15Z310_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>ge</th>\n",
       "      <th>cst</th>\n",
       "      <th>tnp</th>\n",
       "      <th>twp</th>\n",
       "      <th>iap</th>\n",
       "      <th>esp</th>\n",
       "      <th>arr</th>\n",
       "      <th>ms</th>\n",
       "      <th>ls</th>\n",
       "      <th>as</th>\n",
       "      <th>...</th>\n",
       "      <th>fq</th>\n",
       "      <th>mq</th>\n",
       "      <th>fo</th>\n",
       "      <th>mo</th>\n",
       "      <th>nf</th>\n",
       "      <th>sh</th>\n",
       "      <th>ss</th>\n",
       "      <th>me</th>\n",
       "      <th>tt</th>\n",
       "      <th>atd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'F'</td>\n",
       "      <td>b'G'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Vg'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Y'</td>\n",
       "      <td>b'Unmarried'</td>\n",
       "      <td>b'V'</td>\n",
       "      <td>b'Paid'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'Um'</td>\n",
       "      <td>b'10'</td>\n",
       "      <td>b'Farmer'</td>\n",
       "      <td>b'Housewife'</td>\n",
       "      <td>b'Large'</td>\n",
       "      <td>b'Poor'</td>\n",
       "      <td>b'Govt'</td>\n",
       "      <td>b'Asm'</td>\n",
       "      <td>b'Small'</td>\n",
       "      <td>b'Good'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'M'</td>\n",
       "      <td>b'OBC'</td>\n",
       "      <td>b'Vg'</td>\n",
       "      <td>b'Vg'</td>\n",
       "      <td>b'Vg'</td>\n",
       "      <td>b'Vg'</td>\n",
       "      <td>b'N'</td>\n",
       "      <td>b'Unmarried'</td>\n",
       "      <td>b'V'</td>\n",
       "      <td>b'Paid'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'Um'</td>\n",
       "      <td>b'Il'</td>\n",
       "      <td>b'Service'</td>\n",
       "      <td>b'Service'</td>\n",
       "      <td>b'Small'</td>\n",
       "      <td>b'Poor'</td>\n",
       "      <td>b'Govt'</td>\n",
       "      <td>b'Asm'</td>\n",
       "      <td>b'Average'</td>\n",
       "      <td>b'Average'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'F'</td>\n",
       "      <td>b'OBC'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Vg'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'N'</td>\n",
       "      <td>b'Unmarried'</td>\n",
       "      <td>b'V'</td>\n",
       "      <td>b'Paid'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'12'</td>\n",
       "      <td>b'10'</td>\n",
       "      <td>b'Service'</td>\n",
       "      <td>b'Housewife'</td>\n",
       "      <td>b'Average'</td>\n",
       "      <td>b'Average'</td>\n",
       "      <td>b'Govt'</td>\n",
       "      <td>b'Asm'</td>\n",
       "      <td>b'Large'</td>\n",
       "      <td>b'Good'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'M'</td>\n",
       "      <td>b'MOBC'</td>\n",
       "      <td>b'Pass'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Vg'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'N'</td>\n",
       "      <td>b'Unmarried'</td>\n",
       "      <td>b'V'</td>\n",
       "      <td>b'Paid'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'12'</td>\n",
       "      <td>b'Um'</td>\n",
       "      <td>b'Business'</td>\n",
       "      <td>b'Business'</td>\n",
       "      <td>b'Large'</td>\n",
       "      <td>b'Poor'</td>\n",
       "      <td>b'Govt'</td>\n",
       "      <td>b'Asm'</td>\n",
       "      <td>b'Average'</td>\n",
       "      <td>b'Average'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'M'</td>\n",
       "      <td>b'G'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Vg'</td>\n",
       "      <td>b'Vg'</td>\n",
       "      <td>b'N'</td>\n",
       "      <td>b'Unmarried'</td>\n",
       "      <td>b'V'</td>\n",
       "      <td>b'Paid'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'10'</td>\n",
       "      <td>b'12'</td>\n",
       "      <td>b'Service'</td>\n",
       "      <td>b'Housewife'</td>\n",
       "      <td>b'Large'</td>\n",
       "      <td>b'Poor'</td>\n",
       "      <td>b'Private'</td>\n",
       "      <td>b'Asm'</td>\n",
       "      <td>b'Small'</td>\n",
       "      <td>b'Good'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ge      cst      tnp      twp    iap      esp   arr            ms    ls  \\\n",
       "0  b'F'     b'G'  b'Good'  b'Good'  b'Vg'  b'Good'  b'Y'  b'Unmarried'  b'V'   \n",
       "1  b'M'   b'OBC'    b'Vg'    b'Vg'  b'Vg'    b'Vg'  b'N'  b'Unmarried'  b'V'   \n",
       "2  b'F'   b'OBC'  b'Good'  b'Good'  b'Vg'  b'Good'  b'N'  b'Unmarried'  b'V'   \n",
       "3  b'M'  b'MOBC'  b'Pass'  b'Good'  b'Vg'  b'Good'  b'N'  b'Unmarried'  b'V'   \n",
       "4  b'M'     b'G'  b'Good'  b'Good'  b'Vg'    b'Vg'  b'N'  b'Unmarried'  b'V'   \n",
       "\n",
       "        as     ...         fq     mq           fo            mo          nf  \\\n",
       "0  b'Paid'     ...      b'Um'  b'10'    b'Farmer'  b'Housewife'    b'Large'   \n",
       "1  b'Paid'     ...      b'Um'  b'Il'   b'Service'    b'Service'    b'Small'   \n",
       "2  b'Paid'     ...      b'12'  b'10'   b'Service'  b'Housewife'  b'Average'   \n",
       "3  b'Paid'     ...      b'12'  b'Um'  b'Business'   b'Business'    b'Large'   \n",
       "4  b'Paid'     ...      b'10'  b'12'   b'Service'  b'Housewife'    b'Large'   \n",
       "\n",
       "           sh          ss      me          tt         atd  \n",
       "0     b'Poor'     b'Govt'  b'Asm'    b'Small'     b'Good'  \n",
       "1     b'Poor'     b'Govt'  b'Asm'  b'Average'  b'Average'  \n",
       "2  b'Average'     b'Govt'  b'Asm'    b'Large'     b'Good'  \n",
       "3     b'Poor'     b'Govt'  b'Asm'  b'Average'  b'Average'  \n",
       "4     b'Poor'  b'Private'  b'Asm'    b'Small'     b'Good'  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.io import arff\n",
    "import pandas as pd\n",
    "\n",
    "data = arff.loadarff('Sapfile1.arff')\n",
    "df = pd.DataFrame(data[0])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>ge</th>\n",
       "      <th>cst</th>\n",
       "      <th>tnp</th>\n",
       "      <th>twp</th>\n",
       "      <th>iap</th>\n",
       "      <th>esp</th>\n",
       "      <th>arr</th>\n",
       "      <th>ms</th>\n",
       "      <th>ls</th>\n",
       "      <th>as</th>\n",
       "      <th>...</th>\n",
       "      <th>fq</th>\n",
       "      <th>mq</th>\n",
       "      <th>fo</th>\n",
       "      <th>mo</th>\n",
       "      <th>nf</th>\n",
       "      <th>sh</th>\n",
       "      <th>ss</th>\n",
       "      <th>me</th>\n",
       "      <th>tt</th>\n",
       "      <th>atd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>b'M'</td>\n",
       "      <td>b'OBC'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Vg'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'N'</td>\n",
       "      <td>b'Unmarried'</td>\n",
       "      <td>b'V'</td>\n",
       "      <td>b'Paid'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'Um'</td>\n",
       "      <td>b'Um'</td>\n",
       "      <td>b'Service'</td>\n",
       "      <td>b'Housewife'</td>\n",
       "      <td>b'Large'</td>\n",
       "      <td>b'Average'</td>\n",
       "      <td>b'Govt'</td>\n",
       "      <td>b'Eng'</td>\n",
       "      <td>b'Small'</td>\n",
       "      <td>b'Good'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>72</td>\n",
       "      <td>57</td>\n",
       "      <td>59</td>\n",
       "      <td>65</td>\n",
       "      <td>63</td>\n",
       "      <td>54</td>\n",
       "      <td>78</td>\n",
       "      <td>131</td>\n",
       "      <td>92</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>52</td>\n",
       "      <td>38</td>\n",
       "      <td>115</td>\n",
       "      <td>58</td>\n",
       "      <td>59</td>\n",
       "      <td>91</td>\n",
       "      <td>62</td>\n",
       "      <td>78</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ge     cst      tnp      twp    iap      esp   arr            ms  \\\n",
       "count    131     131      131      131    131      131   131           131   \n",
       "unique     2       5        4        4      4        4     2             1   \n",
       "top     b'M'  b'OBC'  b'Good'  b'Good'  b'Vg'  b'Good'  b'N'  b'Unmarried'   \n",
       "freq      72      57       59       65     63       54    78           131   \n",
       "\n",
       "          ls       as   ...        fq     mq          fo            mo  \\\n",
       "count    131      131   ...       131    131         131           131   \n",
       "unique     2        2   ...         6      6           5             5   \n",
       "top     b'V'  b'Paid'   ...     b'Um'  b'Um'  b'Service'  b'Housewife'   \n",
       "freq      92       76   ...        40     52          38           115   \n",
       "\n",
       "              nf          sh       ss      me        tt      atd  \n",
       "count        131         131      131     131       131      131  \n",
       "unique         3           3        2       4         3        3  \n",
       "top     b'Large'  b'Average'  b'Govt'  b'Eng'  b'Small'  b'Good'  \n",
       "freq          58          59       91      62        78       56  \n",
       "\n",
       "[4 rows x 22 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x149b9f42630>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n",
    "ts = ts.cumsum()\n",
    "ts.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ge     b'FMFMMMFFFFMFMMFFFMMMFFFFFFFFMFFFFMMFFMMMMMMM...\n",
       "cst    b'GOBCOBCMOBCGOBCOBCOBCMOBCOBCSTOBCOBCOBCGSTST...\n",
       "tnp    b'GoodVgGoodPassGoodVgGoodGoodVgVgVgVgGoodGood...\n",
       "twp    b'GoodVgGoodGoodGoodGoodVgGoodGoodVgVgVgGoodGo...\n",
       "iap    b'VgVgVgVgVgVgGoodGoodGoodGoodGoodGoodGoodPass...\n",
       "esp    b'GoodVgGoodGoodVgVgGoodGoodVgVgVgVgGoodVgVgVg...\n",
       "arr    b'YNNNNNNNYYNNYNNYNYYYNYNYNNYYYYNNNNNYNNNNYNYN...\n",
       "ms     b'UnmarriedUnmarriedUnmarriedUnmarriedUnmarrie...\n",
       "ls     b'VVVVVVVVVVVVVVVVVVVVVVVVVTTVTVTVVVVVTTVTVTVT...\n",
       "as     b'PaidPaidPaidPaidPaidPaidPaidPaidPaidPaidPaid...\n",
       "fmi    b'MediumLowAmMediumAmMediumHighMediumLowLowVhM...\n",
       "fs     b'AverageAverageAverageSmallAverageAverageAver...\n",
       "fq     b'UmUm1212101012Il12DegreePgDegreeDegreeIlDegr...\n",
       "mq     b'10Il10Um12UmDegreeUm10DegreePg1210Il10101212...\n",
       "fo     b'FarmerServiceServiceBusinessServiceFarmerSer...\n",
       "mo     b'HousewifeServiceHousewifeBusinessHousewifeHo...\n",
       "nf     b'LargeSmallAverageLargeLargeSmallAverageSmall...\n",
       "sh     b'PoorPoorAveragePoorPoorPoorPoorPoorGoodAvera...\n",
       "ss     b'GovtGovtGovtGovtPrivatePrivateGovtPrivateGov...\n",
       "me     b'AsmAsmAsmAsmAsmEngAsmAsmAsmAsmEngEngEngAsmEn...\n",
       "tt     b'SmallAverageLargeAverageSmallAverageSmallAve...\n",
       "atd    b'GoodAverageGoodAverageGoodAverageGoodGoodGoo...\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ge     131\n",
       "cst    131\n",
       "tnp    131\n",
       "twp    131\n",
       "iap    131\n",
       "esp    131\n",
       "arr    131\n",
       "ms     131\n",
       "ls     131\n",
       "as     131\n",
       "fmi    131\n",
       "fs     131\n",
       "fq     131\n",
       "mq     131\n",
       "fo     131\n",
       "mo     131\n",
       "nf     131\n",
       "sh     131\n",
       "ss     131\n",
       "me     131\n",
       "tt     131\n",
       "atd    131\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['ge'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-79200c101cbe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ge'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\New folder\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36mboxplot_frame\u001b[1;34m(self, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[0;32m   2255\u001b[0m     ax = boxplot(self, column=column, by=by, ax=ax, fontsize=fontsize,\n\u001b[0;32m   2256\u001b[0m                  \u001b[0mgrid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgrid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2257\u001b[1;33m                  return_type=return_type, **kwds)\n\u001b[0m\u001b[0;32m   2258\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2259\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\New folder\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36mboxplot\u001b[1;34m(data, column, by, ax, fontsize, rot, grid, figsize, layout, return_type, **kwds)\u001b[0m\n\u001b[0;32m   2239\u001b[0m             \u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2240\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2241\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2242\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2243\u001b[0m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\New folder\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2677\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2678\u001b[0m             \u001b[1;31m# either boolean or fancy integer index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2679\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2680\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2681\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\New folder\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_array\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2721\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_take\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2722\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2723\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2724\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_take\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2725\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\New folder\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[1;34m(self, obj, axis, is_setter)\u001b[0m\n\u001b[0;32m   1325\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1326\u001b[0m                     raise KeyError('{mask} not in index'\n\u001b[1;32m-> 1327\u001b[1;33m                                    .format(mask=objarr[mask]))\n\u001b[0m\u001b[0;32m   1328\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1329\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['ge'] not in index\""
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df.boxplot('ge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for /: 'bytes' and 'int'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-6d1633ca25cf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcorrcoef\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mcorr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcorrcoef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcorr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\New folder\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36mcorrcoef\u001b[1;34m(x, y, rowvar, bias, ddof)\u001b[0m\n\u001b[0;32m   3173\u001b[0m         warnings.warn('bias and ddof have no effect and are deprecated',\n\u001b[0;32m   3174\u001b[0m                       DeprecationWarning, stacklevel=2)\n\u001b[1;32m-> 3175\u001b[1;33m     \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcov\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrowvar\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3176\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3177\u001b[0m         \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\New folder\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36mcov\u001b[1;34m(m, y, rowvar, bias, ddof, fweights, aweights)\u001b[0m\n\u001b[0;32m   3083\u001b[0m             \u001b[0mw\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[0maweights\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3084\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3085\u001b[1;33m     \u001b[0mavg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw_sum\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maverage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturned\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3086\u001b[0m     \u001b[0mw_sum\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mw_sum\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3087\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\New folder\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36maverage\u001b[1;34m(a, axis, weights, returned)\u001b[0m\n\u001b[0;32m   1126\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1127\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mweights\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1128\u001b[1;33m         \u001b[0mavg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1129\u001b[0m         \u001b[0mscl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mavg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mavg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1130\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\New folder\\lib\\site-packages\\numpy\\core\\_methods.py\u001b[0m in \u001b[0;36m_mean\u001b[1;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[0;32m     71\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m         ret = um.true_divide(\n\u001b[1;32m---> 73\u001b[1;33m                 ret, rcount, out=ret, casting='unsafe', subok=False)\n\u001b[0m\u001b[0;32m     74\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_float16_result\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     75\u001b[0m             \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'bytes' and 'int'"
     ]
    }
   ],
   "source": [
    "from numpy import corrcoef\n",
    "corr = corrcoef(df)\n",
    "print(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>ge</th>\n",
       "      <th>cst</th>\n",
       "      <th>tnp</th>\n",
       "      <th>twp</th>\n",
       "      <th>iap</th>\n",
       "      <th>esp</th>\n",
       "      <th>arr</th>\n",
       "      <th>ms</th>\n",
       "      <th>ls</th>\n",
       "      <th>as</th>\n",
       "      <th>...</th>\n",
       "      <th>fq</th>\n",
       "      <th>mq</th>\n",
       "      <th>fo</th>\n",
       "      <th>mo</th>\n",
       "      <th>nf</th>\n",
       "      <th>sh</th>\n",
       "      <th>ss</th>\n",
       "      <th>me</th>\n",
       "      <th>tt</th>\n",
       "      <th>atd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'M'</td>\n",
       "      <td>b'OBC'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Vg'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'N'</td>\n",
       "      <td>b'Unmarried'</td>\n",
       "      <td>b'V'</td>\n",
       "      <td>b'Paid'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'Um'</td>\n",
       "      <td>b'Um'</td>\n",
       "      <td>b'Service'</td>\n",
       "      <td>b'Housewife'</td>\n",
       "      <td>b'Large'</td>\n",
       "      <td>b'Average'</td>\n",
       "      <td>b'Govt'</td>\n",
       "      <td>b'Eng'</td>\n",
       "      <td>b'Small'</td>\n",
       "      <td>b'Good'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ge     cst      tnp      twp    iap      esp   arr            ms    ls  \\\n",
       "0  b'M'  b'OBC'  b'Good'  b'Good'  b'Vg'  b'Good'  b'N'  b'Unmarried'  b'V'   \n",
       "\n",
       "        as   ...        fq     mq          fo            mo        nf  \\\n",
       "0  b'Paid'   ...     b'Um'  b'Um'  b'Service'  b'Housewife'  b'Large'   \n",
       "\n",
       "           sh       ss      me        tt      atd  \n",
       "0  b'Average'  b'Govt'  b'Eng'  b'Small'  b'Good'  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-25-087fbfdb65bc>, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-25-087fbfdb65bc>\"\u001b[1;36m, line \u001b[1;32m3\u001b[0m\n\u001b[1;33m    carrier_count = df.['ge'].value_counts()\u001b[0m\n\u001b[1;37m                       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "carrier_count = df.['ge'].value_counts()\n",
    "sns.set(style=\"darkgrid\")\n",
    "sns.barplot(carrier_count.index, carrier_count.values, alpha=0.9)\n",
    "plt.title('Frequency Distribution')\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Carrier', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "objects = ('G', 'MOBC','OBC','SC','ST')\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [34,6,56,40,23]\n",
    " \n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.ylabel('Persons')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>ge</th>\n",
       "      <th>cst</th>\n",
       "      <th>tnp</th>\n",
       "      <th>twp</th>\n",
       "      <th>iap</th>\n",
       "      <th>esp</th>\n",
       "      <th>arr</th>\n",
       "      <th>ms</th>\n",
       "      <th>ls</th>\n",
       "      <th>as</th>\n",
       "      <th>...</th>\n",
       "      <th>fq</th>\n",
       "      <th>mq</th>\n",
       "      <th>fo</th>\n",
       "      <th>mo</th>\n",
       "      <th>nf</th>\n",
       "      <th>sh</th>\n",
       "      <th>ss</th>\n",
       "      <th>me</th>\n",
       "      <th>tt</th>\n",
       "      <th>atd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>b'M'</td>\n",
       "      <td>b'OBC'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Vg'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'N'</td>\n",
       "      <td>b'Unmarried'</td>\n",
       "      <td>b'V'</td>\n",
       "      <td>b'Paid'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'Um'</td>\n",
       "      <td>b'Um'</td>\n",
       "      <td>b'Service'</td>\n",
       "      <td>b'Housewife'</td>\n",
       "      <td>b'Large'</td>\n",
       "      <td>b'Average'</td>\n",
       "      <td>b'Govt'</td>\n",
       "      <td>b'Eng'</td>\n",
       "      <td>b'Small'</td>\n",
       "      <td>b'Good'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>72</td>\n",
       "      <td>57</td>\n",
       "      <td>59</td>\n",
       "      <td>65</td>\n",
       "      <td>63</td>\n",
       "      <td>54</td>\n",
       "      <td>78</td>\n",
       "      <td>131</td>\n",
       "      <td>92</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>52</td>\n",
       "      <td>38</td>\n",
       "      <td>115</td>\n",
       "      <td>58</td>\n",
       "      <td>59</td>\n",
       "      <td>91</td>\n",
       "      <td>62</td>\n",
       "      <td>78</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ge     cst      tnp      twp    iap      esp   arr            ms  \\\n",
       "count    131     131      131      131    131      131   131           131   \n",
       "unique     2       5        4        4      4        4     2             1   \n",
       "top     b'M'  b'OBC'  b'Good'  b'Good'  b'Vg'  b'Good'  b'N'  b'Unmarried'   \n",
       "freq      72      57       59       65     63       54    78           131   \n",
       "\n",
       "          ls       as   ...        fq     mq          fo            mo  \\\n",
       "count    131      131   ...       131    131         131           131   \n",
       "unique     2        2   ...         6      6           5             5   \n",
       "top     b'V'  b'Paid'   ...     b'Um'  b'Um'  b'Service'  b'Housewife'   \n",
       "freq      92       76   ...        40     52          38           115   \n",
       "\n",
       "              nf          sh       ss      me        tt      atd  \n",
       "count        131         131      131     131       131      131  \n",
       "unique         3           3        2       4         3        3  \n",
       "top     b'Large'  b'Average'  b'Govt'  b'Eng'  b'Small'  b'Good'  \n",
       "freq          58          59       91      62        78       56  \n",
       "\n",
       "[4 rows x 22 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x149ba81a7f0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"cst\"].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([b'Average', b'Small', b'Large'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['fs'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>ge</th>\n",
       "      <th>cst</th>\n",
       "      <th>tnp</th>\n",
       "      <th>twp</th>\n",
       "      <th>iap</th>\n",
       "      <th>esp</th>\n",
       "      <th>arr</th>\n",
       "      <th>ms</th>\n",
       "      <th>ls</th>\n",
       "      <th>as</th>\n",
       "      <th>...</th>\n",
       "      <th>fq</th>\n",
       "      <th>mq</th>\n",
       "      <th>fo</th>\n",
       "      <th>mo</th>\n",
       "      <th>nf</th>\n",
       "      <th>sh</th>\n",
       "      <th>ss</th>\n",
       "      <th>me</th>\n",
       "      <th>tt</th>\n",
       "      <th>atd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>...</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>b'M'</td>\n",
       "      <td>b'OBC'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'Vg'</td>\n",
       "      <td>b'Good'</td>\n",
       "      <td>b'N'</td>\n",
       "      <td>b'Unmarried'</td>\n",
       "      <td>b'V'</td>\n",
       "      <td>b'Paid'</td>\n",
       "      <td>...</td>\n",
       "      <td>b'Um'</td>\n",
       "      <td>b'Um'</td>\n",
       "      <td>b'Service'</td>\n",
       "      <td>b'Housewife'</td>\n",
       "      <td>b'Large'</td>\n",
       "      <td>b'Average'</td>\n",
       "      <td>b'Govt'</td>\n",
       "      <td>b'Eng'</td>\n",
       "      <td>b'Small'</td>\n",
       "      <td>b'Good'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>72</td>\n",
       "      <td>57</td>\n",
       "      <td>59</td>\n",
       "      <td>65</td>\n",
       "      <td>63</td>\n",
       "      <td>54</td>\n",
       "      <td>78</td>\n",
       "      <td>131</td>\n",
       "      <td>92</td>\n",
       "      <td>76</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>52</td>\n",
       "      <td>38</td>\n",
       "      <td>115</td>\n",
       "      <td>58</td>\n",
       "      <td>59</td>\n",
       "      <td>91</td>\n",
       "      <td>62</td>\n",
       "      <td>78</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ge     cst      tnp      twp    iap      esp   arr            ms  \\\n",
       "count    131     131      131      131    131      131   131           131   \n",
       "unique     2       5        4        4      4        4     2             1   \n",
       "top     b'M'  b'OBC'  b'Good'  b'Good'  b'Vg'  b'Good'  b'N'  b'Unmarried'   \n",
       "freq      72      57       59       65     63       54    78           131   \n",
       "\n",
       "          ls       as   ...        fq     mq          fo            mo  \\\n",
       "count    131      131   ...       131    131         131           131   \n",
       "unique     2        2   ...         6      6           5             5   \n",
       "top     b'V'  b'Paid'   ...     b'Um'  b'Um'  b'Service'  b'Housewife'   \n",
       "freq      92       76   ...        40     52          38           115   \n",
       "\n",
       "              nf          sh       ss      me        tt      atd  \n",
       "count        131         131      131     131       131      131  \n",
       "unique         3           3        2       4         3        3  \n",
       "top     b'Large'  b'Average'  b'Govt'  b'Eng'  b'Small'  b'Good'  \n",
       "freq          58          59       91      62        78       56  \n",
       "\n",
       "[4 rows x 22 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([b'Good', b'Vg', b'Pass', b'Best'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['tnp'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([b'Good', b'Vg', b'Best', b'Pass'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['esp'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([b'Unmarried'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ms'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x149bbb451d0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"tnp\"].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x149bbb92c88>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"esp\"].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x149bbc92320>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"ms\"].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: b'Small'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-44-2f1e5fe6f6ec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMinMaxScaler\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[0mscaler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMinMaxScaler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mX_train\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[0mX_test\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mneighbors\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\New folder\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[1;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[0;32m    515\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    516\u001b[0m             \u001b[1;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 517\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    518\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    519\u001b[0m             \u001b[1;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\New folder\\lib\\site-packages\\sklearn\\preprocessing\\data.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    306\u001b[0m         \u001b[1;31m# Reset internal state before fitting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    307\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 308\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartial_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    309\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpartial_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\New folder\\lib\\site-packages\\sklearn\\preprocessing\\data.py\u001b[0m in \u001b[0;36mpartial_fit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    332\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    333\u001b[0m         X = check_array(X, copy=self.copy, warn_on_dtype=True,\n\u001b[1;32m--> 334\u001b[1;33m                         estimator=self, dtype=FLOAT_DTYPES)\n\u001b[0m\u001b[0;32m    335\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    336\u001b[0m         \u001b[0mdata_min\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\New folder\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    431\u001b[0m                                       force_all_finite)\n\u001b[0;32m    432\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 433\u001b[1;33m         \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: b'Small'"
     ]
    }
   ],
   "source": [
    "df=df.replace('Medium',1)\n",
    "df=df.replace('Low',0)\n",
    "df=df.replace('Am',2)\n",
    "df=df.replace('High',3)\n",
    "df=df.replace('Vh',4)\n",
    "df['fmi'].unique()\n",
    "df1 = df[['tnp','twp','esp','fmi','fs','nf','tt']]\n",
    "feature_names = ['tnp', 'twp', 'fmi', 'fs','nf','tt']\n",
    "X = df1[feature_names]\n",
    "y = df1['esp']\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)\n",
    "print('Accuracy on training:'\n",
    "     .format(knn.score(X_train, y_train)))\n",
    "print('Accuracy on testing: '\n",
    "     .format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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