D12_pandas 常見圖表程式設計.ipynb

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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>501</th>\n",
       "      <td>501</td>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
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       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>502</td>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
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       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>503</td>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
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       "      <th>504</th>\n",
       "      <td>504</td>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
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       "      <th>505</th>\n",
       "      <td>505</td>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
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       "<p>506 rows × 14 columns</p>\n",
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      "text/plain": [
       "     Unnamed: 0     CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  \\\n",
       "0             0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0   \n",
       "1             1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0   \n",
       "2             2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0   \n",
       "3             3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0   \n",
       "4             4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0   \n",
       "..          ...      ...   ...    ...   ...    ...    ...   ...     ...  ...   \n",
       "501         501  0.06263   0.0  11.93   0.0  0.573  6.593  69.1  2.4786  1.0   \n",
       "502         502  0.04527   0.0  11.93   0.0  0.573  6.120  76.7  2.2875  1.0   \n",
       "503         503  0.06076   0.0  11.93   0.0  0.573  6.976  91.0  2.1675  1.0   \n",
       "504         504  0.10959   0.0  11.93   0.0  0.573  6.794  89.3  2.3889  1.0   \n",
       "505         505  0.04741   0.0  11.93   0.0  0.573  6.030  80.8  2.5050  1.0   \n",
       "\n",
       "       TAX  PTRATIO       B  LSTAT  \n",
       "0    296.0     15.3  396.90   4.98  \n",
       "1    242.0     17.8  396.90   9.14  \n",
       "2    242.0     17.8  392.83   4.03  \n",
       "3    222.0     18.7  394.63   2.94  \n",
       "4    222.0     18.7  396.90   5.33  \n",
       "..     ...      ...     ...    ...  \n",
       "501  273.0     21.0  391.99   9.67  \n",
       "502  273.0     21.0  396.90   9.08  \n",
       "503  273.0     21.0  396.90   5.64  \n",
       "504  273.0     21.0  393.45   6.48  \n",
       "505  273.0     21.0  396.90   7.88  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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    }
   ],
   "source": [
    "#Day12\n",
    "#作業目標：\n",
    "#在各種情況下靈活運用圖表\n",
    "#圖表的解讀\n",
    "\n",
    "#作業重點：\n",
    "#依據需求畫出圖表\n",
    "#在做圖表解釋時，須了解圖表中的含意\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "\n",
    "#將資料夾中boston.csv讀進來，並用圖表分析欄位。\n",
    "boston_data = pd.read_csv('boston.csv')\n",
    "boston_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "guilty-garden",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='NOX', ylabel='DIS'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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GUSjXXVSSFxFJw92pr6/njDPO4KGHHuKMM86gvr6eQvkjf4/uky/GO76LSGEpKytj0KBB1NXVUVdXB8AxxxzDtm3b8tyyZHp0ktcd30Uk35qbm9m6dSuzZs3i7LPPZuXKle3JvhD06CQvIpJvZsaoUaNYvHgxdXV1lJWVMXr0aNavX5/vpiWiJC8ikoa709jYuN958uqTFxEpAmbGyJEjuf7662lubqasrIyRI0cWzJ68zq4REUnD3Vm3bh3Tp0/nkUceYfr06axbt0578iIixaCsrIzx48d36JM/7bTTWLNmTb6bloiSvIhIGrt372bLli08+uijtLa2UlJSwvTp09m9e3e+m5aIkryISBqjRo1ixIgRTJkypb1PfsqUKQwYcPA3Hj8UlORFRNKorq7mjjvuYMGCBe1n19TW1nL55Zfnu2mJKMmLiKTR0NBAbW0tixcvprGxkZEjR1JbW8uDDz6Y76YlkvHsGjM71swazGy9ma0zs6tC+RFm9oSZvRaeB4VyM7MfmtkGM3vZzE7t7jchItJdGhsbueGGG1i7di319fWsXbuWG264oWBuGpLkFMoW4Bp3HwV8EbjCzEYBc4B6dx8B1IdhgCnAiPCYCRTO/39FRDoZOXIkTz31VIeyp556ipEjR+apRdnJmOTd/S13fyG8/hPQCAwBprLvMmFLgGnh9VRgqUeeBSrMrOO9s0RECsTcuXOZMWMGDQ0NtLS00NDQwIwZM5g7d26+m5aIZXNCv5kNB34FjAHecPeKUG7ANnevMLMVwHx3fyqMqwdq3X1Np1gzifb0qaysrFq+fPl+87t01Q7umbz/EeympibKy8sT189Wqvi5ovjFG7+Q234o4udqG02lu9pfX1/PT37yk/Y7Q1144YVMnDgx5/PJtv3V1dXPu/v4tJXcPdEDKAeeB/5bGP6w0/ht4XkFcHqsvB4Yny52VVWVd2VY7YouyxsaGrKqn61U8XNF8Ys3fiG3/VDEz9U2mkqhL59s4wNrPEPuTnRZAzPrDfwM+Fd3/7dQ/HZbN0x4fieUbwGOjU0+NJSJiMghluTsGgMWAY3u/oPYqIeBS8LrS4CHYuUXh7Nsvghsd/e3cthmEZFDatmyZYwZM4aJEycyZswYli1blu8mJZbkPPnTgIuAV8zsxVB2PTAfuN/MZgCbgPPDuJXA2cAG4GPgslw2WEQKS+d7odqCfa+9AC7ytWzZMubOncuiRYvaL2swY8YMAGpqavLcuswyJnmPDqCmumPtfkceQj/RFQfZLhEpEvFEnuqubj3ZvHnzuOCCC5g9e3b7n6EuuOAC5s2bVxxJXkTkk2z9+vW8/fbb7We97NixgzvvvJP3338/zy1LRkleRCSNkpIS9u7dy+LFi9u7a8477zxKSkry3bREdNMQEZE0Wlpa6NOnT4eyPn360NLSkqcWZUdJXkQkg8suu4zZs2czadIkZs+ezWWXFc75JOquERFJY+jQodx9993ce++97d01F1xwAUOHDs130xJRkhcRSeOmm27iqquuYvr06WzatIlhw4bR2trKD37wg8wT9wDqrhERSaOmpoaFCxcyYMAAzIwBAwawcOHCgjh9ErQnLyKSUU1NDTU1NQV5nr/25EVEipiSvIhIEVOSFxHJoNgvUCYi8olV9BcoExH5JNMFykREitj69ev5+OOP99uT37hxY76bloj65EVE0ujTpw9XXnkl1dXVlJaWUl1dzZVXXrnf9Wx6qh6/Jz98zi+6HrFq//KB/Xp3c2tE5JNm9+7d3HrrrZxyyim0trbS0NDArbfeyu7du/PdtER6dJLfOP+rXZYPn/OLlONERHJp1KhRjBgxgilTptDc3ExZWRlTpkxhwIAB+W5aIj06yYuI5Ft1dTV33HEHCxYsYNSoUaxfv57a2louv/zyfDctESV5EZE0GhoaqK2tZfHixe1n19TW1vLggw/mu2mJZDzwamaLzewdM1sbKzvCzJ4ws9fC86BQbmb2QzPbYGYvm9mp3dn4QlPIf6gQ+aRqbGzkgw8+YMOGDezdu5cNGzbwwQcf0NjYmO+mJZJkT/4e4DZgaaxsDlDv7vPNbE4YrgWmACPC4wtAXXj+xCv0P1SIfFJVVFRQV1fXfru/lpYW6urqOOKII/LcsmQy7sm7+6+ADzoVTwWWhNdLgGmx8qUeeRaoMLPBOWprQZs3bx4AEyZM4KyzzmLChAkdykWkZ9q2bRsAM2fO5JFHHmHmzJkdyns6c/fMlcyGAyvcfUwY/tDdK8JrA7a5e4WZrQDmu/tTYVw9UOvua7qIOROYCVBZWVm1fPnyxI2+dNUO7pmc+yPb9fX1/OQnP+GNN97guOOO48ILL2TixIk5iV1dXZ1yXENDQ07m0aapqan9zvLdQfE70mdb3PGrq6upqKjgww8/bC9rG87351tdXf28u49PW8ndMz6A4cDa2PCHncZvC88rgNNj5fXA+Ezxq6qqPBvDaldkVT+Je++9148//nh/8skn/YknnvAnn3zSjz/+eL/33ntzEh9wwM2sw3P0EeRWQ0NDzmMqfjLdsW7GFfKyKdT4bdvprFmz/JFHHvFZs2b1mG0XWOMZ8uuBnl3ztpkNdve3QnfMO6F8C3BsrN7QUNbjzZs3j0WLFlFdXd1+Y4BFixYxe/bsnPaZmxnu3v4shemkGx9n+849XY7r6g98A/v15qUbvtLdzZJudNddd3Xomy8UB5rkHwYuAeaH54di5Vea2XKiA67b3f2tg27lIdDY2Mjpp5/eoez000/P+RH0vXv3dniWwrR9554u/5CX6s5BKf+5LQWjbaes0HbOMiZ5M1sGnAkcZWabgRuIkvv9ZjYD2AScH6qvBM4GNgAfA5d1Q5u7xciRI7nxxht58MEH28+FnTZtGiNHjsx300Qkz4455hi2bdtGc3MzvXv3ZtCgQWzdujXfzUokY5J391R9FfsdkQx9RFccbKPyobq6mgULFhTsv9pEpPts3bqVQYMG0dzcTP/+/QsmwYP+8dquoaGBc845h+uvv779+hTnnHNOzo+eS3E4bOQcxi6Z0/XIJfsXHTYSQNdbKkSlpaW0tra2nzK5bds2zKxg+uaV5IP169ezY8cOHn300fY/K02fPp1NmzbldD4lJSXt8VtbW3MaWw6dPzXOV5/8J0RZWRk7duxg1qxZnH322axcuZK6ujrKysry3bREdD35oE+fPsyePbvDNaNnz56d82tGtyV2JXiRwrBjxw5OPfVU7rjjDr72ta9xxx13cOqpp7Jjx458Ny0R7ckHu3fv5rbbbutwzejbbrutYK4ZLSLd549//CP19fXtv8IvuOCCfDcpMSX5YNSoUUybNm2/+zgWypXmRKR7lJaW0tzc3KGsubmZ0tLCSJ+F0cpDYO7cuVx11VXtNwLYsWMHd911FwsXLsxzy0Qkn1pbW9m5c2f79aYA+vbtWzBdruqTj9m1axdbtmxh7969bNmyhV27duW7SSKSZ22nTlZWVmJmVFZW0tzczKBBg/LdtESU5IPrrruOkpIShgwZgpkxZMgQSkpKuO6663I6n/Lycurq6rr1Ik0ikjsfffQR/fv3p1+/fpgZ/fr1o3///nz00Uf5bloiSvLB5s2baW5uZsuWLbg7W7Zsobm5mc2bN+d0Pk1NTcyaNYumpqacxhWR7tHS0kK/fv2AfZc06NevHy0tLTmbR3feUEh98jHNzc3cfPPN7f94vfbaa/PdJOnBUp77vqrrC5RJYTIzTjrpJLZu3YqZMWDAAD7zmc/w5JNP5iT+smXLuOiii9r7+NetW8dFF10E5OaGQkryMf37928/hfKUU06hf//+2uOWLnX1RyiIEn+qcVKY3J36+npmzZrF/Pnz2/8MlSsXX3wxra2t9O3bl127drU/X3zxxUryudba2sqkSZPYs2cPvXv37pZTpMrLy7n55pu59tpr9QUiUgDKysro27cvdXV17cl94MCBOTsxo6WlhT59+rBy5cr28/AnT56cs//oqE8+KCkpYefOnezZE10jfM+ePezcuTPn16dQn7xIYWlubmb79u3tJ0uUl5ezffv2/c6dPxhz5szp8G/7OXNSXBfpACjJB6mu757r674PHz6cH//4xwwfPjyncUWk+5hZ+45ZU1MT0V1Pc+e73/0uZkZ1dTVmxne/+92cxVZ3TZDqRgC5vkHAxo0b2w+q5FK6la7QbnIg0tO4O7169WLv3r3tz7lSWlpKS0vLfn3yueouVpI/RMaOHcsrr7zSZXkuxBO5Dv5JJvHbF25acE7KesNqVwA9//aFXe3k5Hrn5uijj+btt99uf86VpUuXctFFF7X38e/atYuSkhKWLl2ak/hK8ofIyy+/zLhx4zok+rFjx/Lyyy/nsVWfLJMmTeKJJ55ov8fuWWedxWOPPZbvZiVy3HHH8eabb7YPH3vssbzxxhsHHK/D7Qvn70uGhXKp5CRfUmaW0y+pd999t8NzrrSdQTNv3jzWrW9k9KiRzJ07N2f3llaSP4TaEnqu9rR1M+nkJk2axOOPP94+7O48/vjjTJo0qccn+rYE/6UvfYmrr76aW265hWeeeYbjjjvuoBJ9Ids7/BoOC6/H3DMmTc3oAGbUubL/L+ms5pnD+zPvv+0eDucsYNg50AR8+yX49kv7tuGD2XaV5LtRdydh3Uw6uXiCT1Lek7Ql+KeffprVq1fz9NNPc9ppp/HMM8/ku2l588ol+xJ2IR6POpTbbrckeTObDCwESoAfufv87phPT9fdH+ShvgXdoej37G6VlZXMnz+fOXPm5LRftbs988wzOT+jo5iYGXv37m3ftnr16tWj181Due3mPMmbWQlwO3AWsBn4rZk97O7rcz2vT7o/NWb33Xkwf62PJ5jzzjuPBx54oL28p25MY5fsO6gd/0n/fb7P0QuO5miO7lAvvneYb51/Bfb+s8/xZxf9c/vwH3/8Lfb88dX2HYNPclcc7Dv75Xvf+x4TJkzI+TppZpSWlrb/UbKlpeWg5nEot93u2JP/PLDB3f8AYGbLgalAj0vy6bpT4nrqhpSPv9a7O6tXr+anP/1pj9+zPFQ/6TvHtgUHH7vrPud9e34n/lMpMIYD7XMuphuRtx1Id/cOfyLKZaJ3d+bPn99+XatrrrnmoOLFt8/u7m6ybvjGOw+Y7O5/G4YvAr7g7ld2qjcTmAlQWVlZtXz58rRxq6urU45raGg4oLbO3jQ762luHXZrj4kfp+WTXncsn640NTXl/DLS3d32Q7VsoHuWT3fHb1s+XZ0nn+/lU11d/by7j09byd1z+gDOI+qHbxu+CLgt3TRVVVWejYaGhqzqZ0vx9wd4tLrsix8vy6XuaH9ZWVl7e+OPsrKynM6nED9bxU+vbd0xsw7PuV533LNvP7DGM+Tk7riswRbg2Njw0FAmRcDMuP3223t8V01nu3btoqysrENZWVmZ7v4lGd1999307t27vevE3enduzd33313nluWTHck+d8CI8zseDPrA/wN8HA3zEcOobYVHGg/6Nq5vKfbtWsX7k5DQwPurgQvidTU1LBkyRJGjx5Nr169GD16NEuWLMnZn5W6W86TvLu3AFcCjwGNwP3uvi7X85FDr+3nX1uSLKQEL3IwampqWLt2LfX19axdu7ZgEjx003ny7r4SWNkdsUVEJDldalhEpIgpyYuIFDEleRGRIqYkLyJSxHL+j9cDaoTZu8CmLCY5Cnivm5qj+IrfU2MrvuJ3Nszdj05XoUck+WyZ2RrP9FdexVf8PMQv5LYrfnHGV3eNiEgRU5IXESlihZrk71J8xe+h8Qu57YpfhPELsk9eRESSKdQ9eRERSUBJXkSkmGW64Hy6BzAcWNup7DvAtw4mbnc8gGOA5cDrwB+JrpB5ArATeJHo9oRLgd6h/pmhzgbgLaKbTHw5Fm9aKDsvxfzODXHjj73ArDDd7Fjd24BLO03fFFvGKesD9wD/CbwE/D68h6Gd48SGLyXcxAU4EXgG2AbsBt4lurDcCZk+V6KL270LzO9U7xzgd6E964G/i41z4Pux4W8B34kNzwReDY/fAKeH8m8Ci2P1vgH8IsPn3RqW+VrgEaCi0/L837G6RwF7yHBzm06f++diZZ8HVgOvAS8AvwDGxpbblk7rQUXCtq8Ly/EaoFdsvVwRXlcCK2LLeuWBLJPY+BeB5Z3K0q5fneoeGXuPWzu970+FZXx5rP5hYVmuD216gGib+32KGH2yeQ/AZbFpdxPdH/FFYD6x7SDdutfFsnspfMZfyrCsm7ooOzGsJy+G93kXMCnWxibgP8LrpV2tb8BzYfwbRNtf27TDU7YlaZJM8UaGUwBJHjDg120rWGjj94G/aGs/UAI8CXwjDF8CfASUESWjZmBRLOZ9YeF2meS7aMNM4JfAp4G3ib48+oRxmZJ8yvpEG+F5sfd5NdFG0iceJxa3feUmuhz0q7HlMhY4Kb5cUn2uwBTgaaIvzbZjO72JvkCHhuEy4MTYNLuIEsZRYbg9yRN9OTwfG3dqWJGPIfpCeRE4DagIMT6ddCMjumvp3Njy/APwu9j4WSF+kiR/H/DvwI1huBLYSGyjB04Hph3o9tCp7Z8C/l9sfmeyL8nfCVwVqzvuQJZJGB5JlAS3AANi5WnXrzTz6ry+zArL7Zed6u0CHguvXwSeSRXjQN9DGLexbd3qYjtIue6lmO+kzu8j3bKOlT0GTI0Nj+00fjUwPt361lX7Mz26tbvGzFab2QIz+42Z/d7M/iKUX2pm/2Zmq8zsNTO7KTZNnZmtMbN1ZnZjrHyjmX3PzF4M4081s8fM7HUzuzxW71oz+62ZvRybvpooWXzTzJ4i+kZ9C3izbTp3byX6Bh8Sik4Dtrh7M9E/0N4G/quZ9TazcuCzRCtlkuVwAvAPRLdC3Ev0DVxP9EWSRKL6HrmFaA9oSoK4nwU+dvc7wvSvuPtLxJZLGjXAQqKN4c9D2WFECfn9EK/Z3f8jNk0L0d7L1V3EqwWudff3wrQvEG3EV3h0j4L/AdwO3ES0V/+HBG1s82v2fa4AHwONZtb2p5OvA/dnChI+99OBGUQ3w4Ho3glL3P2Ztnru/pS7P5hF+1Jy93eIdhCutP1vxzUY2Byr+3IWoTsvkxrgx8DjwNQUbcl2/YqrIfpFMsTMhsbKWwDM7DqiO8o1ZhEz6/eQQsp1L0X9w4l+/War8+eV9s7rKda3rB2KPvlSd/888L+AG2LlJxNtXGOBr5tZ2y0D53r0j69xREl1XGyaN9z9ZKJvtnuI7if7ReBGADP7CjCC6OfzyUCVmZ1BtEIeFsrOBv5LLOYgM7vczPoCXwBWhfKjifYy2jQRrYCTiFagRHe7MrPewL3ANe7+RmzUAuBbZlaSJE6W9V8APpeg3rPAaDN71MyuNrOK2LjPhC/UF83sRSD+RdoX+DLRz+VlRBsX7v4B0XLZZGbLzOwbZtZ5Hbsd+IaZDexUPppobypuTSgnJNHGMN+bSCgsr4ns/3ktB/4mrHetRL9AMpkKrHL33wPvm1lVaN8LGaa7OrYsG5K2vU34Qish2quPux1YZGYNZjbXzP4sSbwUy+TrRMuk/fNMI+n61Ta/Y4HB7v4boi/Tr3eqchXR+v0m0WeeJObBvoe4tOte0C98fq8CPwL+MYv4bW4BnkyxvXWlq/Utaweb5D1B+b+F5+eJfiq3qXf37e6+i6hPblgoP9/MXiDq1x0NjIpN0/aBvgI85+5/cvd3geawwL4SHr9j34o4gqiLZIO7f+zuH9FxxTiCKIG9DbyVYW/oaaJv1L8hWpGS+EdgnbvfFy8MG+5zwAVJgmRZP9MNWNs+n+eI9lh+StQN8KyZtd0I9XV3P7ntAdwRm/4coMHddwI/A6a1ffm4+98SbXy/IeqOWdzpfXxE1K/7PxO8j31vKNqrGU/UJZT2Wh1Bv/DltJWoS+WJTuNXAWcRfZb3kUwNURIhPO+XSMzsOTNrNLOFseJbYsuyOuG8MnL3x4jW7f9LtK7/zszSLZsul0n4RfNe2AmpB04xsyPSxMn2Br/xX0qdl1u/MM+WMLwoQ6xcvYds7Qyf3+eAycDSLn5ZpeXudxN1KXW1vXUl4/qWxMEm+feBQZ3KjqDjBXaaw3MrHe9E1Rx73QqUmtnxRIlhoruPIzqA1beLafZ2mn5viG3A92Ib1GfdfRHRCjE4xXt4PSSxzxDt+f9lKH+307zLifrUxxL13f0+Rbx2ZnYm8FdEP+m78k9EPxWTrixJ65/Cvp+9O8O9dtvEP591RAd0Frv7VKINbUyCdtQAXzazjURf3kcCE9pGhm6fW4iS6F91Mf3/IfoJOiBWth7ovKdSFdoI0a+1nwDziPaIMtkZPtdhRMurw09vd98d2n4N0QG/tELCmAD8KLzva4HzQ/tOjcX9AvD3QOdfKgfMzD5NtI2803mcu3/g7ve6+0VE91c+I02oVMukBvhceF+vE3VHdPW5tYmvX0nUAJeG+A8D48xsRBi3i2hbPp5ofcj0CyFX7yEu07rXgbv/muhgfZKdjc7T/jHJ9pZqfcv2iwUOMsm7exPwlplNiDVsMvDUAYY8HNgBbDezSrLv93sMmB72+jCzIWb2KaK9g6PN7AozOwz4GlHSb+siIvTHzQG+HYqeIeo/LCP6QAcS7Z3OAa7P1BAzGwTcDVzs7n/qqo67v0q0gn0tyZvLVN8i/zO8t7Zup18CF4bx/YgSU1uXQW+gr5nNNLNjiJL14cSWSxfzOJzowOxx7j7c3YcTbWg1ZlYevtjanEwXVxcN3Tr3EyX6NjcBC8zsyDCfk4kOLv2LmY0Fvkr0k/4uYLiZnZWqjZ3m9THRr4ZrzKzz7S6/D9SG9mRyHvBjdx8W3vexRAeAnyBKYF+K1e2fpG1JhD3zO4gOsnmncRPMrH94fRjRjsob+0fpqNMy6UO0ToyNfZ5T6fpXSlfrV6b2nwCUu/uQWPzvxeL3Af7J3TcTnUV1e5JEdqDvIYWU616K9/Q5ou6z9xPGb5tucui+Jba9bUlRPdX69hfZzBNyc4/Xi4k+mB+E4Rvd/fUDCeTuL5nZ74jO+HiTqHskm+kfN7ORwK/DetIEXOjuL5jZD4lOnfpnoj2HKUQbzyAzuzwcfHwQ+I5FB4g3EvXTrifaaH8ZDs4+mrA5lxP1odZ1Wmc7d/PMI+peSqqr+jeb2d+Hdj4LVIc9VYj6O+8MG6cRnZr1qzDuK0R79jcBtxL9evkW0fGTVM4FngwHpNs8FGJcDVxnZncSnZq6g2hj6cr3if3CcfeHzWwI8IyZOfAnoi+nrUQ/b68OXXuY2Syin8snx95nSu7+OzN7mWij//dY+TpS7K11oYboSybuZ6H860RJYgjR3vZ7wHdj9a42swtjw9PcfWOaebV1SfQm2tv7MfCDLupVAbeZWQvRDtuP3P23Sd5MbJl8m+gEg/gxiV8Bo8ys7ddvuvUrkxrg553KfgbcZ2a/Du1eFNr0iJn9d6KcsiRX78Hd38oQp8t1r9N0bZ8JRNvRJSEfpNLfzDbHhn8ADAUWmlnbsb5r3X1riunTrW+/2r96arqsgYhIEdM/XkVEipiSvIhIEVOSFxEpYkryIiJFTEleRKSIKcmLiBQxJXkRkSL2/wGv97YYnHP6wQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Uzp3GRCXEaY7/VgAXA/h9pdT+RNvZAP5NRG5RSq0uQB/zRjcijsYUrv3OqwO/r1g4Dd+/cUFa0bSndxxKK8SWmvppGhUf7+lHQOIbtpICAsyYWJXfN5UimSG00jLHn+toXFc5NCBA1LKDI7nTmKN+otLgNMf/eQCfVEoN5CUqpfaJyHIAGwGUVODXjogtu2aTB6Q3zqhG44xqx0PUTaPimqoKVJaXDRzdCACV5WWeB8h8ZwjdvPg8PPHrdkT6459bTLPUz53GRKXFaaonlBr0kxLz/CW3gcuaMx8ynL6SOop3U31TZ9KYCsdpF93BLMDg9xBUV1Vg7tRxebnAtHf2oDxo/8+EO42JSovTiN8uBSXn9BQRGQfgUQAXIJ4u+kWl1PZcny8bqSPivv5o2jRPUuoo3ikt03QBeCd8ynbaxZR2mY89BPmk+5ZkFY0pZvUQlRCnwD9XRE5o2gXAiEG87jcB/FIpda2IlAPQr4R6JDVn3un0K6cTsuwuDA21ozF57IiMA1ZMaZeV5cG87CHIp+qqCvz9VbNw9zMtCAUF/VGF3v5Y2uaOUjjMh4jOcErnzPvErYiMBXAp4mUgoJTqxSC+PQzWqqVzsOTCybanX9kdom53YUhN83xkc+vAorAp7dJU1M3LFFAnG5oP4p6ftyAUEPT1x3DDxTPw3VfeSTtNrCzIDVxEpaQYZ+7OAHAUwHdFZC6A1wHcrJRKq18sIjcBuAkApk2blvEk+ZI65bLmpX3Gna4NtaON5+DqLgx2i8KmtMsrZtXiiab2jOf3MgXUTuo3k6THtr0zsNCbdLovxsVdohJSjKMXyxCv9PlvSqkPAzgJ4OvWOyml1iilGpVSjRMnTvSkI/k8/cpaltluUbi6qgLLGuvS2pc11mHxrEkZewgCAhz6QF/l02u6IyODAYGlijQqgsLFXaISUozA3w6gXSn1WuL3dYhfCArfEYezcAfDbu4/3B3BWsvIPrkJ6uHrPpyWRRNTyPpiZMoWypbum0k0phAIpGdDSUC4uEtUQgoe+JVShxGv8z8z0bQYQEuh+wHkf6drKrsjGds7e9BrmS7p7Y+hvbMH7Z09qCjL/WKUz5LJupLR9197Ie6/dm5JlZEmonTFmOMHgL8G8MNERs8+xI91LLjB7HR1U/rYtCi8/2hXxkaomIq3X3Leh3K+GOW7SBsQT3+dddaYjPeQz01iRFRYRQn8SqlmAI3FeG2rXHa6ZlP6WLcovGWPvs7dlj1HcfX8qTlfjPJdpA3If5lnIiq+Yo34h5RsauFnO6q2lmsGgDlTxmLDjvcy7jtnylgAuZddyPfUlem9dp3uxz2/aOHFgKhEMfBnyc2oOlngbc/hE3iu5cjA/ZJ5/J+ZX4d/evattE1QAuAz889k+uRyMEu+i7Tp3mswILj7md3ojSqewEVUohj4s6QbVUf6o/jmC2+jvnoUmtuP440DH2gfm1rc7ZvXzcPtT+6AUoAI8MBn5+YlcNp9W0itOOpmQ5ixqJ1lpy6rcxKVFgb+LFlH1d2RfvRGFTYb5u2tkuf65ruKprWP1ufLpQaQ7hvErZefh3/+77fS7sfqnESlhYHfhU0th7GxpQNXzKrF4lmTBoL2z95oxz8++5bzE6RIze/P1zm7TgZzjrD1AtXe2YMRoUDabl5W5yQqLQz8Dq5Y/SLe7ohXk3iiqR0zayvx3C0fR3VVBfYe6c7quZY11uFkbxTh7ohjwNctCudq696MytoD7dnWADItFHMDF1HpYOC3sanl8EDQT9rTcRKbWg5j8axJxto6qS6aNhbXf2Q63j/Ziwef34Of73gPURXD/dfONWbC6M7wTZ74lYtczhFO0qVz5nMBmYgKj4Hfxg9fe1fb/uTrbVg8axIWz5qEmbWV2GO5OHxq9ocws3bMwCJquDuCj/7zC4hv1o1Pidy6tlmbCeN04lcukucIv5Qy3XNJQ7XjaN+Uzrntjsuw7Y7LuIGLqEQx8BukLoZa/XL3Edy5YRdWLZ2D5275ODa1HMaTr7dj8tgR+D8fnZ4RoHcfOgFLhQb0x4CNuw8jFAykTefYFXcbzJSP9RxhN1M8dqmr+Trhi4gKj4FfQ7cYapU6Ck+O/nXC3RHsO6pfC/jGz94c+Dk5neN04tdgJM8RdsvLWkZEVDzFqM455JkWQ62czt1NFky7/7k9js/1+PYDaO3osi3uBsSngtY1taG1o8tVHwdDV6SN8/lEpY8jfg3TYqiV3Shcd4gJAIwMBdAXVei3VmnDmekcU3G3fC/6uuHlfgMiKg6O+DWSi6GpzhpTnva79WxeK12t/8ryIO5ZegF+8qWPah+TeiGxHuxiWvQt1Mifc/pEwwdH/Aa6xdBscuu1h5gohU+c/yG83HoMQRFEU0ofOF1IvFr0dSPbRWEiGtoY+G1YF0Ptzt21MhVMA4Dbn9yRFvQBYGbtGNvny8eiby4BPJdSD0Q0tDHwe8g6Pw4Az+w4GC90ZnHXM2/i0xdMMk6nJBd9H9+ePsfv9kKUSwAfTKkHIL+7j4kofxj4PZasx5PcASsQ7f3KxPnAFNOir5NcA/hgSj0UYyGaiNzh4q7HWju68L1t+7FyXTzDp6dPX8wsBuUqP9666OuGXQC3Yyrp4FTqoZgL0UTkjIHfQ3c+tQuXr96Ku55pQcS6dTdFWQC4/9r81OPXyTWA67Kb3JR6sFuIJqLi41SPR3Sj3lQVZQHc9cezEOmP4eKGGk/nwBtnVOOsMeV470TvQNtZY8pdzdPnUurBy93HRDR4DPweeW73YW17eTCAQCBeovmuZ3YjKAH8i/qtbbXOwS6StnZ0pQV9AHjvRO/ATmEn2ZZ6IKKhjVM9BbassQ4//8rF+NFrBxDpVzjVF0WkX+HWtc0Id0cy7p+cLrp93U5cvnor7tywK6vXa+3own+8tE97m1dTLz967Z2s2omosBj4PfKp2fqibV/4WD0OfXBaW61z96ETaW2DXSRNXjRMZwZ4NfWyP3wqq3YiKiwGfo/YF1vLzOOPS29/uVWfdWNqT+W0xpDNHoBsfewc/aKxqZ2ICouB30MXTZ+A8iBQEQygPAg0Tp8AAGh7vyfjvqGgYPbksWltNYYsH1N7KtM0zuca6/DCLZd6mlP/J/PrsmonosJi4PdIsjpnbxSIRGPojcZPr2rt6MI9v2jJuP9dfzw7I51z4TnVCAbSN3wFA4KF5zgvtNZXj9K2f/aiOtuRfj7KPu83nD9gaieiwmJWj0d0p1cFRfD0jkOIRdMn+MuDggumjLU+BaqrKrB62Vx8bd3Ogbb7r3VXDz9UFkQoKGnlIUJBQagsaHzMYHfbhrsjaO/swc+aD2lv/+XuDmYHEQ0BRQv8IhIE0ATgoFLqqmL1wyt140eiO9Kf1nayN4pHX9qPXsvCbm9UobJcH5BN9fCTQdZUI79u/EgEA+mBPxgQ4+7gwZ71m3oo++l+/e7koAAPbdzjWZVPp8+EiOKKOeK/GcBvAdiXpSxRnSd7tUu4pwwlGw590GMMsMl6P0mpQTZZ9dO6ByBZHfS2tc2ACKCU7elZgyn7rDuUXec7L+0H4E2VTzefCRHFFWWOX0TqAPwRgEeL8fqFkH2OvL54G5A+754aZLsi/TjdF8PK9Tu1ewDWNrWhLwb0RRX6YsCTTW1pt4e7I9jRdhzh7sigdtvqDp1xkiwSlw/ZfCZEVLwR/8MAVgIwDiVF5CYANwHAtGnTTHcbsrLJkS8LALMn67/4WOfdr5xTm7F2EApkVvZ0qsi5ofkgVq7bgaAEEFUx3H/t3JzLPusOnXHjuZbDeZny0a2n6D4TIoor+IhfRK4CcEQp9brd/ZRSa5RSjUqpxokTJxaod/mjy+MPBgSjK8oQCgqCAowKBVFRJnho2TxtgNLNuz+7qwO9lsXhvlgsY+7eriJnuDuC29Y2Z+wcnlk7Rpt+6sR6KHsoaP72kursmipX93Oiu/DoPhMiiivGiH8RgCUiciWAEQDGiMgPlFLLi9AXT100fQKe+HU7BPGtWXdeNQsXTBk7EJB2H/oAgBhH+6bpomvmT8FPf3MwbT7beuE4u0afznl2zSjsPnRCu3P47mda0BsFkBg5r1y/E4saalyNmlMXoSvLg7h89VbHx1xh2N2creSF52vrdiIYEERj9usZRH5X8MCvlPoGgG8AgIh8HMDtwzHoJ+edU8sx3/OLFmy747K0g1nsFiNN00VfXDQDt10x0zaDxVQFOt6u3zmsLO3ZTpckF6E3tegL1AUBBALxy+CDhm85uVLJ/1UC885oIgKYx+8Zu3lnABlZMLrRdUPtaMysrcSejpMDbTNrKwfm3e0Cp91i7fjK8owcfwCZv+c4XfL0Dn0e/x/MrMGVcybn/SjGMxdZBSCeNZXNtxUivynqzl2l1IvDMYcfsJ931mXBpF4Uklo7utKCPgDs6Tjpalft+MryjDwhSbRXV1Xgwc/ORUVZACPKzPPxdeNG5BQ452g2owHA1r1h3P1MC6761st4uvlg1s9r4vbzJKI4lmzwiHXBc0QoMDDvvKutM2Nzl250PZgibbsPnciY8FA4UwF0ybwpeOXrl+GmS88xPkfr0VNYvXGP42tZfWZ+nTY5tT+mPEm3rBs/Ej196Z9nT18/F3eJDDjV4yHdrtsF//w8DlsORUm9KKQaTJG2Ez29ju3VVRVYMncyHtncanyeb21pxYqP1Wc18q+uqsA3r5uH25/cAaXOrB2krjuomMpruqVI+tx+/Hci0mHg91jqrtun3mjLCPoA8P/+8HztLtNkkbZoLL3sgpsibWNGhly1J9NOU/P3U5UHxXWATj0pLPWi19cfxbXfeTXtvpGoQl9/FDvajg+6xEJ7Zw9GlAXRFz0z6h9RFmQeP5EBA38B/XyXPtvlxb3HsPxjMzLa7Yq0OdWlmT15LAICpFwzEBCklX5OBuoVC+rxB+dOxNfX78TRk31pz6PEXN8nlanAW3VVBXa0HceIUACn+1IWuoOC6x99FaFAcGADWa4lFpjHT5QdBv4CumrOJLzw2yPadhPddJGbVNDOk71pQR+IXwQ6T/aiuqoiI1BbhQAEysRVPrxTgTddAE5mEPVF41k4t65tzjkLJ7mestLymXC0T6THwF9AV8+fint/+VbawednjSnH1fOn2j4udbpIVxBNl7poV3QNgG3QB4A+ANLvLh/ebhG6oXY0qqsqsKyxzjidBJw5evLS83LbpW2qYkpEmRj4C2z7334ST73Rhp/vOoyr5kxyDPpWbuvSmA5iqa8e5bqAnAJw+5POI/GaqnLb9nB3BD96zf5Cc+YVc2etYkpEegz8RXD1/KlZB/wkt6mLobKgdo4/VBbMqoCcwHn37sJzarSvtTBxxq6uRISV7ujJbLEeP5E7zOMvQdZURV3qYmV5UDvHX1ke1BaQM+l3sUhaXVWBhz83D+VBQSgoKA8KHv5cakkG55H89R+ZOqhgvaH5IBbduxnLH30Ni+7dnNcNYl5KLY1NVCgM/CWmvbNHew6vdZfqyd4orEUygxJvB4BVS+fghVsuxdeuOM/29dymw6vEfUOBQMZjJo91zq5Z29Sec/Br7ejC19aVXj3+Ur1YUelj4C8xleXBtLRIADjdF8s4urGvPwpL6R1EVbw9qaF2NL582bm2o/+KRD68ndRaOckyz6mB92RvFBUOpZpzLbGwofkgrnzkJfRa5pKGeskGHh5DxcTAX2J0QbQiKAMj+aR3wqe0j9e1r1o6B3935fkZ3xAAoC/qPNXjVCunbvxISMA+8OeSd58Mnr3WKxyA3mh0SOfxmy5KQ/liRcMHA3+J0QVR0RyibpfVY3XnU7vwT8++lfENAQCu/vDktLl33Zy00waqZJ59RZkMHD5zSUP67uNljXVZzfGHuyPY8tYR9BlWjWMK2OaiplGxuP3mRuQFBv4SY1f8LVUyqydVMqsnlW7zVaqnd7w3EOQ3NB/Ewn/ZhGXf2Y6F/7JpYE7aTZ/i1xRJHC0s2L4v/VjIbOb4k3Pjf//ULu3FCohvEBvKUydOazDF1LQ/jIc27snbmcg09DCdswS52azU1x/VZvWkzvEDzofCB+XMwvEtTzQnnjP+xF994kyOv12fdIfSWLk99CV1btxJNM+F4PKpsjyoXYMp9oh/+aOvDpzV/MjmVlzSUI3v37igaP1hiq43GPhLlNNmJbs5/tQDzp1y+pNz/Nt/d0x7Idn+u2O4au4U2z7pNp1lvI7LOX43z3Wm7yrjQjdUnOyNZtQvGhEKFHXE37Q/PBD0k15qDaNpfzjt30yhuClNQrnhVM8w5XaO3ymnPznH3/LeCe3tpvZUujWAVOVB/XRVLs9lZboAFpvpIlfMBemnmvUnp5navcSsJ28x8A9TpwxTIbr2ZE7/NfMnZ9z2s98cQrg7YpyCcDM1kazVY7KscYrrkZx1PaEsAJQFgJDhX3I2u5QLye1aTSGNHamfADC1e4mnqnmLUz3Dlmm3rL69oXY0ls6bgvVvpI/u+qIKuw99gE/NPgv3b9yb8bhPzT7LsSfh7gjWNrUbb8+2VMOihhqs+fxFAASzJ48BEA8UP3zt3bTXWbFw2qDO9vV6fnmoFZb7zIfr8O0X92nbC42ltr3FwD9MmXbL2u2iPdHTZ2yfPXms9lCY8ZX6Am2pnEZpv18/wfE5kjY0H8TKdTsH+nL/tfF53+qqCqx/vc3187h5HdP8cj4vCEOpsNzxU/pT20ztXqquqsC0CSPxdsqZ09MnFP/iOFxwqmeYcrvRK9WYkfogPmZkefyrt+X5QsHMUhE6upz1JIH7FMZwdwS3P7kDkf4YTvVGEemP4bYndyDcHTGeCeDmYPpNLYdxx7od2NRyeOB1TPPLw7nMwnMt+oOCTO1eatofTgv6ALCn4yRTTPOEI/5hamCjV0rOoG6jV6rZk8egLJB+Nm5ZIN7eebI35w1HyYtQRJN0r5CZYmqy+9AHAwe4JPVFFR59aR/OmVilfUxz23Hb6Z4rVr84EGCeaGrHzNpK3HftPG3p692HPnB1FkKpqizXhwNTu5e27tVvvtu691hRMoyGG474h6lcFg+rqyrw0LJ5aTtsH1oWr7J56AP9yN7UnsqpZIPbzJsTPf3a9v/Yug9t4W7tbeGu08bn29RyWDuq3H+0Szu/DAj6o+nt/dGY6wXHoV6Jc9wo/Tc+U7uXLj23Jqt2yg5H/MNYLouH5seYArdz+c7kRej2J3eiN5o55WNKPbUaY8guKQsGsPV3+imAXx84jr8wPN/Glg5t+/Z972PZRXVpU0fLGuswKhTIOFegP+buG0sp5KSPM3y+pnYvNc6oxszaSuxJuTDPrK3kaD9POOIf5qqrKjB36rispiJ0j5k9eUxGiJdEuxtL5k3Bo3/WmNEuyCwjYWJamO6LxtB84APtbXbnGS88W7+oPGfyGKx9PT0LaW1TO14zzC+/eUj/2klDKSfd7lvHbsOeDFN7Lq+RzXO8+376N6l33+8Zst+WSg1H/ORaWVDS5tjLHEotW00eOyKjTcF9mYJkfRtdqQOdkWVie9LZhKrM/gDAB6f7oSzblOO/699vjeF5ktwel+k1p28d+ZjjN2VdZWuofGbDFUf85Ep7Zw+UsgRDpbLaUJMsU5AqmzIFuvo2dnr6lW1Wjyl9dfULezMWoiNRhY/O0H9DWHiO/fRD3fiR6I6kr090RzKPy/SSm28d1qwtp3bda5iyrrJVN34kTvamf2Ynewv7mQ1nBQ/8IjJVRLaISIuI7BaRmwvdB8peX3805/ntpMGWKdBdOModgpLbg+VT9VuLEiW0HtEvIHeetM9z33+0O2PbnEq0F4qbnbDTq/WZUaZ2K1PW1W6HqTCdzpO92tpQTp81uVOMEX8/gNuUUrMALADwZRGZVYR+UBayOdjFZLBlCnQXCN15w6nK8vgvvPXoSW2708XlOcMisqnddX86urCuqc3VXgU3O2EXnlOtPdbT6RtN0kHDtz9Tux3TZ5rLhZwyFTzwK6XeU0q9kfi5C8BvAQyt9AbKYKp5k20tnCXzpmDbHZfhBzd+FNvuuCyr+V/dheP6j5jn8AFg93t2QTGLeSOYF4OdPoPa0foLm6ndjTuf2oXLV2/F7et24vLVW3Hnhl229z9zGE4Ao8qDqCjLvOhWV1Vg9bK5qCgLDPxZtXQ22jvdLaq+bbgAmdrt5OvfG+kVdXFXROoBfBjAa5rbbgJwEwBMm2auHkmFkazi+fj2MymOudbCGUyZAmu66U9+ZT5EBgDOrqnM+jUCgoxpBgDYf0w/4ncyyZCNZGp3YtqlvGJBve3fh0r+rxKYLnqpn++bBz/A3U+/CUEACjE88Nl5thfqrtP6fRamdjsNtaNxcUN1WpnoSxqqB1V7ic4oWuAXkSoA6wF8VSmVkS+mlFoDYA0ANDY2Zjc0I0+sWjoHKxbUo7ntOOZNHVe0/whTLxzTq+0D+xWzzemcJoYpfuw6qE9rdNod3H1av4hsanfycutRY7u1H60dXWhuO4766lGJw3AUgPi6jGnXcfL3q7+9LXF5iE8R3fyTZttdylMNazWmdjvh7gia3u1Ma/v1u50Id0d8ldXjVaHAogR+EQkhHvR/qJT6aTH6QLlpqB09pEZdC8+phm78GgoADyZ2HZuYahOZfGJmDTbsyKxN77QB7bTh5DFTuxPrAqqp/c6ndqV9M7CuhiibE8q2/y6sXZDe/rswrpqbWb4bACKazXl27XaYzhlPjf3akzsR/+QFD3w2f5v+ipHVIwD+E8BvlVIPFfr1aXiprqrA5xekTwVeOacWr/7t5Y7/kej2FZisWDgNZUH9foPDJ+znvy8wbHIztTvpNFTLTG3XTQdZA3kkqox7KN4N66e1TO3AYPZ2Z/J7WeZwdwS3PNGM3mgMvVGF3mgMX32iOW8b2IqR1bMIwOcBXCYizYk/VxahHzQMhLsjGbtsN7+lnwqxcqoz9I9LZuGBay/EC7dcilVL5+CY4T86U3tSqCyoPVjd7Y5lKzeH4rzcqi9yZmX6DCYYym2b2gHg987SX8hM7XaG4kE1hfT87sPadNbnd+enUmrBp3qUUi8jt0EAUYZspwSSc97x7BDzP0MBMGfqeMxNySLJdQ67bvxIhMoCiKZUNw2VBXIevbo5FMd9DX39ZzBlvH76ytQOAAvPqclYGA9IvD0XQ+2gmkIypQ6b2rPFkg1U0rKZErDOeS9rrMsoQ51UEcoMzG2GfPS3DnehZvQIY3BKjl5XWsolZBPIUhf5xleWawNs6qE4ps1mqexqLZmmweymx6qrKvDw5+bh9id3QESglMIDn507qIA9lA6qKaT6CfpBgak9Wwz8VNLcBlXdnPfapnb87R+ejwef34NYDOiLqYGdwbrnqDEEoIc37cW//88+26qbgxm9WmvsfPnjDagsL0NXShmIyvKytG85ERcLxwrxnbC6vujOUHA6yAfw9yg9n941DDJM7dli4KeS5ybYmFIgy8sEr3x9Mdo7e1BZHsTJ3qjxOUwZRH1Rhb5oPAjbHcySy+g1tcZOcjrrW1v2wjpFY/2WU1PlLmPpud2HtVlauRzkk+TXUXo+mRZf87UoyyJtNCw4lZ+2S4FMPrahdrRjCWuniqTW+jeD1d7Zgz7L6L0/qvCVTzTYLnzW2czFpzItTPt9cbXYTN+s3BY0dMIRP/lCr2Hqw9Su097ZgxFlwYHRvc5gqm7qNusc6zqtLUM966zR2HbHZcZvOaZKolajbEpic9qmeCYavrGZ2rPFwE++YNrh67TzN5VuIdnKbt7cjqlW/pY9+imqLXuOYvGsScbXCZUFEbKcn6DjlGrJaZvi0J1UZ9eeLU71kC8MtvIkkDn9Yar8mW0FSbta+VHDhcbp7Jq68SMz3q/VYFItyVujDIffmNqzxcBPvqCrPLl6WfaphqnVRX/ypQXa+2RbQdK0JvDD1w7giV+3a2+bP93+gqWbo1+xcBrKg4KKsgDKg4KHP2df0oKK51OGGlOm9mxxqod8I19z1snpDzd18N2oLA/idF/6yP50XwwPPf+29v4C51O/AP37vXnxeZyzLwH5rIarw8BPvpLPOWu7w0Ky+Q9UlzNv2lgWFGB1FiN16/vlnH3p8LIaLgM/UY7ydViILmce2h0DwK2fPC9vFRpp6POqGi7n+IlylPw6niqXr+O6+fiVn5qpvW++5njJ3zjiJxqEfH0d183Htx8/5dkcL/mbKDX0D7dqbGxUTU1Nxe4GUcGlVhNl0KdsicjrSqlGaztH/ERD2FA78YyGB87xExH5DAM/EZHPMPATEfkMAz8Rkc8w8BMR+UxJpHOKyFEA73r8MjUAjnn8GkORX9834N/37tf3DfjvvU9XSk20NpZE4C8EEWnS5bsOd35934B/37tf3zfg7/eeilM9REQ+w8BPROQzDPxnrCl2B4rEr+8b8O979+v7Bvz93gdwjp+IyGc44ici8hkGfiIin/FV4BeRT4vIHhFpFZGv29zvGhFRIjJs0r6c3ruIfEFEjopIc+LPjcXopxfc/L2LyDIRaRGR3SLyo0L30Qsu/s5Xp/x9vy0ix4vQTU+4eO/TRGSLiPxGRHaKyJXF6GfRKKV88QdAEMDvAJwNoBzADgCzNPcbDWArgFcBNBa734V67wC+AOBbxe5rkd77uQB+A2B84vcPFbvfhXjflvv/NYDHit3vAv6drwHwV4mfZwF4p9j9LuQfP434PwKgVSm1TynVC+AnAJZq7ncPgHsBnC5k5zzm9r0PR27e+5cAfFsp1QkASqkjBe6jF7L9O78ewI8L0jPvuXnvCsCYxM9jARwqYP+Kzk+BfwqAtpTf2xNtA0RkPoCpSqlfFLJjBeD43hOuSXztXSciUwvTNc+5ee/nAThPRLaJyKsi8umC9c47bv/OISLTAcwAsLkA/SoEN+/9LgDLRaQdwLOIf+PxDT8FflsiEgDwEIDbit2XInkGQL1S6kIAzwP4ryL3p5DKEJ/u+TjiI9//EJFxxexQgV0HYJ1SKlrsjhTQ9QC+p5SqA3AlgO8nYoAv+OaNAjgIIHUUW5doSxoN4AIAL4rIOwAWAHh6mCzwOr13KKXCSqlI4tdHAVxUoL55zfG9Iz4ifFop1aeU2g/gbcQvBKXMzftOug7DZ5oHcPfebwCwFgCUUtsBjEC8gJsv+Cnw/xrAuSIyQ0TKEf/H/nTyRqXUB0qpGqVUvVKqHvHF3SVKqeFwyrvtewcAETkr5dclAH5bwP55yfG9A3gK8dE+RKQG8amffQXsoxfcvG+IyPkAxgPYXuD+ecnNez8AYDEAiMjvIR74jxa0l0Xkm8CvlOoH8BUAzyEe1NYqpXaLyCoRWVLc3nnL5Xv/m0Qq4w4Af4N4lk/Jc/nenwMQFpEWAFsAfE0pFS5Oj/Mji3/v1wH4iUqktwwHLt/7bQC+lPj3/mMAXxhOn4ETlmwgIvIZ34z4iYgojoGfiMhnGPiJiHyGgZ+IyGcY+ImIfIaBn8ggUaH1wZTfbxeRu1J+v0lE3kr8+ZWIXJxov1VEHku535+KyHArA0IljIGfyCwC4E8Sm7rSiMhVAP4CwMVKqfMB/CWAH4nIJACPAJgvIosSpR/+ET6rBUNDGwM/kVk/4uV7b9HcdgfiG72OAYBS6g3E6xt9ObGB6P8C+DaA+xAvd1zqO4FpGGHgJ7L3bQB/KiJjLe2zAbxuaWtKtEMp9Qriu0YvRzz4Ew0ZDPxENpRSJwA8jngZC9dEpApAI4AQgIkedI0oZwz8RM4eRryaY2VKWwsyK5heBGB34ue7AfwAwD8BWO1x/4iywsBP5EAp9T7iJXxvSGm+D8C9IlINACIyD/HCdv9fROYA+CPET3JbA6BeRD5ZyD4T2SkrdgeISsSDiFd8BAAopZ4WkSkAXhERBaALwHIAhwE8CeAWpdRpABCRvwLwuIjMSxwFSFRUrM5JROQznOohIvIZBn4iIp9h4Cci8hkGfiIin2HgJyLyGQZ+IiKfYeAnIvKZ/wU0Z9uTJM87MAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1.畫出箱型圖，並判斷哪個欄位的中位數在300~400之間？\n",
    "boston_data.boxplot()\n",
    "#TAX的中位數在300~400之間\n",
    "\n",
    "#2.畫出散佈圖 x='NOX', y='DIS'，並說明這兩欄位有什麼關係？\n",
    "boston_data.plot.scatter(x='NOX',y='DIS')\n",
    "#兩者有負相關的關係"
   ]
  }
 ],
 "metadata": {
  "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}