsOWriPw+Aq1Wy8noGNatiQQg8L23OfjrUU7+HvPI/dt3eJEGjeoS+X2Ebt3B/VF8Ns/0x5ceVn1oV7JvqUjZHU3daa9jVckBzT012TfucH78EmOHZzD30jJY98HXjAkPQlG03L5yk9VBy+k4qCtpt1XE/BJNn/cG4dqiPjnqbO6p0vkycCEA5w+doXq9Wvxvw4fkqHPQ5Gj4csxCI59R4Z6ki7e0mSlPEGVCQgLTpk1j5cqVJRCSafB9bqCxQygzdqYWrZX5b7DOppmxQygz1hZ8N/O/0orLTz8+26NOzyKX/fnq/z318Z5EmZt6JTIyki1btuitmzVrFq6urkaKSAghjO+ZvsurpHh5een9zkQIIYRpdHk9UUKpXbv2v7q7SwghSpu0UIQQQhiEKdw2LAlFCCFMgDGnVCkqSShCCGECpMtLCCGEQUhCEUIIYRDP7F1eQgghSpe0UIQQQhiE3OUlhBDCIDRK2ZgN/XEkoQghhAmQMRQhhBAGIWMoQgghDELGUIQQQhiEVrq8hBBCGEJJtVB27drFsmXLsLS0xMPDA39/f922mzdvEhQUREZGBk5OTsydO7fABxve98SPABZCCFF6NIq2yK+iysjIYPHixaxatYq1a9eyb98+zp8/r9u+cOFCRo4cyfr16+natStLlz7+CafSQhFCCBNQnC6vtLQ00tLS8q13cHDAwcFBt3zw4EE6d+6Mvb09AH369GH//v24ubkBEB0dzezZs3XbBg8e/NjjSkJ5QguOrTR2CGXGAmMHIMqkWcYO4BlTnC6viIgIFi9enG99YGAgY8eO1S1fv36dWrVq6ZZr1KjBpUuXAFCpVDg6Ouq22drakp2d/djjSkIRQggTUJwWiq+vLwMGDMi3/uHWCUB2djZ2dna6ZXNzc8zN80ZCcnJysLCw0Ctvafn4lCEJRQghTEBxWij/7Np6lKpVq5KQkKBbTkpKokaNGgA4OzuTnJys25aVlUW5cuUeW58MygshhAnQKJoiv4qqU6dO/Pzzz2RlZaHRaNixYweenp5AXmulbt26REVFAbB9+3Y8PDweW5+ZYgq/5y+Dbt26a+wQhBAmokqVCk9dh4tz8yKXvZJypshlt27dyrfffouiKHh7e/Piiy9y4MAB/Pz8SExMJCgoCLVaTbVq1ZgzZw62traPrEsSyhOShCKEKCpDJJTazs2KXDYhJeapj/ckZAxFCCFMgCl895eEIoQQJkCmXhFCCGEQMjmkEEIIg5AHbAkhhDAIGUMRQghhEDKGIoQQwiCkhSKEEMIg5BHAQgghDEJaKEIIIQxC7vISQghhEDIoL4QQwiCky0sIIYRByC/lhRBCGIS0UIQQQhiEKYyhyPNQhBBCGIQ8AlgIIYRBSEIRQghhEJJQhBBCGIQkFCGEEAYhCUUIIYRBSEIRQghhEJJQhBBCGIQkFCGEEAYhCUUIIYRBSEIpwyIjI1m6dGmB2xISEnjjjTdKPAZPT88SP0ZxPO6aFEZRFD788MMC50RaunQpkZGRBe5X1q5BVFQUwcHBj9xeGvGa2jUpzNy5c0lPT8+3ftu2bYSGhha4T1m7BmWBJBSh58iRIxw/ftzYYZQIMzMzPvjgA8zMzB5bzpjX4Pz58+zevdsox34cY8ZVGseePHky9vb2Ro/D1ElCEXqOHz/OtWvXjB2GURnzGpw9e5bY2FijHPtxjBlXWbkmZSWOskxmGzaA7OxsZsyYQXx8PGq1mvDwcHbs2MGePXvIzs7Gy8uLwYMHExkZSUxMDDdv3uT27dtUrVqVOXPmYGtry4IFC4iOjubevXuMHDkSLy+vIh9frVbz8ccfc+XKFXJycpg4cSKtWrUiKCgIFxcXoqOjSUhI4L333qNnz55oNBpmz55NTEwMDg4OODs7065dO8zNzdmyZQs2NjacOHGCkJAQABYtWkRUVBRqtZolS5ZQrVq1MnlN/Pz8mDp1KvXq1WPTpk3s37+fRYsWoVarGTZsGJs3b8bT05Ndu3ah0WiYN28ep06dwt7eHkdHR6pXr87WrVsNdg0gr2tyypQpNGjQgAsXLqDVavnkk09wdnYmJCSE5ORkFEVh5syZJCYmEhYWRnZ2NmfOnGHZsmVERETw008/kZOTg6enJ6NHjy7ScQG0Wi0LFy7kzJkzZGVl4e/vT7du3QgNDUWtVhMXF8eVK1cYMmQIvr6+ACxbtoy9e/fi4OCAq6srTk5OvPDCC/niAlizZg07d+7kzp07LFiwgEaNGpXJazJjxgy6d++Ou7s7R44cYf78+bruzX79+hEZGUnPnj3ZtWsXAGFhYezevRt7e3tq165NlSpVOHTokEGvwTNLEU/t559/Vj788ENFURQlNzdXOXz4sDJ79mxFURRFo9EoQ4cOVW7fvq1s3rxZefXVV5W0tDRFURQlNDRUWbZsmaIoinLhwgVFURTl7t27iqenp6IoirJ582ZlyZIlBR7z6tWriq+vr6IoirJo0SJlz549iqIoSnJysjJkyBBFURRl8uTJyvTp0xVFUZRr164pPXr0UBRFUTZs2KDMnDlTURRFycrKUgYOHKhs3rxZV9fWrVt1x2ncuLFy7NgxRVEUZfny5UpoaGiZvSYRERHK6tWrFUVRlAkTJihDhgxRNBqNcvjwYWXOnDmKoiiKh4eHoiiKsnHjxhK/BoqS9//UokUL5dy5c4qiKMrRo0eVN998U5kyZYryxx9/KIqiKLGxscq7775b4Pndvwa5ubmKp6encu/ePeXo0aPK1KlTH3nM++e4efNmZdWqVYqiKEpmZqbSt29fRaPRKIsWLVJGjRql5ObmKnfv3lU6deqkqNVq5fDhw8o777yj5ObmKhqNRgkICFAWLVpUYFxdu3ZVduzYoSiKouzYsUMJCgoqs9dk9+7duv//2bNnK3379lXu3LmjxMfHK2PHjtW7ZkeOHCmVa/CskhaKAbi5ubFw4UJWr17NwIED2b9/P0ePHmXEiBEA3L17l+vXrwPg4eFBhQoVAOjVqxfz5s0DIDc3ly+++IJLly5x48aNYh3/l19+4bfffuObb74BQKVS6bb17NkTgJo1a5KTkwPAoUOHdN/qbGxscHd3f2TdlStXpk2bNgC0bNmS7du3FykmY1yTzp07M3/+fIYMGUJOTg7NmjXj3LlzHD16lM6dO+uVPXDgAO+++26JXoP7GjdujJubGwDt27dn+vTpxMXFcfXq1UL3tba2ZtmyZcTFxaFSqUhJSSnycfft28eNGzf4+eefgbxW4/39PT09sbCwwN7eHhcXF5KTkzl48CDe3t5YWFgA0L1790d2/ZmZmdGjRw8g75ps3LixyHFB6V6TDh06sGLFCgAuX77MgAED+O2330hNTaVTp056Zffv38+gQYNK5Ro8iyShGECdOnXYsGED69evZ9CgQbRo0YLAwEA8PDz0ysXGxmJlZaVbzszMpFy5csTFxRESEsLUqVMZNWoUr732WrGOr9VqWbJkCQ4ODvm2WVtb6/59/02SlZWlF8f9RFMQGxsb3b8tLS3RarVFiskY1+S5554jKSmJqKgoXnzxRVxdXTl69CinT59mzJgxemXVajWWlg/+/EviGtz38PlB3geRlZUVq1evfux+d+7cYfz48UybNo3hw4cTEBBQrKf2aTQaPvzwQ90H98Me/ruwtLREo9EU6+/C3NxcV4eVlVWZvia2trbY29tz6tQpXFxcePnll1m7di0qlYqJEyfqlS3O38XTXoNnkQzKG0BKSgq2trb4+fnRoUMH2rdvz6ZNm9BoNACcO3dOV3bfvn2o1WoANmzYwH/+8x/i4uJo1qwZLVq0ID4+nqSkpGIdv23btqxfvx7IuzX2/Pnzjy3fqlUrtm7dCkB6erqu7xjyPjwzMjKKdfyCGOuatG7dmvDwcLp06UK7du04fPgwdnZ2eh+goN/SSE9PZ8+ePbpthroG98XExJCQkADADz/8QMuWLWnUqBE//vgjkPehFRcXl+/YCQkJ1KxZk7Zt25KRkcHZs2eLddy2bduybt063fLD17wgrVq1Yvv27SiKQnZ2Nt9//71um6lfk06dOrFw4UK6dOlCgwYNiI+PJzU1lerVq+uVu/93cf8a3I/nn3GIgkkLxQBiYmJYsGABTk5OVK9enV69ehEXF4e3tze2trY0adJEd498gwYNGDduHCqVimbNmjFgwAAyMzNZs2YNw4YNo0mTJtSsWbNYxw8MDCQ4OBgfHx8AfHx8CvxWet/IkSMJCgpi8ODBVKlSRTcgD9CtWzfGjBlDbGwsM2fOfLILgvGuSadOnTh48CCurq5AXrdZhw4dHnsNHu7SMuQ1uK9Ro0aEhoaSmJiInZ0dH3/8MWq1muDgYN038oCAAOrXr89LL73E8uXLuXTpEqGhoVhYWODj44OLi0uxB3yHDRvGhx9+yMCBA7G2tqZr1640btz4keV79+7N8ePHGThwIJUqVaJly5a6v4uH47o/IP00SvuadO7cmdDQUN3/c7Vq1ahYsWKB1+DYsWMMHDgQZ2dnmjdvrttm6GvwLJJHAJeiyMhIkpKSCAgIMHYoembMmEHPnj3p2LFjqR+7rF4TQ0lISGDatGmsXLnS2KEUW1hYGE5OTgwaNMig9ZryNRGPJy0UExAWFsaBAwf01i1fvhw7O7snqi8xMREnJyfKly+vG3OYNGmSIUItNYa+JqYoMjKSLVu26K2bNWuWrnVWXMnJyVhYWODo6IhKpWLHjh1PPCuBsRj6mojikYRiAvz9/fH39zdYfQkJCQQEBGBnZ4eiKISEhBT6K+GyxtDXxBR5eXkV6/dKhUlNTWXixInY2Nig1WoJCAigTp06Bqu/NBj6mojikS4vIYQQBiF3eQkhhDAISShCCCEMQhKKEEIIg5CEIoQQwiAkoQghhDAISShCCCEM4v8By46VdTuOcJsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 공분산 히트맵 작성\n",
    "# 공분산은 선형적인 관계를 측정하기 때문에\n",
    "# 두 변수가 비선형적으로 함께 변하는 경우는 잘 측정하지 못함\n",
    "ax = sns.heatmap(df_cov, annot=True)\n",
    "ylim = ax.get_ylim()   # y축 범위\n",
    "ax.set_ylim(ylim[0]+0.5, ylim[1]-0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "227c0241-00cc-433f-9b12-193fd7427089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117570</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.117570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>-0.366126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.817941</td>\n",
       "      <td>-0.366126</td>\n",
       "      <td>0.962865</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      1.000000    -0.117570      0.871754     0.817941\n",
       "sepal_width      -0.117570     1.000000     -0.428440    -0.366126\n",
       "petal_length      0.871754    -0.428440      1.000000     0.962865\n",
       "petal_width       0.817941    -0.366126      0.962865     1.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 상관계수 계산\n",
    "\n",
    "correlation_matrix = data.corr()\n",
    "correlation_matrix\n",
    "\n",
    "# 절대값이 0.3 이상이면 상관관계가 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cd8d81db-3618-481a-b878-b69b17f3b689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 상관계수 히트맵\n",
    "ax = sns.heatmap(correlation_matrix, annot=True)\n",
    "ylim = ax.get_ylim()\n",
    "ax.set_ylim(ylim[0]+0.5, ylim[1]-0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4ac46f8b-b13f-4a64-b2ac-25dbe7ee16e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 산점도 행렬\n",
    "sns.pairplot(data, diag_kind='hist')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9eead722-45a3-49eb-9c52-2a80180e5e60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 산점도\n",
    "data.plot(kind='scatter', x='sepal_length', y='petal_width', c='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4c58a78e-925c-4ea6-9b38-8a37b89d43ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sepal_length', ylabel='petal_width'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+0hbKnJ5YBzwuj/e2Dg88sQ6fmXxlDfQYRmWNyUj0+X0xGYm6c4g4h9YxRIxXKI7B/57E6uvsI923uTh37pxaEICuzxpqamoCjOmbw+FATk4OKioqhByPrEPEffnNure/1nm6+xR6CrRPQcQ5tI7Rc//A+IEhG3OuySAn3X0KBQUFKCoqwo9+9CMAwNGjR/HHP/4RZWVluk40depUbNmyxWtbY2MjkpKSAHR99mCz2fCb3/wmkPy8UtDBCjn1zkDxl9VKs4+C7VMwa/aRvcUtzewjK7xO9ZA9Z9B9Ct2eeOIJ/O53v0NcXBzsdjuqqqqwYMGCgINWVlZi586dKCgoQGlpKfbs2QO73Y6MjAyUlJQEfDySg4h55GbNRdc6j4g+BT3n0Co4WseIHhKDlMuD+wUm0/NGYui+UgCAzs5OHD9+HO3t7Rg5ciQGDhxoZDZdeKWgLdic7Ba9mL+cNburcXLbCaRnj0DqhDSTk3nTGk89Vxtaz72oq4lgr2rMIvtrNKgrhaamJiQkJAD4roP5kksuAQB0dHSgo6MDsbGhnf1DxuI888Bs+sV6nNp+EgDwf4v+geHXp+PWt3NCnMo3vb0O/p57kb0MwfRUkDh+i8LSpUvVt3RuvfVWREREQFEUr///6KOPTAlK5uM888DU7K5WC0K3U9tPomZ3dcivGC4USK+Dr/16jhFojr72VJBYfotCz/f4y8vLDQ9D1sJ71Qfm5LYTvW63WlHQem71PPciXh98jVmP7impd999N1asWIFjx3w311D/w3vVByY9e0RA20NJ67nV89yLeH3wNWY9uovCokWLkJCQgBdffBF33HEHnnvuOezdu9fIbBRinGcemNQJaRh+fbrXtuHXp1vuKgEIrNeht+dedC9DX3sqSKyAZh91+9e//oXly5fj008/xf79+w2IpR9nH2nj7CPxOPsosMdo4ewj8QzvU/jggw+wY8cOHD58GFdccQVmzpyJRYsW9S0tSYXzzAOTOiEt5MVALz29DlrPvahehmB7KkgM3UVh5cqViIiIwOzZs3HddddZokeBiIjE0l0UysrKUFNTg7///e8oKipCdHQ0brjhBtxyyy1G5qMworV4jZ63Zazw1o2It1zMfNuGC+BQT7qLAtB1E7yf//zniIuLw/vvv4/FixezKJAQWgvL6GkKs0LjmIiGL7ObxtiYSD3pnn304osvYubMmcjPz8fJkydRVFTE3gUSoreFZRorGwD4bwrrpucxRuvZiOU654Lb6cbWOR/CWd+m+zEijiEqK4Un3VcKiYmJeOmllzB8+PCL9h05cgSjRo0SGozCR+2+2l63J2Um62oKs0LjmIiGLzaNUajpvlK4//77fRYEAPjzn/8sLBCFn2FXD/O7XU9TmBUax0Q0fLFpjEJNd1Hwpw+tDkQqrYVl9DSFWaFxTETDVyiaxtiYSD31qXntQnl5eVi9erWIPAFj85o2WXJqLV5jpdlHwS4GZNbso2Cb18wky+tU9pzCmteIjKa1eI2epjArNI6JaPgys2ks1MWArEXI20dDhgwRcRgKc876NlTvqQ5qBoyzvg11n9X2eozGygZ8+eYX6swmI2hlILIyv1cK3Qvr+BMbG4vFixcLC0ThScRCK1rz7rV6IUTg3H+Snd+i0HNhHV+4yA6JIGKhFa0FX3rrhbgq/8eaayH35efwlYFIBn6LApvTyAxmzLvX6oUQgXP/qT8I6IPmL774AnV1dV5XDjfccIPwUBRezJh3r9ULIQLn/lN/oPuD5v/8z//EvHnzMG/ePHzwwQd4+umn8c477xgYjcKFiIVWtObda/VCiP45OPefZKX7SuHgwYN466238Mc//hFPPvkkOjo6MH/+fCOzURgZNXM0hk9KD2qhle5j9DbvftKCKbgq/8eo3VeLYVcPE1oQ9GYgsjrdRWHAgAEAgJEjR+Lrr7/GVVddhbq6OsOCUfgRsdCK1rz7pMxkQ4pBIBmIrEx3URg9ejQOHTqE7OxsPP3007jmmmvQ2dmp/Y1keVbpanXWt6H6eHOvVwpWyUnUn+kuCnPnzlX/PWfOHFRUVGDp0qWGhCLzWGVevVafglVyEvV3uj9oXrlypfrva665Bg888ADeffddQ0KROaxyT/2eOdqb2vu0xgARiaFZFM6fP4+WlhZs27YNLS0t6v9qa2s5+0hy/ubVWymHVXIShQPNt49KS0tRVlaGhoYG3HbbbWqPQnR0NO68807DA5JxrDKvXsQaA0QkhuaVwiOPPILy8nL85Cc/wUcffYTy8nKUl5fjf//3f3HfffeZkZEMYpV59Vp9ClbJSRQOAlpPob29HXV1dUhPT9d+sEm4noI2We6p76xv89unYJWcQP957q1Elqyy59RaT0H3B81bt27FrbfeiocffhhA1y0vVqxY0YeoZDXRQ2IwNGtYyH/RRg+JQdo1ab3msEpOov5Md1FYsWIFNmzYgOTkrsafMWPGYNu2bZrfpygKtmzZgnvvvfeifd988w3y8/Nx11134eGHH0ZTU5P+5ARA3/oB+1ftN3T9AFHMWE9BxFoHInISWZXuPoXo6GjExnpfcuh556mwsBAjRozw2f28ZMkS5OXlITs7G+vWrcOyZcvw+OOP640U9qywfoAoZqynIKLXQUROIivTfaWQkJCAQ4cOISIiAgDwwQcfYODAgZrft3TpUpSUlPjct2/fPmRnZwMApk+fjk8++URvnLCnNXe/t/UDrHjFoNWnEOgxfI2HiF4HETmJrE73lUJJSQlKSkpQUVGB66+/HsOGDcPChQs1vy8qKsrn9qamJiQmJqpfx8TEwOVy6Y2j8veBiRlSUuJCct7q482wR9q9FnSxD7DD3uJGyuVxqH7/mM/vazt6FpkTv2dSSn20fhYRxzDjHFYVqtdoX8iStT/n1F0UkpOTsXz5cjidTng8noveSgpUR0cH7Ha7dxhHQMs7AAjf2UeeWAc8Lo/3tg4PPLEOnDlzDjEZiT6/LyYj0XIzJ7R+FhHHMOMcViTLTBlAnqyy5xQ2+wgANm/ejMWLF2PlypX417/+FXjKHgYPHoyGhu/eyjh//nyvVxV0MSusHyCKGespiOh1EJGTyOp09yksXLgQhw4dwvTp0+F0OrF+/XrMmjULd9xxh64TTZ06FVu2bPHa9utf/xr3338/JkyYgLfeegv19fWYPXt2QD9AuF4pdNOau99Y2YC2o2cRk5FoyYLQk1afgt5j+BsPEb0OInKaxQqvUb1kySp7Tq0rBd1FYebMmXj77bfVt3za2trwwAMPYN26dboCdheFyspK7Ny5EwUFBTh9+jSKi4vR3t6OoUOHYsGCBYiJCew/snAvCnrIkhOQJytziidLVtlzahUF3W/iJyQkeH0GEBMTE9DnCt1XCZmZmcjMzAQAXHrppVi9erXuYxARkbF0f6YwdepUrF+/Xv36o48+QlZWliGhiIgoNHRfKSxZsgStra3485//DJvNBqfTibi4OKxevRqKoiAiIgL//Oc/jcxKREQG010U9uzZY2QOIiKygICmpBIRUf/GokBERCoWBSIiUrEoEBGRikWBiIhULApERKRiUSAiIhWLAhERqVgUiIhIxaJAREQqFgUiIlKxKBARkYpFgYiIVCwKRESkYlEgIiIViwIREalYFIiISMWiQEREKhYFIiJSsSgQEZGKRYGIiFSOUAcgYznr21B9vBmeWAeih8SEOg4RWRyLQj9WWfYlts3ZAnukHR6XB5OX3IRRM0eHOhYRWRjfPuqnnPVt2DZnC9xON9qb2uF2urF1zodw1reFOhoRWRiLQj/VXNUMm8P76bU5bGiuag5RIiKSAYtCPxV/WTw63Z1e2zrdnYi/LD5EiYhIBiwK/VT0kBhMXnITHNEODIwfCEe0A5OX3MQPm4nIL37Q3I+Nmjkawyelw97i5uwjItKFRaGfix4Sg5TL43DmzLlQRyEiCZjy9tGWLVswc+ZM3HnnnVixYoXXvvLyckyZMgW5ubnIzc3Ftm3bzIhEPTjr21D3WS1nJhGR8VcKra2tePnll/HGG28gOjoaubm5mDRpEkaP7pov39zcjFmzZuHBBx80Ogr50N3LYHPY0OnuZC8DUZgz/Erh448/xqRJkxAbGwu73Y7p06djx44d6v6mpiYkJiYaHYN86NnL4DrnYi8DERl/pVBTU4O0tDT169TUVHz11Vfq1+3t7Xj33XexYcMGjB8/Ho8++igiIyN1Hz85OVZo3kClpMSF9Px6+cpZfbwZ9kg73E63us0+wA57ixspl4fu55J5TK1IlpyAPFn7c07Di4LL5cKgQYPUr202G2y27y5QCgsLUVhYCLfbjeeffx6lpaWYPXu27uM3NLSgs1MRmlmvlBQ5PsDtLacn1gGPy+O9rcMDT6wjZD+X7GNqNbLkBOTJKntOmy3C7x/Thr99dMkll6Curk79ura2FqmpqRc9zuFwICcnBxUVFUZHon/r2csQGRfJXgYiMv5K4brrrsMDDzyAX/3qVxgwYADee+89LFiwQN3f2NiIpKQkAMDmzZsxZswYoyNRD929DM1VzYi/LJ4FgSjMGV4UkpOTkZ+fj3vvvReKoiAnJwetra34r//6LxQUFKC0tBR79uyB3W5HRkYGSkpKjI5EF4geEsNiQEQATGpeu/3223H77bd7bcvMzAQA/OEPfzAjAhER6cB7H/Vzzvo2VO+p9jvNtLGyAV+++QUaKxsMy1Gzuxq7n9uFmt3Vhp0DYCMeUbB4m4t+TM8iOzuKP0LFqwfUr6/MH4tJC6YIzbHpF+txavtJAMD/LfoHhl+fjlvfzhF6DoCNeEQi8Eqhn9KzyE5jZYNXQQCAilf3C71iqNldrRaEbqe2nxR+xcBGPCIxWBT6KT2L7NTuq/X5vb1t74uT204EtL2vuKgQkRgsCv2UnkV2hl09zOf39ra9L9KzRwS0va+4qBCRGCwK/ZSeRXaSMpNxZf5Yr++7Mn8skjKTheVInZCG4dene20bfn06Uiek9fIdfcNGPCIxIhRFCc09IgThbS78c9a3aS6y01jZgNp9tRh29TChBaGnmt3VOLntBNKzR/gtCMGOqbO+zZRGPBmee0CenIA8WWXPqXWbC84+6uf0LLKTlJlsWDHoljohTfjVgS9sxCMKDt8+6iM98/9FzJkP9hhW6VMwC/sUiILDK4U+0DP/X8Sc+WCPYZU+BbOwT4EoeLxSCJCe+f8i5swHewyr9CmYhX0KRGKwKARIz3x4EXPmgz2GVfoUzMI+BSIxWBQCpGc+vIg588Eewyp9CmZhnwKRGCwKAdIz/1/EnPlgj2GVPgWzsE+BSAz2KfSRnvn/IubMB3sMq/Qp6MU+BbFkyQnIk1X2nOxTMIie+f8i5swHewyr9CmYhX0KRMHh20e9EHH/f61j6OkPCPYYh9cfxNrb1uLw+oOG5tTqD9BzjMbKBuxfZezsJ/YxEPnHt4986Hn/fwC93v/f32Wk1jH09AcEe4zXfvwK2mpa1a9jUgfh/gMPCc+p1R+g5xhm9EuI7GOQ/S0EK5Ilq+w5td4+4pXCBUTc/1/rGHr6A4I9xuH1B70KAgC01bR6XTGIyKnVH6DnGGb0S7CPgUgfFoULiLj/v9Yx9PQHBHuMo5sqfe7vuV1ETq3+AD3HMKNfgn0MRPqwKFxAxP3/tY6hpz8g2GNk3Jrpc3/P7SJyavUH6DmGGf0S7GMg0odF4QIi7v+vdQw9/QHBHuOHOVcgJnWQ1/6Y1EH4Yc4VQnNq9QfoOYYZ/RLsYyDShx8090LP/f+1PnDSOoae/oBgj3F4/UFU/b+vcNm0kV4FQXROrf4APcdorGxA29GziMlINGyKrKg+Btk/bLQiWbLKnlPrg2YWhSDI/uKwIlmyMqd4smSVPSdnH/kg01x1EfP/jc5ARP1H2HU0y3TPfRHz/43OQET9S1hdKcg0V13E/H+jMxBR/xNWRUGmueoi5v8bnYGI+p+wKgoyzVUXMf/f6AxE1P+EVVGQaa66iPn/Rmcgov4nLKekyjRXXcT8f1nWKADkn+5nNbLkBOTJKntOrqfgg0z33NfKasZaCDKNFxEFx5SisGXLFixfvhwOhwM33ngjCgsL1X3ffPMNiouL0draiqSkJDz33HNISEgwIxYREV3A8M8UWltb8fLLL2P16tVYu3Yttm3bhkOHDqn7lyxZgry8PKxbtw6TJ0/GsmXLjI5ERES9MLwofPzxx5g0aRJiY2Nht9sxffp07NixQ92/b98+ZGdnAwCmT5+OTz75xOhIRETUC8OLQk1NDdLSvrvJWmpqKs6cOQMAaGpqQmJiorovJiYGLpfL6EhERNQLwz9TcLlcGDTou1s422w22GxdtaijowN2u907kCOwSP4+RTdDSkpcSM+vlyw5AXmyMqd4smTtzzkNv1K45JJLUFdXp35dW1uL1NRUAMDgwYPR0PDdbRnOnz+PqKgooyMREVEvDC8K1113HT788EOcP38eHo8H7733HqZOndp1cpsNI0eOxO7duwEAmzZtwo033mh0JCIi6oUpzWvvvPMO1qxZA0VRkJOTg3HjxmHnzp0oKCjA6dOnUVxcjPb2dgwdOhQLFixATAznxBMRhYL0Hc1ERCROWN37iIiI/GNRICIiFYsCERGpWBSIiEjFokBERCoWBSIiUrEoEBGRKiwX2emrefPmweVy4ZlnnlG3lZeXY/78+epN/woKCtS7vobCb3/7W9TV1WHgwIEAgNdee029v5SV1q7wl9NKY9rS0oKnnnoKX3/9NRwOB1566SUMG9a1DraVxlMrq1XG9O2338amTZvUr0+cOIGCggLcd999AKwzplo5rTKeALBx40Zs2LABLpcL2dnZmD17trqvT+OpkC6HDx9WbrnlFuVPf/qT1/aNGzcqK1asCFGqi+Xm5ir19fU+9xUXFytbt25VFEVR3nzzTeXZZ581MZk3fzmtNKbFxcXKO++8o37d2dnptc8q49mdp7esVhrTbu3t7crMmTOVc+fOqdusNqaK4junVcazublZmTZtmuJyuZTOzk7ll7/8pXL06FF1f1/Gk28f6eByufDCCy/gkUceuWjfhbf/DrVz5871+peAldau8JfTKmPa0NCAkydP4rbbblO3RUREqP+20nhqZbXKmPZUVlaGadOmITb2uzsdW2lMu/nKaZXxjIyMRGRkJFpaWuB0OuFyuYIeTxYFHebPn4977rnH54ugvb0da9euxd13342FCxdaYj2I++67D3l5edi6dau6zYprV/jKCVhnTCsrK3HppZfisccewz333IMXXngBHo8HgPXG019WwDpj2k1RFDVPN6uNKeA7J2Cd8Rw4cCDuv/9+TJs2DTfddBOuv/56DB06FEDfx5NFQcN///d/IyEhAZMnT/a5v7CwEGVlZVizZg1cLhdKS0tNTuht48aNeOONN/DCCy9g8eLFqKqqAiBm7QqRessJWGdMGxoa8OWXX6KkpARr1qxBY2MjysrKAFhvPP1lBawzpt0+/fRTZGVlIS7uu/v9W21MAd85AeuM5+HDh/E///M/2Lp1K8rLy3HkyBF8+umnAPo+niwKGtauXYtPP/0Ud955J+bNm4fy8nK8+OKLFz3O4XAgJycHFRUV5of0YejQofjpT3+Kw4cPA7Du2hUX5uwp1GOanJyMrKwsDB48GDabDTfffDMqKysBWG88/WXtKdRj2m3Dhg34+c9/7rXNamMK+M7ZU6jHc+fOnbjpppsQExODyMhI3H777SgvLwfQ9/FkUdDw3nvvYf369Xjrrbfw1FNPYcqUKfjd736n7m9sbFT/vXnzZowZMyYEKbu43W6cO3cOQNdMlH/84x/44Q9/CMBaa1f4ywlYZ0zHjh2LgwcPoqWlBQCwa9cuNYuVxlMrK2CdMQW63pI5cOAAxo0b57XdamPaW07AOuM5atQo7Nq1C8q/b3a9Y8cOjBo1CkDfx5O3zg7A7t27sWnTJtx3333qehAvvPAC9uzZA7vdjoyMDJSUlKjTLM3mdDrxy1/+ElFRUfB4PMjLy8OoUaMst3aFVk4rjenHH3+Ml19+GXa7HVdccQXuuOMOfPLJJ5YaTz1ZrTSmR44cwTPPPIPXXnsNQNfnIVZ7jWrltNJ4Ll++HNu3bwcAjBkzBr/4xS+Ceo2yKBARkYpvHxERkYpFgYiIVCwKRESkYlEgIiIViwIREalYFIiISMWiQBSgU6dOed14zpfx48eblEZfHiK9WBSIJPT888+HOgL1UywKRBJ66623Qh2B+imuvEb9jsvlwvz583Ho0CF4PB4UFRVhxIgR+Mtf/gKn0wm3243f//73uPrqq1FWVoa9e/fC4/Hg1KlTcDqd+MMf/oCf/OQnaGxsxOOPP45z586hpaUFubm5yMnJCTjP559/jiVLlsDtdsNms2Hu3Ln4wQ9+gKVLl6K1tRWnT5/G6dOnkZiYiL/+9a8YNGgQmpubMW/ePJw+fRqDBg3CqFGj0NjYiLlz5+Lhhx9GW1sbcnNzMWPGDFx77bUAgL/+9a/Yu3cv6uvrUVJSgokTJ4oeWgoHghcCIgq5LVu2KEVFRerX7e3tyr333qucOnVKURRFqampUX72s58piqIoGzZsUCZOnKicPn1aURRFOXbsmJKdna20tbUpZ8+eVaqqqhRFUZTGxkZlwoQJSmdnp1JVVaXceuutfjOMGzdOUZSulbHuuece5ezZs4qiKMqBAweUvLw8RVEU5aWXXlJycnKUtrY2RVEU5fe//72yZs0aRVEU5YknnlBWrlypKIqiuN1upaioSPmP//iPi46vKIpSVVWljBkzRtm7d6+iKIqya9cuZcaMGYEOG5GiKIrCKwXqd6644gosXLgQy5cvx1133YVvv/0WlZWVKC4uVh/T0dGB8+fPAwAmT56M1NRUAMDIkSORnp6Or776CmPGjMH+/fuxbt06nDhxAm1tbepdSPXat28fTp486bVqX2trq/rvqVOnIjo6GgBwzTXX4OuvvwYAbN++HU8++SQAwG63Y9q0adi2bVuv50lLS1Pv5jl+/HhUV1cHlJOoG4sC9TuXXnopNm7ciE2bNuGee+7BrFmz8P3vfx+vv/66z8dfuPCI0+lETEwM/va3v6Gurg65ublIT0/HjTfeqN6iWC+3241rr70WixYt8rk/MjJS/feAAQPU1dJcLhdstu8+8tNaMavnffJ7HocoUPygmfqdb7/9FpGRkbjrrrtQUFCA6upqfPPNN/jss88AAJ2dnfjiiy/Ux+/YsQNnz54F0PWXvdPpRHp6Oj7//HNMnToVo0aNwsGDB3HmzJmAs2RlZeGf//wnTpw4AaDrl7uvBYUuNHbsWGzYsAFA19KPGzduvOgxHR0dAech0sIrBep3KioqsHDhQiQmJiIqKgpPPvkkbrnlFjz77LOIiIiAoijIyclRF0b50Y9+hLlz5+Ls2bNwOBx48cUXYbfbkZ+fj/nz52PFihUYO3YsLr300oCzDB48GM888wzmzJmDqKgoKIqChx56yGtRIV+eeuopPPHEE9i4cSPi4+Mxbtw4nD59Wt2fk5ODu+66C3l5eab2RFD/x/UUKKyVlZXhyy+/xBNPPBHqKH6tWrUKzc3N+O1vfxvqKNTP8UqBqI/OnDmDxx57zGubzWbDqlWrgj72sWPHMGLECDgcDnz77bd4++23sWDBgqCPS6SFRYGoj1JSUnr98DpYe/fuxWOPPYbY2Fi43W48/PDDuPLKKw05F1FPfPuIiIhUnH1EREQqFgUiIlKxKBARkYpFgYiIVCwKRESkYlEgIiLV/weYidCFLCuWVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 산점도\n",
    "data.plot.scatter(x='sepal_length', y='petal_width', c='purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9bfcdd48-0204-4a98-be0c-1665efa6fca0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "상관계수: 0.8179411262715757\n",
      "p_value:  0.0000000000000000000000000000000000002325498079793166956\n"
     ]
    }
   ],
   "source": [
    "# 상관계수 유의성 검정\n",
    "## 귀무가설 : sepal_length와 petal_width는 상관관계가 없다.\n",
    "## 대립가설 : sepal_length와 petal_width는 상관관계가 있다.\n",
    "import scipy.stats as sp\n",
    "\n",
    "corr_values, pval = sp.pearsonr(data['sepal_length'], data['petal_width'])\n",
    "\n",
    "print(f'상관계수: {corr_values}')\n",
    "print(f'p_value: {pval : .55f}')\n",
    "\n",
    "# p_value 값은 유의수준 0.05보다 작으며, 따라서 기각역에 포함되어 상관관계가 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c32973-84ff-4146-adb8-e4548954b67c",
   "metadata": {},
   "source": [
    "## 교차표 분석 - 2개 이상의 범주형 변수 분석\n",
    "- crosstab() : 교차표 생성 함수\n",
    "- scipy 모듈의 chi2_continggency() : 2개의 범주형 변수에 대한 카이제곱 통계량 계산"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a4eb0796-ba80-4f81-bc8d-38dacdd7c9b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>영양제</th>\n",
       "      <th>감기여부</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>복용</td>\n",
       "      <td>유</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>복용</td>\n",
       "      <td>유</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>복용</td>\n",
       "      <td>유</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>복용</td>\n",
       "      <td>유</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>복용</td>\n",
       "      <td>유</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  영양제 감기여부\n",
       "0  복용    유\n",
       "1  복용    유\n",
       "2  복용    유\n",
       "3  복용    유\n",
       "4  복용    유"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 교차분석하기 위한 범주형 데이터 정의\n",
    "cross_data = pd.DataFrame(\n",
    "                    {'영양제' : ['복용', '복용', '복용', '복용', '복용', '복용', '복용', '복용',\n",
    "                                '복용', '복용', '복용', '복용', '복용', '복용', '복용', '복용',\n",
    "                                '복용', '복용', '복용', '복용',\n",
    "                              '미복용', '미복용', '미복용', '미복용', '미복용', '미복용',\n",
    "                              '미복용', '미복용', '미복용', '미복용', '미복용', '미복용',\n",
    "                              '미복용', '미복용', '미복용', '미복용', '미복용', '미복용',\n",
    "                              '미복용', '미복용'],\n",
    "                     '감기여부' : ['유', '유', '유', '유', '유', '무', '무', '무', '무', '무',\n",
    "                                  '무', '무', '무', '무', '무', '무', '무', '무', '무', '무',\n",
    "                                  '유', '유', '유', '유', '유', '유', '유', '유', '유', '유',\n",
    "                                  '유', '유', '유', '유', '유', '무', '무', '무', '무', '무'] } )\n",
    "\n",
    "cross_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4b6bae66-7d5c-404d-8b41-2fdfe9bf6a11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>감기여부</th>\n",
       "      <th>무</th>\n",
       "      <th>유</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>영양제</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>미복용</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>복용</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "감기여부   무   유  All\n",
       "영양제              \n",
       "미복용    5  15   20\n",
       "복용    15   5   20\n",
       "All   20  20   40"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>감기여부</th>\n",
       "      <th>무</th>\n",
       "      <th>유</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>영양제</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>미복용</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>복용</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "감기여부   무   유\n",
       "영양제         \n",
       "미복용    5  15\n",
       "복용    15   5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 교차분석표 생성\n",
    "## summary 행렬 포함한 교차표\n",
    "display(pd.crosstab(cross_data['영양제'], cross_data['감기여부'], margins=True))\n",
    "\n",
    "## 계산하기 위한 교차표\n",
    "data_crosstab = pd.crosstab(cross_data['영양제'], cross_data['감기여부'], margins=False)\n",
    "display(data_crosstab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7cba67e7-3c10-4a0c-917d-a0c613401a79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. 카이제곱 통계량 : 10.0\n",
      "2. p-value : 0.001565402258002549\n",
      "3. df : 1\n",
      "4. 기대값 행렬 :\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>유</th>\n",
       "      <th>무</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>복용</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>미복용</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        유     무\n",
       "복용   10.0  10.0\n",
       "미복용  10.0  10.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 교차표 분석 : 카이제곱 통계량 산출\n",
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# correction=True를 적용하면 Yates' correction이 적용되어\n",
    "# 검정통계량이 보수적으로 더 낮게 나옴\n",
    "\n",
    "result = chi2_contingency(observed=data_crosstab, correction=False)\n",
    "print(f'1. 카이제곱 통계량 : {result[0]}')\n",
    "print(f'2. p-value : {result[1]}')\n",
    "print(f'3. df : {result[2]}')    # (행의 개수 -1) * (열의 개수 -1)\n",
    "print(f'4. 기대값 행렬 :')\n",
    "pd.DataFrame(result[3]).rename(index={0:\"복용\", 1:\"미복용\"}, columns={0:\"유\", 1:\"무\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d228947e-ef39-4119-9a9b-63c018595a02",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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