data visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "a = pd.read_csv('Iris_Data.csv')\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "a.plot(\n",
    "kind = 'scatter',\n",
    "x = 'sepal_length',b\n",
    "y = 'sepal_width')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x9b31dd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAD4CAYAAADFAawfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAeZElEQVR4nO3df4xeVZkH8O/DMJUpYAdodaHt2GIIZBeKpRMo6UZQdtV1CzaiDSguGNcmu1E07LrBTVdZYiKJSd1l/cNUcdldfmjlx6TU3yuo0djqDIUWLSw/hNIpykBpATvYHz77x5237Uzvfd/7nLnnvOee+/0kZDpn3pl77vvOPNz7vM9zjqgqiIgoXsd0ewJERNQeAzURUeQYqImIIsdATUQUOQZqIqLIHevjh86ePVsXLFjg40cTESVpZGTkBVWdk/c1L4F6wYIFGB4e9vGjiYiSJCLPFH2NqQ8iosgxUBMRRY6Bmogoch0DtYicKSIPHfHfyyLyyRCTIyKiEm8mqupjAN4CACLSA2AUwL2e50VERBOsqY9LADypqoXvThIRUbWsgfoKAHfmfUFEVonIsIgMj42NTX9mRESutqwDvng2cEN/9nHLum7PaFpKB2oRmQHgMgDfzPu6qq5V1UFVHZwzJ7dmm4jIvy3rgPuuBfY8C0Czj/ddW+tgbbmi/isAD6rq73xNhoho2n54I7B/fPLY/vFsvKYsgfpKFKQ9iIiisWeHbbwGSgVqEZkJ4C8B3ON3OkRE0zRrnm28BkoFalXdq6qnqOoe3xMiIpqWSz4D9PZNHuvty8Zrip2JRJSWRSuBS28GZs0HINnHS2/OxmvKy+p5RNQgW9Zlb9Tt2ZGlFy75TPVB0XqMRStrHZinYqAmInetUrhWlUWrFA6oLlCGOEbkmPogInchSuESLLezYqAmIncupXDWrsEEy+2sGKiJyJ21FM6lazDBcjsrBmoicmcthXNJYyRYbmfFQE1E7qylcC5pjATL7axY9UFE02MphZs1byLtkTNe1TESxCtqIgqHaQwnDNREFA7TGE6Y+iCi6Wl412AIDNRE5I5dg0Ew9UFE7tg1GAQDNVHKfO8dyK7BIBioiVIVYu9Adg0GwUBNlKoQaQmW2wXBQE2UqhBpCZbbBcGqD6JUuXYBWrHczjteUROlimmJZDBQE6WKaYlkMPVBlDKXtESIPRB9i3Efx2lgoCaiw1LoNExwH0emPojosBQ6DRPcx7FUoBaRfhG5S0QeFZFtInKhl9kQUbWauD9hiHMI/DyVvaL+dwDfVdWzAJwLYJuX2RBRdZq6P2GIc+g7yTY+TR0DtYi8HsBbAdwCAKq6T1V3e5kNEVWnqfsTpnAOU5S5oj4dwBiA/xSRzSLyVRE5fuqDRGSViAyLyPDY2FjlEyUio6buTxjiHMZfso1PU5mqj2MBnAfg46q6SUT+HcD1AP7lyAep6loAawFgcHBQq54oERnFuj9hCuV/obo+J5S5ot4BYIeqbpr4/C5kgZuIYhZjCiDEin4hjhH4ue0YqFX1twCeFZEzJ4YuAfBrL7MhourEmMZIpXQu8HNbtuHl4wBuF5EZAJ4C8GEvsyFqkhRSAFauZW2W5ypU6VzAxahKBWpVfQjAoOe5EDVHgt1zpbjkdq3nETh/HAI7E4m6IZUUgJVLbtd6HjHm5qeJgZqoGxLsnitl0Urg3A8A0pN9Lj3Z5+2u8K3n4XIMF773ozwCAzVRN4Tonouxy3DLOuDhOwA9mH2uB7PPq+yWdDmGVYjKkiMwUBN1Q4jb8xhTACG6JRNMKzFQE+XxfVsborwrVArAwrVb0nIerimfDdcB/3oycMOs7OOG64ofGzitxPWoiaYKVS0RogMwLwUwsLR7wbrvJGB8V/54Eet5uFR9bLgOGL7l8Od68PDny9dUc4xp4BU10VQxVku4aOp5uKR8Rm61jcfWmUjUODFWS7iI8TxcFjNyqfqwppVaV+tlxyPtTCRqjlQaJmI8D5c5uXyPNa0kPflBuZUXr+IY08AraqKpYqyWcHHy6bbxEM54h20cCPN6LLnGNh4YAzXRVDEuZuTi6Z/axkN4/Pu2cSDM67F8DTD4kcmVJYMfyX8jsQtEtfqlowcHB3V4eLjyn0tEBjfMavO1PeHmMem4/QDyYo4AN1S4cVQNF7wSkRFVzV1TiVfURKkqyq+2y7v6FmKvwcBdgyEwUBOlKvK8qzeplCUegYGaKFWueVefXZkh9hqMsSxxmlieR5Sy5Wtsb4j57soMUTLo0v0YOV5RE9FhvtMGqZQ+BsZATUSH+U4bhCi1C5FeCYypDyI6zHWrLEspnO+Ovhg7MqeJV9REdJg1NRFjKZxL92PkGKiJ6DBraiLGUjiX7sfIMVATVcVa1hZizz2XY2zfCLy8E4BmH7dvLH5sjKVwrnMKuAeiFXPURFWwlrWF2JzA5RjWBfRjLIVz3ZwgxGYRjnhFTVQFawog1n39rAvopyLGFM4RSl1Ri8jTAF4BcBDAgaKFQ4gay3q7PZ3b87IVFi7HsC6gH2MpXIjNCQKzXFG/TVXfwiBNlMO62FBRqVinMjhLhYXLMSC28RCLLFm5nLfTcxUOUx9E3eDSoRdi78AZM23jMXI578g7JssGagXwfREZEZFVeQ8QkVUiMiwiw2NjY9XNkKgOrLfbLh16IfYO3LfXNh5j6sPlvCPfLKJs1ccyVd0pIm8A8AMReVRVf3LkA1R1LYC1QLZxQMXzJIpbiH39XI6RV2rX7pjWY8TaBejS/RhwD0SrUlfUqrpz4uPzAO4FcL7PSRHVToj9Ca23561Su9Ybga1Suw3XVXeMBLsAY9QxUIvI8SJyYuvfAN4B4BHfEyOqlRD7E1pvz11K7azHSLALMEZlUh9vBHCviLQef4eqftfrrIi6zbrQkLWszZXl9tx1TpZjRF7WloqOgVpVnwJwboC5EMXBpUtNevIDYDf3Jwwh1hx1YlieRzSVS5daU/cnjLysLRUM1FRPPhfQcbmdX74GWHjR5LGFF7XfBiviRYBKi7ysLRVclInqx/cCOjNmAvt+nz/ebk47fjF5bMcvsvFuLcoUSsRlbangFTXVj+8FdKxNHy5zCrEI0NQr/E7jFC0Gaqof75UGRf1abfq4Qi3KZHH1+vx0zNXrqzsGBcHUB9WP7339XCo4Yu3oY1BOAq+oqX587+u34M9t44C9Q4/VEmTAQE3143tfv11P2cYBe4ceqyXIgKkPqief3XMu+eNQHXrWjklKAq+oKX0hFvW3sqZjXL+HksBATTSVU/7YWCniUp4X+b5+5A8DdUlDm0ex7Kb7sfD6b2HZTfdjaPNot6fUbJauPpdF/edNWcl33vnVphhiTq9QdBioSxjaPIpP37MVo7vHoQBGd4/j0/dsZbDuFmsKwJr62HAd8JsfTx77zY/br+NsleC+fuQPA3UJX/jeYxjfP7mudnz/QXzhe491aUYN5zsF4LKO8wmn2sYT3NeP/GGgLmHn7nHTOHlmTQFYUx8u6zj/46NHB+UTTs3G8yS4rx/5w/K8Ek7r78NoTlA+rb8v59HknbWrr3cmsD9nkaXegkWWXNeWLgrKRRLb14/84RV1CZ9655no6538R9rX24NPvfPMLs2o4awpgAMFdz5F401dW5qixSvqElYsngsgy1Xv3D2O0/r78Kl3nnlonAJrXVGWbfzQP9rGW2tIj9yaXVlLTxak260tTeRREoF6aPOo9yC6YvFcBua6ckllLF9jD8zsGiRPah+oW6VzraqMVukcAAbWVFkX3T/lDOCFnPzxKWd0b05EBrXPUbN0roGs5XkvPm4bDzEnIoMor6gtqQyWzkXIdwrAWp7nUm5nxa5B8ii6K2prF2BRiRxL57okxMJBMXboxTgnSkZ0gdqaymDpXGRCpADMHXpiHA8xJ6LySgdqEekRkc0issHnhKypjBWL5+LyJXPRI9kfXY8ILl/CCo2uCZECWLQSOPcDh6s2pCf7vDC94rAHosuc2DVInlhy1J8AsA3A6z3NBYC9C3Bo8yjuHhnFQc3+6A6q4u6RUQy+6WQG627oOwkY35U/XpUt64CH7zicY9aD2ecDS7sbGNk1SJ6UuqIWkXkA/hrAV/1Ox57KYNVHA7HCghqm7BX1vwH4JwAnFj1ARFYBWAUAAwMDzhOydgGy6iMy1gWQXLDCghqmY6AWkeUAnlfVERG5uOhxqroWwFoAGBwcnFbyz9IFGGrBpNVDW3HnpmdxUBU9Irjygvn43IpzKj1GEqwLJgH2cj6XY1ixy5AiUib1sQzAZSLyNICvA3i7iNzmdVYGIao+Vg9txW0bt0/Kg9+2cTtWD22t7BjJsFY/uJTznfEO2/jCi2zj3JuQItMxUKvqp1V1nqouAHAFgPtV9SrvMytpxeK5+Px7z8Hc/j4IgLn9ffj8e8+p9I3EOzflXL21GW80a/WDS7758e/bxq9ef3RQXnhRNl7VnIg8irIzMQRLKqN1JV12vPEs1Q+h9g4sCspV/fwGC7EoWohjxMwUqFX1RwB+5GUmjlwWZWqlMlpaqQwAucFakF9xW2G7RHO5lPP5zlGHyIEnIsSiaFx4LcLORCuX8jxrKmPmjPzlMIvGyTPfXYDsMiwtRHksS3ATCNQu5XnWVMbeffmL9xSNN96WdcAXzwZu6M8+tnsTzqWcz3cXILsMSwtRHssS3ARy1H29x2Dv/qN36ujrLf5/UI9IblButaFP1T+zFy/t3Z87TlNY12V2TTP47gJkl2EpIcpjuWdpAlfU4wfyt1MqGgeAKy+Ybxoves+Q7yXmsFZMMM1QayHKY7nwWgJX1C5BtPWGYdmqjz3jR19NtxtvNGvFhHX/Q4pKiP1EuWdpAoHamsZo+dyKc0p3FvLWy8AllREizcBOQ29C7Cca456lIUsGa5/6OH3OTNO4C956GcSYymCnIVXMusHJdNU+UD81ttc07iJE92MyYqyYYKchVSx0yWDtUx+uXYPWRZZC3Hol0321fSPw8k4Amn3cvrG7gZqdhlSx0CWDtb+idhHjIkuhb6W82XAdMHzL5EX9h2/JxruF+xlSxULv1drIQB3jIkvJdF+N3GobDyHGvDnVWuj3rWqf+nDhki7xvR51Mt1XWtCtWTQeQqQlgMmkuiLk8txavid0yWAjA/UxAvwxJyYfU1DRZ13EyUUyJYDSkx+UpcvrokTWaciFhvxxeW5dvidkyWAjUx+vOzb/tIvGQ6RKkikBXHKNbbyhkkl1RcjluY399WjkFfVrOWuDtBsPsR51Mt1Xy9dkH0duza6spScL0q1xApBQqitCLs9t7K9HIwO1Nc3g0v3okiOLsfuK/Egm1eXAd27e5bl1+Z6Q+6jWPvVxXE9+sCwaB+xpBusiTsmU2rmIsTwvQm87a45pPBUh/jZc0ojW7wld4lv7QH3KiceZxgF7p+HnVpyDq5YOHLqC7hHBVUsHCv/vGXu+y6sYy/Mi9MCjY6bxVIT423DpJLZ+T+gS39qnPlxzS9Y0g2URp9jzXV7FWJ7nwHd5V6jfkRC35zGe9zeHtx9KZYzuHsc3h7dXmnoMvY9q7a+oi9LEHRbP8yp011JUisrwul2eZ+Bye279nhC/IyFuz2M87w9+5ef42ZOT9+H82ZO78MGv/LyyYxS9P9Vp1U5XtQ/UefXQ7cZDSKbUzkUC5XkhyrtC/I6EuD2P8bynBulO4y6s71tNV5Spj6n/R1z25pNx+0cv7OKMbJIptXORQHleiPKuEL8jIW7PXc57+Jldk9Ixly+Jo9rJksKxbj4yXdEF6na3LXUL1jH88nXF8jW1CsxTzerrxe6c3Xtm9RXvkXlc7zEYz6nDP67N3p2+f0cEQF5IrvLm3FrWNrR5FHePjE5Kx9w9MorBN53c1b8Xl85Ey/tW0xVd6iPEbQtROy7ve/yhYI/OovEQZs7If1+gaNyFNZURoupjRkFpbtF4qHlNR8crahE5DsBPALxu4vF3qepnfU+MqFt25+w4324ciPO9kr378ittisZdWFM4Iao+9h/Mf9KLxtsdP5ZKrTKpjz8AeLuqvioivQB+KiLfUdWNnudWiuueiVy5LCIO+xlaXz/L41261EJ1r/o+D99c5+T7vGN8ro7UMfWhmVcnPu2d+M/bdcIbT5xhGp99Qn7esGgcaHjnYGwc9jO0vn7Wxy84Jf+Ps2gcsP8ehigBPHAw/8q5aNyFdU4zZ+SHnKJxl2O4vH6xV2qVylGLSI+IPATgeQA/UNVNvib0wqv5t5dF4797ZZ9pHIg/H9UoDvsZWl8/6+M3PvWSaRyw/x6GKAF0+duwss7p8ed/bxp3OYbL6xf7vqilqj5U9SCAt4hIP4B7ReRsVX3kyMeIyCoAqwBgYGDAeUIxlhSRRw77GVpfP+t4rL+DMf7ehphTqNcv5kotU9WHqu4G8CMA78r52lpVHVTVwTlz3BeWCdHx0+jOwdg47Gfo+/Ur2kCiaNyFyznE+HsbYk7WY4TuGgyhY6AWkTkTV9IQkT4AfwHgUV8Tsnb8FJWptilfjT4f1SgO+xlaX7+i66iicevGEoC9JCzECm8hWOe07M0nm8ZdjhG6azCEMlfUpwJ4QES2APglshz1Bl8Tsq5UV1Sm2q58NfZ8VKMsWglcejMwaz4AyT5eenPbqo8Vi+fi8iVzJ/2OVNndZt1YArCXhIVY4c3V0OZRLLvpfiy8/ltYdtP9bd/gXLF4Ls4bmDVp7LyBWYVzuv2jF+KMNxw/aeyMNxzftpnN+npbY0iL5bxD65ijVtUtABYHmMshlo4f17KamPNRjWPcz9B3d5tLZ2Jf7zHYmxPI+yruTLR8j0tnorVDb/XQ1txO4tVDW3P/hoc2j2LHS69NGtvx0msY2jzadj9D6+tt7RqMfQ/L6DoTrWK8HSS/fFftuHQmjhfcwhWNh+DSmWh9bq0LP8W6n2HslWDRrfVh1egFkCLlu5nId6WBS2diUUFBu0ID38+TS2ei7wqLWKtdYqyoOVLtAzXANEZMQtxCuqQmLPpn9uKlnKDcP7P451s7E0M8Ty7n0dsj2JeTV+8teFPUet6xdg3WvjORyCLELaQ1NWGtyHC5OrZWGoR4nlzOIy9Itxu3nnes1S6xp1CTuKKmeIS4hbSmJqwVGXtyrtbbjQP29YlDPE8u52FlPW+XVGWI9GbsKVQGaqpUjLepvh/fEqJaySLU7by1wsJ3tQvglv+POYXK1AdV6m1n5XelFo27sC66Y72tDXEOsd7OuzSkxCbFRdcYqKlSDzw6Zhp3YV10x9ooEuIcQjSvuBzj9o9eeFRQrttWeLGX2rlg6oMq5ZJ7XT201bT3nO9Fk1zzx9bbbZdb7RDrqL9/cABPvzh+6BjvH2y/yFpsa7vHXmrngoGaKmXNi64e2orbNm4/9PlB1UOfFwVr36VwLrndEOV21mO4zCnEMXyLvdTOBVMfVClrXtTa2Qb4L4Vzye3G2D0XogswxjRD7KV2LhioqVLWRXpc0hjWRXest8Iuud0Yu+dCdAHGmGZIcdE1pj6oUtZFekTyGzA6LR3suxTOmj+OsdwuRBdgrGmGmEvtXPCKmiplTWX0FazxXDTuItZSON/HCNEFmGKaIUa8oqZKWVMZ4wVrPBeNu0ils816jBBdgLF39KWisYE6tpKiVIRYpMfF8DO78Ns9r0EB/HbPaxh+Zlflr3eI223rMUJ0AaaWZohRI1MfKXYuxWLp6SeZxkPcOrdKAI9ceP62jduxemhrZccg8qmRgTrGkqJUPP1i/rv9ReMh3qF3KQEkikkjUx8xlhSlwuW59X3r7LuTscXaYUlUViOvqENscd9URYvSt1us3rei/HjRuAumV8inRgZqlhT547JYvW/WTkYXTK+QT41MfbCkyJ8Qi9VbWRe3dxEqvULN1MhADbCkyJdYO9Wsi9tbWcsSiSwamfogf5qaVgqRXqHm6hioRWS+iDwgIttE5Fci8okQE6N6SnFBnDKsC0URWZRJfRwA8A+q+qCInAhgRER+oKq/9jw3olrxnV6h5uoYqFX1OQDPTfz7FRHZBmAuAAZqOkqMC8kT1Z0pRy0iCwAsBrDJx2So/tj1SVS90oFaRE4AcDeAT6rqyzlfXyUiwyIyPDZW3SagpWxZB3zxbOCG/uzjlnX1PEYC2PVJVL1SgVpEepEF6dtV9Z68x6jqWlUdVNXBOXPmVDnH9rasA+67FtjzLADNPt53bbWBNMQxEsGuT6Lqlan6EAC3ANimqmv8T8nohzcC+6dcre0fz8brdIxENLU8j8inMlfUywB8CMDbReShif/e7Xle5e3ZYRuP9RiJaGp5HpFPZao+fgog3vaqWfMmUhI543U6RkLY9UlUrfp3Jl7yGaB3Sv6zty8br9MxiIgK1D9QL1oJXHozMGs+AMk+XnpzNl6nYxARFah/oHblu9yO5XxEVJH6r57XKp1rVWW0SueA4ite6/f4fjwRURv1v6J2KZ2zfo/vxxMRtVH/QO1SOmf9Ht/jLUyXEFGO+gdqF0VldUXjfSfZxq0/H2D3IxEVSiBQF2111GYLJN/ldi4/n+kSIioQ55uJX7oAeOHRw5/PPgv4WIUL9i1aCWzfCIzcCuhBQHqAcz9Q/Ebf+Eu28dbP+eGNWbpj1rwsSLd7I5Hdj0RUIL5APTVIA9nnX7qgumC9ZR3w8B1ZkAayjw/fAQwszQ+mPb3AwX3540UWrbRVeLD7kYgKxJf6mBqkO427sKYZ8oJ0u3EX7H4kogLxBeoQYkwzsPuRiArEl/oIwSWVEYI1XUJEjRDhFXWPcdyBNZWx8CLbOBFRhSIM1H80jgdw9fqjg/LCi7JxIiLP4gvULs0iISy+anL+ePFV3Z0PETVGfIE6RPXDCafaxtk1SERdFF+gDlH98PvnbePsGiSiLoqz6sN39UOr0aXseIzlfETUGPFdUYcgBRUkReOx5s2JqBGaGaiXXGMbZ9cgEXVRnIHa97rMy9cAgx85fAUtPdnny9fkP55dg0TURfHlqK3bWElPfm65KI3RsnxNcWDOw65BIuqS+K6orRUW1jQGEVHNxHdFba2waF0VH7m29JJrbFfLREQR63hFLSJfE5HnReSREBNyqrAYWAq8/jQAkn0cWOplakRE3VAm9XErgHd5nsdh1goLdg0SUeI6BmpV/QmAXQHmkrFWWLBrkIgSV1mOWkRWAVgFAAMDA9P7YZYKC3YNElHiKqv6UNW1qjqoqoNz5syp6sd2xq5BIkpcfOV5VuwaJKLE1T9Qs2uQiBLXMUctIncCuBjAbBHZAeCzqnqL74mZsGuQiBLWMVCr6pUhJkJERPnqn/ogIkocAzURUeQYqImIIsdATUQUOQZqIqLIiapW/0NFxgA8M2V4NoAXKj9Y/Jp63kBzz53n3SxVnfebVDW3rdtLoM49kMiwqg4GOVhEmnreQHPPnefdLCHOm6kPIqLIMVATEUUuZKBeG/BYMWnqeQPNPXeed7N4P+9gOWoiInLD1AcRUeQYqImIIld5oBaRd4nIYyLyhIhcn/P114nINya+vklEFlQ9h24ocd7XiMiYiDw08d/fdmOeVeu0S71kbp54XraIyHmh5+hDifO+WET2HPF6J7GThYjMF5EHRGSbiPxKRD6R85jkXvOS5+3vNVfVyv4D0APgSQCnA5gB4GEAfzrlMX8P4MsT/74CwDeqnEM3/it53tcA+FK35+rh3N8K4DwAjxR8/d0AvgNAACwFsKnbcw503hcD2NDteXo471MBnDfx7xMB/F/O73pyr3nJ8/b2mld9RX0+gCdU9SlV3Qfg6wDeM+Ux7wHwXxP/vgvAJSIiFc8jtDLnnSTtvEv9ewD8t2Y2AugXkVPDzM6fEuedJFV9TlUfnPj3KwC2AZg75WHJveYlz9ubqgP1XADPHvH5Dhx9Moceo6oHAOwBcErF8witzHkDwOUTt4J3icj8MFPrurLPTYouFJGHReQ7IvJn3Z5M1SbSlosBbJrypaRf8zbnDXh6zasO1HlXxlPr/8o8pm7KnNN9ABao6iIA/4vDdxWpS/H1LuNBZGs3nAvgPwAMdXk+lRKREwDcDeCTqvry1C/nfEsSr3mH8/b2mlcdqHcAOPJKcR6AnUWPEZFjAcxC/W8hO563qr6oqn+Y+PQrAJYEmlu3lfmdSI6qvqyqr078+9sAekVkdpenVQkR6UUWrG5X1XtyHpLka97pvH2+5lUH6l8COENEForIDGRvFq6f8pj1AK6e+Pf7ANyvE5n4Gut43lNydJchy3E1wXoAfzNRCbAUwB5Vfa7bk/JNRP6k9d6LiJyP7G/txe7OavomzukWANtUdU3Bw5J7zcuct8/XvOPmthaqekBEPgbge8gqIb6mqr8SkRsBDKvqemQn+z8i8gSyK+krqpxDN5Q872tF5DIAB5Cd9zVdm3CF8napB9ALAKr6ZQDfRlYF8ASAvQA+3J2ZVqvEeb8PwN+JyAEA4wCuSOCCBACWAfgQgK0i8tDE2D8DGACSfs3LnLe315wt5EREkWNnIhFR5BioiYgix0BNRBQ5BmoiosgxUBMRRY6BmogocgzURESR+399of4ttLkiMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = a['petal_width']\n",
    "y = a['sepal_width']\n",
    "z = a['petal_length']\n",
    "plt.scatter(x,y)\n",
    "plt.scatter(x,z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "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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w9xofH69du3b1W9sPmZ6eZmJiYqC569XY9lsB2LbpINfuOW6Nq1k7w9x/q73vvebCvsYN4//7Q3p7T7KkYO/rr2KSHA98HPhYVX2i270vyWnd8dOA/UspWpK0Ovr5q5gAHwburar39By6GdjSPd4C3LTy5UmSlqqfn/HOA94M7EnyhW7fO4BrgOuTXAk8CLx+dUqUJC3FosFeVZ8DssDhC1a2HEnScnnnqSQ1xmCXpMYY7JLUGINdkhpjsEtSYwx2SWqMwS5JjTHYJakxBrskNcZgl6TGGOyS1BiDXZIaY7BLUmMMdklqjMEuSY0x2CWpMQa7JDXGYJekxhjsktQYg12SGmOwS1JjjltsQJKPABcB+6vqpd2+U4DrgDFgL/CGqjqwemXCnocf54rtt67mp5CkJvRzxf5fgdfO2bcduL2qNgK3d9uSpKPAosFeVZ8Bvj1n9yXAzu7xTuDSFa5LkjSgVNXig5Ix4JaepZjHqmpDz/EDVXXyAnO3AlsBRkdHN09NTQ1U6P5vP86+pwaauu6NnsjQ9g7D3X+rvW86/aS+xs3MzDAyMrLK1RydenufnJzcXVXj/c5ddI19uapqB7ADYHx8vCYmJgb6OH/4sZu4ds+ql3tU2rbp4ND2DsPdf6u97718oq9x09PTDJoZ691yeh/0r2L2JTkNoHu/f8CPI0laYYMG+83Alu7xFuCmlSlHkrRciwZ7kj8D/go4O8lDSa4ErgFek+Q+4DXdtiTpKLDo4l1VvWmBQxescC2SpBXgnaeS1BiDXZIaY7BLUmMMdklqjMEuSY0x2CWpMQa7JDXGYJekxhjsktQYg12SGmOwS1JjDHZJakx7z+Av6ag31ucL02/bdLCJF7Hfe82FR/TzecUuSY0x2CWpMQa7JDXGYJekxhjsktQYg12SGmOwS1JjDHZJasyygj3Ja5N8Ncn9SbavVFGSpMENHOxJjgU+ALwOeDHwpiQvXqnCJEmDWc4V+7nA/VX1QFX9AzAFXLIyZUmSBpWqGmxichnw2qp6a7f9ZuAVVXXVnHFbga3d5tnAVwes9VTgWwPOXe+GuXcY7v6HuXcY7v57e39BVT2/34nLeRKwzLPvsO8SVbUD2LGMzzP7yZJdVTW+3I+zHg1z7zDc/Q9z7zDc/S+n9+UsxTwEnNmzfQbwyDI+niRpBSwn2P8G2JjkhUlOAN4I3LwyZUmSBjXwUkxVHUxyFfAp4FjgI1X1pRWr7HDLXs5Zx4a5dxju/oe5dxju/gfufeBfnkqSjk7eeSpJjTHYJakxR1WwJzkzyR1J7k3ypSRXzzMmSf6gexqDv03y02tR60rrs/eJJI8n+UL39h/WotbVkOSZSf46yRe7/t85z5hnJLmuO/d3Jxk78pWuvD57vyLJ/+k5929di1pXS5Jjk3w+yS3zHGvyvPdapP8ln/uj7cWsDwLbquqeJM8Bdie5raq+3DPmdcDG7u0VwB9179e7fnoH+GxVXbQG9a227wHnV9VMkuOBzyX5ZFXd1TPmSuBAVZ2V5I3Au4F/thbFrrB+ege4bu4NgA25GrgXeO48x1o9771+VP+wxHN/VF2xV9U3q+qe7vGTzDZ6+pxhlwD/rWbdBWxIctoRLnXF9dl7s7rzOdNtHt+9zf3N/iXAzu7xDcAFSea7UW5d6bP3ZiU5A7gQ+NACQ5o874f00f+SHVXB3qv7cevlwN1zDp0OfKNn+yEaC8Af0TvAq7of2T+Z5CVHtLBV1v04+gVgP3BbVS147qvqIPA48LwjW+Xq6KN3gF/ulh9vSHLmPMfXq/cC/w74/gLHmz3vncX6hyWe+6My2JOMAB8H3l5VT8w9PM+UZq5uFun9HmafM+JlwB8C/+NI17eaqurpqjqH2buYz03y0jlDmj33ffT+P4GxqvonwP/iB1ew61qSi4D9VbX7Rw2bZ18T573P/pd87o+6YO/WGD8OfKyqPjHPkGafymCx3qvqiUM/slfVnwPHJzn1CJe56qrqMWAaeO2cQ///3Cc5DjgJ+PYRLW6VLdR7VT1aVd/rNj8IbD7Cpa2W84CLk+xl9hliz0/y0TljWj7vi/Y/yLk/qoK9Wzf7MHBvVb1ngWE3A/+i++uYVwKPV9U3j1iRq6Sf3pP8+KG1xSTnMnv+Hj1yVa6eJM9PsqF7fCLwauArc4bdDGzpHl8GfLoauMOun97n/B7pYmZ/B7PuVdVvVdUZVTXG7NOSfLqqfmXOsCbPO/TX/yDn/mj7q5jzgDcDe7r1RoB3AD8JUFV/DPw58EvA/cB3gbesQZ2roZ/eLwN+NclB4Cngja18gQOnATsz+wIuxwDXV9UtSf4jsKuqbmb2G99/T3I/s1dsb1y7cldUP72/LcnFzP711LeBK9as2iNgSM77gpZ77n1KAUlqzFG1FCNJWj6DXZIaY7BLUmMMdklqjMEuSY0x2CWpMQa7JDXm/wGuSGRGX5DfSwAAAABJRU5ErkJggg==\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": [
    "a.hist(column = 'sepal_length', bins = 5)\n",
    "a.hist(column = 'sepal_width', bins = 5)\n",
    "a.hist(column = 'petal_length', bins = 5)\n",
    "plt.show()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>sepal_length</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>sepal_width</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>petal_length</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>petal_width</td>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      1.000000    -0.109369      0.871754     0.817954\n",
       "sepal_width      -0.109369     1.000000     -0.420516    -0.356544\n",
       "petal_length      0.871754    -0.420516      1.000000     0.962757\n",
       "petal_width       0.817954    -0.356544      0.962757     1.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "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": [
    "a.plot(kind = 'scatter',\n",
    "x = 'sepal_length',\n",
    "y = 'petal_width')\n",
    "a.plot(kind = 'scatter',\n",
    "x = 'sepal_length',\n",
    "y = 'petal_length')\n",
    "a.plot(kind = 'scatter',\n",
    "x = 'petal_length',\n",
    "y = 'petal_width')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}