Asignment-1_Q1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6f424f0d",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6a1b9c4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c15b4675",
   "metadata": {},
   "source": [
    "### Q1 iris dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b5c2c5d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "983a1188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
       "       'Species'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d2141ac",
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "Species                                                                 \n",
       "Iris-setosa               5.1           3.5            1.4           0.2\n",
       "Iris-setosa               4.9           3.0            1.4           0.2\n",
       "Iris-setosa               4.7           3.2            1.3           0.2\n",
       "Iris-setosa               4.6           3.1            1.5           0.2\n",
       "Iris-setosa               5.0           3.6            1.4           0.2\n",
       "...                       ...           ...            ...           ...\n",
       "Iris-virginica            6.7           3.0            5.2           2.3\n",
       "Iris-virginica            6.3           2.5            5.0           1.9\n",
       "Iris-virginica            6.5           3.0            5.2           2.0\n",
       "Iris-virginica            6.2           3.4            5.4           2.3\n",
       "Iris-virginica            5.9           3.0            5.1           1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[['Species', 'SepalLengthCm', 'SepalWidthCm','PetalLengthCm', 'PetalWidthCm']]\n",
    "df2.set_index('Species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf26dba7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'SepalWidthCm'}>]], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.hist('SepalLengthCm')\n",
    "df2.hist('PetalLengthCm')\n",
    "df2.hist('PetalWidthCm')\n",
    "df2.hist('SepalWidthCm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "43070b5e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABY8AAAJICAYAAAApRhv0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABO50lEQVR4nO39e7hddX0v+r9Hwr2BaCGiwG6DKCSECQmEGBKBIIqoVD0efVB0n7QUtT8PlnrDIEUix1u3HIu7W3cf6yVpFUkBuWyj3cUWFAg7ISFBLkvKpVE4BoloEUG3AcfvjzmzWFljray5krnmLa/X84xnjTnnZ36/n/EdlzXXZ405RlGWZQAAAAAAYKhJnU4AAAAAAIDuo3gMAAAAAECF4jEAAAAAABWKxwAAAAAAVCgeAwAAAABQoXgMAAAAAEDFbhPR6AEHHFBOnz59IpoGAAAAAKBF1q1b97OyLKeN9NqEFI+nT5+etWvXTkTTAAAAAAC0SFEUPxrtNZetAAAAAACgQvEYAAAAAIAKxWMAAAAAACom5JrHAAAAAEB/2LJlSx555JH85je/6XQq7IS99torhxxySHbfffem36N4DAAAAACM6pFHHsm+++6b6dOnpyiKTqfDDijLMo8//ngeeeSRHHrooU2/z2UrAAAAAIBR/eY3v8n++++vcNzDiqLI/vvvP+6zxxWPAQAAAIDtUjjufTuyDhWPAQAAAICu94lPfCKzZs3K0UcfndmzZ2f16tUta/umm27KGWeckSRZtmxZzj333Ja1PdzGjRtz+eWXDz7eXn+/+tWv8u53vzuHHXZYZs2alZNOOqmlyz0W1zwGAAAAAJo2fcnKlra38dOvGzPmtttuy7e+9a3ccccd2XPPPfOzn/0sv/3tb1uaR7tsLR6fddZZY8aec845OfTQQ3P//fdn0qRJeeihhzIwMNCGLOuceQwAAAAAdLVNmzblgAMOyJ577pkkOeCAA3LQQQdl3bp1Ofnkk3Pcccfl1a9+dTZt2pQkWbRoUf7iL/4iCxYsyFFHHZU1a9YkSdasWZMFCxZkzpw5WbBgQe67776mc/ja176WefPmZfbs2Xn3u9+dZ599NkkyZcqUXHjhhTnmmGMyf/78/PSnP02SPPjgg5k/f36OP/74fPSjH82UKVOSJEuWLMnNN9+c2bNn56//+q+TJD/5yU9y+umn56UvfWnOP//8wfevXr06H//4xzNpUr2M++IXvzive93rsnHjxsyYMSPnnHNOjjrqqLz97W/Pd7/73SxcuDAvfelLB5d3ZykeAwAAAABd7bTTTsvDDz+cww8/PO95z3vyve99L1u2bMl73/veXHXVVVm3bl3OPvvsXHjhhYPveeqpp7Jq1ap84QtfyNlnn50kmTFjRr7//e9n/fr1ueSSS/KRj3ykqf4HBgayYsWK3HrrrdmwYUMmT56cr3/964P9zJ8/P3feeWdOOumk/N3f/V2S5Lzzzst5552X22+/PQcddNBgW5/+9Kdz4oknZsOGDXnf+96XJNmwYUNWrFiRu+66KytWrMjDDz+ce+65J7Nnz87kyZNHzOmBBx7Ieeedlx/84Af54Q9/mMsvvzy33HJLLr300nzyk58c/yCPwGUrAAAAAICuNmXKlKxbty4333xzbrzxxpx55pn5y7/8y9x999151atelSR59tln86IXvWjwPW9729uSJCeddFJ++ctf5j/+4z/y5JNPZvHixbn//vtTFEW2bNnSVP//8i//knXr1uX4449Pkvz617/OC17wgiTJHnvsMXi95OOOOy433HBDkvqlNq699tokyVlnnZUPfvCDo7Z/6qmnZurUqUmSI488Mj/60Y/GzOnQQw9NrVZLksyaNSunnnpqiqJIrVbLxo0bm1qusSgeAwAAAABdb/LkyVm0aFEWLVqUWq2Wz3/+85k1a1Zuu+22EeOLoqg8vuiii3LKKafkmmuuycaNG7No0aKm+i7LMosXL86nPvWpymu77777YF+TJ0/OM888M74FSwYvxzG0jVmzZuXOO+/M7373u8HLVoz2nkmTJg0+njRp0g7lMBKXrQAAAAAAutp9992X+++/f/Dxhg0bMnPmzGzevHmweLxly5bcc889gzErVqxIktxyyy2ZOnVqpk6dmieeeCIHH3xwkmTZsmVN93/qqafmqquuymOPPZYk+fnPfz7m2cHz58/P1VdfnSS54oorBp/fd9998+STT47Z52GHHZa5c+fm4osvTlmWSZL7778/1113XdN576ymisdFUbyvKIp7iqK4uyiKbxRFsddEJwYAAAAAkCS/+tWvsnjx4hx55JE5+uijc++99+aSSy7JVVddlQ9/+MM55phjMnv27KxatWrwPc9//vOzYMGC/Nmf/Vm+/OUvJ0nOP//8XHDBBVm4cOHgDe9GsmzZshxyyCGD03777ZePf/zjOe2003L00UfnVa961eDN+UZz2WWX5bOf/WzmzZuXTZs2DV6W4uijj85uu+2WY445ZvCGeaP50pe+lEcffTQveclLUqvV8s53vnOb6ydPtGJr1XrUgKI4OMktSY4sy/LXRVH8Y5Jvl2W5bLT3zJ07t1y7dm1LEwUAAAAA2m9gYCAzZ87sdBrjsmjRolx66aWZO3dux3J4+umns/fee6coilxxxRX5xje+0dazhkcy0rosimJdWZYjDlSz1zzeLcneRVFsSbJPkp/sVJYAAAAAAH1s3bp1Offcc1OWZZ73vOflK1/5SqdTGrcxi8dlWf5/RVFcmuTHSX6d5J/Lsvzn4XFFUbwrybuS5A/+4A9anScAAAAAQFNuuummTqeQE088MXfeeWen09gpY17zuCiK5yd5Q5JDkxyU5PeKonjH8LiyLL9YluXcsiznTps2rfWZAgAAAADQNs3cMO+VSf69LMvNZVluSfLNJAsmNi0AAAAAADqpmeLxj5PML4pin6IoiiSnJhmY2LQAAAAAAOikMYvHZVmuTnJVkjuS3NV4zxcnOC8AAAAAADqomTOPU5blxWVZzijL8qiyLP9zWZb/e6ITAwAAAADY6hOf+ERmzZqVo48+OrNnz87q1atb1vZNN92UM844I2VZ5oADDsgvfvGLJMmmTZtSFEVuueWWwdhp06bl8ccfzznnnJN777230tayZcty7rnnJkmuvfbabWIWLVqUtWvXjpjDmjVrctJJJ+WII47IjBkzcs455+Tpp59u2TLuiN062jsAAAAA0FuWTm1xe0+MGXLbbbflW9/6Vu64447sueee+dnPfpbf/va3rc0jSVEUednLXpbbbrstr33ta7Nq1arMmTMnq1atystf/vLcd999OeCAA7L//vvnS1/60pjtXXvttTnjjDNy5JFHbjfupz/9ad7ylrfkiiuuyAknnJCyLHP11VfnySefzD777NOqxRu3ps48BgAAAABGN33Jyk6n0Nc2bdqUAw44IHvuuWeS5IADDshBBx2UdevW5eSTT85xxx2XV7/61dm0aVOS+hm+f/EXf5EFCxbkqKOOypo1a5LUz+5dsGBB5syZkwULFuS+++6r9LVw4cKsWrUqSbJq1aq8//3vz2233Tb4eMGCBYN9bD2L+Ktf/WoOP/zwnHzyybn11lsHY6+//vp86EMfyuzZs/Pggw8mSa688srMmzcvhx9+eG6++eYkyec///ksXrw4J5xwQpJ6EfvNb35zDjzwwCxdujSLFy/OaaedlunTp+eb3/xmzj///NRqtZx++unZsmVL6we8QfEYAAAAAOhqp512Wh5++OEcfvjhec973pPvfe972bJlS9773vfmqquuyrp163L22WfnwgsvHHzPU089lVWrVuULX/hCzj777CTJjBkz8v3vfz/r16/PJZdcko985COVvhYsWDBYPF6zZk3e+MY35uGHH05SLwgvXLhwm/hNmzbl4osvzq233pobbrhh8DIVCxYsyOtf//p85jOfyYYNG3LYYYclSZ555pmsWbMml112WT72sY8lSe6+++4cd9xxoy7/gw8+mJUrV+a6667LO97xjpxyyim56667svfee2flyon7x4XLVgAAAAAAXW3KlClZt25dbr755tx4440588wz85d/+Ze5++6786pXvSpJ8uyzz+ZFL3rR4Hve9ra3JUlOOumk/PKXv8x//Md/5Mknn8zixYtz//33pyiKEc/anTdvXtavX5+nnnoqW7ZsyZQpU/LiF784DzzwQFatWpUPfOAD28SvXr06ixYtyrRp05IkZ555Zv7t3/5t1GV505velCQ57rjjsnHjxqaW/zWveU1233331Gq1PPvsszn99NOTJLVarek2doTiMQAAAADQ9SZPnpxFixZl0aJFqdVq+fznP59Zs2YNXlJiuKIoKo8vuuiinHLKKbnmmmuycePGLFq0qPK+ffbZJy95yUvyla98Jccee2ySZP78+fn2t7+dxx57LEccccSYfW3P1ktvTJ48Oc8880ySZNasWVm3bl3e8IY3bPc9kyZNyu677z7Y36RJkwbbmAguWwEAAAAAdLX77rsv999//+DjDRs2ZObMmdm8efNg8XjLli255557BmNWrFiRJLnlllsyderUTJ06NU888UQOPvjgJMmyZctG7W/hwoW57LLLBq9BfMIJJ+Rzn/tc5s+fXykUv+xlL8tNN92Uxx9/PFu2bMmVV145+Nq+++6bJ598cszlO/fcc7N8+fKsXr168Lmvfe1refTRR8d870RSPAYAAAAAutqvfvWrLF68OEceeWSOPvro3Hvvvbnkkkty1VVX5cMf/nCOOeaYzJ49e/BaxUny/Oc/PwsWLMif/dmf5ctf/nKS5Pzzz88FF1yQhQsX5tlnnx21v4ULF+ahhx4aLB4fe+yxeeSRRwZvljfUi170oixdujQnnHBCXvnKVw6erZwkb33rW/OZz3wmc+bMGbxh3kgOPPDAXHHFFfngBz+YI444IjNnzszNN9+c/fbbb9xj1UpFWZYtb3Tu3Lnl1jsNAgAAAEC/m75kZTZ++nWdTmNCDAwMZObMmZ1OY1wWLVqUSy+9NHPnzu10Kl1lpHVZFMW6sixHHChnHgMAAAAAUOGGeQAAAABAX7nppps6nUJfcOYxAAAAAAAViscAAAAAAFQoHgMAAAAAUKF4DAAAAABAheIxAAAAwE6YvmRlS2I6oVvzojWmL1nZN+t48uTJmT17do466qi85S1vydNPPz1q7IYNG/Ltb397zDZvuummnHHGGUmSZcuW5dxzz21ZvsNt3Lgxl19++eDj7fX3q1/9Ku9+97tz2GGHZdasWTnppJOyevXqCctte3brSK8AAAAAQE+qLa+1tL27Ft81Zszee++dDRs2JEne/va352//9m/z/ve/f8TYDRs2ZO3atXnta1/byjR3ytbi8VlnnTVm7DnnnJNDDz00999/fyZNmpSHHnooAwMDbciyypnHAAAAAEDPOPHEE/PAAw/kqaeeytlnn53jjz8+c+bMyXXXXZff/va3+ehHP5oVK1Zk9uzZWbFiRdasWZMFCxZkzpw5WbBgQe67776m+/ra176WefPmZfbs2Xn3u9+dZ599NkkyZcqUXHjhhTnmmGMyf/78/PSnP02SPPjgg5k/f36OP/74fPSjH82UKVOSJEuWLMnNN9+c2bNn56//+q+TJD/5yU9y+umn56UvfWnOP//8wfevXr06H//4xzNpUr10++IXvzive93rsnHjxsyYMSPnnHNOjjrqqLz97W/Pd7/73SxcuDAvfelLs2bNmpaN8VaKxwAAAABAT3jmmWfyne98J7VaLZ/4xCfyile8IrfffntuvPHGfOhDH8qWLVtyySWX5Mwzz8yGDRty5plnZsaMGfn+97+f9evX55JLLslHPvKRpvoaGBjIihUrcuutt2bDhg2ZPHlyvv71rydJnnrqqcyfPz933nlnTjrppPzd3/1dkuS8887Leeedl9tvvz0HHXTQYFuf/vSnc+KJJ2bDhg153/vel6R+hvSKFSty1113ZcWKFXn44Ydzzz33ZPbs2Zk8efKIOT3wwAM577zz8oMf/CA//OEPc/nll+eWW27JpZdemk9+8pM7M7QjctkKAAAAAKCr/frXv87s2bOT1M88/tM//dMsWLAg119/fS699NIkyW9+85v8+Mc/rrz3iSeeyOLFi3P//fenKIps2bKlqT7/5V/+JevWrcvxxx8/mMMLXvCCJMkee+wxeL3k4447LjfccEOS5Lbbbsu1116bJDnrrLPywQ9+cNT2Tz311EydOjVJcuSRR+ZHP/rRmDkdeuihqdXqlw2ZNWtWTj311BRFkVqtlo0bNza1XOOheAwAAAAAdLWh1zzeqizLXH311TniiCO2eX74zeUuuuiinHLKKbnmmmuycePGLFq0qKk+y7LM4sWL86lPfary2u67756iKJLUb+b3zDPPNL8wDXvuuefg/NY2Zs2alTvvvDO/+93vBi9bMdp7Jk2aNPh40qRJO5TDWFy2AgAAAADoOa9+9avzN3/zNynLMkmyfv36JMm+++6bJ598cjDuiSeeyMEHH5wkWbZsWdPtn3rqqbnqqqvy2GOPJUl+/vOfj3l28Pz583P11VcnSa644orB54fnNJrDDjssc+fOzcUXXzy4XPfff3+uu+66pvNuJcVjAAAA6LDpS1Zm+pKVnU4DoKdcdNFF2bJlS44++ugcddRRueiii5Ikp5xySu69997BG+adf/75ueCCC7Jw4cLBG96NZNmyZTnkkEMGp/322y8f//jHc9ppp+Xoo4/Oq171qmzatGm7OV122WX57Gc/m3nz5mXTpk2Dl6U4+uijs9tuu+WYY44ZvGHeaL70pS/l0UcfzUte8pLUarW8853v3Ob6ye1UbK1gt9LcuXPLtWvXtrxdAAAA6EdbC8cbP/26DmfCjpi+ZOWY666ZmE7o1rx6UTeOZauOLQMDA5k5c2YrUup7Tz/9dPbee+8URZErrrgi3/jGNzp21vBIRlqXRVGsK8ty7kjxrnkMAAAAANAC69aty7nnnpuyLPO85z0vX/nKVzqd0k5RPAYAAAAAaIETTzwxd955Z6fTaBnXPAYAAAAAoELxGAAAAACACsVjAAAAAAAqFI8BAAAAAKhQPAYAAAAAutrkyZMze/bsHHXUUXnLW96Sp59+etTYDRs25Nvf/vaYbd50000544wzUpZlDjjggPziF79IkmzatClFUeSWW24ZjJ02bVoef/zxnHPOObn33nsrbS1btiznnntukuTaa6/dJmbRokVZu3btiDmsWbMmJ510Uo444ojMmDEj55xzznaXrd1263QCAAAAAEDvGJgxs6XtzfzhwJgxe++9dzZs2JAkefvb356//du/zfvf//4RYzds2JC1a9fmta99bVP9F0WRl73sZbntttvy2te+NqtWrcqcOXOyatWqvPzlL899992XAw44IPvvv3++9KUvjdnetddemzPOOCNHHnnkduN++tOf5i1veUuuuOKKnHDCCSnLMldffXWefPLJ7LPPPk3lPtGceQwAAAAA9IwTTzwxDzzwQJ566qmcffbZOf744zNnzpxcd911+e1vf5uPfvSjWbFiRWbPnp0VK1ZkzZo1WbBgQebMmZMFCxbkvvvuq7S5cOHCrFq1KkmyatWqvP/9789tt902+HjBggVJtj2L+Ktf/WoOP/zwnHzyybn11lsHY6+//vp86EMfyuzZs/Pggw8mSa688srMmzcvhx9+eG6++eYkyec///ksXrw4J5xwQpJ6EfvNb35zDjzwwCxdujSLFy/OaaedlunTp+eb3/xmzj///NRqtZx++unZsmXLBI7wcxSPAQAAAICe8Mwzz+Q73/lOarVaPvGJT+QVr3hFbr/99tx444350Ic+lC1btuSSSy7JmWeemQ0bNuTMM8/MjBkz8v3vfz/r16/PJZdcko985COVdhcsWDBYPF6zZk3e+MY35uGHH05SLwgvXLhwm/hNmzbl4osvzq233pobbrhh8DIVCxYsyOtf//p85jOfyYYNG3LYYYcN5r1mzZpcdtll+djHPpYkufvuu3PccceNuqwPPvhgVq5cmeuuuy7veMc7csopp+Suu+7K3nvvnZUrV+78YDbBZSsAAAAAgK7261//OrNnz05SP/P4T//0T7NgwYJcf/31ufTSS5Mkv/nNb/LjH/+48t4nnngiixcvzv3335+iKEY8a3fevHlZv359nnrqqWzZsiVTpkzJi1/84jzwwANZtWpVPvCBD2wTv3r16ixatCjTpk1Lkpx55pn5t3/7t1Hzf9Ob3pQkOe6447Jx48amlvk1r3lNdt9999RqtTz77LM5/fTTkyS1Wq3pNnaW4jEAAAAAbMf0JSuz8dOv63Qau7Sh1zzeaus1go844ohtnl+9evU2jy+66KKccsopueaaa7Jx48YsWrSo0v4+++yTl7zkJfnKV76SY489Nkkyf/78fPvb385jjz1W6SOpX2aiWXvuuWeS+o3/nnnmmSTJrFmzsm7durzhDW/Y7nsmTZqU3XfffbC/SZMmDbYx0Vy2AgAAAADoOa9+9avzN3/zNynLMkmyfv36JMm+++6bJ598cjDuiSeeyMEHH5wkWbZs2ajtLVy4MJdddtngNYhPOOGEfO5zn8v8+fMrheKXvexluemmm/L4449ny5YtufLKKwdfG97/aM4999wsX758m2L31772tTz66KNjvrddFI8BAAAAgJ5z0UUXZcuWLTn66KNz1FFH5aKLLkqSnHLKKbn33nsHb5h3/vnn54ILLsjChQvz7LPPjtrewoUL89BDDw0Wj4899tg88sgjgzfLG+pFL3pRli5dmhNOOCGvfOUrB89WTpK3vvWt+cxnPpM5c+YM3jBvJAceeGCuuOKKfPCDH8wRRxyRmTNn5uabb85+++23o0PScsXWynwrzZ07t9x610EAAABg+6Yvqd/4yNfie1MzlzTo1ssedGte3aZX13Grji0DAwOZOXNmK1Kiw0Zal0VRrCvLcu5I8c48BgAAAACgQvEYAAAAAIAKxWMAAADoI1u/pg4AO0vxGAAAAADYrom4bxrttSPrUPEYAAAAABjVXnvtlccff1wBuYeVZZnHH388e+2117jet9sE5QMAAAAA9IFDDjkkjzzySDZv3tzpVNgJe+21Vw455JBxvUfxGAAAAAAY1e67755DDz2002nQAS5bAQAAAABAheIxAAAAAAAViscAAADAiKYvWdnpFOgj05es7Mptqhtzgm6heAwAAAAAQIXiMQAAAAAAFYrHAAAAAABUKB4DAAAAAFCheAwAAAAAQIXiMQAAAIxg+pKVnU4BADpK8RgAAAAAgArFYwAAAAAAKsYsHhdFcURRFBuGTL8siuIv2pAbAAAAAAAdsttYAWVZ3pdkdpIURTE5yf+X5JqJTQsAAAAAgE4a72UrTk3yYFmWP5qIZAAAAAAA6A7jLR6/Nck3JiIRAAAAAAC6R9PF46Io9kjy+iRXjvL6u4qiWFsUxdrNmze3Kj8A2CVNX7Ky0ykAAACwixvPmcevSXJHWZY/HenFsiy/WJbl3LIs506bNq012QEAAAAA0BHjKR6/LS5ZAQAAAACwS2iqeFwUxT5JXpXkmxObDgAAAAAA3WC3ZoLKsnw6yf4TnAsAAAAAAF1iPJetAAAAAABgF6F4DAB9bvqSlZ1OAQCAIWrLazv1fp/v6HWt2obtCxNP8RgAAAAAgArFYwAAAAAAKhSPAQAAAACoUDwGAAAAAKBC8RgAAAAAgArFYwAAAAAAKhSPAQBgAkxfsrLTKQD0HMdOoFMcf0ameAwAAAAAQIXiMQAAAAAAFYrHAAAAAABUKB4DAAAAAFCheAwAAAAAQIXiMQAAAAAAFYrHAAA9bPqSlZm+ZGWn02AHWXetZTyhefaXiVFbXut0CjtsZ3O3TdFq3bhNNZNTv30+VzwGAAAAAKBC8RgAAAAAgArFYwAAAAAAKhSPAQAAAACoUDwGAAAAAKBC8RgA6Fn9dBdjeke/3UEb6D615bVOpwAASRSPAQAAAAAYgeIxAAAAAAAViscAAAAAAFQoHgMAAAAAUKF4DAAAAABAheIxAAAAAAAViscAQFOmL1nZ6RQAoCf1++9QywcwMbrh+KN4DAAAAABAheIxAAAAAAAViscAAAAAAFQoHgMAAAAAUKF4DAAAAABAheIxAPSobrjzLvQb+xXAxOjG4+v0JSu7Mi9ap5Prt7a8Nuprvbrd9Wre7BzFYwAAAAAAKhSPAQAAAACoUDwGAAAAAKBC8RgAAAAAgArFYwAAAAAAKhSPAQAAAACoUDwGANpu+pKVnU6BPmObolm2lf7X7nXczdtUbXmt0ynQJ7p5O+9XjmX9r1fGXPEYAAAAAIAKxWMAAAAAACoUjwEAAAAAqFA8BgAAAACgQvEYAAAAAIAKxWMAAJrWK3eFBrqH4wYwXo4b0LyJ3l8UjwEAAAAAqFA8BgAAAACgQvEYAAAAAIAKxWMAAAAAACoUjwEAAAAAqFA8BgAAAACgQvEYAKADpi9Z2ekUekIrx8mY061sm/SC2vJap1OAbXTq2NlN+8J4x6Cbcqd3KB4DAAAAAFCheAwAAAAAQEVTxeOiKJ5XFMVVRVH8sCiKgaIoTpjoxAAAAAAA6Jzdmoz7XJJ/KsvyzUVR7JFknwnMCQAAAACADhuzeFwUxX5JTkryx0lSluVvk/x2YtMCAAAAAKCTmrlsxYuTbE7y1aIo1hdF8aWiKH5veFBRFO8qimJtURRrN2/e3PJEAQB6Rafu/k1/6uXtqVW5T1+ysqfHgc6oLa91OgXYIbvK8c4+2p92le13V9JM8Xi3JMcm+e9lWc5J8lSSJcODyrL8YlmWc8uynDtt2rQWpwkAAAAAQDs1Uzx+JMkjZVmubjy+KvViMgAAAAAAfWrM4nFZlo8mebgoiiMaT52a5N4JzQoAAAAAgI4a84Z5De9N8vWiKPZI8lCSP5m4lAAAAAAA6LSmisdlWW5IMndiUwEAAAAAoFs0c81jAAAAAAB2MYrHAABNmr5kZadT6CvGE+gVteW1TqcAQJv06zF/Rz97Kx4DAAAAAFCheAwAAAAAQIXiMQAAAAAAFYrHAAAAAABUKB4DAAAAAFCheAwAAAAAQIXiMRNu+pKVnU4BAGiC39mMh+2l/Yx563RqLGvLa6ktr3Wk7+3pxpx2lv2FHdGP+wJ1jgk7TvEYAAAAAIAKxWMAAAAAACoUjwEAAAAAqFA8BgAAAACgQvEYAAAAAIAKxWO6wvQlK9t650t32QSYGO0+ngPPse/RSs0ez9u93dnOSWwHQ9WW1zqdQkVtea0r84JW2pW2c8VjAAAAAAAqFI8BAAAAAKhQPAYAAAAAoELxGAAAAACACsVjAAAAAAAqFI8BAAAAAKhQPAYA6FLTl6zsyra6sT9opX7ffvt9+TqltrzW6RRgG/b1bXXjPtqqnGrLay1tq9t0Y067EsVjAAAAAAAqFI8BAAAAAKhQPAYAAAAAoELxGAAAAACACsVjAAAAAAAqFI8BxtDuuxS7KzKdYLuDXUMz+7rjQfOMJ0yc2vJap1Oo6MacoBPsC7sWxWMAAAAAACoUjwEAAAAAqFA8BgAAAACgQvEYAAAAAIAKxWMAAAAAACoUjwEAAAAAqFA8HsH0JSszfcnKTqexQ3o172Z14/J1Y05YLwDQK/zOBoBdV215rdMp9JWJGE/FYwAAAAAAKhSPAQAAAACoUDwGAAAAAKBC8RgAAAAAgArFYwAAAAAAKhSPAaBFpi9Z2ekUAAC6Xm15rdMpVHRjTgDdQPEYAAAAAIAKxWMAAAAAACoUjwEAAAAAqFA8BgAAAACgQvEYAAAAAIAKxWMAAAAAACoUj4GdNn3JypbEdEK35gW0ln0dAABot9ryWle1syMUjwEAAAAAqFA8BgAAAACgQvEYAAAAAIAKxWMAAAAAACoUjwEAAAAAqJjQ4rE7m9MJrdzuWtVWN+YEW/XqNmW/AugdnbxDOL2hk7+LbZ+t041jWVte68q8oFm23+Y1M1bGc/xj4MxjAAAAAAAqFI8BAAAAAKjYrZmgoig2JnkyybNJninLcu5EJgUAAAAAQGc1VTxuOKUsy59NWCYAAAAAAHQNl60AAAAAAKCi2eJxmeSfi6JYVxTFuyYyIQAAAAAAOq/Z4vHCsiyPTfKaJP93URQnDQ8oiuJdRVGsLYpi7ebNm1uaZCtNX7Kyr/uDZk1fsrKp7dM23DrNjnk3cuxsrX5fvl5lvdBJteW1TqdAn3AsY1fS78fOfl8+oK7b9/WmisdlWf6k8fOxJNckmTdCzBfLspxbluXcadOmtTZLAAAAAADaaszicVEUv1cUxb5b55OcluTuiU4MAAAAAIDO2a2JmAOTXFMUxdb4y8uy/KcJzQoAAAAAgI4as3hcluVDSY5pQy4AAAAAAHSJZm+YBwAAAADALkTxGAAAAACAio4Xj6cvWdnpFHqGsYLu1e790/GAHVFbXut0CuNiO2dH9Np2DjBefj+yI1r1+7Ebf892Y07QTzpePAYAAAAAoPsoHgMAAAAAUKF4DAAAAABAheIxAAAAAAAViscAAAAAAFQoHu+Cmr07r7v49q5+X3f9vny0zvQlK20v0AP6/S7p/b58ADBR/A7tbdZff1A8BgAAAACgQvEYAAAAAIAKxWMAAAAAACoUjwEAAAAAqFA8BgAAAACgQvEYAAAAAICKnigeT1+yMtOXrOx0GkAb2NdhW7XltU6nMKH6ffmaYQzaz5j3P+sYAKA1eqJ4DAAAAABAeykeAwAAAABQoXgMAAAAAECF4jEAAAAAABWKxwAAAAAAVPRV8Xj6kpV93R/9zzYF3cv++Zza8lpqy2udTqOiG3MCGC/HMgCgm/RV8RgAAAAAgNZQPAYAAAAAoELxGAAAAACACsVjAAAAAAAqFI8BAAAAAKhQPAYAAAAAoELxGICWmL5kZadT2KXVltc6nUJFN+ZEa9eLddx+/TzmteW1ppavn8cAxsO+AEA7KB4DAAAAAFCheAwAAAAAQIXiMQAAAAAAFYrHAAAAAABUKB4DAAAAAFCheNwFpi9Z2ekUusfSqZ3OAPpGtx5bujUv6Ae15bVOp7DLMea9q93rzrYCAPQixWMAAAAAACoUjwEAAAAAqFA8BgAAAACgQvEYAAAAAIAKxWMAAAAAACoUjwEAAAAAqFA8ZqdMX7Ky0ykAALRcbXktteW1TqcBfcU+BQC9R/EYAAAAAIAKxWMAAAAAACoUjwEAAAAAqFA8BgAAAACgQvEYAAAAAIAKxWOAZiyd2ukMgC5TW17rdAoAO6W2vNbUsaxVMQBA71E8BgAAAACgQvEYAAAAAIAKxWMAAAAAACoUjwEAAAAAqFA8BgAAAACgQvEYAAAAAIAKxWOApVM7nQFAV6ktr3U6Bega9ofuZL0AQHsoHgMAAAAAUKF4DAAAAABARdPF46IoJhdFsb4oim9NZEIAAAAAAHTeeM48Pi/JwEQlAgAAAABA92iqeFwUxSFJXpfkSxObDgAAAAAA3aDZM48vS3J+kt9NXCoAAAAAAHSLMYvHRVGckeSxsizXjRH3rqIo1hZFsXbz5s0tS5A+sXRqpzMAoAm15bVOp9BXjGdrGU+6Vbu3TfsCANAuzZx5vDDJ64ui2JjkiiSvKIria8ODyrL8YlmWc8uynDtt2rQWpwkAAAAAQDuNWTwuy/KCsiwPKctyepK3JvnXsizfMeGZAQAAAADQMc1e8xgAAAAAgF3IbuMJLsvypiQ3TUgmAAAAAAB0DWceAwAAAABQoXi8PUundjoDRmK9ALCDastrnU5hl2PM+591DADQvxSPAQAAAACoUDwGAAAAAKBC8RgAAAAAgArFYwAAAAAAKhSPAQAAAACoUDwGAAAAAKBC8Zhd19Kp9amZOKA7tXL/tK8Du5Da8lqnUwDoPz5PkjRfa4AeoXgMAAAAAECF4jEAAAAAABWKxwAAAAAAVCgeAwAAAABQoXgMAAAAAECF4nE/clfP7uSOq7BrsK8DfaS2vNbpFAAA6CDFYwAAAAAAKhSPAQAAAACoUDwGAAAAAKBC8RgAAAAAgArFYwAAAAAAKhSPAQAAAACo2DWLx0undjoDGJ3tE4BhastrqS2vdToNAHZV/kYB2GXtmsVjAAAAAAC2S/EYAAAAAIAKxWMAAAAAACoUjwEAAAAAqFA8BgAAAACgYuKLx+7KSifY7trPmLdWN45nu3PqxjFgl1BbXut0CrBTbMMAALSKM48BAAAAAKhQPAYAAAAAoELxGAAAAACACsVjAAAAAAAqFI8BAAAAAKhQPAYAAAAAoELxeGctndqamHZbOrU78+pV3TiWrcypVW21e7uznTfPOAHQC/y+AoCJ4/ds75rA+ofiMQAAAAAAFYrHAAAAAABUKB4DAAAAAFCheAwAAAAAQIXiMQAAAAAAFYrH0IvcARV6n/246TGoLa9NcCIA/WeXP3b6PQudYd9jV7CLbeeKxwAAAAAAVCgeAwAAAABQoXgMAAAAAECF4jEAAAAAABWKxwAAAAAAVCgeAwAAAABQ0VvF46VTO50B0A79vq/3+/I1o1fHoN15N9Pf0qm9O56Q2H4B+kUvH897NfdezTvp7dzpb7bNit4qHgMAAAAA0BaKxwAAAAAAVCgeAwAAAABQoXgMAAAAAECF4jEAAAAAABWKxwAAAAAAVPRf8Xjp1E5nMLH6ffmgWd24L7Q7p24cA7qX7aX/WcdQZ18AgInTyt+zzbTVbIzf/xOm/4rHAAAAAADstDGLx0VR7FUUxZqiKO4siuKeoig+1o7EAAAAAADonN2aiPnfSV5RluWviqLYPcktRVF8pyzL/zXBuQEAAAAA0CFjFo/LsiyT/KrxcPfGVE5kUgAAAAAAdFZT1zwuimJyURQbkjyW5IayLFdPaFYAAAAAAHRUU8XjsiyfLctydpJDkswriuKo4TFFUbyrKIq1RVGs3bx58/iycFfE7tWqO1/Svay//ub4WmcMaKVe3q96Ne9u1arxtF66cwzava9367GlAznVltfa3ud2tXtf78btoN26cQy6MSdau176fR37ndZ+3fhZcQfaaqp4vFVZlv+R5KYkp4/w2hfLspxbluXcadOmjTsRAAAAAAC6x5jF46IophVF8bzG/N5JXpnkhxOcFwAAAAAAHTTmDfOSvCjJ8qIoJqdebP7Hsiy/NbFpAQAAAADQSWMWj8uy/EGSOW3IBQAAAACALjGuax4DAAAAALBrUDwGAAAAAKBC8bhbLJ1anwCA8enl36HN5N2ry9bLunHMe3k7p/85lnXn8nVjTgD0HMVjAAAAAAAqFI8BAAAAAKhQPAYAAAAAoELxGAAAAACACsVjAAAAAAAqFI8BgOY1e+d2d3inlZZObd021e5t074ArWe/olm2leZ/h3bjWLXyd383fo7o1fXCLkfxGAAAAACACsVjAAAAAAAqFI8BAAAAAKhQPAYAAAAAoELxGAAAAACACsVjAAAAAAAqFI8BgF3D0qmdzqA3GCfGo5ntxTbVnayX+hgYh/Yy5r3NuqPX2YZ3iOIxAAAAAAAViscAAAAAAFQoHgMAAAAAUKF4DAAAAABAheIxAAAAAAAViscAANAv3EW8O7VyvVjH0NuWTrUf01q2JyaY4jEAAAAAABWKxwAAAAAAVCgeAwAAAABQoXgMAAAAAECF4jEAAAAAABWKxwAAAAAAVCgeAwAwMZZO7e/+ILHdtdLSqS0fz4EZMzMwY2ZL2+wKrRqncY55V42lfQ/oAjt9XOyBY5niMQAAAAAAFYrHAAAAAABUKB4DAAAAAFCheAwAAAAAQIXiMQAAAAAAFYrHAED36oG7D0PPaWa/WjrV/gf0lYEZMzudAvSMbtxfBmbM7Mq8dgWKxwAAAAAAVCgeAwAAAABQoXgMAAAAAECF4jEAAAAAABWKxwAAAAAAVCgeAwAAAABQoXgMQOssnVqf6E3WHbCr6PfjXb8vH0yQgRkze7K/gRkz2547vavft5VW7let0qvHlq0UjwEAAAAAqFA8BgAAAACgQvEYAAAAAIAKxWMAAAAAACoUjwEAAAAAqFA8BgAAAACgQvEYAGAiLJ3a6QwmztKp/b18ABNsYMbMTqfABBqYMXPn17Hfs7sMxwO6neIxAAAAAAAViscAAAAAAFQoHgMAAAAAUKF4DAAAAABAheIxAAAAAAAViscAAAAwhoEZM/u6v25kDHpbN66/bswJ66XbKR4DAAAAAFCheAwAAAAAQMWYxeOiKP5TURQ3FkUxUBTFPUVRnNeOxAAAAAAA6Jzdmoh5JskHyrK8oyiKfZOsK4rihrIs753g3AAAAAAA6JAxzzwuy3JTWZZ3NOafTDKQ5OCJTgwAAAAAgM4Z1zWPi6KYnmROktUTkg0AAAAAAF2h6eJxURRTklyd5C/KsvzlCK+/qyiKtUVRrN28eXMrcwQAaI+lUzudAQD0tIEZMzMwY2Zb+6M5zYyV8Wwt40krtfv4ulVTxeOiKHZPvXD89bIsvzlSTFmWXyzLcm5ZlnOnTZvWyhwBAAAAAGizMYvHRVEUSb6cZKAsy89OfEoAAAAAAHRaM2ceL0zyn5O8oiiKDY3ptROcFwAAAAAAHbTbWAFlWd6SpGhDLgAAAAAAdImmb5gHAAAAAMCuQ/EY+lhteS215bVOpwETynYOAOysTty9fldnzOllAzNm2oZ7WDeuu27MaSvFYwAAAAAAKhSPAQAAAACoUDwGAAAAAKBC8RgAAAAAgArFYwAAAAAAKhSPAQAAAACoUDwGAKDjBmbMzMCMmZ1OA2CndONxrNnjazfm3o2MU2vHoFXbps8R7efY0tvGs14UjwEAAAAAqFA8BgAAAACgQvEYAAAAAIAKxWMAAAAAACoUjwEAAAAAqFA8HkNtea3TKTDBWrWO272t2DYBoL+4UzwwXo4ZAEw0xWMAAAAAACoUjwEAAAAAqFA8BgAAAACgQvEYAAAAAIAKxWMAAAAAACoUjwEAAAAAqFA83sXVltc6nQJdoJntoN3bSjfmRPt143ZH6w3MmNnpFIA2aOW+7rgBwETxOwa2pXgMAAAAAECF4jEAAAAAABWKxwAAAAAAVCgeAwAAAABQoXgMAAAAAEBFXxaPa8trnU6hopU5NdNWN45Bs7ox93avv17VrcvWzn2mtrzW1nGwbXavToxnM3eGbuXdo92JunfHoN+3g27MqZUsX3vbAegkxzIS2wGd1ZfFYwAAAAAAdo7iMQAAAAAAFYrHAAAAAABUKB4DAAAAAFCheAwAAAAAQIXiMQAAAAAAFW0pHteW11oa1wq15bWW9dfKvNs5Bs3qxpya0at57wp6dd30at7U9fIxf2DGzAzMmNmyfnd13TiWrcypG5ev3zUz5tYL42F7AWBH+P3BRHDmMQAAAAAAFYrHAAAAAABUKB4DAAAAAFCheAwAAAAAQIXiMQAAAAAAFT1XPG7lXe7pXbYDdgWt3M5b1Vaz7TQTZz/uXq28S3O77/jc73eY7vfla4ZtilYamDGzqXXcqphu1arcmx2ndvYHAOycniseAwAAAAAw8RSPAQAAAACoUDwGAAAAAKBC8RgAAAAAgArFYwAAAAAAKhSPAQAAAACoUDymbWrLa6ktr3U6DegK9gWAzhqYMbPTKfQV49mcgRkzWzZW7R5z6xgAdk2KxwAAAAAAVCgeAwAAAABQoXgMAAAAAECF4jEAAAAAABWKxwAAAAAAVCgeAwAAAABQoXhMS9SW1zqdAgDQpQZmzOx0CoxgYMZM64auZdsEgO6geAwAAAAAQMWYxeOiKL5SFMVjRVHc3Y6EAAAAAADovGbOPF6W5PQJzgMAAAAAgC4yZvG4LMvvJ/l5G3IBAAAAAKBLuOYxAAAAAAAVLSseF0XxrqIo1hZFsXbz5s2tahZ2SbXltU6nANBVBmbMzMCMmT3ZXzvzbqV2jzmt1cp1ZzvoXdYdALCzWlY8Lsvyi2VZzi3Lcu60adNa1SwAAAAAAB3gshUAAAAAAFSMWTwuiuIbSW5LckRRFI8URfGnE58WAAAAAACdtNtYAWVZvq0diQAAAAAA0D1ctgIAAAAAgArFYwAAAAAAKhSPAQAAAACoUDwGAAAAAKBC8RgAAAAAgArFYwAAAAAAKhSPAQAAAACoUDwGAAAAAKBC8RgAAAAAgArFYwAAAAAAKhSPAQAAAACoUDwGAAAAAKBC8RgAAAAAgArFYwAAAAAAKhSPAQAAAACoUDwGAAAAAKBC8RgAAAAAgArFYwAAAAAAKhSPAQAAAACoUDwGAAAAAKBC8RgAAAAAgArFYwC62sCMmZ1OAQAAAHZJiscAAAAAAFQoHgMAAAAAUKF4DAAAAABAheIxAAAAAAAViscAAAAAAFQoHgMAAAAAUKF4DD1sYMbMTqcAAAAAQJ9SPAYAAAAAoELxGAAAAACACsVjAAAAAAAqFI8BAAAAAKhQPAYAAAAAoELxuAkDM2Z2OoVdTjNj3svrpVdz79W8k97OneZYxwAAANBaiscAAAAAAFQoHgMAAAAAUKF4DAAAAABAheIxAAAAAAAViscAAAAAAFQoHgMAAAAAUKF43CIDM2Z2OgVGMDBjZlPrplUxsCto5X7Vbt2YEwAAAHQrxWMAAAAAACoUjwEAAAAAqFA8BgAAAACgQvEYAAAAAIAKxWMAAAAAACoUjwEAAAAAqGhb8XhgxswMzJjZVFy7YsYT1wqtHINu7K9Z7e6vnZod82bbapVe3e6ajWn3ftzOtnq5v2Z04/GgldtdN445AAAANMuZxwAAAAAAVCgeAwAAAABQoXgMAAAAAECF4jEAAAAAABWKxwAAAAAAVCge96iBGTO7rr925wSt1sptuJ37w8CMmfY/AAAAoOUUjwEAAAAAqFA8BgAAAACgoqnicVEUpxdFcV9RFA8URbFkopMCAAAAAKCzxiweF0UxOcnnk7wmyZFJ3lYUxZETnRgAAAAAAJ3TzJnH85I8UJblQ2VZ/jbJFUneMLFpAQAAAADQSc0Ujw9O8vCQx480ngMAAAAAoE8VZVluP6Ao3pLk1WVZntN4/J+TzCvL8r3D4t6V5F2Nh0ckuW/Iywck+VkT+TQT184Y/fV/Tv3eXzfm1O/9dWNO/d5fN+bU7/11Y0793l835tTv/XVjTv3eXzfm1O/9dWNO/d5fN+bU7/11Y0793l835tTv/XVjTv3eXzfmtDNt/WFZltNGjC7LcrtTkhOS/M8hjy9IcsFY7xvWxtpWxbUzRn/9n1O/99eNOfV7f92YU7/314059Xt/3ZhTv/fXjTn1e3/dmFO/99eNOfV7f92YU7/314059Xt/3ZhTv/fXjTn1e3/dmFO/99eNObW6ra1TM5etuD3JS4uiOLQoij2SvDXJ9U28DwAAAACAHrXbWAFlWT5TFMW5Sf5nkslJvlKW5T0TnhkAAAAAAB0zZvE4Scqy/HaSb+9EP19sYVw7Y/TX/hj9tT9Gf+2P0V/7Y/TX/hj9tT9Gf+2P0V/7Y/TX/hj9tT9Gf+2P0V/7Y/TX/hj9tT+ml/sbNOYN8wAAAAAA2PU0c81jAAAAAAB2MYrHAAAAAABUNHXN4/EqimJGkjckOThJmeQnSa4vy3JgB9s6OMnqsix/NeT508uy/KfG/LwkZVmWtxdFcWSS05P8sHGt5tHa/fuyLP+vMfp+eZJ5Se4uy/KfG8+9LMlAWZa/LIpi7yRLkhyb5N4knyzL8omiKP48yTVlWT68nbb3SPLWJD8py/K7RVGclWRBkoEkXyzLcsuQ2MOS/B9J/lOSZ5Lcn+QbZVk+sb38AQAAAAB2VMvPPC6K4sNJrkhSJFmT5PbG/DeKoljSZBt/0vj550muS/LeJHcXRfGGIWGfbMRcnOS/JvnvRVF8Ksl/SzIlyZKiKC5sxFw/bPofSd609fGQftcMmX9no619k1w8JPevJHm6Mf+5JFOT/FXjua82nv9/kqwuiuLmoijeUxTFtBEW86tJXpfkvKIo/iHJW5KsTnJ8ki8NyePPk/xtkr0ar+2dehH5tqIoFo05mOzSiqJ4QQvb2r9VbfUzY95+rRpz490823n7GfP2M+btZ8zbz5gDAGMqy7KlU5J/S7L7CM/vkeT+Jtv4cePnXUmmNOanJ1mb5LzG4/VDYiYn2SfJL5Ps13h+7yQ/aMzfkeRrSRYlObnxc1Nj/uQh/a4fMn97kmmN+d9LcldjfmBIzB3D8t6wtZ3UC/OnJflyks1J/inJ4iT7NmK25rZbkp8mmdx4XGx9bejyNeb3SXJTY/4PhubbK1OSF7Sonf07vBxTk3w6yQ+TPN6YBhrPPa+J939nyPx+ST6V5B+SnDUs7guNny9M8t+TfD7J/kmWNraNf0zyokbM7w+b9k+yMcnzk/x+I+b0Ycvw5SQ/SHJ5kgOHvPbpJAc05ucmeSjJA0l+tHWfaexXf5nksO0s59wkNzb2v/+U5IYkTzT2rzlD4qYkuSTJPY3XNyf5X0n+2Jj3/pg3M97tHvNmxruVY97MePfydt7KcW/VmDu29P+xxXbueL4rbOfGvPkxb8QVSV6W5E2pf3PzZWncJL6ZKcmMYY9H+rv2gCHzk5JMaszvkfo3Un+/iX7eM8brUxptPW/Ic3sMXZYkpyT5QJLXDHnu6HEs6x9sbT/1v7XfnOSoUdbR/5Hkj4aPjzE35sbcmBvz/hnz7fYxnuAmk/5hkj8c4fk/THLfkMc/GGW6K8n/bsTcO8Kg/1OSz2ZIoXbI6+uHxW+NmZTkfal/GJndeO6hEXK8M/UPT/snWTvstfWNn1cm+ZPG/FeTzG3MH57k9sb88KLy7klen+QbSTY3nru7scE8P8mTee4D217ZtkB9V5I9G/PPT7JuyGt3N37u1IfTRhtNf0BNEx9OG3FtK/KkiQ+nQ96/3Q+oae4Pgv+Z5MNJXjjkuRc2nruh8fjYUabjkmwa8r6rG8v4xiTXNx5vXed3NH7+U+pn4C9pjM+HUz9AvDfJdY2Y3yX592HTlsbPh4Zvm6mf4f7x1PfN9yW5duh2N2T+xiTHD9nO1zbm/z3JpUl+nPq3DN6X5KBh470myWuSvC3Jw0ne3Hj+1CS3DYm7LskfJzkkyfuTXJTkpUmWp345GGPew2PezHi3e8ybGe9Wjnkz493L23krx71VY+7Y0v/HFtu54/musJ0b83GN+Wmp/43wnUbuX2qMywNJThva72hTnjuJ6ZQkj6T+d8A/J5k+/DjQWCc/Tf2kpDek/i3Sf22874+GxL9/2PSBJD/b+rgRM/QfAS9vjNmNjTF5beP5O5M8vzH/oSSrUv/754Ykn2o8/2xjef+fJEduZzmXNNbPD5Oc0/j55dT//tma08mpn7z13SS/SPKtJLcmuSnJfzLmxtyYG3Nj3j9jPuZ4NhM0nin16w1vXclfbExbV/LQQuFPk8xO/cPI0Gl66tcBTmNlzR7W/m5J/j7Js43Hq5Ps05ifNCRuaqpF3ENSL/7+t60b0rDXN6ZemPz3xs8XNp6fkucK0VOTLEvyYKPvLY3Y7yU5phGzfjvjs3fj5/sa7/tRkj9P8i9J/i71IuzFQ+LPS/0D4BcbG8HWwvW0JN9vzI/54bTxXEs+oKaJD6eNuLYVedLEh9NGXEuKPBnyj5AR+rhvyM7+r42ch0+/HhK/Ydj7L0x9R95/yJivH/L6j4fFb902P9hYN7Uhr/37sNg7ttPvhiHzP0yyW2P+fw2Lu2uEtk5M8oUkjzaW711N5D30tTuHvbb1HzGTUr9+uTHv4TFvZrzbPebNjHcrx7yZ8e7l7byV496qMW/3dt7uMW9mvI257bzVY97MeNvOjXkHx3wgQ4oCQ2IPzbYn5/zXUaa/SfLLre0nmdWYf3Pq95yZPzSv1L9t+sJG+79MckTj+T/Mtv+IfjLJiiQfTXJxY/rF1vkRxurGJMc25l+c5/7euXtIzNo893flbnnuW63rkxyV5BOp//19Z+p/s00fNib3pP5N3f0b+Q39xu3dQ9qaNmQMr2nMvyrJPxtzY27Mjbkx758xH2saM2BHptR/ic9P8n82Vs78NC69MCTmy0lePsr7L2/8PCRDCqLDYhY2fu45yusHZMgHo2GvvS5DzvRqYnn2SXLosOf2TXJM6oXXA4e9dniT7R6URoEzyfMaYzVvhLhZjddGPK08TXw4bcy35ANqmvhw2phvW5EnTXw4bSL39Y2fzXw4/eck52fbr+8dmHoh/buNx3cneeko6+XhIfMDGfKPj8Zzi1M/CPxoeE5JPj7SGAzZZ65M/ez8fTPsDPvU/3O29T9kD2Xbr0oMvVzKexvL+IrUzyy/LMlJST6W5B+Gj/mQ901O/R9IX208vi31/xq+JfV/lLyx8fzJ2fYgvCqN40HqX6H4n8O34S4b86FjZczHGPNmxrvdY97MeLdyzJsZ717ezls57q0ac8eWcR1b1hvztm/nPTnmzYx3F2/nPXlsmcAx/+seHvPXjzLm96fxN8OwPvdI8sCQx08meVdjHIdPPxs+no3Hs5Lcl/rXfUf6m+juYfFD/zb5gyRXpX6fnK0nPQ0f96Hx64a9tn7IGBzVmP+nPHfW2l55rkAw/OSpeanvXw8nWTV8XTbWx2PZ9iSsu4fGDIkbmuM9xtyYG3Njbsz7Z8zHmsYMMHX/lCY+nDaea8kH1DRZyGw8bkuRZ/iO1Xhumw+njedaUuRJ/dIbf5V6YfsXSX7eGLu/ynOX5HhzGv+xGiG3Nw6Z/y9JXjlCzOlpXCc89ctoTBkh5iVJrhrh+T9K/VIbjw57/uJh09b/Pr0wyd8Pi12U+n/V1qd+Rvy3Uz8w7954/Yomts1jUj8z/jtJZqR+k8n/aGxPC4bFrWm8dkue+0/ftCR/bsx3esx/0RjzhdsZ88ObGPNfNMb8v4xnzJsZ7x0c89fvzJin/hWm4eP97gy5HlaTYz57rDEfMt5PZJRtvJe383GO+6IRxn282/mYY74T23lPjHmz492BY8vR/TrmLTi+TNTv0K1jvvX4MmFj3sx47+CY79TxvAPb+XiOLQON8e62MW/m2PLRJsf8jozwe7QDY35BY/1/OMlZjenDjecuGNLWv2bIPjQsn39v/FybYScxpf53zYYkTzYer89z18ecNyRucoYVHxrPvyH1k3LenOrfRE/nuUs4PpnnCgmT8twf/0enfubZ3zemB1O/mfvaNC43mFG+AZv6tUNPHvJ4WeqXCbwu9Usr/kOSt6d+gtc/NmK+0nh8VmM9f7bx/D557oQaY27Mjbkxb9eYLzHmEzfmY01jBpi6f8q2fxAM/3D6/CFxLfmAmnF+OG28tlN//Gb0Pwi2npE85ofTRtwxGeMPsTTxh2/j8Ywkrxw+Ftn28iwzUr8kxqgxY8S9ZjxtDY1J/asLR01ATuNqK8nMJvub2cR4zstzly2Zlfo/HV47LH5ozJGp/3Nim5hm43Ygppb6tYpakdMOL1/qNxPYbjsjxI2a17D3/MP2Xm/E/H0rYpqJa2znV3ZTTi0epxMb62/Ua3ulfi2t928vptm4JmNObGznreivJcvXTDujtdXYD6Y25vdJ/Xfct1L/HTp1SMx+Q7a5S5L8j6ExI7Q1YtwIbX1sjJh9Uv/d/N0RYobnPVpO++3s8o2ybN8apb+heY20fH+eMa6v1kxMK9saHpMhv0Pb0V8bxmCP1E8GeGXj8dtTv3fF/53nCn17Jvm/hsSclfql3sYVM4629hgW859T/yNweH+Lm+hveFs7unxjjtMocaPl9ZLUr2X4X1M/keLPMmR/acQclvq39T6X5P/d0ZgdbOuzqZ+kMTxma97N9NeK5Rve3/9vlP6GjudobR2ZeoHhbxrrZEmGXSsy9Xux7DPGPvPKNC5JOOz55yW5sDF/fJK9RoiZnuQdo7T7e0k+k8blB4c8/4fDpj0azx+Q5E1D4ianfgm+81L/vXdmtr0J01nbW64hcbulfhm/tzbmFzbG6/wkv9eI2T3JexrPvzPP3cR97wy5x1Hqn+Encsyn7uSY79PkmG89DrRrzBfsxJhP9HZuzNs/5s9r05i3+9iyM2Pu2NLmMd/eVDTeQJ8qiuJPyrL8aividjamKIq9U7+h3d3t6G88MeNtqyiKP0/9D4SB1M+AO68sy+saMXeUZXlsMzGN+fcmOXeMtpqJaSanMdtpzLekrUY770n9Hxtj9bfduKIoLk794Lpb6heSn5f6tcZfmfrZ4Z8YIeZlqV8EfjCm0eaYcTsY08qcdqitZtoZxxhcn6pXpP7HfcqyfP0IMUXqZ9yNK6aR04601UxOOxTT7pwa/a0py3JeY/6c1PfDa1P/xsT/KMvy08Ni3tmIuWZozAhtjRi3gzHvGZ7TKLmfO0ZbO7x8Lc7pntQ/UD5TFMUXkzyV+vX+T208/6YRYp5O/StrgzGNPsaM28GYVua0Q2010844xuCJxvsfTP1MiSvLsvxZhhgW841GzOYM00zcDsb84/CcdrCtHV6+HRyDy1P/R/7wtr6e+vF+79TPUP691PeFU1P/1tfiITH7pP4P9ClJvjmOmJRl+cfD+hs1boSY8ea0vf5atXyVdkYZz5Ha+vMkZyT5fpLXpn6G0y9S/5rse8qyvKlVMY2cmm3rj1L/XLCz/Z2X+qUA29VfU3EA9KeiKF5QluVjrYhrVUy7+2t3Ttsom6gwm3p3ygg3BtzRuFbFtLu/icgp9bOfpzTmp6f+9YLzGo/XNxvTyrZ6NadxtjU59T/ofpltz5L7QbMxrWyrV3MaR1t3JPla6mf+n9z4uakxf/LW9dOKmHG01aqcxmynlcs3njEYMn97tr3hwV3NxrSyrW7sr8U5Db3pxvBri21oNqaVbfVqTuNoa33qX907LfWv0G1O/Xpvi5Ps22xMK9vqxv5anNPW4/puqd+0eusZJ8WQ11oSo79tfx835vdJclNj/g8y7LPNzsbob7Ctqanf+PuHSR5vTAON5543pK0x41oVM0LczyeyvwnKaWB7bY02JfnOWDHNxrUqpt39TUROSfZL8qnUv5r+tmExXxgyPzTurJHiWhUzAf19envL14GcXpjkv6f+TZT9U7+U5g+S/GOSFw2JHynurqFxrYppNq+d6G/c7YyzrS+MkdPvjzBtTP1b978/pL/hMfsPj2tVTLN57WB/O9ROq9va3rRb6HlFUfxgtJdSv/Zx03Gtiml3f+3OKfUPr79KkrIsNxZFsSjJVUVR/GEjrtmYVrbVqzk1G/dMWZbPJnm6KIoHy7L8ZSP+10VR/G4cMa1sq1dzajZubupfo7kwyYfKstxQFMWvy7L83pB2jmtRTLNxrcqpmXZauXzNjsGkoiien3ohqCgbZw+WZflUURTPjCOmlW11Y3+tzGnot2HuLIpiblmWa4uiODzJlnHEtLKtXs2p2biyLMvfpX4/g38uimL31L8J8bYkl6Z+mahmYlrZVjf218qcJhVFsUfq/zjZJ/Wi0M9Tv5TD7o12WhXTyrZ6ub+kXlx+tvH8vklSluWPG+uo1TH6qxce/jXJorIsH02SoihemOSPU78Hy6vGiFs8JK5VMe3ub2dz+uMR2jpltLaKojg2IytS/zZhGu8bM65VMe3ur905Jflq6jcTuzrJ2UVRvDn1ouf/TjJ/yHuGx/2fI8S1KmYi+xtp+dqd07IkK1M/5t+Y5OupfxPiDUn+tvFztLjXDYtrVUyzee1ofzvSTitz+lnq96ga6uDUTwAqk7y48Vwzca2KaXd/7c5p+8omKsym7p5SP+NhdqrXVpme5CfjiWtVTLv760BO/5pk9rD1sFvqFzt/ttmYVrbVqzmNo63Vee6OpkPvIDo1z90ldcyYVrbVqzmNJ67x3NYbX/63jHKGfqti2t1ft+WU+n+AH0ry742fL2w8PyXPnbU5Zkwr2+rG/lqc09TUP8Q+mPp+saUR+700ro/WTEwr2+rVnMbR1vrt7Gt7NxvTyra6sb8W5/S+xnr4UerXSP6XJH+X+pk+F7cyRn+DbZ2X+tlWX0z97M4/aTw/LY3rI7YqRn+Dbd23nX3hvpHmR4trVUy7++tATs+m/jn+xhGmXw+JHzOuVTHt7q8DOW0Yti4uTP3GXftn28/6Y8a1Kqbd/XUgp/VDXv/xsPgNQ+bHjGtVTLv760BOH0z9W1S1Ia/9+9DYZuNaFdPu/tqd01hT04Gm7p1S/4riy0d57fLxxLUqpt39dSCnQzLsjp1DYhY2G9PKtno1p3G0tecorx+w9SDYTEwr2+rVnMYTN+y11yX55EivtTqm3f11Y07D4vdJcujOxrSyrW7sb2dySv0stmNSP0v8wFHeN2ZMK9vq1ZzGikvjRrRjrKMxY1rZVjf218qcGnEHJTmoMf+81G+kPG8iYvQ3GDOr8fyM7ayXlsTor0zqZ9+fnyHHnNS/NfjhJN8dT1yrYtrdXwdyujvJS0dZHw8PmR8zrlUx7e6vAzkNZMiJH43nFqd+A/gfDXluzLhWxbS7vw7kdOeQ1z4+LHbo5dLGjGtVTLv7a3dOjfmtJ918NvXPlQ+Nsm+MGdeqmHb31+6ctjc1HWgymUwmk8lkMplMJlM3Tqlfu/Gv8tx1fH+eemHor5I8fzxxrYppd38dyOnNSY4YZX28ccj8mHGtiml3fx3I6b8keeUIr5+e5P4hj8eMa1VMu/vrQE6XpHF/nmExL0n9JrVpNq5VMe3ur905DXv+j5L8rySPjrRvjCeuVTHt7q/dOY34vvEEm0wmk8lkMplMJpPJ1EtTGpe5aEVcq2La3V835tTv/XVjTv3eXzfm1O/9tSOn1G/qftRY7TQT16qYdvfX7pyGT0UjGAAAAPpOURQ/LsvyD1oR16qYdvfXjTn1e3/dmFO/99eNOfV7f92YU7/31+6ckvoNqQAAAKBnFUXxg9FeSv1avU3HtSqm3f11Y0793l835tTv/XVjTv3eXzfm1O/9tTunsSgeAwAA0OsOTPLqJL8Y9nyRZNU441oV0+7+ujGnfu+vG3Pq9/66Mad+768bc+r3/tqd03YpHgMAANDrvpX6TZg2DH+hKIqbxhnXqph299eNOfV7f92YU7/314059Xt/3ZhTv/fX7py2yzWPAQAAAAComNTpBAAAAAAA6D6KxwAAAAAAVCgeAwAAAABQoXgMAAAAAECF4jEAAAAAABX/f6VCynDmQltFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.plot.bar(figsize = (25,10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf4c9cef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2.hist(by='Species', figsize=(10, 7));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "48185f05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Species', ylabel='PetalWidthCm'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = 'scatter', x = 'Species', y = 'PetalLengthCm')\n",
    "df.plot(kind = 'scatter', x = 'Species', y = 'SepalLengthCm')\n",
    "df.plot(kind = 'scatter', x = 'Species', y = 'SepalWidthCm')\n",
    "df.plot(kind = 'scatter', x = 'Species', y = 'PetalWidthCm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "841087f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Iris-setosa', 'Iris-versicolor', 'Iris-virginica'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df['Species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "808a5953",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = ['Red', 'Green', 'Blue']\n",
    "groups = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "10dd981b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x27516194700>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAEJCAYAAAB2T0usAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAucElEQVR4nO3de3xcdZ3/8denSbQNhZbauqilCfwEEZJe6IWr0BKWa60LC1YpCC5tJJUFF1Fw+/t1oT8r+NNF1kvBKiKYcFmqKCKsK71AK6K2pbSUgiK0EOkKFCgtpdAkn98fM0lzmcw5k5w5cybzfj4e55GZ7znznc/55jTfnvO9mbsjIiKlbVChAxARkcJTZSAiIqoMRERElYGIiKDKQEREUGUgIiLEUBmYWZmZPW5m92fYN9XMtpvZuvQ2P9/xiIhIT+UxfMflwCZgv172r3T36THEISIivchrZWBmo4EzgYXAFVHkOXLkSK+uro4iKxGRkrFmzZpX3X1Ub/vzfWdwI/BlYN8sxxxjZk8ALwFXuvvGbBlWV1ezevXq6CIUESkBZrYl2/68tRmY2XTgZXdfk+WwtUCVu48DvgP8vJe86s1stZmtfuWVV6IPVkSkxOWzAfk4YIaZbQbuAk4ys8bOB7j7m+6+M/36AaDCzEZ2z8jdF7v7JHefNGpUr3c5IiLSR3mrDNz9K+4+2t2rgU8By9z9/M7HmNkBZmbp11PS8WzLV0wiIpJZHL2JujCzSwDc/WbgHKDBzFqAt4FPuaZRFSkKe/bsobm5md27dxc6FOlk8ODBjB49moqKipw+Z8X2t3fSpEmuBmSRwnv++efZd999ed/73kf6Bl8KzN3Ztm0bO3bs4KCDDuqyz8zWuPuk3j6rEchS3JqaoLoaBg1K/WxqKnREJWP37t2qCBLGzHjf+97Xp7u12B8TiUSmqQnq62HXrtT7LVtS7wFmzSpcXCVEFUHy9PV3ojsDKV7z5u2tCNrt2pVKF5GcqDKQ4vXCC7mly4AzdOjQXvcde+yxefver33ta3nLu1BUGUjxGjMmt3QpCa2trQA8+uijefsOVQYiSbJwIVRWdk2rrEylS/LksbF/xYoVTJs2jfPOO4/a2lpg713D1q1bOeGEExg/fjw1NTWsXLmyx+c3btzIlClTGD9+PGPHjuXPf/4zAI2NjR3pn/vc52htbeXqq6/m7bffZvz48cxKt03dcMMN1NTUUFNTw4033gjAW2+9xZlnnsm4ceOoqanh7rvvBmDBggVMnjyZmpoa6uvrSUyPTncvqm3ixIku0qGx0b2qyt0s9bOxsdARlYynnnoq/MGNje6Vle6wd6us7Pfva5999nF39+XLl3tlZaU/99xzPfZ985vf9K9+9avu7t7S0uJvvvlmj3wuvfRSb0zH8s477/iuXbv8qaee8unTp/u7777r7u4NDQ1+2223dcnb3X316tVeU1PjO3fu9B07dvjhhx/ua9eu9SVLlvjs2bM7jnvjjTfc3X3btm0daeeff77fd999/SqDTDL9boDVnuVvq+4MpLjNmgWbN0NbW+qnehElUwyN/VOmTOnRtx5g8uTJ3HrrrVxzzTVs2LCBffftOW/mMcccw9e+9jW+/vWvs2XLFoYMGcLSpUtZs2YNkydPZvz48SxdupTnnnuux2dXrVrFWWedxT777MPQoUM5++yzWblyJbW1tTz00ENcddVVrFy5kmHDhgGwfPlyjjrqKGpra1m2bBkbN2admzM2qgxEJP9iaOzfZ599MqafcMIJPPLII3zoQx/iggsu4Pbbb+fee+9l/PjxjB8/ntWrV3Peeedx3333MWTIEE499VSWLVuGu3PhhReybt061q1bxzPPPMM111zTI3/v5THPoYceypo1a6itreUrX/kKCxYsYPfu3cydO5clS5awYcMG5syZk5gR3KoMRCT/CtjYv2XLFt7//vczZ84cLr74YtauXctZZ53V8Ud+0qRJPPfccxx88MFcdtllzJgxg/Xr11NXV8eSJUt4+eWXAXjttdfYsiU1C3RFRQV79uwBUpXNz3/+c3bt2sVbb73Fvffey8c+9jFeeuklKisrOf/887nyyitZu3Ztxx/+kSNHsnPnTpYsWZL38w9Lg85EJP8WLuw6QBBia+xfsWIF3/jGN6ioqGDo0KHcfvvtPY65++67aWxspKKiggMOOID58+czYsQIvvrVr3LKKafQ1tZGRUUF3/ve96iqqqK+vp6xY8dy5JFH0tTUxEUXXcSUKVMAmD17NhMmTODXv/41X/rSlxg0aBAVFRXcdNNNDB8+nDlz5lBbW0t1dTWTJ0/O+/mHpbmJRKRPNm3axEc/+tHwH2hqSrURvPBC6o5g4UK18eRJpt9N0NxEujMQkXjMmqU//gmmNgMREVFlICIiqgxERARVBiIigioDKSQtTCOSGKoMpDDaF6bZsiU1U037wjSqECQHhZrCOoyXXnqJc845p0+fnTp1KnF3oVdlIIWhhWkkT+KYwrqzlpaWjOkf/OAHYxth3H7O/aHKQApDC9OUnKYNTVTfWM2gawdRfWM1TRuSMYX19u3bqa6upq2tDYBdu3Zx4IEHsmfPHv7yl79w2mmnMXHiRD72sY/x9NNPA3DRRRdxxRVXMG3aNK666ioefvjhjrmOJkyYwI4dO9i8eTM1NTVA6o/1lVdeSW1tLWPHjuU73/kOAEuXLmXChAnU1tbyT//0T7zzzjs9zu3OO++ktraWmpoarrrqqo70oUOHMn/+fI466ih+97vf9b8Qs01pmsRNU1gPEFVVXaczbt+qqgodmYSUyxTWjesbvXJhpXMNHVvlwkpvXJ+MKaxnzJjhy5Ytc3f3u+66yy+++GJ3dz/ppJP8T3/6k7u7P/bYYz5t2jR3d7/wwgv9zDPP9JaWFnd3nz59uq9atcrd3Xfs2OF79uzx559/3o844gh3d1+0aJGfffbZvmfPHndPTWP99ttv++jRo/2ZZ55xd/cLLrjAv/Wtb7m7+4knnuh//OMf/a9//asfeOCB/vLLL/uePXt82rRpfu+997q7O+B33313xnLRFNZSPLQwTUmZt3Qeu/Z0fSy4a88u5i1NxhTWM2fO7Fh85q677mLmzJns3LmTRx99lHPPPbdjcZutW7d2fObcc8+lrKwMgOOOO44rrriCb3/727zxxhuUl3ed3OGhhx7ikksu6UgfMWIEzzzzDAcddBCHHnooABdeeCGPPPJIl8/98Y9/ZOrUqYwaNYry8nJmzZrVcUxZWRn/+I//2Nfi6kGVgRTGrFmweDFUVYFZ6ufixZquYIB6YXvmx3+9pfdFf6awnjFjBg8++CCvvfYaa9as4aSTTqKtrY3hw4d3zG66bt06Nm3alPH7rr76an74wx/y9ttvc/TRR3c8Tmrn7phZj7Qg2Y4ZPHhwR2UUBVUGUjhamKZkjBmWearq3tKjFGYK66FDhzJlyhQuv/xypk+fTllZGfvttx8HHXQQ99xzD5D6w/zEE09k/I6//OUv1NbWctVVVzFp0qQelcEpp5zCzTff3NHY/Nprr3HYYYexefNmnn32WQB+8pOfcOKJJ3b53FFHHcXDDz/Mq6++SmtrK3feeWePY6KiykAy0xgAidDCuoVUVnR9LFhZUcnCunimsG5v2P3pT3/K5ZdfnvG4mTNn0tjYyMyZMzvSmpqauOWWWxg3bhxHHHEEv/jFLzJ+9sYbb6SmpoZx48YxZMgQTj/99C77Z8+ezZgxYxg7dizjxo3jjjvuYPDgwdx6662ce+651NbWMmjQIC655JIun/vABz7Addddx7Rp0xg3bhxHHnkkn/jEJ/pZIplpCmvpqX0MQPe55/UYRzrJdQrrpg1NzFs6jxe2v8CYYWNYWLeQWbW6nvJBU1hLNLKNAVBlIH00q3aW/vgnmB4TSU8aAyBSclQZSE8FXK9WRApDlYH0pDEAIiVHlYH0pDEAIiUn7w3IZlYGrAb+6u7Tu+0z4D+AM4BdwEXuvjbfMUkIWq9WpKTEcWdwObCpl32nA4ekt3rgphjikVKi8RIDWr6nsJ4/fz4PPfRQTp+57777uP7667Me05/prfMlr+MMzGw0cBuwELgiw53B94EV7n5n+v0zwFR339ojszSNM5DQNF4ir3IdZ5APQ4cOZefOnV3SWltbI52mIZM4vqM/+jLOIN93BjcCXwbaetn/IeDFTu+b02ki/ac1ExIlnzdp+ZrC+qKLLupYk6C6upoFCxZw/PHHc8899/DAAw9w2GGHcfzxx3PZZZcxfXrq/7o//vGPufTSS4HUVNeXXXYZxx57LAcffHBHXmGmt16wYAGTJ0+mpqaG+vr6UHMZ9Ufe2gzMbDrwsruvMbOpvR2WIa3HGZtZPanHSIxR90YJS+MlEqP7TVr7wnYQ3U3aH/7wB5588skeM5fecccdnHrqqcybN4/W1lZ2dfsPwrBhwxg3bhwPP/ww06ZN45e//CWnnnoqFRUVPb5j8ODBrFq1it27d3PIIYfwyCOPcNBBB/HpT3+617i2bt3KqlWrePrpp5kxY0aPx0OLFy/m+eef5/HHH6e8vJzXXnsNgEsvvZT58+cDcMEFF3D//ffz8Y9/vE9lE0Y+7wyOA2aY2WbgLuAkM2vsdkwzcGCn96OBl7pn5O6L3X2Su08aNWpUvuKVgUbjJRIjjpu0qKewzqQ9/emnn+bggw/u+L5slcE//MM/MGjQIA4//HD+9re/9difaXprgOXLl3PUUUdRW1vLsmXL2LhxY7bT77e8VQbu/hV3H+3u1cCngGXufn63w+4DPmMpRwPbs7UXiORE4yUSI46btKinsM72Hbk8snnve9/b8TrT5zJNb717927mzp3LkiVL2LBhA3PmzGH37t2hv7MvYh9nYGaXmFn71HwPAM8BzwI/AObGHY8MYBovkRiFvEnr6xTW2Rx22GE899xzbN68GaDjrqIvMk1v3f6Hf+TIkezcuTOWtZRjmajO3VcAK9Kvb+6U7sDn44hBSpTGSyTCwoWZO3bFcZO2YsUKvvGNb1BRUcHQoUO5/fbbMx43c+ZMzj33XFasWBGY55AhQ1i0aBGnnXYaI0eOZMqUKX2Ob/bs2fzpT39i7NixVFRUMGfOHC699FLmzJlDbW0t1dXVTJ48uc/5h5ZtTcwkbloDuUg0NLiXlaXWNS4rS72XASWXNZDd3RsbU0tcm6V+NvZv+eOC27Fjh7u7t7W1eUNDg99www0FjmivvqyBrCmsJXpz58JNncYPtrbufb9oUWFikoIbaDdpP/jBD7jtttt49913mTBhAp/73OcKHVK/aHEbiV55eaoC6K6sDNLPRaX4JWHQmWSWxEFnUooyVQTZ0qVoFdt/JktBX38nqgwker31xEjw8H3J3eDBg9m2bZsqhARxd7Zt28bgwYNz/qzaDCR69fVd2ww6p8uAMXr0aJqbm3nllVcKHYp0MnjwYEaPHp3z51QZSPTaG4kXL049GiorS1UEajweUCoqKjKO+JXipMpA8mPRIv3xFykiajMQERFVBiXp5JNT0zO0byefXOiI+k6L10jCRXGJxnKZZxuRlsRNI5D7qa4uNSq4+1ZXV+jIctfY6F5Z2fU8KiuLf2irDBhRXKJRXeYEjEDWoLNSY5mWkEgrsmuB6urUxPjdVVVBegIxkUKK4hKN6jLXoDMZuLR4jSRcFJdoXJe5KgMpXlq8RhIuiks0rstclUGpqavLLT3JtHiNJFwUl2hcl7kqg1Lz0EM9//DX1aXSi40Wr5GEi+ISjesyVwOyiEgJUAOy9BRHx2f1/xcpKpqOotQ0NXVdf3DLlr0TyIW97wzKI4rvEJFY6TFRqYmj47P6/4skjh4TSVdxdHxW/3+RoqPKoNTE0fFZ/f9Fio4qg1ITR8dn9f8XKTqqDEpNHB2f1f9fpOioAVlEpASoATlOSelbn5Q4RPJIl3m0NM4gKknpW5+UOETySJd59PSYKCpJ6VuflDhE8kiXee70mCguSelbn5Q4RPJIl3n0VBlEJSl965MSh0ge6TKPniqDqCSlb31S4hDJI13m0VNlEJWk9K1PShwieaTLPHpqQBYRKQH9bkA2s7PN7M9mtt3M3jSzHWb2ZojPDTazP5jZE2a20cyuzXDM1HS+69Lb/OBTkkBz50J5eeq/TOXlqfe57Id4OnGro7hIcrh71g14Fvho0HEZPmfA0PTrCuD3wNHdjpkK3J9LvhMnTnTJoqHBHXpuDQ3h9ru7Nza6V1Z23V9ZmUqPShzfISIdgNWe5W9r4GMiM/utux/XnwrHzCqBVUCDu/++U/pU4Ep3nx42Lz0mClBeDq2tPdPLyqClJXg/xNOJWx3FRWIV9Jio1xHIZnZ2+uVqM7sb+DnwTvt+d/9ZiC8vA9YAHwa+17ki6OQYM3sCeIlUxbAxQz71QD3AGPUdyy7TH/rO6UH7IZ5O3OooLpIo2doMPp7e9gN2Aad0Sgv1P3l3b3X38cBoYIqZ1XQ7ZC1Q5e7jgO+QqnAy5bPY3Se5+6RRo0aF+erSVVaWPT1oP8TTiVsdxUUSpdfKwN0/6+6fBX7Y/rpT2i25fIm7vwGsAE7rlv6mu+9Mv34AqDCzkTmeg3TWPkFLb+lB+yGeTtzqKC6SLNkaFNLtCWvDpGU4ZhQwPP16CLASmN7tmAPY2711CvBC+/veNjUgh9DQ4F5WlmqULSvr2jgcZr97qiG3qsrdLPUzHw27cXyHiLh7PxqQzewY4FjgC8C3Ou3aDzjLU492emVmY4HbgDJSdyD/6e4LzOySdCV0s5ldCjQALcDbwBXu/mi2fNWALCKSuz43IAPvAYamj9m3U/qbwDlBX+zu64EJGdJv7vT6u8B3g/ISEZH8ytZm8LC7X0tqbMC1nbYb3P3PMcZYPKIYRBVmQFh/8wgTZ3/PJYrzSIimDU1U31jNoGsHUX1jNU0bcv+9xlHkIv3S2/Mj4JfAfb1t2Z495XNLbJtBFIOowgwI628eYeLs77lEcR4J0bi+0SsXVjrX0LFVLqz0xvXhf69xFLlIEPrRZnBi+uXZpBp6G9PvPw1sdvd/zVcFlU1i2wyiGEQVZkBYf/MIE2d/zyWK80iI6hur2bK9Z1lUDati8xc2h8ujOv9FLhIkqM0gzAjkR9z9hKC0uCS2Mhg0KPUfuu7MoK0tXB5mve8L+D2FziNMnP09lyjOIyEGXTsIp2fMhtH2b+F+r3EUuUiQKFY6G2VmB3fK8CBS3UalsygGUYUZENbfPMLE2d9zieI8EmLMsMzn3Ft6xmNjKHKR/gpTGfwLsMLMVpjZCmA5qe6m0lkUg6jCDAjrbx5h4uzvuURxHgmxsG4hlRVdy6KyopKFdeF/r3EUuUi/ZWtQaN+A9wLj0tt7w3wmX1tiG5DdoxlEFWZAWH/zCBNnf88livNIiMb1jV71rSq3a8yrvlWVU+NxRx4xFLlINvSjAfkkd1/WacK67pVI4ER1+ZDYNgMRkQTrT5tBe2+ij2fYQk85LQUQ1GFdHdoTae71qygf0YxZG+Ujmpl7/ar4Yxg4w0MkV73dMgDjCZgnqBBboh8TJUFQh3V1aE+khutWOhU7uw7NqNjpDdetjC+GgTM8RDKgH4+JVgMHkZpm+rfAo8Bj7h645GU+6TFRgKAO6+rQnkjlI5ppfX10j/Sy/Ztpea1nel5iGDjDQySDPj8mSn/oQGAh8C5wGfDn9JrGiyKPVKIRtGiMFpVJpNbXP5hTel5iCLHukQxcWbuWuvsud18B/AepmUu/B+xDt3UJJEGCOqyrQ3sile3/Uk7peYlh4AwPkT7otTIws/PM7LtmtorUfER/D2wAjnf3g3v7nBRYUId1dWhPpPovb4aKt7omVryVSo8rhoEzPET6orfGBGAnsBqYDRyareEhzk0NyCEEdVhXh/ZEarhupZft/6JDq5ft/2KsjccdMQyc4SHSDf1oQC4jNcjs2PT2EWAr8Dvgd+6+LP9VVU9qQBYRyV1/GpBb3X2tu3/X3c8DzgAeBD4L/Cb6UAssir73QXnE1Ylb4whyEsV6BXEIGocQ1689imEsca0RITno7ZYBGAtcAtwOPEtqfeK7gcuBSdluN/K55eUxURR974PyiKsTt8YR5CSK9QriEDQOIa5fexTDWOJaI0K6oh+PiTqPL3jU3TN0To9fXh4TRdH3PiiPuDpxaxxBTqJYryAOQeMQ4vq1RzGMJa41IqSrfq9nkDR5qQyimEw+KI+45vjXxPg5iWK9gjiYtZH5qW4b7oNi+7UHfU+otRtiWiNCuopiPYOBL4q+90F5xNWJW+MIchLFegVxCBqHENevPYphLHGtESG5UWUA0fS9D8ojrk7cGkeQkyjWK4hD0DiEuH7tUQxjiWuNCMlRtgaFJG55G2cQRd/7oDzi6sStcQQ5iWK9gjgEjUOI69cexTCWuNaIkL3oRwPyLyHDg729lciMPNVPWWmcgYhI7vrTZvBN4N+zbNJdHGMVTj451UrWvp18cv/jlqIQR7/6k6/8ETZ8C2Zt2PAtnHzlj3LPY+bTWFkLZo6VtXDyzKejD1Sil+22IYlbYqejiGOsQl1d133tW11dfs5JEiOOfvV1X7wl41iGui/eEj6PT25yaOt2ibZ53Sc3RReo9Al9fUzUzswOAa4DDgcGd6pECjJZXWIfE8UxViGu7qmSOHH0q7fhW2B7Vc8dw7bgb2RIz5RHWQu0lffcMagFb82QLrGJomvprcBNQAswjdSI5J9EE94AEsU6AVprQHoRy6Wx/cDc0jNp66WrdG/pkhhhKoMh7r6U1AC1Le5+DXBSfsMqQnGMVZCSFculMezF3NIzGdTLSji9pUtihKkMdpvZIFKrnF1qZmcB789zXMUnjrEKdXWZP9dbugwYcfSrr5u9NONYhrrZS8Pncc6z9OyE6Ol0SbRsDQrp9oTJwFBgNKlHRj8Djg76XL62xDYgu8czVqF7I7Iaj0tGHP3q6754izNss0OrM2xzTo3HHXl8cpMzaE+qIXnQHjUeJwT9bUBuZ2b7peoO35GfaimcxDYgi4gkWL8bkM1skpltANYDG8zsCTObGOJzg83sD+njN5rZtRmOMTP7tpk9a2brzezIoHxFRCR6YdoMfgTMdfdqd68GPk/qcVGQd4CT3H0cMB44zcyO7nbM6cAh6a2eVK+l6IVabSMhK2UELYBTJOcSxeIlc381l/IF5di1RvmCcub+qudiQJF8TwSLxgTlEYcw4xGDyivMecRxeRXJZV40cYaS7RlS+hHSb8OkBeRRCawFjuqW/n3g053ePwN8IFteObcZhFptIyErZQQtgFMk5xLF4iUN9zd0+Xz71nD/3vmcIvmeCBaNCcojDmHGIwaVV5jziOPyKpLLvGjibEcEg86+lf5jfiepbgIzgdeBn6Yrk7VZPlsGrAE+DHzP3a/qtv9+4Hp3X5V+vxS4yt17bRTIuc0g1GobIY6JQ9ACOEVyLlEsXlK+oJxW71kWZVZGy/yW6L4ngkVjgvKIQ5jxiEHlFeY84ri8iuQyL5o42/V7cRszW55lt7t74JgDMxsO3Av8s7s/2Sn9V8B13SqDL7v7mm6fryf1GIkxY8ZM3JKpdHsTarWNhKyUEfQvukjOJYrFS+za3svC/82j+54IFo0JyiMOYSqDoPIKcx5xXF5FcpkXTZx7v7OfDcjuPi3LFmrwmbu/AawATuu2qxnoPLxxNNBjFQ93X+zuk9x90qhRo8J85V6hVttIyGCvoAVwiuRcoli8pMwyl0Xn9Ei+J4JFY4LySIqg8gpzHnFcXkVymRdNnGGF6U30d2Z2i5k9mH5/uJldHOJzo9J3BJjZEOBkoPv0hfcBn0n3Kjoa2O7uW3M9iaxCrbaRkJUyghbAKZJziWLxkvqJmcuic3ok3xPBojFBecQhzHjEoPIKcx5xXF5FcpkXTZyhZWtQSD9CehD4JPBE+n05sCHE58YCj5PqkvokMD+dfglwSfq1Ad8D/gJsACYF5dunQWehVttIyEoZQQvgFMm5RLF4ScP9DV52bZlzDV52bVmXxuNIvyeCRWOC8ohDmPGIQeUV5jziuLyK5DIvmjjdo2lA/qO7Tzazx919QjptnbuPj7RWCkmDzkREchfFrKVvmdn7SE840v44J6L4kqNoOgOXjjBjCKIYZxBHHKHyCLgEozjXOMorKfRPOjdh7gyOBL4D1JB63DMKOMfd1+c/vJ7ycmfQ1JR6Lr9r1960ykpYvBhmzYr2uySUpg1N1P+ynl179v5OKisqWfzxxcyqnRX6mCTEESqPgEswinONo7ySQv+ke+p319J0JuXAR0g943/G3fdEF2Ju8lIZJKkzsADhxhBEMc4gjjhC5VGd/RKM4lzjKK+k0D/pnvr8mMjMJpvZAQDu3gJMBBYC/25mIyKPtJC0qEzivLA9c9l3Tg9zTBLiCJVHwCUYxbnGUV5JoX/SucvWZvB94F0AMzsBuJ7UKmfbgcX5Dy1GxdQZuESEGUMQxTiDOOIIlUfAJRjFucZRXkmhf9K5y1YZlLn7a+nXM4HF7v5Td/8/pKaXGDiKqjNwaQgzhiCKcQZxxBEqj4BLMIpzjaO8kkL/pPugtz6npBqLy9OvnwZO6LwvW3/VfG55W9wmKZ2BpUOYMQRRjDOII45QeQRcglGcaxzllRT6J90VfR1nYGbzgDOAV4ExwJHu7mb2YeA2dz8u7zVVBhpnICKSuz43ILv7QuCLwI+B431vrTEI+OcogxTJJNQ6AgFrHsTV1zyKOIKOCVzqIqYxBANprEJS1mZIhGy3DUncEr0GskQm1DoCAWsexDWXfBRxBB0TuNRFBGs7hBHX98QhKWszxIWo1kBOCj0mKg2h1hEIWPMgrr7mUcQRdEzgUhcxjSEYSGMVkrI2Q1yimI5CJHZh+oln+gPcOT2uvuZRxBF0TKaKoHN6XGMIBtJYhTiuj2Ia76DKQBIp1DoCAWsexNXXPIo4go4JXOoipjEEA2msQlLWZkgKVQaSSKHWEQhY8yCuvuZRxBF0TOBSFzGNIRhIYxWSsjZDYmRrUEjipgbk0hFqHYGANQ/i6mseRRxBxwQudRHTGIKBNFYhKWszxAE1IIuIiBqQpU+S0Jc8ihiOOHMFNqgFM8cGtXDEmSsKEkeo7wlaz6BY+qtLUdKdgfSQhHnvo4jhiDNX8NQDJ5Kaeb2dc/gZD7PxV1NjiyPU9wStZ6D5+aWfIlnPIElUGeRfEvqSRxGDDWoBL8+wowVvy5CepzhCfU91wHoGAftFgugxkeQsCX3JI4nBe+mP2Vt6vuII8z1B6xkUUX91KU6qDKSHJPQljyQG62WkVm/p+YojzPcErWdQRP3VpTipMpAektCXPIoYDj99FdD9Main0+OLI9T3BK1nUEz91aU4Zet3msRN4wzikYS+5FHEcPgZyx3b49Dm2B4//IzlBYkj1PcErWeQkP7qUpzQOAMREVEDsiRWFP33g/JIyhgBKV3Fcm2E618nErHu/fe3bN9C/S9TE+2E7b8flEcU3xEqjm5jALZs2TtnkMYAlLZiujb0mEgKIor++0F5JGWMgJSuJF0bekwkiRRF//2gPJIyRkBKVzFdG6oMpCCi6L8flEdSxghI6Sqma0OVgRREFP33g/JIyhgBKV3FdG2oMpCCmFU7i8UfX0zVsCoMo2pYVc6TvwXlEcV3hIpjVmrCuKoqMEv91ARyAsV1bagBWUSkBBSsAdnMDjSz5Wa2ycw2mtnlGY6ZambbzWxdepufr3hERKR3+XxM1AJ80d0/ChwNfN7MDs9w3Ep3H5/eFuQxngEhigEsSVi4JkwcYeIslgE9Ycy9fhXlI5oxa6N8RDNzrw8/h1JUBlJ5So6yzVUR5Qb8Avj7bmlTgftzyaeU5yZqbHSvrEytgdu+VVbmNkdN4/pGr1xY6VxDx1a5sDL2uYeC4ggTZxTlkRQN1610KnZ2ORcqdnrDdStji2Eglaf0RBLmJjKzauARoMbd3+yUPhX4KdAMvARc6e4bs+VVym0GUQxgScLCNWHiCBNnkgb09Ff5iGZaXx/dI71s/2ZaXuuZng8DqTylp6A2g7xPR2FmQ0n9wf9C54ogbS1Q5e47zewM4OfAIRnyqAfqAcYksYNuTKIYwJKEhWvCxBEmzmIa0BOk9fUP5pSeDwOpPCV3ee1aamYVpCqCJnf/Wff97v6mu+9Mv34AqDCzkRmOW+zuk9x90qhRo/IZcqJFMYAlCQvXhIkjTJzFNKAnSNn+L+WUng8DqTwld/nsTWTALcAmd7+hl2MOSB+HmU1Jx7MtXzEVuygGsCRh4ZowcYSJs5gG9ASp//JmqHira2LFW6n0mAyk8pQ+yNag0J8NOJ7UMlPrgXXp7QzgEuCS9DGXAhuBJ4DHgGOD8i3lBmT3aBY4ScLCNWHiCBPnQFrwpeG6lV62/4sOrV62/4uxNh63G0jlKV2RhAbkKJVyA7KISF9p1tIBJiljBKIw91dzKV9Qjl1rlC8oZ+6v5hY6JJGSpcVtikhci7XEYe6v5nLT6ps63rd6a8f7RWcuKlRYIiVLdwZFZN7SeR0VQbtde3Yxb+m8AkXUd4vXLM4pXUTyS5VBEUnKGIEotHprTukikl+qDIpIUsYIRKHMynJKF5H8UmVQRJIyRiAK9RPrc0oXkfxSZVBE4lqsJQ6LzlxEw6SGjjuBMiujYVKDGo9FCkTjDERESoDGGUSliCZ6L5axCMUSZ1xUHlJIGmcQRlMT1NfDrnS3zi1bUu8hcYuZFstYhGKJMy4qDyk0PSYKo4gmek/KegVBiiXOuKg8JN/0mCgKRTTRe7GMRSiWOOOi8pBCU2UQRhFN9F4sYxGKJc64qDyk0FQZhFFEE70Xy1iEYokzLioPKTRVBmHMmgWLF6faCMxSPxcvTlzjMRTPWIRiiTMuKg8pNDUgi4iUADUgi/TT3OtXUT6iGbM2ykc0M/f6VTnnoTEEknQaZyCSxdzrV3HT/AmwZx8AWl8fzU3z9wdWsejq40PloTEEUgz0mEgki/IRzbS+PrpHetn+zbS81jM9E40hkCTQYyKRfmh9/YM5pWeiMQRSDFQZiGRRtv9LOaVnojEEUgxUGYhkUf/lzVDxVtfEirdS6SFpDIEUA1UGIlksuvp4GhY8Ttn+zUAbZfs307Dg8dCNx6AxBFIc1IAsIlIC1IAsIiKBVBmIiIgqAxERUWUgIiKoMhAREVQZiIgIqgxERARVBiIiQh4rAzM70MyWm9kmM9toZpdnOMbM7Ntm9qyZrTezI/MVj4iI9C6fdwYtwBfd/aPA0cDnzezwbsecDhyS3uqBm/IYT8nQQioikqu8LW7j7luBrenXO8xsE/Ah4KlOh30CuN1Tc2I8ZmbDzewD6c9KH2ghFRHpi1jaDMysGpgA/L7brg8BL3Z635xOkz6at3ReR0XQbteeXcxbOq9AEYlIMch7ZWBmQ4GfAl9w9ze7787wkR4z55lZvZmtNrPVr7zySj7CHDC0kIqI9EVeKwMzqyBVETS5+88yHNIMHNjp/Wigx6oh7r7Y3Se5+6RRo0blJ9gBQgupiEhf5LM3kQG3AJvc/YZeDrsP+Ey6V9HRwHa1F/SPFlIRkb7IWwMycBxwAbDBzNal0/4VGAPg7jcDDwBnAM8Cu4DP5jGektDeSDxv6Txe2P4CY4aNYWHdQjUei0hWWtxGRKQEaHEbEREJpMpARERUGYiIiCoDERFBlYGIiFCEvYnM7BVgSwFDGAm8WsDvz0WxxKo4o1UscULxxDoQ4qxy915H7RZdZVBoZrY6W/esJCmWWBVntIolTiieWEshTj0mEhERVQYiIqLKoC8WFzqAHBRLrIozWsUSJxRPrAM+TrUZiIiI7gxERESVQVZmVmZmj5vZ/Rn2TTWz7Wa2Lr3NL1CMm81sQzqGHjP4pacH/7aZPWtm683syELEmY4lKNaklOlwM1tiZk+b2SYzO6bb/kSUaYg4k1KeH+kUwzoze9PMvtDtmIKXacg4k1Km/2JmG83sSTO708wGd9ufe3m6u7ZeNuAK4A7g/gz7pmZKL0CMm4GRWfafATxIalW5o4HfJzjWpJTpbcDs9Ov3AMOTWKYh4kxEeXaLqQz4H1J93hNXpiHiLHiZkloa+HlgSPr9fwIX9bc8dWfQCzMbDZwJ/LDQsfTTJ4DbPeUxYLiZfaDQQSWVme0HnEBqYSbc/V13f6PbYQUv05BxJlEd8Bd37z5wtOBl2k1vcSZFOTDEzMqBSnquEJlzeaoy6N2NwJeBtizHHGNmT5jZg2Z2RDxh9eDAf5vZGjOrz7D/Q8CLnd43p9MKIShWKHyZHgy8AtyafkT4QzPbp9sxSSjTMHFC4cuzu08Bd2ZIT0KZdtZbnFDgMnX3vwLfBF4AtpJaIfK/ux2Wc3mqMsjAzKYDL7v7miyHrSV1CzkO+A7w8zhiy+A4dz8SOB34vJmd0G2/ZfhMobqQBcWahDItB44EbnL3CcBbwNXdjklCmYaJMwnl2cHM3gPMAO7JtDtDWkGu04A4C16mZrY/qf/5HwR8ENjHzM7vfliGj2YtT1UGmR0HzDCzzcBdwElm1tj5AHd/0913pl8/AFSY2ci4A3X3l9I/XwbuBaZ0O6QZOLDT+9H0vKWMRVCsCSnTZqDZ3X+ffr+E1B/d7scUukwD40xIeXZ2OrDW3f+WYV8SyrRdr3EmpExPBp5391fcfQ/wM+DYbsfkXJ6qDDJw96+4+2h3ryZ1u7jM3bvUvGZ2gJlZ+vUUUmW5Lc44zWwfM9u3/TVwCvBkt8PuAz6T7l1wNKlbyq1xxtkeX1CsSShTd/8f4EUz+0g6qQ54qtthBS/TMHEmoTy7+TS9P3opeJl20mucCSnTF4CjzawyHUsdsKnbMTmXZ3l+Yh2YzOwSAHe/GTgHaDCzFuBt4FOebsaP0d8B96avzXLgDnf/r25xPkCqZ8GzwC7gszHHmEusSShTgH8GmtKPC54DPpvQMg2KMynliZlVAn8PfK5TWuLKNEScBS9Td/+9mS0h9ciqBXgcWNzf8tQIZBER0WMiERFRZSAiIqgyEBERVBmIiAiqDEREBFUGMsCY2bz0bI7rLTWr5FER5z/Vep/Ftkd6hN873MzmxvV9Uno0zkAGDEtN4TwdONLd30mPDH1PgcOKynBgLrCowHHIAKU7AxlIPgC86u7vALj7q+1TYJjZRDN7OD1J3q/bZ3A0sxVmdqOZPWqpueGnpNOnpNMeT//8SK/fmoWZnWJmvzOztWZ2j5kNTadvNrNr0+kbzOywdPooM/tNOv37ZrYlXaldD/yv9N3ON9LZD7W96xk0tY+MFekLVQYykPw3cKCZ/cnMFpnZiQBmVkFqUrFz3H0i8CNgYafP7ePux5L6n/eP0mlPAyekJ4GbD3wt12DSf8T/N3ByeoK+1aTWyGj3ajr9JuDKdNq/kZr+5EhS8zeNSadfTWpK5fHu/qV02gTgC8DhpGYxPS7XGEXa6TGRDBjuvtPMJgIfA6YBd5vZ1aT+CNcAv0n/57mM1NS/7e5Mf/4RM9vPzIYD+wK3mdkhpGZ7rOhDSEeT+kP92/T3vgf4Xaf9P0v/XAOcnX59PHBWOp7/MrPXs+T/B3dvBjCzdUA1sKoPcYqoMpCBxd1bgRXACjPbAFxI6o/tRnc/prePZXj/f4Hl7n6WmVWn88yVAb9x90/3sv+d9M9W9v5bzOVRzzudXnfOQyRnekwkA4al1rA9pFPSeGAL8AwwKt3AjJlVWNdFSWam048nNbvjdmAY8Nf0/ov6GNJjwHFm9uF0/pVmdmjAZ1YBn0wffwqwfzp9B6m7FZG8UGUgA8lQUo92njKz9aQe0Vzj7u+Smm3y62b2BLCOrvO/v25mjwI3Axen0/4fcJ2Z/ZbUY6Uw6sysuX0DPkyqIrkzHc9jwGEBeVwLnGJma0nNq78V2OHu20g9bnqyUwOySGQ0a6mUNDNbAVzp7qsLHQuAmb0XaHX3lvSdzE3uPr7AYUkJ0DNGkWQZA/ynmQ0C3gXmFDgeKRG6MxAREbUZiIiIKgMREUGVgYiIoMpARERQZSAiIqgyEBER4P8DzkAVJ3TGytkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    x = df[df['Species'] == groups[i]]\n",
    "    plt.scatter(x['SepalLengthCm'], x['SepalWidthCm'], c = colors[i], label=groups[i])\n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Sepal Width\")\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a90403a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2751450ad90>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for j in range(3):\n",
    "    y = df[df['Species'] == groups[j]]\n",
    "    plt.scatter(y['PetalLengthCm'], y['PetalWidthCm'], c = colors[j], label=groups[j])\n",
    "plt.xlabel(\"Petal Length\")\n",
    "plt.ylabel(\"Petal Width\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "76009e92",
   "metadata": {
    "scrolled": true
   },
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "mean   75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std    43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min     1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%    38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%    75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%   112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max   150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6f91cb9a",
   "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>Species</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Species  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "0   Iris-setosa            5.1           3.5            1.4           0.2\n",
       "1   Iris-setosa            4.9           3.0            1.4           0.2\n",
       "2   Iris-setosa            4.7           3.2            1.3           0.2\n",
       "3   Iris-setosa            4.6           3.1            1.5           0.2\n",
       "4   Iris-setosa            5.0           3.6            1.4           0.2\n",
       "5   Iris-setosa            5.4           3.9            1.7           0.4\n",
       "6   Iris-setosa            4.6           3.4            1.4           0.3\n",
       "7   Iris-setosa            5.0           3.4            1.5           0.2\n",
       "8   Iris-setosa            4.4           2.9            1.4           0.2\n",
       "9   Iris-setosa            4.9           3.1            1.5           0.1\n",
       "10  Iris-setosa            5.4           3.7            1.5           0.2\n",
       "11  Iris-setosa            4.8           3.4            1.6           0.2\n",
       "12  Iris-setosa            4.8           3.0            1.4           0.1\n",
       "13  Iris-setosa            4.3           3.0            1.1           0.1\n",
       "14  Iris-setosa            5.8           4.0            1.2           0.2\n",
       "15  Iris-setosa            5.7           4.4            1.5           0.4\n",
       "16  Iris-setosa            5.4           3.9            1.3           0.4\n",
       "17  Iris-setosa            5.1           3.5            1.4           0.3\n",
       "18  Iris-setosa            5.7           3.8            1.7           0.3\n",
       "19  Iris-setosa            5.1           3.8            1.5           0.3\n",
       "20  Iris-setosa            5.4           3.4            1.7           0.2\n",
       "21  Iris-setosa            5.1           3.7            1.5           0.4\n",
       "22  Iris-setosa            4.6           3.6            1.0           0.2\n",
       "23  Iris-setosa            5.1           3.3            1.7           0.5\n",
       "24  Iris-setosa            4.8           3.4            1.9           0.2\n",
       "25  Iris-setosa            5.0           3.0            1.6           0.2\n",
       "26  Iris-setosa            5.0           3.4            1.6           0.4\n",
       "27  Iris-setosa            5.2           3.5            1.5           0.2\n",
       "28  Iris-setosa            5.2           3.4            1.4           0.2\n",
       "29  Iris-setosa            4.7           3.2            1.6           0.2\n",
       "30  Iris-setosa            4.8           3.1            1.6           0.2\n",
       "31  Iris-setosa            5.4           3.4            1.5           0.4\n",
       "32  Iris-setosa            5.2           4.1            1.5           0.1\n",
       "33  Iris-setosa            5.5           4.2            1.4           0.2\n",
       "34  Iris-setosa            4.9           3.1            1.5           0.1\n",
       "35  Iris-setosa            5.0           3.2            1.2           0.2\n",
       "36  Iris-setosa            5.5           3.5            1.3           0.2\n",
       "37  Iris-setosa            4.9           3.1            1.5           0.1\n",
       "38  Iris-setosa            4.4           3.0            1.3           0.2\n",
       "39  Iris-setosa            5.1           3.4            1.5           0.2\n",
       "40  Iris-setosa            5.0           3.5            1.3           0.3\n",
       "41  Iris-setosa            4.5           2.3            1.3           0.3\n",
       "42  Iris-setosa            4.4           3.2            1.3           0.2\n",
       "43  Iris-setosa            5.0           3.5            1.6           0.6\n",
       "44  Iris-setosa            5.1           3.8            1.9           0.4\n",
       "45  Iris-setosa            4.8           3.0            1.4           0.3\n",
       "46  Iris-setosa            5.1           3.8            1.6           0.2\n",
       "47  Iris-setosa            4.6           3.2            1.4           0.2\n",
       "48  Iris-setosa            5.3           3.7            1.5           0.2\n",
       "49  Iris-setosa            5.0           3.3            1.4           0.2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[(df2['Species'] == groups[0])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5cb683d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91628\\AppData\\Local\\Temp/ipykernel_37624/2092522783.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df2[(df2['Species'] == groups[0])].median()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SepalLengthCm    5.0\n",
       "SepalWidthCm     3.4\n",
       "PetalLengthCm    1.5\n",
       "PetalWidthCm     0.2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[(df2['Species'] == groups[0])].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "57da222e",
   "metadata": {
    "scrolled": false
   },
   "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>Species</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Species  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "0  Iris-setosa            5.0           3.4            1.5           0.2\n",
       "1          NaN            5.1           NaN            NaN           NaN"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[(df2['Species'] == groups[0])].mode()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}