ProjectQues2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "import operator\n",
    "from collections import Counter\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from sklearn.metrics import accuracy_score  \n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_excel('DATASET_1.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Current Place</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Place of interest</th>\n",
       "      <th>Latitude.1</th>\n",
       "      <th>Longitude.1</th>\n",
       "      <th>Food you like most</th>\n",
       "      <th>Leisure time activity</th>\n",
       "      <th>Future plan</th>\n",
       "      <th>Typically your days are started at:</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151340976</td>\n",
       "      <td>Goran</td>\n",
       "      <td>23.750270</td>\n",
       "      <td>90.434130</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.411270</td>\n",
       "      <td>91.995891</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151340977</td>\n",
       "      <td>Banasree</td>\n",
       "      <td>22.513531</td>\n",
       "      <td>88.301537</td>\n",
       "      <td>Sea Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kachchi</td>\n",
       "      <td>Sports</td>\n",
       "      <td>BCS/Business</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151340978</td>\n",
       "      <td>Azimpur</td>\n",
       "      <td>23.727040</td>\n",
       "      <td>90.384689</td>\n",
       "      <td>Kaptai Lake</td>\n",
       "      <td>22.755208</td>\n",
       "      <td>92.277031</td>\n",
       "      <td>kacchi biryani</td>\n",
       "      <td>Series</td>\n",
       "      <td>Higher Study in North America</td>\n",
       "      <td>ER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151340980</td>\n",
       "      <td>Niketon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>home</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>any</td>\n",
       "      <td>movies,music</td>\n",
       "      <td>software Engineer</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151340981</td>\n",
       "      <td>Shaymoli</td>\n",
       "      <td>22.301411</td>\n",
       "      <td>70.822357</td>\n",
       "      <td>home</td>\n",
       "      <td>22.301411</td>\n",
       "      <td>70.822357</td>\n",
       "      <td>sea food</td>\n",
       "      <td>music</td>\n",
       "      <td>businessman</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>151340982</td>\n",
       "      <td>Bangshal</td>\n",
       "      <td>-32.348610</td>\n",
       "      <td>151.924576</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.411270</td>\n",
       "      <td>91.995891</td>\n",
       "      <td>Burger</td>\n",
       "      <td>Series</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>151340983</td>\n",
       "      <td>Dania</td>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.442194</td>\n",
       "      <td>Bandorban</td>\n",
       "      <td>21.479600</td>\n",
       "      <td>92.559400</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>movies</td>\n",
       "      <td>Network Engineer</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>151340984</td>\n",
       "      <td>Jurain</td>\n",
       "      <td>23.697285</td>\n",
       "      <td>90.431817</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.697285</td>\n",
       "      <td>90.431817</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CEO</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>151340985</td>\n",
       "      <td>Lalbag</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>Any</td>\n",
       "      <td>All</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>151340986</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>151340988</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>Bandorban</td>\n",
       "      <td>21.479600</td>\n",
       "      <td>92.559400</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>movies, music</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>151340990</td>\n",
       "      <td>Dania</td>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.442194</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.411270</td>\n",
       "      <td>91.995891</td>\n",
       "      <td>khichuri</td>\n",
       "      <td>movies</td>\n",
       "      <td>job</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>151340991</td>\n",
       "      <td>Mohammadpur</td>\n",
       "      <td>23.765800</td>\n",
       "      <td>90.358400</td>\n",
       "      <td>Sea Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>Sci Fi Movie</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>151340992</td>\n",
       "      <td>Jigatola</td>\n",
       "      <td>23.737286</td>\n",
       "      <td>90.371610</td>\n",
       "      <td>Cox’s Bazar</td>\n",
       "      <td>21.411270</td>\n",
       "      <td>91.995891</td>\n",
       "      <td>Cheesy fries</td>\n",
       "      <td>Movies</td>\n",
       "      <td>BCS</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>151340994</td>\n",
       "      <td>Shamoli</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.733103</td>\n",
       "      <td>90.368848</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Game Development</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>151340995</td>\n",
       "      <td>Green Corner</td>\n",
       "      <td>23.745173</td>\n",
       "      <td>90.386421</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.745173</td>\n",
       "      <td>90.386421</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>BCS</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>151340996</td>\n",
       "      <td>Shahjahanpur</td>\n",
       "      <td>27.883743</td>\n",
       "      <td>79.912247</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.411270</td>\n",
       "      <td>91.995891</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Web Development</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>151340998</td>\n",
       "      <td>Khilgaon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Tv series</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>151341000</td>\n",
       "      <td>Banani</td>\n",
       "      <td>23.793993</td>\n",
       "      <td>90.404274</td>\n",
       "      <td>Sea Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spicy Foods</td>\n",
       "      <td>Tv series/movies</td>\n",
       "      <td>not fixed</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>151341001</td>\n",
       "      <td>Uttara</td>\n",
       "      <td>24.098379</td>\n",
       "      <td>90.328712</td>\n",
       "      <td>Home</td>\n",
       "      <td>24.098379</td>\n",
       "      <td>90.328712</td>\n",
       "      <td>Burger</td>\n",
       "      <td>Tv series</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID Current Place   Latitude   Longitude Place of interest  \\\n",
       "0   151340976         Goran  23.750270   90.434130       Cox's Bazar   \n",
       "1   151340977      Banasree  22.513531   88.301537         Sea Beach   \n",
       "2   151340978       Azimpur  23.727040   90.384689       Kaptai Lake   \n",
       "3   151340980       Niketon  23.707310   90.415480              home   \n",
       "4   151340981      Shaymoli  22.301411   70.822357              home   \n",
       "5   151340982      Bangshal -32.348610  151.924576       Cox's Bazar   \n",
       "6   151340983         Dania  23.701443   90.442194         Bandorban   \n",
       "7   151340984        Jurain  23.697285   90.431817              Home   \n",
       "8   151340985        Lalbag  23.707310   90.415480              Home   \n",
       "9   151340986       Rampura  23.761227   90.420769              Home   \n",
       "10  151340988       Rampura  23.761227   90.420769         Bandorban   \n",
       "11  151340990         Dania  23.701443   90.442194       Cox's Bazar   \n",
       "12  151340991   Mohammadpur  23.765800   90.358400         Sea Beach   \n",
       "13  151340992      Jigatola  23.737286   90.371610       Cox’s Bazar   \n",
       "14  151340994       Shamoli  23.707310   90.415480           Tejgaon   \n",
       "15  151340995  Green Corner  23.745173   90.386421              Home   \n",
       "16  151340996  Shahjahanpur  27.883743   79.912247       Cox's Bazar   \n",
       "17  151340998      Khilgaon  23.707310   90.415480              Home   \n",
       "18  151341000        Banani  23.793993   90.404274         Sea Beach   \n",
       "19  151341001        Uttara  24.098379   90.328712              Home   \n",
       "\n",
       "    Latitude.1  Longitude.1 Food you like most Leisure time activity  \\\n",
       "0    21.411270    91.995891           Biriyani                Movies   \n",
       "1          NaN          NaN            Kachchi                Sports   \n",
       "2    22.755208    92.277031     kacchi biryani                Series   \n",
       "3    23.707310    90.415480                any          movies,music   \n",
       "4    22.301411    70.822357           sea food                 music   \n",
       "5    21.411270    91.995891             Burger                Series   \n",
       "6    21.479600    92.559400           Biriyani                movies   \n",
       "7    23.697285    90.431817           Biriyani                   NaN   \n",
       "8    23.707310    90.415480                Any                   All   \n",
       "9    23.761227    90.420769           Biriyani                Movies   \n",
       "10   21.479600    92.559400            Chicken         movies, music   \n",
       "11   21.411270    91.995891           khichuri                movies   \n",
       "12         NaN          NaN          Fast Food          Sci Fi Movie   \n",
       "13   21.411270    91.995891       Cheesy fries                Movies   \n",
       "14   23.733103    90.368848           Biriyani                Movies   \n",
       "15   23.745173    90.386421           Biriyani                Movies   \n",
       "16   21.411270    91.995891           Biriyani                Movies   \n",
       "17   23.707310    90.415480           Biriyani             Tv series   \n",
       "18         NaN          NaN        Spicy Foods      Tv series/movies   \n",
       "19   24.098379    90.328712             Burger             Tv series   \n",
       "\n",
       "                      Future plan Typically your days are started at:  \n",
       "0               Software Engineer                                  LR  \n",
       "1                    BCS/Business                                  LR  \n",
       "2   Higher Study in North America                                  ER  \n",
       "3               software Engineer                                  LR  \n",
       "4                     businessman                                  LR  \n",
       "5                    Entrepreneur                                  LR  \n",
       "6                Network Engineer                                  LR  \n",
       "7                             CEO                                  LR  \n",
       "8                  Higher Studies                                  LR  \n",
       "9               Software Engineer                                  LR  \n",
       "10              Software Engineer                                  LR  \n",
       "11                            job                                  LR  \n",
       "12                 Higher Studies                                  LR  \n",
       "13                            BCS                                  LR  \n",
       "14               Game Development                                  LR  \n",
       "15                            BCS                                  LR  \n",
       "16                Web Development                                  LR  \n",
       "17                 Higher Studies                                  LR  \n",
       "18                      not fixed                                  LR  \n",
       "19                   Entrepreneur                                  LR  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_excel('DATASET_2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.read_excel('DATASET_3.xlsx')\n",
    "df4 = pd.read_excel('DATASET_4.xlsx')\n",
    "df5 = pd.read_excel('DATASET_5.xlsx')\n",
    "df6 = pd.read_excel('DATASET_6.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df7=pd.concat([df1, df2,df3,df4, df5,df6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Current Place</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Place of interest</th>\n",
       "      <th>Latitude.1</th>\n",
       "      <th>Longitude.1</th>\n",
       "      <th>Food you like most</th>\n",
       "      <th>Leisure time activity</th>\n",
       "      <th>Future plan</th>\n",
       "      <th>Typically your days are started at:</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151340976</td>\n",
       "      <td>Goran</td>\n",
       "      <td>23.750270</td>\n",
       "      <td>90.4341</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.4113</td>\n",
       "      <td>91.9959</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151340977</td>\n",
       "      <td>Banasree</td>\n",
       "      <td>22.513531</td>\n",
       "      <td>88.3015</td>\n",
       "      <td>Sea Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kachchi</td>\n",
       "      <td>Sports</td>\n",
       "      <td>BCS/Business</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151340978</td>\n",
       "      <td>Azimpur</td>\n",
       "      <td>23.727040</td>\n",
       "      <td>90.3847</td>\n",
       "      <td>Kaptai Lake</td>\n",
       "      <td>22.7552</td>\n",
       "      <td>92.277</td>\n",
       "      <td>kacchi biryani</td>\n",
       "      <td>Series</td>\n",
       "      <td>Higher Study in North America</td>\n",
       "      <td>ER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151340980</td>\n",
       "      <td>Niketon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>home</td>\n",
       "      <td>23.7073</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>any</td>\n",
       "      <td>movies,music</td>\n",
       "      <td>software Engineer</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151340981</td>\n",
       "      <td>Shaymoli</td>\n",
       "      <td>22.301411</td>\n",
       "      <td>70.8224</td>\n",
       "      <td>home</td>\n",
       "      <td>22.3014</td>\n",
       "      <td>70.8224</td>\n",
       "      <td>sea food</td>\n",
       "      <td>music</td>\n",
       "      <td>businessman</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>151340982</td>\n",
       "      <td>Bangshal</td>\n",
       "      <td>-32.348610</td>\n",
       "      <td>151.925</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.4113</td>\n",
       "      <td>91.9959</td>\n",
       "      <td>Burger</td>\n",
       "      <td>Series</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>151340983</td>\n",
       "      <td>Dania</td>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.4422</td>\n",
       "      <td>Bandorban</td>\n",
       "      <td>21.4796</td>\n",
       "      <td>92.5594</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>movies</td>\n",
       "      <td>Network Engineer</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>151340984</td>\n",
       "      <td>Jurain</td>\n",
       "      <td>23.697285</td>\n",
       "      <td>90.4318</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.6973</td>\n",
       "      <td>90.4318</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CEO</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>151340985</td>\n",
       "      <td>Lalbag</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.7073</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Any</td>\n",
       "      <td>All</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>151340986</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.7612</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>151340988</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Bandorban</td>\n",
       "      <td>21.4796</td>\n",
       "      <td>92.5594</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>movies, music</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>151340990</td>\n",
       "      <td>Dania</td>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.4422</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.4113</td>\n",
       "      <td>91.9959</td>\n",
       "      <td>khichuri</td>\n",
       "      <td>movies</td>\n",
       "      <td>job</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>151340991</td>\n",
       "      <td>Mohammadpur</td>\n",
       "      <td>23.765800</td>\n",
       "      <td>90.3584</td>\n",
       "      <td>Sea Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>Sci Fi Movie</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>151340992</td>\n",
       "      <td>Jigatola</td>\n",
       "      <td>23.737286</td>\n",
       "      <td>90.3716</td>\n",
       "      <td>Cox’s Bazar</td>\n",
       "      <td>21.4113</td>\n",
       "      <td>91.9959</td>\n",
       "      <td>Cheesy fries</td>\n",
       "      <td>Movies</td>\n",
       "      <td>BCS</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>151340994</td>\n",
       "      <td>Shamoli</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.7331</td>\n",
       "      <td>90.3688</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Game Development</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>151340995</td>\n",
       "      <td>Green Corner</td>\n",
       "      <td>23.745173</td>\n",
       "      <td>90.3864</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.7452</td>\n",
       "      <td>90.3864</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>BCS</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>151340996</td>\n",
       "      <td>Shahjahanpur</td>\n",
       "      <td>27.883743</td>\n",
       "      <td>79.9122</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.4113</td>\n",
       "      <td>91.9959</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Web Development</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>151340998</td>\n",
       "      <td>Khilgaon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.7073</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Tv series</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>151341000</td>\n",
       "      <td>Banani</td>\n",
       "      <td>23.793993</td>\n",
       "      <td>90.4043</td>\n",
       "      <td>Sea Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spicy Foods</td>\n",
       "      <td>Tv series/movies</td>\n",
       "      <td>not fixed</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>151341001</td>\n",
       "      <td>Uttara</td>\n",
       "      <td>24.098379</td>\n",
       "      <td>90.3287</td>\n",
       "      <td>Home</td>\n",
       "      <td>24.0984</td>\n",
       "      <td>90.3287</td>\n",
       "      <td>Burger</td>\n",
       "      <td>Tv series</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>LR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151340894</td>\n",
       "      <td>Mirpur</td>\n",
       "      <td>23.776604</td>\n",
       "      <td>90.383</td>\n",
       "      <td>Saint Martin</td>\n",
       "      <td>20.6233</td>\n",
       "      <td>92.323</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>listening music</td>\n",
       "      <td>Higher Study</td>\n",
       "      <td>08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151340897</td>\n",
       "      <td>Mohanogor</td>\n",
       "      <td>23.734558</td>\n",
       "      <td>90.385</td>\n",
       "      <td>Canada</td>\n",
       "      <td>56.1304</td>\n",
       "      <td>-106.347</td>\n",
       "      <td>Egg and Milk</td>\n",
       "      <td>Reading Newspapers</td>\n",
       "      <td>Higher Study</td>\n",
       "      <td>06:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151340898</td>\n",
       "      <td>Gopibag</td>\n",
       "      <td>23.722109</td>\n",
       "      <td>90.4262</td>\n",
       "      <td>Bandarban</td>\n",
       "      <td>-7.73352</td>\n",
       "      <td>112.959</td>\n",
       "      <td>Tea</td>\n",
       "      <td>ChitChat with friends</td>\n",
       "      <td>Traveling</td>\n",
       "      <td>10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151340900</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Norway</td>\n",
       "      <td>22.32</td>\n",
       "      <td>39.1008</td>\n",
       "      <td>Hilsa</td>\n",
       "      <td>Reading Magazines</td>\n",
       "      <td>BCS</td>\n",
       "      <td>08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151340901</td>\n",
       "      <td>Shantinagar</td>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.4143</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>4.2105</td>\n",
       "      <td>101.976</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Online Gaming</td>\n",
       "      <td>BCS</td>\n",
       "      <td>09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>151340902</td>\n",
       "      <td>Siddeswari</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Santorini</td>\n",
       "      <td>36.3932</td>\n",
       "      <td>25.4615</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Watching movies, series</td>\n",
       "      <td>Higher Study</td>\n",
       "      <td>10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>151340903</td>\n",
       "      <td>Narinda</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Istanbul</td>\n",
       "      <td>41.0082</td>\n",
       "      <td>28.9784</td>\n",
       "      <td>Mutton</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Afflulent</td>\n",
       "      <td>11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>151340904</td>\n",
       "      <td>Mugdapara</td>\n",
       "      <td>23.730534</td>\n",
       "      <td>90.4462</td>\n",
       "      <td>Niagara Falls</td>\n",
       "      <td>43.0896</td>\n",
       "      <td>-79.0849</td>\n",
       "      <td>Chicken,Egg &amp; Milk</td>\n",
       "      <td>Reading Books</td>\n",
       "      <td>Higher Study</td>\n",
       "      <td>06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>151340905</td>\n",
       "      <td>Shahidbag</td>\n",
       "      <td>23.828959</td>\n",
       "      <td>90.3757</td>\n",
       "      <td>Paris</td>\n",
       "      <td>48.8566</td>\n",
       "      <td>2.35222</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>Cooking</td>\n",
       "      <td>Teaching</td>\n",
       "      <td>09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>151340906</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.759815</td>\n",
       "      <td>90.3913</td>\n",
       "      <td>Chittagong</td>\n",
       "      <td>22.3569</td>\n",
       "      <td>91.7832</td>\n",
       "      <td>Khola pitha</td>\n",
       "      <td>Exploring</td>\n",
       "      <td>BCS</td>\n",
       "      <td>11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>151341029</td>\n",
       "      <td>Segunbagicha</td>\n",
       "      <td>23.732598</td>\n",
       "      <td>90.4026</td>\n",
       "      <td>Spain</td>\n",
       "      <td>40.4637</td>\n",
       "      <td>-3.74922</td>\n",
       "      <td>Mix Food</td>\n",
       "      <td>watching movie and anime</td>\n",
       "      <td>give own IT Farm</td>\n",
       "      <td>10:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>151341010</td>\n",
       "      <td>Farmgate</td>\n",
       "      <td>23.756107</td>\n",
       "      <td>90.3872</td>\n",
       "      <td>Sunamganj</td>\n",
       "      <td>25.0456</td>\n",
       "      <td>91.3871</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>Watching movie</td>\n",
       "      <td>Web Developer</td>\n",
       "      <td>08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>151341020</td>\n",
       "      <td>Mirpur 60 Feet</td>\n",
       "      <td>23.790579</td>\n",
       "      <td>90.3653</td>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>25.5725</td>\n",
       "      <td>91.3108</td>\n",
       "      <td>Kacchi Biriyani</td>\n",
       "      <td>Playing games and watching Movies</td>\n",
       "      <td>Higher Study</td>\n",
       "      <td>9.30 Am</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>151341012</td>\n",
       "      <td>Zigatola</td>\n",
       "      <td>23.738078</td>\n",
       "      <td>90.3725</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.4339</td>\n",
       "      <td>91.987</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Don't have</td>\n",
       "      <td>09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>151341021</td>\n",
       "      <td>Rayerbag</td>\n",
       "      <td>23.693846</td>\n",
       "      <td>90.4553</td>\n",
       "      <td>China</td>\n",
       "      <td>34.626</td>\n",
       "      <td>103.65</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>TV Series, Documentaries</td>\n",
       "      <td>Higher Study</td>\n",
       "      <td>10.00 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>151341019</td>\n",
       "      <td>Shyamoli</td>\n",
       "      <td>23.771800</td>\n",
       "      <td>90.3631</td>\n",
       "      <td>Sylhet</td>\n",
       "      <td>24.8949</td>\n",
       "      <td>91.8687</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Don't have</td>\n",
       "      <td>09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151340921</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>saint martin</td>\n",
       "      <td>49.4355</td>\n",
       "      <td>-2.56735</td>\n",
       "      <td>Alu Vorta</td>\n",
       "      <td>playing game</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>10.00.00 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151340923</td>\n",
       "      <td>Siddheshwari</td>\n",
       "      <td>23.744602</td>\n",
       "      <td>90.4115</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.427</td>\n",
       "      <td>91.9712</td>\n",
       "      <td>Pasta</td>\n",
       "      <td>Watching Movies &amp; TV-Series</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>09:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151340925</td>\n",
       "      <td>Maniknagar</td>\n",
       "      <td>23.721389</td>\n",
       "      <td>90.4299</td>\n",
       "      <td>My Room</td>\n",
       "      <td>23.7214</td>\n",
       "      <td>90.4299</td>\n",
       "      <td>Cashew nut</td>\n",
       "      <td>Traversing Foreign Universities</td>\n",
       "      <td>PhD</td>\n",
       "      <td>06:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151340926</td>\n",
       "      <td>Maniknagar</td>\n",
       "      <td>23.722595</td>\n",
       "      <td>90.4296</td>\n",
       "      <td>Sylhet</td>\n",
       "      <td>24.8949</td>\n",
       "      <td>91.8687</td>\n",
       "      <td>Cashew nut</td>\n",
       "      <td>Photography</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151340927</td>\n",
       "      <td>Mirpur-12</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Khulna</td>\n",
       "      <td>22.8419</td>\n",
       "      <td>89.5581</td>\n",
       "      <td>Spicy food</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>08:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>151340928</td>\n",
       "      <td>Mirpur</td>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.3686</td>\n",
       "      <td>Shylet</td>\n",
       "      <td>24.8258</td>\n",
       "      <td>91.7175</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>09:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>151340930</td>\n",
       "      <td>Motijheel</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Sajek Valley</td>\n",
       "      <td>22.343</td>\n",
       "      <td>91.8427</td>\n",
       "      <td>Porota Dalbhaji</td>\n",
       "      <td>Twitch live Streaming</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>151340931</td>\n",
       "      <td>Hazaribagh</td>\n",
       "      <td>23.727835</td>\n",
       "      <td>90.374</td>\n",
       "      <td>Sylhet</td>\n",
       "      <td>24.8948</td>\n",
       "      <td>91.869</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Watching youtube</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>151340932</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.763697</td>\n",
       "      <td>90.3707</td>\n",
       "      <td>Gopalganj</td>\n",
       "      <td>23.0894</td>\n",
       "      <td>89.6213</td>\n",
       "      <td>Grill &amp; Nun</td>\n",
       "      <td>Watching Movie</td>\n",
       "      <td>Full Stack Developer</td>\n",
       "      <td>09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>151340933</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Sajek Valley</td>\n",
       "      <td>22.3304</td>\n",
       "      <td>91.8326</td>\n",
       "      <td>Beef</td>\n",
       "      <td>Watching anime</td>\n",
       "      <td>Database Analyst</td>\n",
       "      <td>10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>151340934</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Bandarban</td>\n",
       "      <td>21.8311</td>\n",
       "      <td>92.3686</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Playing online game</td>\n",
       "      <td>Network Specialist</td>\n",
       "      <td>10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>151340935</td>\n",
       "      <td>Gulshan 1</td>\n",
       "      <td>23.778600</td>\n",
       "      <td>90.4156</td>\n",
       "      <td>Khulna</td>\n",
       "      <td>22.8092</td>\n",
       "      <td>89.5697</td>\n",
       "      <td>Any well cooked food</td>\n",
       "      <td>Nothing fixed</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>151340936</td>\n",
       "      <td>Mohanagar</td>\n",
       "      <td>23.764510</td>\n",
       "      <td>90.4131</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>4.21048</td>\n",
       "      <td>101.976</td>\n",
       "      <td>Lasi</td>\n",
       "      <td>Exploring digital library &amp; surfing internet</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>151340937</td>\n",
       "      <td>Mirpur-1</td>\n",
       "      <td>23.795600</td>\n",
       "      <td>90.3537</td>\n",
       "      <td>Sajek Valley</td>\n",
       "      <td>23.4056</td>\n",
       "      <td>92.2793</td>\n",
       "      <td>Spicy Food</td>\n",
       "      <td>Watching Movies &amp; Series</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>151340938</td>\n",
       "      <td>Malibagh</td>\n",
       "      <td>23.748975</td>\n",
       "      <td>90.4107</td>\n",
       "      <td>Paris</td>\n",
       "      <td>48.8566</td>\n",
       "      <td>2.3522</td>\n",
       "      <td>Pasta</td>\n",
       "      <td>watching tv series,reading books and blogs</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>151340939</td>\n",
       "      <td>Malibagh</td>\n",
       "      <td>23.758353</td>\n",
       "      <td>90.4162</td>\n",
       "      <td>Sylhet</td>\n",
       "      <td>24.8949</td>\n",
       "      <td>91.8687</td>\n",
       "      <td>Burger</td>\n",
       "      <td>Facebooking</td>\n",
       "      <td>Web Developer</td>\n",
       "      <td>11:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>151340940</td>\n",
       "      <td>Khilgaon</td>\n",
       "      <td>23.755814</td>\n",
       "      <td>90.4233</td>\n",
       "      <td>My Rooftop</td>\n",
       "      <td>23.7558</td>\n",
       "      <td>90.4233</td>\n",
       "      <td>Kacchi</td>\n",
       "      <td>Writing novellas, reading books, analyzing bus...</td>\n",
       "      <td>Author, Entrepreneur, Journalist</td>\n",
       "      <td>07:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>151340941</td>\n",
       "      <td>Niketon</td>\n",
       "      <td>23.797911</td>\n",
       "      <td>90.4144</td>\n",
       "      <td>Gazipur</td>\n",
       "      <td>24.0958</td>\n",
       "      <td>90.4125</td>\n",
       "      <td>Spicy food</td>\n",
       "      <td>Cooking</td>\n",
       "      <td>Not decided yet</td>\n",
       "      <td>10.00 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>151340942</td>\n",
       "      <td>Shantinagar</td>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.4143</td>\n",
       "      <td>Sylhet</td>\n",
       "      <td>24.8949</td>\n",
       "      <td>91.8687</td>\n",
       "      <td>Italian</td>\n",
       "      <td>spending time with  pet pigeons</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>06:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>151340943</td>\n",
       "      <td>Mohakhali</td>\n",
       "      <td>23.780232</td>\n",
       "      <td>90.4011</td>\n",
       "      <td>Saint Martins</td>\n",
       "      <td>20.6316</td>\n",
       "      <td>92.3209</td>\n",
       "      <td>Fast food</td>\n",
       "      <td>Watching Football and TV Series</td>\n",
       "      <td>Database Analyst</td>\n",
       "      <td>09:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>151340944</td>\n",
       "      <td>Kathalbagan</td>\n",
       "      <td>23.746188</td>\n",
       "      <td>90.3899</td>\n",
       "      <td>Saint Martins</td>\n",
       "      <td>20.6316</td>\n",
       "      <td>92.3209</td>\n",
       "      <td>Beef curry with curds</td>\n",
       "      <td>Gaming, watching football, cricket matches</td>\n",
       "      <td>Full Stack Developer</td>\n",
       "      <td>10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>151340945</td>\n",
       "      <td>Panthapath</td>\n",
       "      <td>23.751791</td>\n",
       "      <td>90.3859</td>\n",
       "      <td>Saint Martins</td>\n",
       "      <td>20.6316</td>\n",
       "      <td>92.3209</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Watching Cricket,Movies</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>10:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>151340946</td>\n",
       "      <td>Mirpur-10</td>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.3686</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>39.0595</td>\n",
       "      <td>-80.873</td>\n",
       "      <td>Luchi Dalvaji</td>\n",
       "      <td>spending timme with family members</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>05:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>151340947</td>\n",
       "      <td>Dhaka Cantonment</td>\n",
       "      <td>26.782930</td>\n",
       "      <td>82.1501</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>39.0595</td>\n",
       "      <td>-80.873</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Watching TV</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>10:30:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>134 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID     Current Place   Latitude Longitude Place of interest  \\\n",
       "0   151340976             Goran  23.750270   90.4341       Cox's Bazar   \n",
       "1   151340977          Banasree  22.513531   88.3015         Sea Beach   \n",
       "2   151340978           Azimpur  23.727040   90.3847       Kaptai Lake   \n",
       "3   151340980           Niketon  23.707310   90.4155              home   \n",
       "4   151340981          Shaymoli  22.301411   70.8224              home   \n",
       "5   151340982          Bangshal -32.348610   151.925       Cox's Bazar   \n",
       "6   151340983             Dania  23.701443   90.4422         Bandorban   \n",
       "7   151340984            Jurain  23.697285   90.4318              Home   \n",
       "8   151340985            Lalbag  23.707310   90.4155              Home   \n",
       "9   151340986           Rampura  23.761227   90.4208              Home   \n",
       "10  151340988           Rampura  23.761227   90.4208         Bandorban   \n",
       "11  151340990             Dania  23.701443   90.4422       Cox's Bazar   \n",
       "12  151340991       Mohammadpur  23.765800   90.3584         Sea Beach   \n",
       "13  151340992          Jigatola  23.737286   90.3716       Cox’s Bazar   \n",
       "14  151340994           Shamoli  23.707310   90.4155           Tejgaon   \n",
       "15  151340995      Green Corner  23.745173   90.3864              Home   \n",
       "16  151340996      Shahjahanpur  27.883743   79.9122       Cox's Bazar   \n",
       "17  151340998          Khilgaon  23.707310   90.4155              Home   \n",
       "18  151341000            Banani  23.793993   90.4043         Sea Beach   \n",
       "19  151341001            Uttara  24.098379   90.3287              Home   \n",
       "0   151340894            Mirpur  23.776604    90.383      Saint Martin   \n",
       "1   151340897         Mohanogor  23.734558    90.385            Canada   \n",
       "2   151340898           Gopibag  23.722109   90.4262         Bandarban   \n",
       "3   151340900           Rampura  23.761227   90.4208            Norway   \n",
       "4   151340901       Shantinagar  23.738200   90.4143          Malaysia   \n",
       "5   151340902        Siddeswari  23.707310   90.4155         Santorini   \n",
       "6   151340903           Narinda  23.707310   90.4155          Istanbul   \n",
       "7   151340904         Mugdapara  23.730534   90.4462     Niagara Falls   \n",
       "8   151340905         Shahidbag  23.828959   90.3757             Paris   \n",
       "9   151340906           Tejgaon  23.759815   90.3913        Chittagong   \n",
       "..        ...               ...        ...       ...               ...   \n",
       "16  151341029      Segunbagicha  23.732598   90.4026             Spain   \n",
       "17  151341010          Farmgate  23.756107   90.3872         Sunamganj   \n",
       "18  151341020    Mirpur 60 Feet  23.790579   90.3653         Meghalaya   \n",
       "19  151341012          Zigatola  23.738078   90.3725       Cox's Bazar   \n",
       "20  151341021          Rayerbag  23.693846   90.4553             China   \n",
       "21  151341019          Shyamoli  23.771800   90.3631            Sylhet   \n",
       "0   151340921           Tejgaon  23.707310   90.4155      saint martin   \n",
       "1   151340923      Siddheshwari  23.744602   90.4115       Cox's Bazar   \n",
       "2   151340925        Maniknagar  23.721389   90.4299           My Room   \n",
       "3   151340926        Maniknagar  23.722595   90.4296            Sylhet   \n",
       "4   151340927         Mirpur-12  23.707310   90.4155            Khulna   \n",
       "5   151340928            Mirpur  23.807085   90.3686            Shylet   \n",
       "6   151340930         Motijheel  23.707310   90.4155      Sajek Valley   \n",
       "7   151340931        Hazaribagh  23.727835    90.374            Sylhet   \n",
       "8   151340932           Tejgaon  23.763697   90.3707         Gopalganj   \n",
       "9   151340933           Rampura  23.707310   90.4155      Sajek Valley   \n",
       "10  151340934           Rampura  23.707310   90.4155         Bandarban   \n",
       "11  151340935         Gulshan 1  23.778600   90.4156            Khulna   \n",
       "12  151340936         Mohanagar  23.764510   90.4131          Malaysia   \n",
       "13  151340937          Mirpur-1  23.795600   90.3537      Sajek Valley   \n",
       "14  151340938          Malibagh  23.748975   90.4107             Paris   \n",
       "15  151340939          Malibagh  23.758353   90.4162            Sylhet   \n",
       "16  151340940          Khilgaon  23.755814   90.4233        My Rooftop   \n",
       "17  151340941           Niketon  23.797911   90.4144           Gazipur   \n",
       "18  151340942       Shantinagar  23.738200   90.4143            Sylhet   \n",
       "19  151340943         Mohakhali  23.780232   90.4011     Saint Martins   \n",
       "20  151340944       Kathalbagan  23.746188   90.3899     Saint Martins   \n",
       "21  151340945        Panthapath  23.751791   90.3859     Saint Martins   \n",
       "22  151340946         Mirpur-10  23.807085   90.3686       Cox's Bazar   \n",
       "23  151340947  Dhaka Cantonment  26.782930   82.1501       Cox's Bazar   \n",
       "\n",
       "   Latitude.1 Longitude.1     Food you like most  \\\n",
       "0     21.4113     91.9959               Biriyani   \n",
       "1         NaN         NaN                Kachchi   \n",
       "2     22.7552      92.277         kacchi biryani   \n",
       "3     23.7073     90.4155                    any   \n",
       "4     22.3014     70.8224               sea food   \n",
       "5     21.4113     91.9959                 Burger   \n",
       "6     21.4796     92.5594               Biriyani   \n",
       "7     23.6973     90.4318               Biriyani   \n",
       "8     23.7073     90.4155                    Any   \n",
       "9     23.7612     90.4208               Biriyani   \n",
       "10    21.4796     92.5594                Chicken   \n",
       "11    21.4113     91.9959               khichuri   \n",
       "12        NaN         NaN              Fast Food   \n",
       "13    21.4113     91.9959           Cheesy fries   \n",
       "14    23.7331     90.3688               Biriyani   \n",
       "15    23.7452     90.3864               Biriyani   \n",
       "16    21.4113     91.9959               Biriyani   \n",
       "17    23.7073     90.4155               Biriyani   \n",
       "18        NaN         NaN            Spicy Foods   \n",
       "19    24.0984     90.3287                 Burger   \n",
       "0     20.6233      92.323               Biriyani   \n",
       "1     56.1304    -106.347           Egg and Milk   \n",
       "2    -7.73352     112.959                    Tea   \n",
       "3       22.32     39.1008                  Hilsa   \n",
       "4      4.2105     101.976               Biriyani   \n",
       "5     36.3932     25.4615               Biriyani   \n",
       "6     41.0082     28.9784                 Mutton   \n",
       "7     43.0896    -79.0849     Chicken,Egg & Milk   \n",
       "8     48.8566     2.35222                  Pizza   \n",
       "9     22.3569     91.7832            Khola pitha   \n",
       "..        ...         ...                    ...   \n",
       "16    40.4637    -3.74922               Mix Food   \n",
       "17    25.0456     91.3871           Regular Food   \n",
       "18    25.5725     91.3108        Kacchi Biriyani   \n",
       "19    21.4339      91.987           Regular Food   \n",
       "20     34.626      103.65           Regular Food   \n",
       "21    24.8949     91.8687           Regular Food   \n",
       "0     49.4355    -2.56735              Alu Vorta   \n",
       "1      21.427     91.9712                  Pasta   \n",
       "2     23.7214     90.4299             Cashew nut   \n",
       "3     24.8949     91.8687             Cashew nut   \n",
       "4     22.8419     89.5581             Spicy food   \n",
       "5     24.8258     91.7175               Biriyani   \n",
       "6      22.343     91.8427        Porota Dalbhaji   \n",
       "7     24.8948      91.869               Biriyani   \n",
       "8     23.0894     89.6213            Grill & Nun   \n",
       "9     22.3304     91.8326                   Beef   \n",
       "10    21.8311     92.3686               Biriyani   \n",
       "11    22.8092     89.5697   Any well cooked food   \n",
       "12    4.21048     101.976                   Lasi   \n",
       "13    23.4056     92.2793             Spicy Food   \n",
       "14    48.8566      2.3522                  Pasta   \n",
       "15    24.8949     91.8687                 Burger   \n",
       "16    23.7558     90.4233                 Kacchi   \n",
       "17    24.0958     90.4125             Spicy food   \n",
       "18    24.8949     91.8687                Italian   \n",
       "19    20.6316     92.3209              Fast food   \n",
       "20    20.6316     92.3209  Beef curry with curds   \n",
       "21    20.6316     92.3209               Biriyani   \n",
       "22    39.0595     -80.873          Luchi Dalvaji   \n",
       "23    39.0595     -80.873               Chicken    \n",
       "\n",
       "                                Leisure time activity  \\\n",
       "0                                              Movies   \n",
       "1                                              Sports   \n",
       "2                                              Series   \n",
       "3                                        movies,music   \n",
       "4                                               music   \n",
       "5                                              Series   \n",
       "6                                              movies   \n",
       "7                                                 NaN   \n",
       "8                                                 All   \n",
       "9                                              Movies   \n",
       "10                                      movies, music   \n",
       "11                                             movies   \n",
       "12                                       Sci Fi Movie   \n",
       "13                                             Movies   \n",
       "14                                             Movies   \n",
       "15                                             Movies   \n",
       "16                                             Movies   \n",
       "17                                          Tv series   \n",
       "18                                   Tv series/movies   \n",
       "19                                          Tv series   \n",
       "0                                     listening music   \n",
       "1                                  Reading Newspapers   \n",
       "2                               ChitChat with friends   \n",
       "3                                   Reading Magazines   \n",
       "4                                       Online Gaming   \n",
       "5                             Watching movies, series   \n",
       "6                                         Documentary   \n",
       "7                                       Reading Books   \n",
       "8                                             Cooking   \n",
       "9                                           Exploring   \n",
       "..                                                ...   \n",
       "16                           watching movie and anime   \n",
       "17                                     Watching movie   \n",
       "18                  Playing games and watching Movies   \n",
       "19                                             Sports   \n",
       "20                           TV Series, Documentaries   \n",
       "21                                             Sports   \n",
       "0                                        playing game   \n",
       "1                         Watching Movies & TV-Series   \n",
       "2                     Traversing Foreign Universities   \n",
       "3                                         Photography   \n",
       "4                                               Movie   \n",
       "5                                              Gaming   \n",
       "6                               Twitch live Streaming   \n",
       "7                                    Watching youtube   \n",
       "8                                      Watching Movie   \n",
       "9                                      Watching anime   \n",
       "10                                Playing online game   \n",
       "11                                      Nothing fixed   \n",
       "12       Exploring digital library & surfing internet   \n",
       "13                           Watching Movies & Series   \n",
       "14         watching tv series,reading books and blogs   \n",
       "15                                        Facebooking   \n",
       "16  Writing novellas, reading books, analyzing bus...   \n",
       "17                                            Cooking   \n",
       "18                    spending time with  pet pigeons   \n",
       "19                    Watching Football and TV Series   \n",
       "20         Gaming, watching football, cricket matches   \n",
       "21                            Watching Cricket,Movies   \n",
       "22                 spending timme with family members   \n",
       "23                                        Watching TV   \n",
       "\n",
       "                         Future plan  \\\n",
       "0                  Software Engineer   \n",
       "1                       BCS/Business   \n",
       "2      Higher Study in North America   \n",
       "3                  software Engineer   \n",
       "4                        businessman   \n",
       "5                       Entrepreneur   \n",
       "6                   Network Engineer   \n",
       "7                                CEO   \n",
       "8                     Higher Studies   \n",
       "9                  Software Engineer   \n",
       "10                 Software Engineer   \n",
       "11                               job   \n",
       "12                    Higher Studies   \n",
       "13                               BCS   \n",
       "14                  Game Development   \n",
       "15                               BCS   \n",
       "16                   Web Development   \n",
       "17                    Higher Studies   \n",
       "18                         not fixed   \n",
       "19                      Entrepreneur   \n",
       "0                       Higher Study   \n",
       "1                       Higher Study   \n",
       "2                          Traveling   \n",
       "3                                BCS   \n",
       "4                                BCS   \n",
       "5                       Higher Study   \n",
       "6                          Afflulent   \n",
       "7                       Higher Study   \n",
       "8                           Teaching   \n",
       "9                                BCS   \n",
       "..                               ...   \n",
       "16                  give own IT Farm   \n",
       "17                     Web Developer   \n",
       "18                      Higher Study   \n",
       "19                        Don't have   \n",
       "20                      Higher Study   \n",
       "21                        Don't have   \n",
       "0                     Higher Studies   \n",
       "1                     Higher Studies   \n",
       "2                                PhD   \n",
       "3                 Software Developer   \n",
       "4                            Teacher   \n",
       "5                 Software Developer   \n",
       "6                 Software Developer   \n",
       "7                 Software Developer   \n",
       "8               Full Stack Developer   \n",
       "9                   Database Analyst   \n",
       "10                Network Specialist   \n",
       "11                Software Developer   \n",
       "12                      Entrepreneur   \n",
       "13                Software Developer   \n",
       "14                    Higher Studies   \n",
       "15                     Web Developer   \n",
       "16  Author, Entrepreneur, Journalist   \n",
       "17                   Not decided yet   \n",
       "18                    Data Scientist   \n",
       "19                  Database Analyst   \n",
       "20              Full Stack Developer   \n",
       "21                Software Developer   \n",
       "22                Software Developer   \n",
       "23                Software Developer   \n",
       "\n",
       "                  Typically your days are started at:  \n",
       "0                                                  LR  \n",
       "1                                                  LR  \n",
       "2                                                  ER  \n",
       "3                                                  LR  \n",
       "4                                                  LR  \n",
       "5                                                  LR  \n",
       "6                                                  LR  \n",
       "7                                                  LR  \n",
       "8                                                  LR  \n",
       "9                                                  LR  \n",
       "10                                                 LR  \n",
       "11                                                 LR  \n",
       "12                                                 LR  \n",
       "13                                                 LR  \n",
       "14                                                 LR  \n",
       "15                                                 LR  \n",
       "16                                                 LR  \n",
       "17                                                 LR  \n",
       "18                                                 LR  \n",
       "19                                                 LR  \n",
       "0                                            08:00:00  \n",
       "1                                            06:30:00  \n",
       "2                                            10:00:00  \n",
       "3                                            08:00:00  \n",
       "4                                            09:00:00  \n",
       "5                                            10:00:00  \n",
       "6                                            11:00:00  \n",
       "7                                            06:00:00  \n",
       "8                                            09:00:00  \n",
       "9                                            11:00:00  \n",
       "..                                                ...  \n",
       "16                                           10:30:00  \n",
       "17                                           08:00:00  \n",
       "18                                            9.30 Am  \n",
       "19                                           09:00:00  \n",
       "20                                           10.00 AM  \n",
       "21                                           09:00:00  \n",
       "0                                         10.00.00 AM  \n",
       "1                                            09:30:00  \n",
       "2                                            06:30:00  \n",
       "3                                            09:00:00  \n",
       "4                                            08:00:00  \n",
       "5                                            09:30:00  \n",
       "6                                            06:00:00  \n",
       "7                                            09:00:00  \n",
       "8                                            09:00:00  \n",
       "9                                            10:00:00  \n",
       "10                                           10:00:00  \n",
       "11                                           09:00:00  \n",
       "12                                           09:00:00  \n",
       "13                                           11:00:00  \n",
       "14                                                ...  \n",
       "15                                           11:00:00  \n",
       "16                                           07:00:00  \n",
       "17                                           10.00 AM  \n",
       "18                                           06:30:00  \n",
       "19                                           09:00:00  \n",
       "20                                           10:00:00  \n",
       "21                                           10:00:00  \n",
       "22                                           05:00:00  \n",
       "23                                           10:30:00  \n",
       "\n",
       "[134 rows x 11 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df8 = pd.read_excel('EXTENDED_1.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf=pd.merge(df7,df8,on=\"ID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Current Place</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Place of interest</th>\n",
       "      <th>Latitude.1</th>\n",
       "      <th>Longitude.1</th>\n",
       "      <th>Food you like most</th>\n",
       "      <th>Leisure time activity</th>\n",
       "      <th>Future plan</th>\n",
       "      <th>...</th>\n",
       "      <th>Are you eager to harm yourself to defend your beliefs?</th>\n",
       "      <th>How frequently you visit a doctor within a year\"</th>\n",
       "      <th>How many people do you follow on social media?</th>\n",
       "      <th>How much time you spend in university without academic curriculum(in hour)?</th>\n",
       "      <th>Are you rich in vocabulary?</th>\n",
       "      <th>Class note taking tendency(High,Low,Medium)</th>\n",
       "      <th>Theory class attendance(h,l,m)</th>\n",
       "      <th>Theory class interest(h,l,m)</th>\n",
       "      <th>Lab interest(h,l,m)</th>\n",
       "      <th>Preference(class lectures cl or online tutorials)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151340976</td>\n",
       "      <td>Goran</td>\n",
       "      <td>23.750270</td>\n",
       "      <td>90.4341</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.4113</td>\n",
       "      <td>91.9959</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>often</td>\n",
       "      <td>500</td>\n",
       "      <td>1.5</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>m</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151340977</td>\n",
       "      <td>Banasree</td>\n",
       "      <td>22.513531</td>\n",
       "      <td>88.3015</td>\n",
       "      <td>Sea Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kachchi</td>\n",
       "      <td>Sports</td>\n",
       "      <td>BCS/Business</td>\n",
       "      <td>...</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>2019-02-01 00:00:00</td>\n",
       "      <td>2019-05-03 00:00:00</td>\n",
       "      <td>1-2 hours</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Medium</td>\n",
       "      <td>m</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151340980</td>\n",
       "      <td>Niketon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>home</td>\n",
       "      <td>23.7073</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>any</td>\n",
       "      <td>movies,music</td>\n",
       "      <td>software Engineer</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>5 years ago last visitted</td>\n",
       "      <td>more than 100</td>\n",
       "      <td>1 hour</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>m</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151340981</td>\n",
       "      <td>Shaymoli</td>\n",
       "      <td>22.301411</td>\n",
       "      <td>70.8224</td>\n",
       "      <td>home</td>\n",
       "      <td>22.3014</td>\n",
       "      <td>70.8224</td>\n",
       "      <td>sea food</td>\n",
       "      <td>music</td>\n",
       "      <td>businessman</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>very rare</td>\n",
       "      <td>no one apart from friends</td>\n",
       "      <td>0,rarely 1 hour</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151340982</td>\n",
       "      <td>Bangshal</td>\n",
       "      <td>-32.348610</td>\n",
       "      <td>151.925</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.4113</td>\n",
       "      <td>91.9959</td>\n",
       "      <td>Burger</td>\n",
       "      <td>Series</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>151340983</td>\n",
       "      <td>Dania</td>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.4422</td>\n",
       "      <td>Bandorban</td>\n",
       "      <td>21.4796</td>\n",
       "      <td>92.5594</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>movies</td>\n",
       "      <td>Network Engineer</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>m</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>151340984</td>\n",
       "      <td>Jurain</td>\n",
       "      <td>23.697285</td>\n",
       "      <td>90.4318</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.6973</td>\n",
       "      <td>90.4318</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CEO</td>\n",
       "      <td>...</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Medium</td>\n",
       "      <td>m</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>151340985</td>\n",
       "      <td>Lalbag</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.7073</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Any</td>\n",
       "      <td>All</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>...</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>once or twice a year</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>l</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>151340986</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.7612</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>1 hour</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>m</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>151340988</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Bandorban</td>\n",
       "      <td>21.4796</td>\n",
       "      <td>92.5594</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>movies, music</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>l</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>151340990</td>\n",
       "      <td>Dania</td>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.4422</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.4113</td>\n",
       "      <td>91.9959</td>\n",
       "      <td>khichuri</td>\n",
       "      <td>movies</td>\n",
       "      <td>job</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>m</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>151340991</td>\n",
       "      <td>Mohammadpur</td>\n",
       "      <td>23.765800</td>\n",
       "      <td>90.3584</td>\n",
       "      <td>Sea Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>Sci Fi Movie</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>2 hour</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>High</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>151340992</td>\n",
       "      <td>Jigatola</td>\n",
       "      <td>23.737286</td>\n",
       "      <td>90.3716</td>\n",
       "      <td>Cox’s Bazar</td>\n",
       "      <td>21.4113</td>\n",
       "      <td>91.9959</td>\n",
       "      <td>Cheesy fries</td>\n",
       "      <td>Movies</td>\n",
       "      <td>BCS</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>151340994</td>\n",
       "      <td>Shamoli</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.7331</td>\n",
       "      <td>90.3688</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Game Development</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>151340995</td>\n",
       "      <td>Green Corner</td>\n",
       "      <td>23.745173</td>\n",
       "      <td>90.3864</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.7452</td>\n",
       "      <td>90.3864</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>BCS</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>2019-04-03 00:00:00</td>\n",
       "      <td>20-25</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>151340996</td>\n",
       "      <td>Shahjahanpur</td>\n",
       "      <td>27.883743</td>\n",
       "      <td>79.9122</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.4113</td>\n",
       "      <td>91.9959</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Movies</td>\n",
       "      <td>Web Development</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>151340998</td>\n",
       "      <td>Khilgaon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Home</td>\n",
       "      <td>23.7073</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Tv series</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-10-01 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>Low</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>151341000</td>\n",
       "      <td>Banani</td>\n",
       "      <td>23.793993</td>\n",
       "      <td>90.4043</td>\n",
       "      <td>Sea Beach</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Spicy Foods</td>\n",
       "      <td>Tv series/movies</td>\n",
       "      <td>not fixed</td>\n",
       "      <td>...</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>rarely</td>\n",
       "      <td>300</td>\n",
       "      <td>30min depending on time</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>h</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>151341001</td>\n",
       "      <td>Uttara</td>\n",
       "      <td>24.098379</td>\n",
       "      <td>90.3287</td>\n",
       "      <td>Home</td>\n",
       "      <td>24.0984</td>\n",
       "      <td>90.3287</td>\n",
       "      <td>Burger</td>\n",
       "      <td>Tv series</td>\n",
       "      <td>Entrepreneur</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>m</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>151340894</td>\n",
       "      <td>Mirpur</td>\n",
       "      <td>23.776604</td>\n",
       "      <td>90.383</td>\n",
       "      <td>Saint Martin</td>\n",
       "      <td>20.6233</td>\n",
       "      <td>92.323</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>listening music</td>\n",
       "      <td>Higher Study</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Low</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>151340897</td>\n",
       "      <td>Mohanogor</td>\n",
       "      <td>23.734558</td>\n",
       "      <td>90.385</td>\n",
       "      <td>Canada</td>\n",
       "      <td>56.1304</td>\n",
       "      <td>-106.347</td>\n",
       "      <td>Egg and Milk</td>\n",
       "      <td>Reading Newspapers</td>\n",
       "      <td>Higher Study</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>less frequent</td>\n",
       "      <td>very few</td>\n",
       "      <td>around 1 hour</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>151340898</td>\n",
       "      <td>Gopibag</td>\n",
       "      <td>23.722109</td>\n",
       "      <td>90.4262</td>\n",
       "      <td>Bandarban</td>\n",
       "      <td>-7.73352</td>\n",
       "      <td>112.959</td>\n",
       "      <td>Tea</td>\n",
       "      <td>ChitChat with friends</td>\n",
       "      <td>Traveling</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>m</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>151340900</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Norway</td>\n",
       "      <td>22.32</td>\n",
       "      <td>39.1008</td>\n",
       "      <td>Hilsa</td>\n",
       "      <td>Reading Magazines</td>\n",
       "      <td>BCS</td>\n",
       "      <td>...</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Not.</td>\n",
       "      <td>15</td>\n",
       "      <td>5 hours.</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High.</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>151340901</td>\n",
       "      <td>Shantinagar</td>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.4143</td>\n",
       "      <td>Malaysia</td>\n",
       "      <td>4.2105</td>\n",
       "      <td>101.976</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Online Gaming</td>\n",
       "      <td>BCS</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Medium</td>\n",
       "      <td>m</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>151340902</td>\n",
       "      <td>Siddeswari</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Santorini</td>\n",
       "      <td>36.3932</td>\n",
       "      <td>25.4615</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Watching movies, series</td>\n",
       "      <td>Higher Study</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>400</td>\n",
       "      <td>4</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Low</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>151340903</td>\n",
       "      <td>Narinda</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Istanbul</td>\n",
       "      <td>41.0082</td>\n",
       "      <td>28.9784</td>\n",
       "      <td>Mutton</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Afflulent</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Low</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>151340904</td>\n",
       "      <td>Mugdapara</td>\n",
       "      <td>23.730534</td>\n",
       "      <td>90.4462</td>\n",
       "      <td>Niagara Falls</td>\n",
       "      <td>43.0896</td>\n",
       "      <td>-79.0849</td>\n",
       "      <td>Chicken,Egg &amp; Milk</td>\n",
       "      <td>Reading Books</td>\n",
       "      <td>Higher Study</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>High</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>151340905</td>\n",
       "      <td>Shahidbag</td>\n",
       "      <td>23.828959</td>\n",
       "      <td>90.3757</td>\n",
       "      <td>Paris</td>\n",
       "      <td>48.8566</td>\n",
       "      <td>2.35222</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>Cooking</td>\n",
       "      <td>Teaching</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>High</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>151340906</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.759815</td>\n",
       "      <td>90.3913</td>\n",
       "      <td>Chittagong</td>\n",
       "      <td>22.3569</td>\n",
       "      <td>91.7832</td>\n",
       "      <td>Khola pitha</td>\n",
       "      <td>Exploring</td>\n",
       "      <td>BCS</td>\n",
       "      <td>...</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Low</td>\n",
       "      <td>M</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>151340907</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Chittagong</td>\n",
       "      <td>22.3569</td>\n",
       "      <td>91.7832</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Reading Books</td>\n",
       "      <td>Teaching</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>151341005</td>\n",
       "      <td>Bashabo</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>46.5475</td>\n",
       "      <td>7.98529</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>Playing Violine</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>151341009</td>\n",
       "      <td>East Rampura</td>\n",
       "      <td>23.708022</td>\n",
       "      <td>90.4179</td>\n",
       "      <td>Mount Everest</td>\n",
       "      <td>27.9881</td>\n",
       "      <td>86.925</td>\n",
       "      <td>Chinese food</td>\n",
       "      <td>Reading Books</td>\n",
       "      <td>Higher Study</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>2019-10-05 00:00:00</td>\n",
       "      <td>0.5hr</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>151341022</td>\n",
       "      <td>Mohanogor Project</td>\n",
       "      <td>23.767600</td>\n",
       "      <td>90.4147</td>\n",
       "      <td>Sajek</td>\n",
       "      <td>23.382</td>\n",
       "      <td>92.2938</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>Watching movie</td>\n",
       "      <td>Don't have</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>2019-04-03 00:00:00</td>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Low</td>\n",
       "      <td>m</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>151341029</td>\n",
       "      <td>Segunbagicha</td>\n",
       "      <td>23.732598</td>\n",
       "      <td>90.4026</td>\n",
       "      <td>Spain</td>\n",
       "      <td>40.4637</td>\n",
       "      <td>-3.74922</td>\n",
       "      <td>Mix Food</td>\n",
       "      <td>watching movie and anime</td>\n",
       "      <td>give own IT Farm</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>1 or 2</td>\n",
       "      <td>15+</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>151341010</td>\n",
       "      <td>Farmgate</td>\n",
       "      <td>23.756107</td>\n",
       "      <td>90.3872</td>\n",
       "      <td>Sunamganj</td>\n",
       "      <td>25.0456</td>\n",
       "      <td>91.3871</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>Watching movie</td>\n",
       "      <td>Web Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>m</td>\n",
       "      <td>Low</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>151341020</td>\n",
       "      <td>Mirpur 60 Feet</td>\n",
       "      <td>23.790579</td>\n",
       "      <td>90.3653</td>\n",
       "      <td>Meghalaya</td>\n",
       "      <td>25.5725</td>\n",
       "      <td>91.3108</td>\n",
       "      <td>Kacchi Biriyani</td>\n",
       "      <td>Playing games and watching Movies</td>\n",
       "      <td>Higher Study</td>\n",
       "      <td>...</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-03-02 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Low</td>\n",
       "      <td>M</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>151341012</td>\n",
       "      <td>Zigatola</td>\n",
       "      <td>23.738078</td>\n",
       "      <td>90.3725</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.4339</td>\n",
       "      <td>91.987</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Don't have</td>\n",
       "      <td>...</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>2019-04-03 00:00:00</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>151341021</td>\n",
       "      <td>Rayerbag</td>\n",
       "      <td>23.693846</td>\n",
       "      <td>90.4553</td>\n",
       "      <td>China</td>\n",
       "      <td>34.626</td>\n",
       "      <td>103.65</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>TV Series, Documentaries</td>\n",
       "      <td>Higher Study</td>\n",
       "      <td>...</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Low</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>151341019</td>\n",
       "      <td>Shyamoli</td>\n",
       "      <td>23.771800</td>\n",
       "      <td>90.3631</td>\n",
       "      <td>Sylhet</td>\n",
       "      <td>24.8949</td>\n",
       "      <td>91.8687</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>Sports</td>\n",
       "      <td>Don't have</td>\n",
       "      <td>...</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>2 or 3 times</td>\n",
       "      <td>20</td>\n",
       "      <td>2 to 3 hours</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>m</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>151340921</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>saint martin</td>\n",
       "      <td>49.4355</td>\n",
       "      <td>-2.56735</td>\n",
       "      <td>Alu Vorta</td>\n",
       "      <td>playing game</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>m</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>151340923</td>\n",
       "      <td>Siddheshwari</td>\n",
       "      <td>23.744602</td>\n",
       "      <td>90.4115</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>21.427</td>\n",
       "      <td>91.9712</td>\n",
       "      <td>Pasta</td>\n",
       "      <td>Watching Movies &amp; TV-Series</td>\n",
       "      <td>Higher Studies</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>High</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>151340925</td>\n",
       "      <td>Maniknagar</td>\n",
       "      <td>23.721389</td>\n",
       "      <td>90.4299</td>\n",
       "      <td>My Room</td>\n",
       "      <td>23.7214</td>\n",
       "      <td>90.4299</td>\n",
       "      <td>Cashew nut</td>\n",
       "      <td>Traversing Foreign Universities</td>\n",
       "      <td>PhD</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>High</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>151340926</td>\n",
       "      <td>Maniknagar</td>\n",
       "      <td>23.722595</td>\n",
       "      <td>90.4296</td>\n",
       "      <td>Sylhet</td>\n",
       "      <td>24.8949</td>\n",
       "      <td>91.8687</td>\n",
       "      <td>Cashew nut</td>\n",
       "      <td>Photography</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Medium</td>\n",
       "      <td>high</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>151340927</td>\n",
       "      <td>Mirpur-12</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Khulna</td>\n",
       "      <td>22.8419</td>\n",
       "      <td>89.5581</td>\n",
       "      <td>Spicy food</td>\n",
       "      <td>Movie</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>420</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>151340928</td>\n",
       "      <td>Mirpur</td>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.3686</td>\n",
       "      <td>Shylet</td>\n",
       "      <td>24.8258</td>\n",
       "      <td>91.7175</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Low</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>151340930</td>\n",
       "      <td>Motijheel</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Sajek Valley</td>\n",
       "      <td>22.343</td>\n",
       "      <td>91.8427</td>\n",
       "      <td>Porota Dalbhaji</td>\n",
       "      <td>Twitch live Streaming</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>151340931</td>\n",
       "      <td>Hazaribagh</td>\n",
       "      <td>23.727835</td>\n",
       "      <td>90.374</td>\n",
       "      <td>Sylhet</td>\n",
       "      <td>24.8948</td>\n",
       "      <td>91.869</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Watching youtube</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>151340932</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.763697</td>\n",
       "      <td>90.3707</td>\n",
       "      <td>Gopalganj</td>\n",
       "      <td>23.0894</td>\n",
       "      <td>89.6213</td>\n",
       "      <td>Grill &amp; Nun</td>\n",
       "      <td>Watching Movie</td>\n",
       "      <td>Full Stack Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Low</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>151340933</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Sajek Valley</td>\n",
       "      <td>22.3304</td>\n",
       "      <td>91.8326</td>\n",
       "      <td>Beef</td>\n",
       "      <td>Watching anime</td>\n",
       "      <td>Database Analyst</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>2</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>High</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>151340934</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Bandarban</td>\n",
       "      <td>21.8311</td>\n",
       "      <td>92.3686</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Playing online game</td>\n",
       "      <td>Network Specialist</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>151340935</td>\n",
       "      <td>Gulshan 1</td>\n",
       "      <td>23.778600</td>\n",
       "      <td>90.4156</td>\n",
       "      <td>Khulna</td>\n",
       "      <td>22.8092</td>\n",
       "      <td>89.5697</td>\n",
       "      <td>Any well cooked food</td>\n",
       "      <td>Nothing fixed</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>2019-06-05 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>151340937</td>\n",
       "      <td>Mirpur-1</td>\n",
       "      <td>23.795600</td>\n",
       "      <td>90.3537</td>\n",
       "      <td>Sajek Valley</td>\n",
       "      <td>23.4056</td>\n",
       "      <td>92.2793</td>\n",
       "      <td>Spicy Food</td>\n",
       "      <td>Watching Movies &amp; Series</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>300</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>151340939</td>\n",
       "      <td>Malibagh</td>\n",
       "      <td>23.758353</td>\n",
       "      <td>90.4162</td>\n",
       "      <td>Sylhet</td>\n",
       "      <td>24.8949</td>\n",
       "      <td>91.8687</td>\n",
       "      <td>Burger</td>\n",
       "      <td>Facebooking</td>\n",
       "      <td>Web Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>m</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>151340941</td>\n",
       "      <td>Niketon</td>\n",
       "      <td>23.797911</td>\n",
       "      <td>90.4144</td>\n",
       "      <td>Gazipur</td>\n",
       "      <td>24.0958</td>\n",
       "      <td>90.4125</td>\n",
       "      <td>Spicy food</td>\n",
       "      <td>Cooking</td>\n",
       "      <td>Not decided yet</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>m</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>151340942</td>\n",
       "      <td>Shantinagar</td>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.4143</td>\n",
       "      <td>Sylhet</td>\n",
       "      <td>24.8949</td>\n",
       "      <td>91.8687</td>\n",
       "      <td>Italian</td>\n",
       "      <td>spending time with  pet pigeons</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>very few</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>151340943</td>\n",
       "      <td>Mohakhali</td>\n",
       "      <td>23.780232</td>\n",
       "      <td>90.4011</td>\n",
       "      <td>Saint Martins</td>\n",
       "      <td>20.6316</td>\n",
       "      <td>92.3209</td>\n",
       "      <td>Fast food</td>\n",
       "      <td>Watching Football and TV Series</td>\n",
       "      <td>Database Analyst</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Low</td>\n",
       "      <td>m</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>151340944</td>\n",
       "      <td>Kathalbagan</td>\n",
       "      <td>23.746188</td>\n",
       "      <td>90.3899</td>\n",
       "      <td>Saint Martins</td>\n",
       "      <td>20.6316</td>\n",
       "      <td>92.3209</td>\n",
       "      <td>Beef curry with curds</td>\n",
       "      <td>Gaming, watching football, cricket matches</td>\n",
       "      <td>Full Stack Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>166</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>High</td>\n",
       "      <td>h</td>\n",
       "      <td>Medium</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>151340945</td>\n",
       "      <td>Panthapath</td>\n",
       "      <td>23.751791</td>\n",
       "      <td>90.3859</td>\n",
       "      <td>Saint Martins</td>\n",
       "      <td>20.6316</td>\n",
       "      <td>92.3209</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>Watching Cricket,Movies</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Class lectures</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>151340946</td>\n",
       "      <td>Mirpur-10</td>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.3686</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>39.0595</td>\n",
       "      <td>-80.873</td>\n",
       "      <td>Luchi Dalvaji</td>\n",
       "      <td>spending timme with family members</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>High</td>\n",
       "      <td>h</td>\n",
       "      <td>High</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>151340947</td>\n",
       "      <td>Dhaka Cantonment</td>\n",
       "      <td>26.782930</td>\n",
       "      <td>82.1501</td>\n",
       "      <td>Cox's Bazar</td>\n",
       "      <td>39.0595</td>\n",
       "      <td>-80.873</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>Watching TV</td>\n",
       "      <td>Software Developer</td>\n",
       "      <td>...</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>2019-04-03 00:00:00</td>\n",
       "      <td>around 350</td>\n",
       "      <td>1</td>\n",
       "      <td>Maybe</td>\n",
       "      <td>Medium</td>\n",
       "      <td>h</td>\n",
       "      <td>Low</td>\n",
       "      <td>High</td>\n",
       "      <td>Online tutorials</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID       Current Place   Latitude Longitude Place of interest  \\\n",
       "0    151340976               Goran  23.750270   90.4341       Cox's Bazar   \n",
       "1    151340977            Banasree  22.513531   88.3015         Sea Beach   \n",
       "2    151340980             Niketon  23.707310   90.4155              home   \n",
       "3    151340981            Shaymoli  22.301411   70.8224              home   \n",
       "4    151340982            Bangshal -32.348610   151.925       Cox's Bazar   \n",
       "5    151340983               Dania  23.701443   90.4422         Bandorban   \n",
       "6    151340984              Jurain  23.697285   90.4318              Home   \n",
       "7    151340985              Lalbag  23.707310   90.4155              Home   \n",
       "8    151340986             Rampura  23.761227   90.4208              Home   \n",
       "9    151340988             Rampura  23.761227   90.4208         Bandorban   \n",
       "10   151340990               Dania  23.701443   90.4422       Cox's Bazar   \n",
       "11   151340991         Mohammadpur  23.765800   90.3584         Sea Beach   \n",
       "12   151340992            Jigatola  23.737286   90.3716       Cox’s Bazar   \n",
       "13   151340994             Shamoli  23.707310   90.4155           Tejgaon   \n",
       "14   151340995        Green Corner  23.745173   90.3864              Home   \n",
       "15   151340996        Shahjahanpur  27.883743   79.9122       Cox's Bazar   \n",
       "16   151340998            Khilgaon  23.707310   90.4155              Home   \n",
       "17   151341000              Banani  23.793993   90.4043         Sea Beach   \n",
       "18   151341001              Uttara  24.098379   90.3287              Home   \n",
       "19   151340894              Mirpur  23.776604    90.383      Saint Martin   \n",
       "20   151340897           Mohanogor  23.734558    90.385            Canada   \n",
       "21   151340898             Gopibag  23.722109   90.4262         Bandarban   \n",
       "22   151340900             Rampura  23.761227   90.4208            Norway   \n",
       "23   151340901         Shantinagar  23.738200   90.4143          Malaysia   \n",
       "24   151340902          Siddeswari  23.707310   90.4155         Santorini   \n",
       "25   151340903             Narinda  23.707310   90.4155          Istanbul   \n",
       "26   151340904           Mugdapara  23.730534   90.4462     Niagara Falls   \n",
       "27   151340905           Shahidbag  23.828959   90.3757             Paris   \n",
       "28   151340906             Tejgaon  23.759815   90.3913        Chittagong   \n",
       "29   151340907             Rampura  23.707310   90.4155        Chittagong   \n",
       "..         ...                 ...        ...       ...               ...   \n",
       "73   151341005             Bashabo  23.707310   90.4155       Switzerland   \n",
       "74   151341009        East Rampura  23.708022   90.4179     Mount Everest   \n",
       "75   151341022  Mohanogor Project   23.767600   90.4147             Sajek   \n",
       "76   151341029        Segunbagicha  23.732598   90.4026             Spain   \n",
       "77   151341010            Farmgate  23.756107   90.3872         Sunamganj   \n",
       "78   151341020      Mirpur 60 Feet  23.790579   90.3653         Meghalaya   \n",
       "79   151341012            Zigatola  23.738078   90.3725       Cox's Bazar   \n",
       "80   151341021            Rayerbag  23.693846   90.4553             China   \n",
       "81   151341019            Shyamoli  23.771800   90.3631            Sylhet   \n",
       "82   151340921             Tejgaon  23.707310   90.4155      saint martin   \n",
       "83   151340923        Siddheshwari  23.744602   90.4115       Cox's Bazar   \n",
       "84   151340925          Maniknagar  23.721389   90.4299           My Room   \n",
       "85   151340926          Maniknagar  23.722595   90.4296            Sylhet   \n",
       "86   151340927           Mirpur-12  23.707310   90.4155            Khulna   \n",
       "87   151340928              Mirpur  23.807085   90.3686            Shylet   \n",
       "88   151340930           Motijheel  23.707310   90.4155      Sajek Valley   \n",
       "89   151340931          Hazaribagh  23.727835    90.374            Sylhet   \n",
       "90   151340932             Tejgaon  23.763697   90.3707         Gopalganj   \n",
       "91   151340933             Rampura  23.707310   90.4155      Sajek Valley   \n",
       "92   151340934             Rampura  23.707310   90.4155         Bandarban   \n",
       "93   151340935           Gulshan 1  23.778600   90.4156            Khulna   \n",
       "94   151340937            Mirpur-1  23.795600   90.3537      Sajek Valley   \n",
       "95   151340939            Malibagh  23.758353   90.4162            Sylhet   \n",
       "96   151340941             Niketon  23.797911   90.4144           Gazipur   \n",
       "97   151340942         Shantinagar  23.738200   90.4143            Sylhet   \n",
       "98   151340943           Mohakhali  23.780232   90.4011     Saint Martins   \n",
       "99   151340944         Kathalbagan  23.746188   90.3899     Saint Martins   \n",
       "100  151340945          Panthapath  23.751791   90.3859     Saint Martins   \n",
       "101  151340946           Mirpur-10  23.807085   90.3686       Cox's Bazar   \n",
       "102  151340947    Dhaka Cantonment  26.782930   82.1501       Cox's Bazar   \n",
       "\n",
       "    Latitude.1 Longitude.1     Food you like most  \\\n",
       "0      21.4113     91.9959               Biriyani   \n",
       "1          NaN         NaN                Kachchi   \n",
       "2      23.7073     90.4155                    any   \n",
       "3      22.3014     70.8224               sea food   \n",
       "4      21.4113     91.9959                 Burger   \n",
       "5      21.4796     92.5594               Biriyani   \n",
       "6      23.6973     90.4318               Biriyani   \n",
       "7      23.7073     90.4155                    Any   \n",
       "8      23.7612     90.4208               Biriyani   \n",
       "9      21.4796     92.5594                Chicken   \n",
       "10     21.4113     91.9959               khichuri   \n",
       "11         NaN         NaN              Fast Food   \n",
       "12     21.4113     91.9959           Cheesy fries   \n",
       "13     23.7331     90.3688               Biriyani   \n",
       "14     23.7452     90.3864               Biriyani   \n",
       "15     21.4113     91.9959               Biriyani   \n",
       "16     23.7073     90.4155               Biriyani   \n",
       "17         NaN         NaN            Spicy Foods   \n",
       "18     24.0984     90.3287                 Burger   \n",
       "19     20.6233      92.323               Biriyani   \n",
       "20     56.1304    -106.347           Egg and Milk   \n",
       "21    -7.73352     112.959                    Tea   \n",
       "22       22.32     39.1008                  Hilsa   \n",
       "23      4.2105     101.976               Biriyani   \n",
       "24     36.3932     25.4615               Biriyani   \n",
       "25     41.0082     28.9784                 Mutton   \n",
       "26     43.0896    -79.0849     Chicken,Egg & Milk   \n",
       "27     48.8566     2.35222                  Pizza   \n",
       "28     22.3569     91.7832            Khola pitha   \n",
       "29     22.3569     91.7832               Biriyani   \n",
       "..         ...         ...                    ...   \n",
       "73     46.5475     7.98529                Chinese   \n",
       "74     27.9881      86.925           Chinese food   \n",
       "75      23.382     92.2938              Fast Food   \n",
       "76     40.4637    -3.74922               Mix Food   \n",
       "77     25.0456     91.3871           Regular Food   \n",
       "78     25.5725     91.3108        Kacchi Biriyani   \n",
       "79     21.4339      91.987           Regular Food   \n",
       "80      34.626      103.65           Regular Food   \n",
       "81     24.8949     91.8687           Regular Food   \n",
       "82     49.4355    -2.56735              Alu Vorta   \n",
       "83      21.427     91.9712                  Pasta   \n",
       "84     23.7214     90.4299             Cashew nut   \n",
       "85     24.8949     91.8687             Cashew nut   \n",
       "86     22.8419     89.5581             Spicy food   \n",
       "87     24.8258     91.7175               Biriyani   \n",
       "88      22.343     91.8427        Porota Dalbhaji   \n",
       "89     24.8948      91.869               Biriyani   \n",
       "90     23.0894     89.6213            Grill & Nun   \n",
       "91     22.3304     91.8326                   Beef   \n",
       "92     21.8311     92.3686               Biriyani   \n",
       "93     22.8092     89.5697   Any well cooked food   \n",
       "94     23.4056     92.2793             Spicy Food   \n",
       "95     24.8949     91.8687                 Burger   \n",
       "96     24.0958     90.4125             Spicy food   \n",
       "97     24.8949     91.8687                Italian   \n",
       "98     20.6316     92.3209              Fast food   \n",
       "99     20.6316     92.3209  Beef curry with curds   \n",
       "100    20.6316     92.3209               Biriyani   \n",
       "101    39.0595     -80.873          Luchi Dalvaji   \n",
       "102    39.0595     -80.873               Chicken    \n",
       "\n",
       "                          Leisure time activity           Future plan  \\\n",
       "0                                        Movies     Software Engineer   \n",
       "1                                        Sports          BCS/Business   \n",
       "2                                  movies,music     software Engineer   \n",
       "3                                         music           businessman   \n",
       "4                                        Series          Entrepreneur   \n",
       "5                                        movies      Network Engineer   \n",
       "6                                           NaN                   CEO   \n",
       "7                                           All        Higher Studies   \n",
       "8                                        Movies     Software Engineer   \n",
       "9                                 movies, music     Software Engineer   \n",
       "10                                       movies                   job   \n",
       "11                                 Sci Fi Movie        Higher Studies   \n",
       "12                                       Movies                   BCS   \n",
       "13                                       Movies      Game Development   \n",
       "14                                       Movies                   BCS   \n",
       "15                                       Movies       Web Development   \n",
       "16                                    Tv series        Higher Studies   \n",
       "17                             Tv series/movies             not fixed   \n",
       "18                                    Tv series          Entrepreneur   \n",
       "19                              listening music          Higher Study   \n",
       "20                           Reading Newspapers          Higher Study   \n",
       "21                        ChitChat with friends             Traveling   \n",
       "22                            Reading Magazines                   BCS   \n",
       "23                                Online Gaming                   BCS   \n",
       "24                      Watching movies, series          Higher Study   \n",
       "25                                  Documentary             Afflulent   \n",
       "26                                Reading Books          Higher Study   \n",
       "27                                      Cooking              Teaching   \n",
       "28                                    Exploring                   BCS   \n",
       "29                                Reading Books              Teaching   \n",
       "..                                          ...                   ...   \n",
       "73                              Playing Violine    Software Developer   \n",
       "74                                Reading Books          Higher Study   \n",
       "75                               Watching movie            Don't have   \n",
       "76                     watching movie and anime      give own IT Farm   \n",
       "77                               Watching movie         Web Developer   \n",
       "78            Playing games and watching Movies          Higher Study   \n",
       "79                                       Sports            Don't have   \n",
       "80                     TV Series, Documentaries          Higher Study   \n",
       "81                                       Sports            Don't have   \n",
       "82                                 playing game        Higher Studies   \n",
       "83                  Watching Movies & TV-Series        Higher Studies   \n",
       "84              Traversing Foreign Universities                   PhD   \n",
       "85                                  Photography    Software Developer   \n",
       "86                                        Movie               Teacher   \n",
       "87                                       Gaming    Software Developer   \n",
       "88                        Twitch live Streaming    Software Developer   \n",
       "89                             Watching youtube    Software Developer   \n",
       "90                               Watching Movie  Full Stack Developer   \n",
       "91                               Watching anime      Database Analyst   \n",
       "92                          Playing online game    Network Specialist   \n",
       "93                                Nothing fixed    Software Developer   \n",
       "94                     Watching Movies & Series    Software Developer   \n",
       "95                                  Facebooking         Web Developer   \n",
       "96                                      Cooking       Not decided yet   \n",
       "97              spending time with  pet pigeons        Data Scientist   \n",
       "98              Watching Football and TV Series      Database Analyst   \n",
       "99   Gaming, watching football, cricket matches  Full Stack Developer   \n",
       "100                     Watching Cricket,Movies    Software Developer   \n",
       "101          spending timme with family members    Software Developer   \n",
       "102                                 Watching TV    Software Developer   \n",
       "\n",
       "                           ...                         \\\n",
       "0                          ...                          \n",
       "1                          ...                          \n",
       "2                          ...                          \n",
       "3                          ...                          \n",
       "4                          ...                          \n",
       "5                          ...                          \n",
       "6                          ...                          \n",
       "7                          ...                          \n",
       "8                          ...                          \n",
       "9                          ...                          \n",
       "10                         ...                          \n",
       "11                         ...                          \n",
       "12                         ...                          \n",
       "13                         ...                          \n",
       "14                         ...                          \n",
       "15                         ...                          \n",
       "16                         ...                          \n",
       "17                         ...                          \n",
       "18                         ...                          \n",
       "19                         ...                          \n",
       "20                         ...                          \n",
       "21                         ...                          \n",
       "22                         ...                          \n",
       "23                         ...                          \n",
       "24                         ...                          \n",
       "25                         ...                          \n",
       "26                         ...                          \n",
       "27                         ...                          \n",
       "28                         ...                          \n",
       "29                         ...                          \n",
       "..                         ...                          \n",
       "73                         ...                          \n",
       "74                         ...                          \n",
       "75                         ...                          \n",
       "76                         ...                          \n",
       "77                         ...                          \n",
       "78                         ...                          \n",
       "79                         ...                          \n",
       "80                         ...                          \n",
       "81                         ...                          \n",
       "82                         ...                          \n",
       "83                         ...                          \n",
       "84                         ...                          \n",
       "85                         ...                          \n",
       "86                         ...                          \n",
       "87                         ...                          \n",
       "88                         ...                          \n",
       "89                         ...                          \n",
       "90                         ...                          \n",
       "91                         ...                          \n",
       "92                         ...                          \n",
       "93                         ...                          \n",
       "94                         ...                          \n",
       "95                         ...                          \n",
       "96                         ...                          \n",
       "97                         ...                          \n",
       "98                         ...                          \n",
       "99                         ...                          \n",
       "100                        ...                          \n",
       "101                        ...                          \n",
       "102                        ...                          \n",
       "\n",
       "    Are you eager to harm yourself to defend your beliefs?  \\\n",
       "0                                                   No       \n",
       "1                                                Maybe       \n",
       "2                                                   No       \n",
       "3                                                   No       \n",
       "4                                                   No       \n",
       "5                                                   No       \n",
       "6                                                Maybe       \n",
       "7                                                Maybe       \n",
       "8                                                   No       \n",
       "9                                                   No       \n",
       "10                                                  No       \n",
       "11                                                 Yes       \n",
       "12                                                  No       \n",
       "13                                                  No       \n",
       "14                                                  No       \n",
       "15                                                  No       \n",
       "16                                                  No       \n",
       "17                                               Maybe       \n",
       "18                                                  No       \n",
       "19                                                  No       \n",
       "20                                                  No       \n",
       "21                                                  No       \n",
       "22                                               Maybe       \n",
       "23                                                  No       \n",
       "24                                                  No       \n",
       "25                                                  No       \n",
       "26                                                  No       \n",
       "27                                                  No       \n",
       "28                                               Maybe       \n",
       "29                                                  No       \n",
       "..                                                 ...       \n",
       "73                                                  No       \n",
       "74                                                  No       \n",
       "75                                                  No       \n",
       "76                                                  No       \n",
       "77                                                  No       \n",
       "78                                               Maybe       \n",
       "79                                               Maybe       \n",
       "80                                               Maybe       \n",
       "81                                               Maybe       \n",
       "82                                                  No       \n",
       "83                                                  No       \n",
       "84                                                  No       \n",
       "85                                                  No       \n",
       "86                                                 Yes       \n",
       "87                                                  No       \n",
       "88                                                 Yes       \n",
       "89                                                  No       \n",
       "90                                                  No       \n",
       "91                                                 Yes       \n",
       "92                                                  No       \n",
       "93                                                 Yes       \n",
       "94                                                 Yes       \n",
       "95                                                 Yes       \n",
       "96                                                  No       \n",
       "97                                                  No       \n",
       "98                                                  No       \n",
       "99                                                  No       \n",
       "100                                              Maybe       \n",
       "101                                                Yes       \n",
       "102                                              Maybe       \n",
       "\n",
       "    How frequently you visit a doctor within a year\"  \\\n",
       "0                                              often   \n",
       "1                                2019-02-01 00:00:00   \n",
       "2                          5 years ago last visitted   \n",
       "3                                          very rare   \n",
       "4                                                  2   \n",
       "5                                                  1   \n",
       "6                                                  4   \n",
       "7                               once or twice a year   \n",
       "8                                                  2   \n",
       "9                                                  3   \n",
       "10                                                 3   \n",
       "11                                                 0   \n",
       "12                                                 5   \n",
       "13                                                 2   \n",
       "14                               2019-04-03 00:00:00   \n",
       "15                                                 1   \n",
       "16                                                 1   \n",
       "17                                            rarely   \n",
       "18                                                 2   \n",
       "19                                                 2   \n",
       "20                                     less frequent   \n",
       "21                                                 1   \n",
       "22                                              Not.   \n",
       "23                                                 1   \n",
       "24                                                 1   \n",
       "25                                                 2   \n",
       "26                                                 0   \n",
       "27                                                 2   \n",
       "28                                                 2   \n",
       "29                                                 1   \n",
       "..                                               ...   \n",
       "73                                                 5   \n",
       "74                                                 3   \n",
       "75                               2019-04-03 00:00:00   \n",
       "76                                            1 or 2   \n",
       "77                                                 0   \n",
       "78                                                 1   \n",
       "79                                                 1   \n",
       "80                                                 4   \n",
       "81                                      2 or 3 times   \n",
       "82                                                 0   \n",
       "83                                                 1   \n",
       "84                                                 6   \n",
       "85                                                 2   \n",
       "86                                                 1   \n",
       "87                                                 3   \n",
       "88                                                 1   \n",
       "89                                                 3   \n",
       "90                                                 3   \n",
       "91                                                 3   \n",
       "92                                                 0   \n",
       "93                                                 2   \n",
       "94                                                 2   \n",
       "95                                                 2   \n",
       "96                                                 2   \n",
       "97                                                 3   \n",
       "98                                                 2   \n",
       "99                                                 1   \n",
       "100                                                1   \n",
       "101                                                3   \n",
       "102                              2019-04-03 00:00:00   \n",
       "\n",
       "    How many people do you follow on social media?  \\\n",
       "0                                              500   \n",
       "1                              2019-05-03 00:00:00   \n",
       "2                                    more than 100   \n",
       "3                        no one apart from friends   \n",
       "4                                               10   \n",
       "5                                                3   \n",
       "6                                               50   \n",
       "7                                                3   \n",
       "8                                              100   \n",
       "9                                              100   \n",
       "10                                              10   \n",
       "11                                             150   \n",
       "12                                             100   \n",
       "13                                              50   \n",
       "14                                           20-25   \n",
       "15                                              10   \n",
       "16                             2020-10-01 00:00:00   \n",
       "17                                             300   \n",
       "18                                              20   \n",
       "19                                              15   \n",
       "20                                        very few   \n",
       "21                                              20   \n",
       "22                                              15   \n",
       "23                                               1   \n",
       "24                                             400   \n",
       "25                                             300   \n",
       "26                                               0   \n",
       "27                                              20   \n",
       "28                                              10   \n",
       "29                                              10   \n",
       "..                                             ...   \n",
       "73                                              25   \n",
       "74                             2019-10-05 00:00:00   \n",
       "75                                              30   \n",
       "76                                             15+   \n",
       "77                                               0   \n",
       "78                             2019-03-02 00:00:00   \n",
       "79                                              40   \n",
       "80                                             200   \n",
       "81                                              20   \n",
       "82                                            1000   \n",
       "83                                              50   \n",
       "84                                              50   \n",
       "85                                             300   \n",
       "86                                             420   \n",
       "87                                              30   \n",
       "88                                             200   \n",
       "89                                              20   \n",
       "90                                             500   \n",
       "91                                             500   \n",
       "92                                             100   \n",
       "93                             2019-06-05 00:00:00   \n",
       "94                                             300   \n",
       "95                                             100   \n",
       "96                                             120   \n",
       "97                                        very few   \n",
       "98                                            1000   \n",
       "99                                             166   \n",
       "100                                              0   \n",
       "101                                             50   \n",
       "102                                     around 350   \n",
       "\n",
       "    How much time you spend in university without academic curriculum(in hour)?  \\\n",
       "0                                                  1.5                            \n",
       "1                                            1-2 hours                            \n",
       "2                                               1 hour                            \n",
       "3                                      0,rarely 1 hour                            \n",
       "4                                                    0                            \n",
       "5                                                    1                            \n",
       "6                                                    6                            \n",
       "7                                                    4                            \n",
       "8                                               1 hour                            \n",
       "9                                                    0                            \n",
       "10                                                   1                            \n",
       "11                                              2 hour                            \n",
       "12                                                   0                            \n",
       "13                                                   1                            \n",
       "14                                                   1                            \n",
       "15                                                   1                            \n",
       "16                                                   1                            \n",
       "17                             30min depending on time                            \n",
       "18                                                   1                            \n",
       "19                                                   3                            \n",
       "20                                       around 1 hour                            \n",
       "21                                                   3                            \n",
       "22                                            5 hours.                            \n",
       "23                                                   2                            \n",
       "24                                                   4                            \n",
       "25                                                   2                            \n",
       "26                                                   0                            \n",
       "27                                                   1                            \n",
       "28                                                   5                            \n",
       "29                                                   2                            \n",
       "..                                                 ...                            \n",
       "73                                                   4                            \n",
       "74                                               0.5hr                            \n",
       "75                                                   2                            \n",
       "76                                                   2                            \n",
       "77                                                   1                            \n",
       "78                                                   1                            \n",
       "79                                 2019-04-03 00:00:00                            \n",
       "80                                                   1                            \n",
       "81                                        2 to 3 hours                            \n",
       "82                                                   3                            \n",
       "83                                                   1                            \n",
       "84                                                   6                            \n",
       "85                                                   1                            \n",
       "86                                                 0.5                            \n",
       "87                                                   1                            \n",
       "88                                                   2                            \n",
       "89                                                   0                            \n",
       "90                                                   2                            \n",
       "91                                                   2                            \n",
       "92                                                   2                            \n",
       "93                                                   1                            \n",
       "94                                                   1                            \n",
       "95                                                   5                            \n",
       "96                                                   2                            \n",
       "97                                                   0                            \n",
       "98                                                   1                            \n",
       "99                                                   2                            \n",
       "100                                                  6                            \n",
       "101                                                  0                            \n",
       "102                                                  1                            \n",
       "\n",
       "    Are you rich in vocabulary?  Class note taking tendency(High,Low,Medium)  \\\n",
       "0                            No                                       Medium   \n",
       "1                           Yes                                       Medium   \n",
       "2                            No                                       Medium   \n",
       "3                           Yes                                          Low   \n",
       "4                         Maybe                                       Medium   \n",
       "5                            No                                          Low   \n",
       "6                         Maybe                                       Medium   \n",
       "7                           Yes                                          Low   \n",
       "8                            No                                          Low   \n",
       "9                            No                                          Low   \n",
       "10                           No                                          Low   \n",
       "11                        Maybe                                         High   \n",
       "12                        Maybe                                       Medium   \n",
       "13                           No                                       Medium   \n",
       "14                           No                                       Medium   \n",
       "15                           No                                       Medium   \n",
       "16                        Maybe                                       Medium   \n",
       "17                           No                                         High   \n",
       "18                           No                                          Low   \n",
       "19                        Maybe                                          Low   \n",
       "20                        Maybe                                       Medium   \n",
       "21                          Yes                                          Low   \n",
       "22                          Yes                                       Medium   \n",
       "23                        Maybe                                       Medium   \n",
       "24                        Maybe                                          Low   \n",
       "25                          Yes                                          Low   \n",
       "26                        Maybe                                         High   \n",
       "27                        Maybe                                         High   \n",
       "28                        Maybe                                          Low   \n",
       "29                           No                                       Medium   \n",
       "..                          ...                                          ...   \n",
       "73                           No                                          Low   \n",
       "74                           No                                         High   \n",
       "75                        Maybe                                          Low   \n",
       "76                           No                                       Medium   \n",
       "77                           No                                          Low   \n",
       "78                        Maybe                                          Low   \n",
       "79                        Maybe                                       Medium   \n",
       "80                        Maybe                                          Low   \n",
       "81                           No                                          Low   \n",
       "82                           No                                          Low   \n",
       "83                          Yes                                         High   \n",
       "84                          Yes                                         High   \n",
       "85                          Yes                                       Medium   \n",
       "86                        Maybe                                       Medium   \n",
       "87                        Maybe                                          Low   \n",
       "88                           No                                         High   \n",
       "89                           No                                          Low   \n",
       "90                           No                                          Low   \n",
       "91                        Maybe                                         High   \n",
       "92                           No                                         High   \n",
       "93                           No                                       Medium   \n",
       "94                           No                                       Medium   \n",
       "95                           No                                         High   \n",
       "96                           No                                         High   \n",
       "97                          Yes                                       Medium   \n",
       "98                        Maybe                                          Low   \n",
       "99                          Yes                                         High   \n",
       "100                       Maybe                                       Medium   \n",
       "101                         Yes                                         High   \n",
       "102                       Maybe                                       Medium   \n",
       "\n",
       "     Theory class attendance(h,l,m) Theory class interest(h,l,m)  \\\n",
       "0                                 m                       Medium   \n",
       "1                                 m                       Medium   \n",
       "2                                 m                       Medium   \n",
       "3                            medium                       Medium   \n",
       "4                                 h                         High   \n",
       "5                                 m                       Medium   \n",
       "6                                 m                       Medium   \n",
       "7                                 l                          Low   \n",
       "8                                 m                          Low   \n",
       "9                                 l                          Low   \n",
       "10                                m                       Medium   \n",
       "11                                h                       Medium   \n",
       "12                                h                         High   \n",
       "13                                h                       Medium   \n",
       "14                                h                       Medium   \n",
       "15                                h                       Medium   \n",
       "16                                h                         High   \n",
       "17                                h                          Low   \n",
       "18                                m                          Low   \n",
       "19                              0.9                          Low   \n",
       "20                                h                         High   \n",
       "21                                m                       Medium   \n",
       "22                            High.                       Medium   \n",
       "23                                m                       Medium   \n",
       "24                                h                       Medium   \n",
       "25                                h                         High   \n",
       "26                                h                         High   \n",
       "27                                h                       Medium   \n",
       "28                                M                       Medium   \n",
       "29                                h                       Medium   \n",
       "..                              ...                          ...   \n",
       "73                                h                       Medium   \n",
       "74                                h                         High   \n",
       "75                                m                       Medium   \n",
       "76                                h                       Medium   \n",
       "77                                m                          Low   \n",
       "78                                M                          Low   \n",
       "79                                h                       Medium   \n",
       "80                                h                         High   \n",
       "81                                m                       Medium   \n",
       "82                                m                       Medium   \n",
       "83                                h                         High   \n",
       "84                                h                         High   \n",
       "85                             high                       Medium   \n",
       "86                                h                         High   \n",
       "87                                h                       Medium   \n",
       "88                                h                         High   \n",
       "89                                h                       Medium   \n",
       "90                                h                       Medium   \n",
       "91                                h                       Medium   \n",
       "92                             High                       Medium   \n",
       "93                                h                         High   \n",
       "94                                h                         High   \n",
       "95                                m                       Medium   \n",
       "96                                m                         High   \n",
       "97                                h                         High   \n",
       "98                                m                       Medium   \n",
       "99                                h                       Medium   \n",
       "100                               h                         High   \n",
       "101                               h                         High   \n",
       "102                               h                          Low   \n",
       "\n",
       "    Lab interest(h,l,m) Preference(class lectures cl or online tutorials)  \n",
       "0                  High                                    Class lectures  \n",
       "1                  High                                  Online tutorials  \n",
       "2                Medium                                  Online tutorials  \n",
       "3                   Low                                  Online tutorials  \n",
       "4                Medium                                  Online tutorials  \n",
       "5                  High                                  Online tutorials  \n",
       "6                  High                                  Online tutorials  \n",
       "7                  High                                  Online tutorials  \n",
       "8                  High                                  Online tutorials  \n",
       "9                  High                                  Online tutorials  \n",
       "10               Medium                                  Online tutorials  \n",
       "11                 High                                    Class lectures  \n",
       "12                 High                                  Online tutorials  \n",
       "13                 High                                  Online tutorials  \n",
       "14               Medium                                    Class lectures  \n",
       "15               Medium                                    Class lectures  \n",
       "16                  Low                                    Class lectures  \n",
       "17               Medium                                  Online tutorials  \n",
       "18               Medium                                  Online tutorials  \n",
       "19               Medium                                  Online tutorials  \n",
       "20               Medium                                  Online tutorials  \n",
       "21                 High                                  Online tutorials  \n",
       "22               Medium                                    Class lectures  \n",
       "23                 High                                  Online tutorials  \n",
       "24                 High                                  Online tutorials  \n",
       "25                 High                                    Class lectures  \n",
       "26               Medium                                    Class lectures  \n",
       "27               Medium                                  Online tutorials  \n",
       "28                 High                                  Online tutorials  \n",
       "29               Medium                                    Class lectures  \n",
       "..                  ...                                               ...  \n",
       "73                 High                                  Online tutorials  \n",
       "74                 High                                    Class lectures  \n",
       "75               Medium                                  Online tutorials  \n",
       "76                 High                                  Online tutorials  \n",
       "77               Medium                                  Online tutorials  \n",
       "78                 High                                  Online tutorials  \n",
       "79               Medium                                  Online tutorials  \n",
       "80                 High                                  Online tutorials  \n",
       "81               Medium                                  Online tutorials  \n",
       "82                 High                                    Class lectures  \n",
       "83                 High                                    Class lectures  \n",
       "84                 High                                  Online tutorials  \n",
       "85               Medium                                  Online tutorials  \n",
       "86               Medium                                  Online tutorials  \n",
       "87               Medium                                    Class lectures  \n",
       "88                 High                                  Online tutorials  \n",
       "89               Medium                                  Online tutorials  \n",
       "90                 High                                  Online tutorials  \n",
       "91                 High                                    Class lectures  \n",
       "92               Medium                                    Class lectures  \n",
       "93                 High                                    Class lectures  \n",
       "94                 High                                    Class lectures  \n",
       "95                 High                                  Online tutorials  \n",
       "96                 High                                    Class lectures  \n",
       "97               Medium                                  Online tutorials  \n",
       "98                 High                                  Online tutorials  \n",
       "99                 High                                  Online tutorials  \n",
       "100                High                                    Class lectures  \n",
       "101              Medium                                  Online tutorials  \n",
       "102                High                                  Online tutorials  \n",
       "\n",
       "[103 rows x 81 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf.drop(fdf.columns.difference(['ID','Current Place','Latitude','Longitude','Food you like most','Expected food price of that favorite food','Restaurant location']), 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>ID</th>\n",
       "      <th>Current Place</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Food you like most</th>\n",
       "      <th>Expected food price of that favorite food</th>\n",
       "      <th>Restaurant location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151340976</td>\n",
       "      <td>Goran</td>\n",
       "      <td>23.750270</td>\n",
       "      <td>90.4341</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>150</td>\n",
       "      <td>Khilgaon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151340977</td>\n",
       "      <td>Banasree</td>\n",
       "      <td>22.513531</td>\n",
       "      <td>88.3015</td>\n",
       "      <td>Kachchi</td>\n",
       "      <td>200</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151340980</td>\n",
       "      <td>Niketon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>any</td>\n",
       "      <td>200</td>\n",
       "      <td>dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151340981</td>\n",
       "      <td>Shaymoli</td>\n",
       "      <td>22.301411</td>\n",
       "      <td>70.8224</td>\n",
       "      <td>sea food</td>\n",
       "      <td>70</td>\n",
       "      <td>many outlets in dhaka,i go to dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151340982</td>\n",
       "      <td>Bangshal</td>\n",
       "      <td>-32.348610</td>\n",
       "      <td>151.925</td>\n",
       "      <td>Burger</td>\n",
       "      <td>1000</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>151340983</td>\n",
       "      <td>Dania</td>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.4422</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>180</td>\n",
       "      <td>shantinagor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>151340984</td>\n",
       "      <td>Jurain</td>\n",
       "      <td>23.697285</td>\n",
       "      <td>90.4318</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>250</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>151340985</td>\n",
       "      <td>Lalbag</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Any</td>\n",
       "      <td>400</td>\n",
       "      <td>dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>151340986</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>150</td>\n",
       "      <td>Aust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>151340988</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>350</td>\n",
       "      <td>basundhara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>151340990</td>\n",
       "      <td>Dania</td>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.4422</td>\n",
       "      <td>khichuri</td>\n",
       "      <td>150</td>\n",
       "      <td>khilgao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>151340991</td>\n",
       "      <td>Mohammadpur</td>\n",
       "      <td>23.765800</td>\n",
       "      <td>90.3584</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>250</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>151340992</td>\n",
       "      <td>Jigatola</td>\n",
       "      <td>23.737286</td>\n",
       "      <td>90.3716</td>\n",
       "      <td>Cheesy fries</td>\n",
       "      <td>100</td>\n",
       "      <td>dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>151340994</td>\n",
       "      <td>Shamoli</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>200</td>\n",
       "      <td>Shyamoli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>151340995</td>\n",
       "      <td>Green Corner</td>\n",
       "      <td>23.745173</td>\n",
       "      <td>90.3864</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>200</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>151340996</td>\n",
       "      <td>Shahjahanpur</td>\n",
       "      <td>27.883743</td>\n",
       "      <td>79.9122</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>220</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>151340998</td>\n",
       "      <td>Khilgaon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>150</td>\n",
       "      <td>dhaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>151341000</td>\n",
       "      <td>Banani</td>\n",
       "      <td>23.793993</td>\n",
       "      <td>90.4043</td>\n",
       "      <td>Spicy Foods</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>151341001</td>\n",
       "      <td>Uttara</td>\n",
       "      <td>24.098379</td>\n",
       "      <td>90.3287</td>\n",
       "      <td>Burger</td>\n",
       "      <td>50</td>\n",
       "      <td>lat:23.707310 long: 90.415482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>151340894</td>\n",
       "      <td>Mirpur</td>\n",
       "      <td>23.776604</td>\n",
       "      <td>90.383</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>120</td>\n",
       "      <td>Mirpur 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>151340897</td>\n",
       "      <td>Mohanogor</td>\n",
       "      <td>23.734558</td>\n",
       "      <td>90.385</td>\n",
       "      <td>Egg and Milk</td>\n",
       "      <td>200</td>\n",
       "      <td>B Baria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>151340898</td>\n",
       "      <td>Gopibag</td>\n",
       "      <td>23.722109</td>\n",
       "      <td>90.4262</td>\n",
       "      <td>Tea</td>\n",
       "      <td>80</td>\n",
       "      <td>Gulshan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>151340900</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Hilsa</td>\n",
       "      <td>800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>151340901</td>\n",
       "      <td>Shantinagar</td>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.4143</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>140</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>151340902</td>\n",
       "      <td>Siddeswari</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>250</td>\n",
       "      <td>Banani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>151340903</td>\n",
       "      <td>Narinda</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Mutton</td>\n",
       "      <td>150</td>\n",
       "      <td>Banani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>151340904</td>\n",
       "      <td>Mugdapara</td>\n",
       "      <td>23.730534</td>\n",
       "      <td>90.4462</td>\n",
       "      <td>Chicken,Egg &amp; Milk</td>\n",
       "      <td>100</td>\n",
       "      <td>shantinagar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>151340905</td>\n",
       "      <td>Shahidbag</td>\n",
       "      <td>23.828959</td>\n",
       "      <td>90.3757</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>500</td>\n",
       "      <td>Malibag Chowdhurypara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>151340906</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.759815</td>\n",
       "      <td>90.3913</td>\n",
       "      <td>Khola pitha</td>\n",
       "      <td>120</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>151340907</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>160</td>\n",
       "      <td>Khilgaon, Dhaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>151341005</td>\n",
       "      <td>Bashabo</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>300</td>\n",
       "      <td>Gulshan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>151341009</td>\n",
       "      <td>East Rampura</td>\n",
       "      <td>23.708022</td>\n",
       "      <td>90.4179</td>\n",
       "      <td>Chinese food</td>\n",
       "      <td>300</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>151341022</td>\n",
       "      <td>Mohanogor Project</td>\n",
       "      <td>23.767600</td>\n",
       "      <td>90.4147</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>300</td>\n",
       "      <td>Null</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>151341029</td>\n",
       "      <td>Segunbagicha</td>\n",
       "      <td>23.732598</td>\n",
       "      <td>90.4026</td>\n",
       "      <td>Mix Food</td>\n",
       "      <td>1500</td>\n",
       "      <td>Khilgaon, Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>151341010</td>\n",
       "      <td>Farmgate</td>\n",
       "      <td>23.756107</td>\n",
       "      <td>90.3872</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>200</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>151341020</td>\n",
       "      <td>Mirpur 60 Feet</td>\n",
       "      <td>23.790579</td>\n",
       "      <td>90.3653</td>\n",
       "      <td>Kacchi Biriyani</td>\n",
       "      <td>150</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>151341012</td>\n",
       "      <td>Zigatola</td>\n",
       "      <td>23.738078</td>\n",
       "      <td>90.3725</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>200</td>\n",
       "      <td>Dhanmundi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>151341021</td>\n",
       "      <td>Rayerbag</td>\n",
       "      <td>23.693846</td>\n",
       "      <td>90.4553</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>400</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>151341019</td>\n",
       "      <td>Shyamoli</td>\n",
       "      <td>23.771800</td>\n",
       "      <td>90.3631</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>200</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>151340921</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Alu Vorta</td>\n",
       "      <td>150</td>\n",
       "      <td>2 no. gate, Chittagong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>151340923</td>\n",
       "      <td>Siddheshwari</td>\n",
       "      <td>23.744602</td>\n",
       "      <td>90.4115</td>\n",
       "      <td>Pasta</td>\n",
       "      <td>220</td>\n",
       "      <td>Khilgaon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>151340925</td>\n",
       "      <td>Maniknagar</td>\n",
       "      <td>23.721389</td>\n",
       "      <td>90.4299</td>\n",
       "      <td>Cashew nut</td>\n",
       "      <td>250</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>151340926</td>\n",
       "      <td>Maniknagar</td>\n",
       "      <td>23.722595</td>\n",
       "      <td>90.4296</td>\n",
       "      <td>Cashew nut</td>\n",
       "      <td>200</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>151340927</td>\n",
       "      <td>Mirpur-12</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Spicy food</td>\n",
       "      <td>500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>151340928</td>\n",
       "      <td>Mirpur</td>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.3686</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>250</td>\n",
       "      <td>Mirpur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>151340930</td>\n",
       "      <td>Motijheel</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Porota Dalbhaji</td>\n",
       "      <td>50</td>\n",
       "      <td>Gulshan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>151340931</td>\n",
       "      <td>Hazaribagh</td>\n",
       "      <td>23.727835</td>\n",
       "      <td>90.374</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>300</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>151340932</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.763697</td>\n",
       "      <td>90.3707</td>\n",
       "      <td>Grill &amp; Nun</td>\n",
       "      <td>120</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>151340933</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Beef</td>\n",
       "      <td>150</td>\n",
       "      <td>Rampura, Dhaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>151340934</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>180</td>\n",
       "      <td>Gulsan 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>151340935</td>\n",
       "      <td>Gulshan 1</td>\n",
       "      <td>23.778600</td>\n",
       "      <td>90.4156</td>\n",
       "      <td>Any well cooked food</td>\n",
       "      <td>200</td>\n",
       "      <td>Jigatola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>151340937</td>\n",
       "      <td>Mirpur-1</td>\n",
       "      <td>23.795600</td>\n",
       "      <td>90.3537</td>\n",
       "      <td>Spicy Food</td>\n",
       "      <td>150</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>151340939</td>\n",
       "      <td>Malibagh</td>\n",
       "      <td>23.758353</td>\n",
       "      <td>90.4162</td>\n",
       "      <td>Burger</td>\n",
       "      <td>60</td>\n",
       "      <td>Khilgaon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>151340941</td>\n",
       "      <td>Niketon</td>\n",
       "      <td>23.797911</td>\n",
       "      <td>90.4144</td>\n",
       "      <td>Spicy food</td>\n",
       "      <td>300</td>\n",
       "      <td>Khilgoan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>151340942</td>\n",
       "      <td>Shantinagar</td>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.4143</td>\n",
       "      <td>Italian</td>\n",
       "      <td>300</td>\n",
       "      <td>Bailey Road</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>151340943</td>\n",
       "      <td>Mohakhali</td>\n",
       "      <td>23.780232</td>\n",
       "      <td>90.4011</td>\n",
       "      <td>Fast food</td>\n",
       "      <td>600</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>151340944</td>\n",
       "      <td>Kathalbagan</td>\n",
       "      <td>23.746188</td>\n",
       "      <td>90.3899</td>\n",
       "      <td>Beef curry with curds</td>\n",
       "      <td>160</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>151340945</td>\n",
       "      <td>Panthapath</td>\n",
       "      <td>23.751791</td>\n",
       "      <td>90.3859</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>180</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>151340946</td>\n",
       "      <td>Mirpur-10</td>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.3686</td>\n",
       "      <td>Luchi Dalvaji</td>\n",
       "      <td>100</td>\n",
       "      <td>Gulshan-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>151340947</td>\n",
       "      <td>Dhaka Cantonment</td>\n",
       "      <td>26.782930</td>\n",
       "      <td>82.1501</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>250</td>\n",
       "      <td>gulshan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID       Current Place   Latitude Longitude  \\\n",
       "0    151340976               Goran  23.750270   90.4341   \n",
       "1    151340977            Banasree  22.513531   88.3015   \n",
       "2    151340980             Niketon  23.707310   90.4155   \n",
       "3    151340981            Shaymoli  22.301411   70.8224   \n",
       "4    151340982            Bangshal -32.348610   151.925   \n",
       "5    151340983               Dania  23.701443   90.4422   \n",
       "6    151340984              Jurain  23.697285   90.4318   \n",
       "7    151340985              Lalbag  23.707310   90.4155   \n",
       "8    151340986             Rampura  23.761227   90.4208   \n",
       "9    151340988             Rampura  23.761227   90.4208   \n",
       "10   151340990               Dania  23.701443   90.4422   \n",
       "11   151340991         Mohammadpur  23.765800   90.3584   \n",
       "12   151340992            Jigatola  23.737286   90.3716   \n",
       "13   151340994             Shamoli  23.707310   90.4155   \n",
       "14   151340995        Green Corner  23.745173   90.3864   \n",
       "15   151340996        Shahjahanpur  27.883743   79.9122   \n",
       "16   151340998            Khilgaon  23.707310   90.4155   \n",
       "17   151341000              Banani  23.793993   90.4043   \n",
       "18   151341001              Uttara  24.098379   90.3287   \n",
       "19   151340894              Mirpur  23.776604    90.383   \n",
       "20   151340897           Mohanogor  23.734558    90.385   \n",
       "21   151340898             Gopibag  23.722109   90.4262   \n",
       "22   151340900             Rampura  23.761227   90.4208   \n",
       "23   151340901         Shantinagar  23.738200   90.4143   \n",
       "24   151340902          Siddeswari  23.707310   90.4155   \n",
       "25   151340903             Narinda  23.707310   90.4155   \n",
       "26   151340904           Mugdapara  23.730534   90.4462   \n",
       "27   151340905           Shahidbag  23.828959   90.3757   \n",
       "28   151340906             Tejgaon  23.759815   90.3913   \n",
       "29   151340907             Rampura  23.707310   90.4155   \n",
       "..         ...                 ...        ...       ...   \n",
       "73   151341005             Bashabo  23.707310   90.4155   \n",
       "74   151341009        East Rampura  23.708022   90.4179   \n",
       "75   151341022  Mohanogor Project   23.767600   90.4147   \n",
       "76   151341029        Segunbagicha  23.732598   90.4026   \n",
       "77   151341010            Farmgate  23.756107   90.3872   \n",
       "78   151341020      Mirpur 60 Feet  23.790579   90.3653   \n",
       "79   151341012            Zigatola  23.738078   90.3725   \n",
       "80   151341021            Rayerbag  23.693846   90.4553   \n",
       "81   151341019            Shyamoli  23.771800   90.3631   \n",
       "82   151340921             Tejgaon  23.707310   90.4155   \n",
       "83   151340923        Siddheshwari  23.744602   90.4115   \n",
       "84   151340925          Maniknagar  23.721389   90.4299   \n",
       "85   151340926          Maniknagar  23.722595   90.4296   \n",
       "86   151340927           Mirpur-12  23.707310   90.4155   \n",
       "87   151340928              Mirpur  23.807085   90.3686   \n",
       "88   151340930           Motijheel  23.707310   90.4155   \n",
       "89   151340931          Hazaribagh  23.727835    90.374   \n",
       "90   151340932             Tejgaon  23.763697   90.3707   \n",
       "91   151340933             Rampura  23.707310   90.4155   \n",
       "92   151340934             Rampura  23.707310   90.4155   \n",
       "93   151340935           Gulshan 1  23.778600   90.4156   \n",
       "94   151340937            Mirpur-1  23.795600   90.3537   \n",
       "95   151340939            Malibagh  23.758353   90.4162   \n",
       "96   151340941             Niketon  23.797911   90.4144   \n",
       "97   151340942         Shantinagar  23.738200   90.4143   \n",
       "98   151340943           Mohakhali  23.780232   90.4011   \n",
       "99   151340944         Kathalbagan  23.746188   90.3899   \n",
       "100  151340945          Panthapath  23.751791   90.3859   \n",
       "101  151340946           Mirpur-10  23.807085   90.3686   \n",
       "102  151340947    Dhaka Cantonment  26.782930   82.1501   \n",
       "\n",
       "        Food you like most Expected food price of that favorite food  \\\n",
       "0                 Biriyani                                       150   \n",
       "1                  Kachchi                                       200   \n",
       "2                      any                                       200   \n",
       "3                 sea food                                        70   \n",
       "4                   Burger                                      1000   \n",
       "5                 Biriyani                                       180   \n",
       "6                 Biriyani                                       250   \n",
       "7                      Any                                       400   \n",
       "8                 Biriyani                                       150   \n",
       "9                  Chicken                                       350   \n",
       "10                khichuri                                       150   \n",
       "11               Fast Food                                       250   \n",
       "12            Cheesy fries                                       100   \n",
       "13                Biriyani                                       200   \n",
       "14                Biriyani                                       200   \n",
       "15                Biriyani                                       220   \n",
       "16                Biriyani                                       150   \n",
       "17             Spicy Foods                                        20   \n",
       "18                  Burger                                        50   \n",
       "19                Biriyani                                       120   \n",
       "20            Egg and Milk                                       200   \n",
       "21                     Tea                                        80   \n",
       "22                   Hilsa                                       800   \n",
       "23                Biriyani                                       140   \n",
       "24                Biriyani                                       250   \n",
       "25                  Mutton                                       150   \n",
       "26      Chicken,Egg & Milk                                       100   \n",
       "27                   Pizza                                       500   \n",
       "28             Khola pitha                                       120   \n",
       "29                Biriyani                                       160   \n",
       "..                     ...                                       ...   \n",
       "73                 Chinese                                       300   \n",
       "74            Chinese food                                       300   \n",
       "75               Fast Food                                       300   \n",
       "76                Mix Food                                      1500   \n",
       "77            Regular Food                                       200   \n",
       "78         Kacchi Biriyani                                       150   \n",
       "79            Regular Food                                       200   \n",
       "80            Regular Food                                       400   \n",
       "81            Regular Food                                       200   \n",
       "82               Alu Vorta                                       150   \n",
       "83                   Pasta                                       220   \n",
       "84              Cashew nut                                       250   \n",
       "85              Cashew nut                                       200   \n",
       "86              Spicy food                                       500   \n",
       "87                Biriyani                                       250   \n",
       "88         Porota Dalbhaji                                        50   \n",
       "89                Biriyani                                       300   \n",
       "90             Grill & Nun                                       120   \n",
       "91                    Beef                                       150   \n",
       "92                Biriyani                                       180   \n",
       "93    Any well cooked food                                       200   \n",
       "94              Spicy Food                                       150   \n",
       "95                  Burger                                        60   \n",
       "96              Spicy food                                       300   \n",
       "97                 Italian                                       300   \n",
       "98               Fast food                                       600   \n",
       "99   Beef curry with curds                                       160   \n",
       "100               Biriyani                                       180   \n",
       "101          Luchi Dalvaji                                       100   \n",
       "102               Chicken                                        250   \n",
       "\n",
       "                         Restaurant location  \n",
       "0                                   Khilgaon  \n",
       "1                                  Dhanmondi  \n",
       "2                                  dhanmondi  \n",
       "3    many outlets in dhaka,i go to dhanmondi  \n",
       "4                                  Dhanmondi  \n",
       "5                                shantinagor  \n",
       "6                                  Dhanmondi  \n",
       "7                                  dhanmondi  \n",
       "8                                       Aust  \n",
       "9                                 basundhara  \n",
       "10                                   khilgao  \n",
       "11                                 Dhanmondi  \n",
       "12                                 dhanmondi  \n",
       "13                                  Shyamoli  \n",
       "14                                 Dhanmondi  \n",
       "15                                 Dhanmondi  \n",
       "16                                     dhaka  \n",
       "17                                       NaN  \n",
       "18             lat:23.707310 long: 90.415482  \n",
       "19                                  Mirpur 1  \n",
       "20                                   B Baria  \n",
       "21                                   Gulshan  \n",
       "22                                       NaN  \n",
       "23                                 Dhanmondi  \n",
       "24                                    Banani  \n",
       "25                                    Banani  \n",
       "26                               shantinagar  \n",
       "27                     Malibag Chowdhurypara  \n",
       "28                                 Dhanmondi  \n",
       "29                           Khilgaon, Dhaka  \n",
       "..                                       ...  \n",
       "73                                   Gulshan  \n",
       "74                                 Dhanmondi  \n",
       "75                                      Null  \n",
       "76                       Khilgaon, Dhanmondi  \n",
       "77                                       NaN  \n",
       "78                                 Dhanmondi  \n",
       "79                                 Dhanmundi  \n",
       "80                                 Dhanmondi  \n",
       "81                                 Dhanmondi  \n",
       "82                    2 no. gate, Chittagong  \n",
       "83                                  Khilgaon  \n",
       "84                                 Dhanmondi  \n",
       "85                                 Dhanmondi  \n",
       "86                                       NaN  \n",
       "87                                    Mirpur  \n",
       "88                                   Gulshan  \n",
       "89                                 Dhanmondi  \n",
       "90                                 Dhanmondi  \n",
       "91                            Rampura, Dhaka  \n",
       "92                                  Gulsan 2  \n",
       "93                                  Jigatola  \n",
       "94                                 Dhanmondi  \n",
       "95                                  Khilgaon  \n",
       "96                                  Khilgoan  \n",
       "97                               Bailey Road  \n",
       "98                                 Dhanmondi  \n",
       "99                                 Dhanmondi  \n",
       "100                                Dhanmondi  \n",
       "101                                Gulshan-2  \n",
       "102                                  gulshan  \n",
       "\n",
       "[103 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "new=fdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>ID</th>\n",
       "      <th>Current Place</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Food you like most</th>\n",
       "      <th>Expected food price of that favorite food</th>\n",
       "      <th>Restaurant location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151340976</td>\n",
       "      <td>Goran</td>\n",
       "      <td>23.750270</td>\n",
       "      <td>90.4341</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>150</td>\n",
       "      <td>Khilgaon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151340977</td>\n",
       "      <td>Banasree</td>\n",
       "      <td>22.513531</td>\n",
       "      <td>88.3015</td>\n",
       "      <td>Kachchi</td>\n",
       "      <td>200</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151340980</td>\n",
       "      <td>Niketon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>any</td>\n",
       "      <td>200</td>\n",
       "      <td>dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151340981</td>\n",
       "      <td>Shaymoli</td>\n",
       "      <td>22.301411</td>\n",
       "      <td>70.8224</td>\n",
       "      <td>sea food</td>\n",
       "      <td>70</td>\n",
       "      <td>many outlets in dhaka,i go to dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151340982</td>\n",
       "      <td>Bangshal</td>\n",
       "      <td>-32.348610</td>\n",
       "      <td>151.925</td>\n",
       "      <td>Burger</td>\n",
       "      <td>1000</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>151340983</td>\n",
       "      <td>Dania</td>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.4422</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>180</td>\n",
       "      <td>shantinagor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>151340984</td>\n",
       "      <td>Jurain</td>\n",
       "      <td>23.697285</td>\n",
       "      <td>90.4318</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>250</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>151340985</td>\n",
       "      <td>Lalbag</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Any</td>\n",
       "      <td>400</td>\n",
       "      <td>dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>151340986</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>150</td>\n",
       "      <td>Aust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>151340988</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>350</td>\n",
       "      <td>basundhara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>151340990</td>\n",
       "      <td>Dania</td>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.4422</td>\n",
       "      <td>khichuri</td>\n",
       "      <td>150</td>\n",
       "      <td>khilgao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>151340991</td>\n",
       "      <td>Mohammadpur</td>\n",
       "      <td>23.765800</td>\n",
       "      <td>90.3584</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>250</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>151340992</td>\n",
       "      <td>Jigatola</td>\n",
       "      <td>23.737286</td>\n",
       "      <td>90.3716</td>\n",
       "      <td>Cheesy fries</td>\n",
       "      <td>100</td>\n",
       "      <td>dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>151340994</td>\n",
       "      <td>Shamoli</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>200</td>\n",
       "      <td>Shyamoli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>151340995</td>\n",
       "      <td>Green Corner</td>\n",
       "      <td>23.745173</td>\n",
       "      <td>90.3864</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>200</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>151340996</td>\n",
       "      <td>Shahjahanpur</td>\n",
       "      <td>27.883743</td>\n",
       "      <td>79.9122</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>220</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>151340998</td>\n",
       "      <td>Khilgaon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>150</td>\n",
       "      <td>dhaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>151341000</td>\n",
       "      <td>Banani</td>\n",
       "      <td>23.793993</td>\n",
       "      <td>90.4043</td>\n",
       "      <td>Spicy Foods</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>151341001</td>\n",
       "      <td>Uttara</td>\n",
       "      <td>24.098379</td>\n",
       "      <td>90.3287</td>\n",
       "      <td>Burger</td>\n",
       "      <td>50</td>\n",
       "      <td>lat:23.707310 long: 90.415482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>151340894</td>\n",
       "      <td>Mirpur</td>\n",
       "      <td>23.776604</td>\n",
       "      <td>90.383</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>120</td>\n",
       "      <td>Mirpur 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>151340897</td>\n",
       "      <td>Mohanogor</td>\n",
       "      <td>23.734558</td>\n",
       "      <td>90.385</td>\n",
       "      <td>Egg and Milk</td>\n",
       "      <td>200</td>\n",
       "      <td>B Baria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>151340898</td>\n",
       "      <td>Gopibag</td>\n",
       "      <td>23.722109</td>\n",
       "      <td>90.4262</td>\n",
       "      <td>Tea</td>\n",
       "      <td>80</td>\n",
       "      <td>Gulshan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>151340900</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.4208</td>\n",
       "      <td>Hilsa</td>\n",
       "      <td>800</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>151340901</td>\n",
       "      <td>Shantinagar</td>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.4143</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>140</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>151340902</td>\n",
       "      <td>Siddeswari</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>250</td>\n",
       "      <td>Banani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>151340903</td>\n",
       "      <td>Narinda</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Mutton</td>\n",
       "      <td>150</td>\n",
       "      <td>Banani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>151340904</td>\n",
       "      <td>Mugdapara</td>\n",
       "      <td>23.730534</td>\n",
       "      <td>90.4462</td>\n",
       "      <td>Chicken,Egg &amp; Milk</td>\n",
       "      <td>100</td>\n",
       "      <td>shantinagar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>151340905</td>\n",
       "      <td>Shahidbag</td>\n",
       "      <td>23.828959</td>\n",
       "      <td>90.3757</td>\n",
       "      <td>Pizza</td>\n",
       "      <td>500</td>\n",
       "      <td>Malibag Chowdhurypara</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>151340906</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.759815</td>\n",
       "      <td>90.3913</td>\n",
       "      <td>Khola pitha</td>\n",
       "      <td>120</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>151340907</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>160</td>\n",
       "      <td>Khilgaon, Dhaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>151341005</td>\n",
       "      <td>Bashabo</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Chinese</td>\n",
       "      <td>300</td>\n",
       "      <td>Gulshan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>151341009</td>\n",
       "      <td>East Rampura</td>\n",
       "      <td>23.708022</td>\n",
       "      <td>90.4179</td>\n",
       "      <td>Chinese food</td>\n",
       "      <td>300</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>151341022</td>\n",
       "      <td>Mohanogor Project</td>\n",
       "      <td>23.767600</td>\n",
       "      <td>90.4147</td>\n",
       "      <td>Fast Food</td>\n",
       "      <td>300</td>\n",
       "      <td>Null</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>151341029</td>\n",
       "      <td>Segunbagicha</td>\n",
       "      <td>23.732598</td>\n",
       "      <td>90.4026</td>\n",
       "      <td>Mix Food</td>\n",
       "      <td>1500</td>\n",
       "      <td>Khilgaon, Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>151341010</td>\n",
       "      <td>Farmgate</td>\n",
       "      <td>23.756107</td>\n",
       "      <td>90.3872</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>200</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>151341020</td>\n",
       "      <td>Mirpur 60 Feet</td>\n",
       "      <td>23.790579</td>\n",
       "      <td>90.3653</td>\n",
       "      <td>Kacchi Biriyani</td>\n",
       "      <td>150</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>151341012</td>\n",
       "      <td>Zigatola</td>\n",
       "      <td>23.738078</td>\n",
       "      <td>90.3725</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>200</td>\n",
       "      <td>Dhanmundi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>151341021</td>\n",
       "      <td>Rayerbag</td>\n",
       "      <td>23.693846</td>\n",
       "      <td>90.4553</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>400</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>151341019</td>\n",
       "      <td>Shyamoli</td>\n",
       "      <td>23.771800</td>\n",
       "      <td>90.3631</td>\n",
       "      <td>Regular Food</td>\n",
       "      <td>200</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>151340921</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Alu Vorta</td>\n",
       "      <td>150</td>\n",
       "      <td>2 no. gate, Chittagong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>151340923</td>\n",
       "      <td>Siddheshwari</td>\n",
       "      <td>23.744602</td>\n",
       "      <td>90.4115</td>\n",
       "      <td>Pasta</td>\n",
       "      <td>220</td>\n",
       "      <td>Khilgaon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>151340925</td>\n",
       "      <td>Maniknagar</td>\n",
       "      <td>23.721389</td>\n",
       "      <td>90.4299</td>\n",
       "      <td>Cashew nut</td>\n",
       "      <td>250</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>151340926</td>\n",
       "      <td>Maniknagar</td>\n",
       "      <td>23.722595</td>\n",
       "      <td>90.4296</td>\n",
       "      <td>Cashew nut</td>\n",
       "      <td>200</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>151340927</td>\n",
       "      <td>Mirpur-12</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Spicy food</td>\n",
       "      <td>500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>151340928</td>\n",
       "      <td>Mirpur</td>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.3686</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>250</td>\n",
       "      <td>Mirpur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>151340930</td>\n",
       "      <td>Motijheel</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Porota Dalbhaji</td>\n",
       "      <td>50</td>\n",
       "      <td>Gulshan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>151340931</td>\n",
       "      <td>Hazaribagh</td>\n",
       "      <td>23.727835</td>\n",
       "      <td>90.374</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>300</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>151340932</td>\n",
       "      <td>Tejgaon</td>\n",
       "      <td>23.763697</td>\n",
       "      <td>90.3707</td>\n",
       "      <td>Grill &amp; Nun</td>\n",
       "      <td>120</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>151340933</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Beef</td>\n",
       "      <td>150</td>\n",
       "      <td>Rampura, Dhaka</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>151340934</td>\n",
       "      <td>Rampura</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.4155</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>180</td>\n",
       "      <td>Gulsan 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>151340935</td>\n",
       "      <td>Gulshan 1</td>\n",
       "      <td>23.778600</td>\n",
       "      <td>90.4156</td>\n",
       "      <td>Any well cooked food</td>\n",
       "      <td>200</td>\n",
       "      <td>Jigatola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>151340937</td>\n",
       "      <td>Mirpur-1</td>\n",
       "      <td>23.795600</td>\n",
       "      <td>90.3537</td>\n",
       "      <td>Spicy Food</td>\n",
       "      <td>150</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>151340939</td>\n",
       "      <td>Malibagh</td>\n",
       "      <td>23.758353</td>\n",
       "      <td>90.4162</td>\n",
       "      <td>Burger</td>\n",
       "      <td>60</td>\n",
       "      <td>Khilgaon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>151340941</td>\n",
       "      <td>Niketon</td>\n",
       "      <td>23.797911</td>\n",
       "      <td>90.4144</td>\n",
       "      <td>Spicy food</td>\n",
       "      <td>300</td>\n",
       "      <td>Khilgoan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>151340942</td>\n",
       "      <td>Shantinagar</td>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.4143</td>\n",
       "      <td>Italian</td>\n",
       "      <td>300</td>\n",
       "      <td>Bailey Road</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>151340943</td>\n",
       "      <td>Mohakhali</td>\n",
       "      <td>23.780232</td>\n",
       "      <td>90.4011</td>\n",
       "      <td>Fast food</td>\n",
       "      <td>600</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>151340944</td>\n",
       "      <td>Kathalbagan</td>\n",
       "      <td>23.746188</td>\n",
       "      <td>90.3899</td>\n",
       "      <td>Beef curry with curds</td>\n",
       "      <td>160</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>151340945</td>\n",
       "      <td>Panthapath</td>\n",
       "      <td>23.751791</td>\n",
       "      <td>90.3859</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>180</td>\n",
       "      <td>Dhanmondi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>151340946</td>\n",
       "      <td>Mirpur-10</td>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.3686</td>\n",
       "      <td>Luchi Dalvaji</td>\n",
       "      <td>100</td>\n",
       "      <td>Gulshan-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>151340947</td>\n",
       "      <td>Dhaka Cantonment</td>\n",
       "      <td>26.782930</td>\n",
       "      <td>82.1501</td>\n",
       "      <td>Chicken</td>\n",
       "      <td>250</td>\n",
       "      <td>gulshan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID       Current Place   Latitude Longitude  \\\n",
       "0    151340976               Goran  23.750270   90.4341   \n",
       "1    151340977            Banasree  22.513531   88.3015   \n",
       "2    151340980             Niketon  23.707310   90.4155   \n",
       "3    151340981            Shaymoli  22.301411   70.8224   \n",
       "4    151340982            Bangshal -32.348610   151.925   \n",
       "5    151340983               Dania  23.701443   90.4422   \n",
       "6    151340984              Jurain  23.697285   90.4318   \n",
       "7    151340985              Lalbag  23.707310   90.4155   \n",
       "8    151340986             Rampura  23.761227   90.4208   \n",
       "9    151340988             Rampura  23.761227   90.4208   \n",
       "10   151340990               Dania  23.701443   90.4422   \n",
       "11   151340991         Mohammadpur  23.765800   90.3584   \n",
       "12   151340992            Jigatola  23.737286   90.3716   \n",
       "13   151340994             Shamoli  23.707310   90.4155   \n",
       "14   151340995        Green Corner  23.745173   90.3864   \n",
       "15   151340996        Shahjahanpur  27.883743   79.9122   \n",
       "16   151340998            Khilgaon  23.707310   90.4155   \n",
       "17   151341000              Banani  23.793993   90.4043   \n",
       "18   151341001              Uttara  24.098379   90.3287   \n",
       "19   151340894              Mirpur  23.776604    90.383   \n",
       "20   151340897           Mohanogor  23.734558    90.385   \n",
       "21   151340898             Gopibag  23.722109   90.4262   \n",
       "22   151340900             Rampura  23.761227   90.4208   \n",
       "23   151340901         Shantinagar  23.738200   90.4143   \n",
       "24   151340902          Siddeswari  23.707310   90.4155   \n",
       "25   151340903             Narinda  23.707310   90.4155   \n",
       "26   151340904           Mugdapara  23.730534   90.4462   \n",
       "27   151340905           Shahidbag  23.828959   90.3757   \n",
       "28   151340906             Tejgaon  23.759815   90.3913   \n",
       "29   151340907             Rampura  23.707310   90.4155   \n",
       "..         ...                 ...        ...       ...   \n",
       "73   151341005             Bashabo  23.707310   90.4155   \n",
       "74   151341009        East Rampura  23.708022   90.4179   \n",
       "75   151341022  Mohanogor Project   23.767600   90.4147   \n",
       "76   151341029        Segunbagicha  23.732598   90.4026   \n",
       "77   151341010            Farmgate  23.756107   90.3872   \n",
       "78   151341020      Mirpur 60 Feet  23.790579   90.3653   \n",
       "79   151341012            Zigatola  23.738078   90.3725   \n",
       "80   151341021            Rayerbag  23.693846   90.4553   \n",
       "81   151341019            Shyamoli  23.771800   90.3631   \n",
       "82   151340921             Tejgaon  23.707310   90.4155   \n",
       "83   151340923        Siddheshwari  23.744602   90.4115   \n",
       "84   151340925          Maniknagar  23.721389   90.4299   \n",
       "85   151340926          Maniknagar  23.722595   90.4296   \n",
       "86   151340927           Mirpur-12  23.707310   90.4155   \n",
       "87   151340928              Mirpur  23.807085   90.3686   \n",
       "88   151340930           Motijheel  23.707310   90.4155   \n",
       "89   151340931          Hazaribagh  23.727835    90.374   \n",
       "90   151340932             Tejgaon  23.763697   90.3707   \n",
       "91   151340933             Rampura  23.707310   90.4155   \n",
       "92   151340934             Rampura  23.707310   90.4155   \n",
       "93   151340935           Gulshan 1  23.778600   90.4156   \n",
       "94   151340937            Mirpur-1  23.795600   90.3537   \n",
       "95   151340939            Malibagh  23.758353   90.4162   \n",
       "96   151340941             Niketon  23.797911   90.4144   \n",
       "97   151340942         Shantinagar  23.738200   90.4143   \n",
       "98   151340943           Mohakhali  23.780232   90.4011   \n",
       "99   151340944         Kathalbagan  23.746188   90.3899   \n",
       "100  151340945          Panthapath  23.751791   90.3859   \n",
       "101  151340946           Mirpur-10  23.807085   90.3686   \n",
       "102  151340947    Dhaka Cantonment  26.782930   82.1501   \n",
       "\n",
       "        Food you like most Expected food price of that favorite food  \\\n",
       "0                 Biriyani                                       150   \n",
       "1                  Kachchi                                       200   \n",
       "2                      any                                       200   \n",
       "3                 sea food                                        70   \n",
       "4                   Burger                                      1000   \n",
       "5                 Biriyani                                       180   \n",
       "6                 Biriyani                                       250   \n",
       "7                      Any                                       400   \n",
       "8                 Biriyani                                       150   \n",
       "9                  Chicken                                       350   \n",
       "10                khichuri                                       150   \n",
       "11               Fast Food                                       250   \n",
       "12            Cheesy fries                                       100   \n",
       "13                Biriyani                                       200   \n",
       "14                Biriyani                                       200   \n",
       "15                Biriyani                                       220   \n",
       "16                Biriyani                                       150   \n",
       "17             Spicy Foods                                        20   \n",
       "18                  Burger                                        50   \n",
       "19                Biriyani                                       120   \n",
       "20            Egg and Milk                                       200   \n",
       "21                     Tea                                        80   \n",
       "22                   Hilsa                                       800   \n",
       "23                Biriyani                                       140   \n",
       "24                Biriyani                                       250   \n",
       "25                  Mutton                                       150   \n",
       "26      Chicken,Egg & Milk                                       100   \n",
       "27                   Pizza                                       500   \n",
       "28             Khola pitha                                       120   \n",
       "29                Biriyani                                       160   \n",
       "..                     ...                                       ...   \n",
       "73                 Chinese                                       300   \n",
       "74            Chinese food                                       300   \n",
       "75               Fast Food                                       300   \n",
       "76                Mix Food                                      1500   \n",
       "77            Regular Food                                       200   \n",
       "78         Kacchi Biriyani                                       150   \n",
       "79            Regular Food                                       200   \n",
       "80            Regular Food                                       400   \n",
       "81            Regular Food                                       200   \n",
       "82               Alu Vorta                                       150   \n",
       "83                   Pasta                                       220   \n",
       "84              Cashew nut                                       250   \n",
       "85              Cashew nut                                       200   \n",
       "86              Spicy food                                       500   \n",
       "87                Biriyani                                       250   \n",
       "88         Porota Dalbhaji                                        50   \n",
       "89                Biriyani                                       300   \n",
       "90             Grill & Nun                                       120   \n",
       "91                    Beef                                       150   \n",
       "92                Biriyani                                       180   \n",
       "93    Any well cooked food                                       200   \n",
       "94              Spicy Food                                       150   \n",
       "95                  Burger                                        60   \n",
       "96              Spicy food                                       300   \n",
       "97                 Italian                                       300   \n",
       "98               Fast food                                       600   \n",
       "99   Beef curry with curds                                       160   \n",
       "100               Biriyani                                       180   \n",
       "101          Luchi Dalvaji                                       100   \n",
       "102               Chicken                                        250   \n",
       "\n",
       "                         Restaurant location  \n",
       "0                                   Khilgaon  \n",
       "1                                  Dhanmondi  \n",
       "2                                  dhanmondi  \n",
       "3    many outlets in dhaka,i go to dhanmondi  \n",
       "4                                  Dhanmondi  \n",
       "5                                shantinagor  \n",
       "6                                  Dhanmondi  \n",
       "7                                  dhanmondi  \n",
       "8                                       Aust  \n",
       "9                                 basundhara  \n",
       "10                                   khilgao  \n",
       "11                                 Dhanmondi  \n",
       "12                                 dhanmondi  \n",
       "13                                  Shyamoli  \n",
       "14                                 Dhanmondi  \n",
       "15                                 Dhanmondi  \n",
       "16                                     dhaka  \n",
       "17                                       NaN  \n",
       "18             lat:23.707310 long: 90.415482  \n",
       "19                                  Mirpur 1  \n",
       "20                                   B Baria  \n",
       "21                                   Gulshan  \n",
       "22                                       NaN  \n",
       "23                                 Dhanmondi  \n",
       "24                                    Banani  \n",
       "25                                    Banani  \n",
       "26                               shantinagar  \n",
       "27                     Malibag Chowdhurypara  \n",
       "28                                 Dhanmondi  \n",
       "29                           Khilgaon, Dhaka  \n",
       "..                                       ...  \n",
       "73                                   Gulshan  \n",
       "74                                 Dhanmondi  \n",
       "75                                      Null  \n",
       "76                       Khilgaon, Dhanmondi  \n",
       "77                                       NaN  \n",
       "78                                 Dhanmondi  \n",
       "79                                 Dhanmundi  \n",
       "80                                 Dhanmondi  \n",
       "81                                 Dhanmondi  \n",
       "82                    2 no. gate, Chittagong  \n",
       "83                                  Khilgaon  \n",
       "84                                 Dhanmondi  \n",
       "85                                 Dhanmondi  \n",
       "86                                       NaN  \n",
       "87                                    Mirpur  \n",
       "88                                   Gulshan  \n",
       "89                                 Dhanmondi  \n",
       "90                                 Dhanmondi  \n",
       "91                            Rampura, Dhaka  \n",
       "92                                  Gulsan 2  \n",
       "93                                  Jigatola  \n",
       "94                                 Dhanmondi  \n",
       "95                                  Khilgaon  \n",
       "96                                  Khilgoan  \n",
       "97                               Bailey Road  \n",
       "98                                 Dhanmondi  \n",
       "99                                 Dhanmondi  \n",
       "100                                Dhanmondi  \n",
       "101                                Gulshan-2  \n",
       "102                                  gulshan  \n",
       "\n",
       "[103 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new.to_csv('q1.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf= pd.read_csv('q2.csv')\n",
    "fdf = fdf.drop('Unnamed: 0', axis=1)\n",
    "\n",
    "fdf.columns = ['id','u','v','w','x','y','z']\n",
    "\n",
    "fdf.loc[fdf.z == 'Khilgaon', 'RL'] = 1\n",
    "fdf.loc[fdf.z == 'Dhanmondi', 'RL'] = 2\n",
    "fdf.loc[fdf.z == 'Shyamoli', 'RL'] = 3\n",
    "fdf.loc[fdf.z == 'Mirpur', 'RL'] = 3\n",
    "fdf.loc[fdf.z == 'Shantinagar', 'RL'] = 4\n",
    "fdf.loc[fdf.z == 'BailyRoad', 'RL'] = 4\n",
    "fdf.loc[fdf.z == 'Gulshan', 'RL'] = 5\n",
    "fdf.loc[fdf.z == 'Banani', 'RL'] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>id</th>\n",
       "      <th>u</th>\n",
       "      <th>v</th>\n",
       "      <th>w</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>RL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151340976</td>\n",
       "      <td>Goran</td>\n",
       "      <td>23.750270</td>\n",
       "      <td>90.434130</td>\n",
       "      <td>Biriyani</td>\n",
       "      <td>150</td>\n",
       "      <td>Khilgaon</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>151340977</td>\n",
       "      <td>Banasree</td>\n",
       "      <td>22.513531</td>\n",
       "      <td>88.301537</td>\n",
       "      <td>Kacchi</td>\n",
       "      <td>200</td>\n",
       "      <td>Dhanmondi</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151340980</td>\n",
       "      <td>Niketon</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200</td>\n",
       "      <td>Dhanmondi</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151340981</td>\n",
       "      <td>Shaymoli</td>\n",
       "      <td>22.301411</td>\n",
       "      <td>70.822357</td>\n",
       "      <td>Burger</td>\n",
       "      <td>70</td>\n",
       "      <td>Dhanmondi</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151340982</td>\n",
       "      <td>Bangshal</td>\n",
       "      <td>-32.348610</td>\n",
       "      <td>151.924576</td>\n",
       "      <td>Burger</td>\n",
       "      <td>1000</td>\n",
       "      <td>Dhanmondi</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id         u          v           w         x     y          z   RL\n",
       "0  151340976     Goran  23.750270   90.434130  Biriyani   150   Khilgaon  1.0\n",
       "1  151340977  Banasree  22.513531   88.301537    Kacchi   200  Dhanmondi  2.0\n",
       "2  151340980   Niketon  23.707310   90.415480       NaN   200  Dhanmondi  2.0\n",
       "3  151340981  Shaymoli  22.301411   70.822357    Burger    70  Dhanmondi  2.0\n",
       "4  151340982  Bangshal -32.348610  151.924576    Burger  1000  Dhanmondi  2.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf.loc[fdf.x == 'Biriyani', 'fc'] = 1\n",
    "fdf.loc[fdf.x == 'Kacchi', 'fc'] = 1\n",
    "fdf.loc[fdf.x == 'RegularFood', 'fc'] = 1\n",
    "fdf.loc[fdf.x == 'khichuri', 'fc'] = 1\n",
    "\n",
    "\n",
    "fdf.loc[fdf.x == 'Burger', 'fc'] = 2\n",
    "fdf.loc[fdf.x == 'Pizza', 'fc'] = 2\n",
    "fdf.loc[fdf.x == 'Pasta', 'fc'] = 2\n",
    "fdf.loc[fdf.x == 'FastFood', 'fc'] = 2\n",
    "fdf.loc[fdf.x == 'ChineseFood', 'fc'] = 2\n",
    "\n",
    "fdf.loc[fdf.x == 'Chicken', 'fc'] = 3\n",
    "fdf.loc[fdf.x == 'Egg', 'fc'] = 3\n",
    "fdf.loc[fdf.x == 'Tea', 'fc'] = 3\n",
    "fdf.loc[fdf.x == 'Hilsha', 'fc'] = 3\n",
    "fdf.loc[fdf.x == 'Hilsa', 'fc'] = 3\n",
    "fdf.loc[fdf.x == 'Mutton', 'fc'] = 3\n",
    "fdf.loc[fdf.x == 'Beef', 'fc'] = 3\n",
    "\n",
    "fdf.loc[fdf.x == 'Waffle', 'fc'] = 4\n",
    "fdf.loc[fdf.x == 'Cake', 'fc'] = 4\n",
    "fdf.loc[fdf.x == 'Cashewnut', 'fc'] = 4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>x</th>\n",
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       "      <th>RL</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>151340976</td>\n",
       "      <td>Goran</td>\n",
       "      <td>23.750270</td>\n",
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       "      <td>Banasree</td>\n",
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       "      <td>200</td>\n",
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       "      <th>3</th>\n",
       "      <td>151340981</td>\n",
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       "      <td>70.822357</td>\n",
       "      <td>Burger</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151340982</td>\n",
       "      <td>Bangshal</td>\n",
       "      <td>-32.348610</td>\n",
       "      <td>151.924576</td>\n",
       "      <td>Burger</td>\n",
       "      <td>1000</td>\n",
       "      <td>Dhanmondi</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "          id         u          v           w         x     y          z   RL  \\\n",
       "0  151340976     Goran  23.750270   90.434130  Biriyani   150   Khilgaon  1.0   \n",
       "1  151340977  Banasree  22.513531   88.301537    Kacchi   200  Dhanmondi  2.0   \n",
       "2  151340980   Niketon  23.707310   90.415480       NaN   200  Dhanmondi  2.0   \n",
       "3  151340981  Shaymoli  22.301411   70.822357    Burger    70  Dhanmondi  2.0   \n",
       "4  151340982  Bangshal -32.348610  151.924576    Burger  1000  Dhanmondi  2.0   \n",
       "\n",
       "    fc  \n",
       "0  1.0  \n",
       "1  1.0  \n",
       "2  NaN  \n",
       "3  2.0  \n",
       "4  2.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf.drop(fdf.columns.difference(['v','w','y','RL','fc']), 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.442194</td>\n",
       "      <td>180</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.697285</td>\n",
       "      <td>90.431817</td>\n",
       "      <td>250</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23.707310</td>\n",
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       "      <td>400</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>350</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.442194</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>23.765800</td>\n",
       "      <td>90.358400</td>\n",
       "      <td>250</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23.737286</td>\n",
       "      <td>90.371610</td>\n",
       "      <td>100</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>200</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>23.745173</td>\n",
       "      <td>90.386421</td>\n",
       "      <td>200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>27.883743</td>\n",
       "      <td>79.912247</td>\n",
       "      <td>220</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>23.793993</td>\n",
       "      <td>90.404274</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24.098379</td>\n",
       "      <td>90.328712</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>23.776604</td>\n",
       "      <td>90.382988</td>\n",
       "      <td>120</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23.734558</td>\n",
       "      <td>90.384998</td>\n",
       "      <td>200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23.722109</td>\n",
       "      <td>90.426212</td>\n",
       "      <td>80</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.414300</td>\n",
       "      <td>140</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>250</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23.730534</td>\n",
       "      <td>90.446159</td>\n",
       "      <td>100</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>23.828959</td>\n",
       "      <td>90.375672</td>\n",
       "      <td>500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23.759815</td>\n",
       "      <td>90.391319</td>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>160</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415482</td>\n",
       "      <td>300</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>23.708022</td>\n",
       "      <td>90.417885</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>23.767600</td>\n",
       "      <td>90.414700</td>\n",
       "      <td>300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>23.732598</td>\n",
       "      <td>90.402584</td>\n",
       "      <td>1500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>23.756107</td>\n",
       "      <td>90.387196</td>\n",
       "      <td>200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>23.790579</td>\n",
       "      <td>90.365308</td>\n",
       "      <td>150</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>23.738078</td>\n",
       "      <td>90.372485</td>\n",
       "      <td>200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>23.693846</td>\n",
       "      <td>90.455336</td>\n",
       "      <td>400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>23.771800</td>\n",
       "      <td>90.363100</td>\n",
       "      <td>200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>23.744602</td>\n",
       "      <td>90.411456</td>\n",
       "      <td>220</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>23.721389</td>\n",
       "      <td>90.429928</td>\n",
       "      <td>250</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>23.722595</td>\n",
       "      <td>90.429585</td>\n",
       "      <td>200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.368613</td>\n",
       "      <td>250</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415482</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>23.727835</td>\n",
       "      <td>90.374046</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>23.763697</td>\n",
       "      <td>90.370691</td>\n",
       "      <td>120</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>180</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>23.778600</td>\n",
       "      <td>90.415600</td>\n",
       "      <td>200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>23.795600</td>\n",
       "      <td>90.353700</td>\n",
       "      <td>150</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>23.758353</td>\n",
       "      <td>90.416229</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>23.797911</td>\n",
       "      <td>90.414391</td>\n",
       "      <td>300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.414300</td>\n",
       "      <td>300</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>23.780232</td>\n",
       "      <td>90.401148</td>\n",
       "      <td>600</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>23.746188</td>\n",
       "      <td>90.389870</td>\n",
       "      <td>160</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>23.751791</td>\n",
       "      <td>90.385936</td>\n",
       "      <td>180</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.368613</td>\n",
       "      <td>100</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>26.782930</td>\n",
       "      <td>82.150100</td>\n",
       "      <td>250</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             v           w     y   RL   fc\n",
       "0    23.750270   90.434130   150  1.0  1.0\n",
       "1    22.513531   88.301537   200  2.0  1.0\n",
       "2    23.707310   90.415480   200  2.0  NaN\n",
       "3    22.301411   70.822357    70  2.0  2.0\n",
       "4   -32.348610  151.924576  1000  2.0  2.0\n",
       "5    23.701443   90.442194   180  4.0  1.0\n",
       "6    23.697285   90.431817   250  2.0  1.0\n",
       "7    23.707310   90.415480   400  2.0  1.0\n",
       "8    23.761227   90.420769   150  NaN  1.0\n",
       "9    23.761227   90.420769   350  2.0  3.0\n",
       "10   23.701443   90.442194   150  1.0  1.0\n",
       "11   23.765800   90.358400   250  2.0  2.0\n",
       "12   23.737286   90.371610   100  2.0  3.0\n",
       "13   23.707310   90.415480   200  3.0  1.0\n",
       "14   23.745173   90.386421   200  2.0  1.0\n",
       "15   27.883743   79.912247   220  2.0  1.0\n",
       "16   23.707310   90.415480   150  1.0  1.0\n",
       "17   23.793993   90.404274    20  NaN  1.0\n",
       "18   24.098379   90.328712    50  1.0  2.0\n",
       "19   23.776604   90.382988   120  3.0  1.0\n",
       "20   23.734558   90.384998   200  NaN  3.0\n",
       "21   23.722109   90.426212    80  5.0  3.0\n",
       "22   23.761227   90.420769   800  NaN  3.0\n",
       "23   23.738200   90.414300   140  2.0  1.0\n",
       "24   23.707310   90.415480   250  5.0  1.0\n",
       "25   23.707310   90.415480   150  5.0  3.0\n",
       "26   23.730534   90.446159   100  4.0  3.0\n",
       "27   23.828959   90.375672   500  1.0  2.0\n",
       "28   23.759815   90.391319   120  2.0  NaN\n",
       "29   23.707310   90.415480   160  1.0  1.0\n",
       "..         ...         ...   ...  ...  ...\n",
       "73   23.707310   90.415482   300  5.0  2.0\n",
       "74   23.708022   90.417885   300  2.0  2.0\n",
       "75   23.767600   90.414700   300  NaN  2.0\n",
       "76   23.732598   90.402584  1500  1.0  2.0\n",
       "77   23.756107   90.387196   200  NaN  1.0\n",
       "78   23.790579   90.365308   150  2.0  1.0\n",
       "79   23.738078   90.372485   200  2.0  1.0\n",
       "80   23.693846   90.455336   400  2.0  1.0\n",
       "81   23.771800   90.363100   200  2.0  1.0\n",
       "82   23.707310   90.415480   150  NaN  NaN\n",
       "83   23.744602   90.411456   220  1.0  2.0\n",
       "84   23.721389   90.429928   250  2.0  4.0\n",
       "85   23.722595   90.429585   200  2.0  4.0\n",
       "86   23.707310   90.415480   500  NaN  1.0\n",
       "87   23.807085   90.368613   250  3.0  1.0\n",
       "88   23.707310   90.415482    50  5.0  1.0\n",
       "89   23.727835   90.374046   300  2.0  1.0\n",
       "90   23.763697   90.370691   120  2.0  1.0\n",
       "91   23.707310   90.415480   150  1.0  3.0\n",
       "92   23.707310   90.415480   180  5.0  1.0\n",
       "93   23.778600   90.415600   200  2.0  2.0\n",
       "94   23.795600   90.353700   150  2.0  2.0\n",
       "95   23.758353   90.416229    60  1.0  2.0\n",
       "96   23.797911   90.414391   300  1.0  2.0\n",
       "97   23.738200   90.414300   300  4.0  2.0\n",
       "98   23.780232   90.401148   600  2.0  2.0\n",
       "99   23.746188   90.389870   160  2.0  3.0\n",
       "100  23.751791   90.385936   180  2.0  1.0\n",
       "101  23.807085   90.368613   100  5.0  1.0\n",
       "102  26.782930   82.150100   250  5.0  3.0\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "cT=['v','w','y','fc','RL']\n",
    "fdf=fdf.reindex(columns=cT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v</th>\n",
       "      <th>w</th>\n",
       "      <th>y</th>\n",
       "      <th>fc</th>\n",
       "      <th>RL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.750270</td>\n",
       "      <td>90.434130</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.513531</td>\n",
       "      <td>88.301537</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.301411</td>\n",
       "      <td>70.822357</td>\n",
       "      <td>70</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-32.348610</td>\n",
       "      <td>151.924576</td>\n",
       "      <td>1000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.442194</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.697285</td>\n",
       "      <td>90.431817</td>\n",
       "      <td>250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>350</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.442194</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>23.765800</td>\n",
       "      <td>90.358400</td>\n",
       "      <td>250</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23.737286</td>\n",
       "      <td>90.371610</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>23.745173</td>\n",
       "      <td>90.386421</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>27.883743</td>\n",
       "      <td>79.912247</td>\n",
       "      <td>220</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>23.793993</td>\n",
       "      <td>90.404274</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24.098379</td>\n",
       "      <td>90.328712</td>\n",
       "      <td>50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>23.776604</td>\n",
       "      <td>90.382988</td>\n",
       "      <td>120</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23.734558</td>\n",
       "      <td>90.384998</td>\n",
       "      <td>200</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23.722109</td>\n",
       "      <td>90.426212</td>\n",
       "      <td>80</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>800</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.414300</td>\n",
       "      <td>140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23.730534</td>\n",
       "      <td>90.446159</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>23.828959</td>\n",
       "      <td>90.375672</td>\n",
       "      <td>500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23.759815</td>\n",
       "      <td>90.391319</td>\n",
       "      <td>120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>160</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415482</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>23.708022</td>\n",
       "      <td>90.417885</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>23.767600</td>\n",
       "      <td>90.414700</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>23.732598</td>\n",
       "      <td>90.402584</td>\n",
       "      <td>1500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>23.756107</td>\n",
       "      <td>90.387196</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>23.790579</td>\n",
       "      <td>90.365308</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>23.738078</td>\n",
       "      <td>90.372485</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>23.693846</td>\n",
       "      <td>90.455336</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>23.771800</td>\n",
       "      <td>90.363100</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>23.744602</td>\n",
       "      <td>90.411456</td>\n",
       "      <td>220</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>23.721389</td>\n",
       "      <td>90.429928</td>\n",
       "      <td>250</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>23.722595</td>\n",
       "      <td>90.429585</td>\n",
       "      <td>200</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.368613</td>\n",
       "      <td>250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415482</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>23.727835</td>\n",
       "      <td>90.374046</td>\n",
       "      <td>300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>23.763697</td>\n",
       "      <td>90.370691</td>\n",
       "      <td>120</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>23.778600</td>\n",
       "      <td>90.415600</td>\n",
       "      <td>200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>23.795600</td>\n",
       "      <td>90.353700</td>\n",
       "      <td>150</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>23.758353</td>\n",
       "      <td>90.416229</td>\n",
       "      <td>60</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>23.797911</td>\n",
       "      <td>90.414391</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.414300</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>23.780232</td>\n",
       "      <td>90.401148</td>\n",
       "      <td>600</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>23.746188</td>\n",
       "      <td>90.389870</td>\n",
       "      <td>160</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>23.751791</td>\n",
       "      <td>90.385936</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.368613</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>26.782930</td>\n",
       "      <td>82.150100</td>\n",
       "      <td>250</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             v           w     y   fc   RL\n",
       "0    23.750270   90.434130   150  1.0  1.0\n",
       "1    22.513531   88.301537   200  1.0  2.0\n",
       "2    23.707310   90.415480   200  NaN  2.0\n",
       "3    22.301411   70.822357    70  2.0  2.0\n",
       "4   -32.348610  151.924576  1000  2.0  2.0\n",
       "5    23.701443   90.442194   180  1.0  4.0\n",
       "6    23.697285   90.431817   250  1.0  2.0\n",
       "7    23.707310   90.415480   400  1.0  2.0\n",
       "8    23.761227   90.420769   150  1.0  NaN\n",
       "9    23.761227   90.420769   350  3.0  2.0\n",
       "10   23.701443   90.442194   150  1.0  1.0\n",
       "11   23.765800   90.358400   250  2.0  2.0\n",
       "12   23.737286   90.371610   100  3.0  2.0\n",
       "13   23.707310   90.415480   200  1.0  3.0\n",
       "14   23.745173   90.386421   200  1.0  2.0\n",
       "15   27.883743   79.912247   220  1.0  2.0\n",
       "16   23.707310   90.415480   150  1.0  1.0\n",
       "17   23.793993   90.404274    20  1.0  NaN\n",
       "18   24.098379   90.328712    50  2.0  1.0\n",
       "19   23.776604   90.382988   120  1.0  3.0\n",
       "20   23.734558   90.384998   200  3.0  NaN\n",
       "21   23.722109   90.426212    80  3.0  5.0\n",
       "22   23.761227   90.420769   800  3.0  NaN\n",
       "23   23.738200   90.414300   140  1.0  2.0\n",
       "24   23.707310   90.415480   250  1.0  5.0\n",
       "25   23.707310   90.415480   150  3.0  5.0\n",
       "26   23.730534   90.446159   100  3.0  4.0\n",
       "27   23.828959   90.375672   500  2.0  1.0\n",
       "28   23.759815   90.391319   120  NaN  2.0\n",
       "29   23.707310   90.415480   160  1.0  1.0\n",
       "..         ...         ...   ...  ...  ...\n",
       "73   23.707310   90.415482   300  2.0  5.0\n",
       "74   23.708022   90.417885   300  2.0  2.0\n",
       "75   23.767600   90.414700   300  2.0  NaN\n",
       "76   23.732598   90.402584  1500  2.0  1.0\n",
       "77   23.756107   90.387196   200  1.0  NaN\n",
       "78   23.790579   90.365308   150  1.0  2.0\n",
       "79   23.738078   90.372485   200  1.0  2.0\n",
       "80   23.693846   90.455336   400  1.0  2.0\n",
       "81   23.771800   90.363100   200  1.0  2.0\n",
       "82   23.707310   90.415480   150  NaN  NaN\n",
       "83   23.744602   90.411456   220  2.0  1.0\n",
       "84   23.721389   90.429928   250  4.0  2.0\n",
       "85   23.722595   90.429585   200  4.0  2.0\n",
       "86   23.707310   90.415480   500  1.0  NaN\n",
       "87   23.807085   90.368613   250  1.0  3.0\n",
       "88   23.707310   90.415482    50  1.0  5.0\n",
       "89   23.727835   90.374046   300  1.0  2.0\n",
       "90   23.763697   90.370691   120  1.0  2.0\n",
       "91   23.707310   90.415480   150  3.0  1.0\n",
       "92   23.707310   90.415480   180  1.0  5.0\n",
       "93   23.778600   90.415600   200  2.0  2.0\n",
       "94   23.795600   90.353700   150  2.0  2.0\n",
       "95   23.758353   90.416229    60  2.0  1.0\n",
       "96   23.797911   90.414391   300  2.0  1.0\n",
       "97   23.738200   90.414300   300  2.0  4.0\n",
       "98   23.780232   90.401148   600  2.0  2.0\n",
       "99   23.746188   90.389870   160  3.0  2.0\n",
       "100  23.751791   90.385936   180  1.0  2.0\n",
       "101  23.807085   90.368613   100  1.0  5.0\n",
       "102  26.782930   82.150100   250  3.0  5.0\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf=fdf.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v</th>\n",
       "      <th>w</th>\n",
       "      <th>y</th>\n",
       "      <th>fc</th>\n",
       "      <th>RL</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.750270</td>\n",
       "      <td>90.434130</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.513531</td>\n",
       "      <td>88.301537</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.301411</td>\n",
       "      <td>70.822357</td>\n",
       "      <td>70</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-32.348610</td>\n",
       "      <td>151.924576</td>\n",
       "      <td>1000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.442194</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.697285</td>\n",
       "      <td>90.431817</td>\n",
       "      <td>250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>350</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.442194</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>23.765800</td>\n",
       "      <td>90.358400</td>\n",
       "      <td>250</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23.737286</td>\n",
       "      <td>90.371610</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>23.745173</td>\n",
       "      <td>90.386421</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>27.883743</td>\n",
       "      <td>79.912247</td>\n",
       "      <td>220</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>23.793993</td>\n",
       "      <td>90.404274</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24.098379</td>\n",
       "      <td>90.328712</td>\n",
       "      <td>50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>23.776604</td>\n",
       "      <td>90.382988</td>\n",
       "      <td>120</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23.734558</td>\n",
       "      <td>90.384998</td>\n",
       "      <td>200</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23.722109</td>\n",
       "      <td>90.426212</td>\n",
       "      <td>80</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>800</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.414300</td>\n",
       "      <td>140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23.730534</td>\n",
       "      <td>90.446159</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>23.828959</td>\n",
       "      <td>90.375672</td>\n",
       "      <td>500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23.759815</td>\n",
       "      <td>90.391319</td>\n",
       "      <td>120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>160</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415482</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>23.708022</td>\n",
       "      <td>90.417885</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>23.767600</td>\n",
       "      <td>90.414700</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>23.732598</td>\n",
       "      <td>90.402584</td>\n",
       "      <td>1500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>23.756107</td>\n",
       "      <td>90.387196</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>23.790579</td>\n",
       "      <td>90.365308</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>23.738078</td>\n",
       "      <td>90.372485</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>23.693846</td>\n",
       "      <td>90.455336</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>23.771800</td>\n",
       "      <td>90.363100</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>23.744602</td>\n",
       "      <td>90.411456</td>\n",
       "      <td>220</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>23.721389</td>\n",
       "      <td>90.429928</td>\n",
       "      <td>250</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>23.722595</td>\n",
       "      <td>90.429585</td>\n",
       "      <td>200</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.368613</td>\n",
       "      <td>250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415482</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>23.727835</td>\n",
       "      <td>90.374046</td>\n",
       "      <td>300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>23.763697</td>\n",
       "      <td>90.370691</td>\n",
       "      <td>120</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>23.778600</td>\n",
       "      <td>90.415600</td>\n",
       "      <td>200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>23.795600</td>\n",
       "      <td>90.353700</td>\n",
       "      <td>150</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>23.758353</td>\n",
       "      <td>90.416229</td>\n",
       "      <td>60</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>23.797911</td>\n",
       "      <td>90.414391</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.414300</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>23.780232</td>\n",
       "      <td>90.401148</td>\n",
       "      <td>600</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>23.746188</td>\n",
       "      <td>90.389870</td>\n",
       "      <td>160</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>23.751791</td>\n",
       "      <td>90.385936</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.368613</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>26.782930</td>\n",
       "      <td>82.150100</td>\n",
       "      <td>250</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             v           w     y   fc   RL\n",
       "0    23.750270   90.434130   150  1.0  1.0\n",
       "1    22.513531   88.301537   200  1.0  2.0\n",
       "2    23.707310   90.415480   200  0.0  2.0\n",
       "3    22.301411   70.822357    70  2.0  2.0\n",
       "4   -32.348610  151.924576  1000  2.0  2.0\n",
       "5    23.701443   90.442194   180  1.0  4.0\n",
       "6    23.697285   90.431817   250  1.0  2.0\n",
       "7    23.707310   90.415480   400  1.0  2.0\n",
       "8    23.761227   90.420769   150  1.0  0.0\n",
       "9    23.761227   90.420769   350  3.0  2.0\n",
       "10   23.701443   90.442194   150  1.0  1.0\n",
       "11   23.765800   90.358400   250  2.0  2.0\n",
       "12   23.737286   90.371610   100  3.0  2.0\n",
       "13   23.707310   90.415480   200  1.0  3.0\n",
       "14   23.745173   90.386421   200  1.0  2.0\n",
       "15   27.883743   79.912247   220  1.0  2.0\n",
       "16   23.707310   90.415480   150  1.0  1.0\n",
       "17   23.793993   90.404274    20  1.0  0.0\n",
       "18   24.098379   90.328712    50  2.0  1.0\n",
       "19   23.776604   90.382988   120  1.0  3.0\n",
       "20   23.734558   90.384998   200  3.0  0.0\n",
       "21   23.722109   90.426212    80  3.0  5.0\n",
       "22   23.761227   90.420769   800  3.0  0.0\n",
       "23   23.738200   90.414300   140  1.0  2.0\n",
       "24   23.707310   90.415480   250  1.0  5.0\n",
       "25   23.707310   90.415480   150  3.0  5.0\n",
       "26   23.730534   90.446159   100  3.0  4.0\n",
       "27   23.828959   90.375672   500  2.0  1.0\n",
       "28   23.759815   90.391319   120  0.0  2.0\n",
       "29   23.707310   90.415480   160  1.0  1.0\n",
       "..         ...         ...   ...  ...  ...\n",
       "73   23.707310   90.415482   300  2.0  5.0\n",
       "74   23.708022   90.417885   300  2.0  2.0\n",
       "75   23.767600   90.414700   300  2.0  0.0\n",
       "76   23.732598   90.402584  1500  2.0  1.0\n",
       "77   23.756107   90.387196   200  1.0  0.0\n",
       "78   23.790579   90.365308   150  1.0  2.0\n",
       "79   23.738078   90.372485   200  1.0  2.0\n",
       "80   23.693846   90.455336   400  1.0  2.0\n",
       "81   23.771800   90.363100   200  1.0  2.0\n",
       "82   23.707310   90.415480   150  0.0  0.0\n",
       "83   23.744602   90.411456   220  2.0  1.0\n",
       "84   23.721389   90.429928   250  4.0  2.0\n",
       "85   23.722595   90.429585   200  4.0  2.0\n",
       "86   23.707310   90.415480   500  1.0  0.0\n",
       "87   23.807085   90.368613   250  1.0  3.0\n",
       "88   23.707310   90.415482    50  1.0  5.0\n",
       "89   23.727835   90.374046   300  1.0  2.0\n",
       "90   23.763697   90.370691   120  1.0  2.0\n",
       "91   23.707310   90.415480   150  3.0  1.0\n",
       "92   23.707310   90.415480   180  1.0  5.0\n",
       "93   23.778600   90.415600   200  2.0  2.0\n",
       "94   23.795600   90.353700   150  2.0  2.0\n",
       "95   23.758353   90.416229    60  2.0  1.0\n",
       "96   23.797911   90.414391   300  2.0  1.0\n",
       "97   23.738200   90.414300   300  2.0  4.0\n",
       "98   23.780232   90.401148   600  2.0  2.0\n",
       "99   23.746188   90.389870   160  3.0  2.0\n",
       "100  23.751791   90.385936   180  1.0  2.0\n",
       "101  23.807085   90.368613   100  1.0  5.0\n",
       "102  26.782930   82.150100   250  3.0  5.0\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf[pd.to_numeric(fdf['v'],errors='coerce').notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>v</th>\n",
       "      <th>w</th>\n",
       "      <th>y</th>\n",
       "      <th>fc</th>\n",
       "      <th>RL</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>23.750270</td>\n",
       "      <td>90.434130</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.513531</td>\n",
       "      <td>88.301537</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.301411</td>\n",
       "      <td>70.822357</td>\n",
       "      <td>70</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-32.348610</td>\n",
       "      <td>151.924576</td>\n",
       "      <td>1000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.442194</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.697285</td>\n",
       "      <td>90.431817</td>\n",
       "      <td>250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>350</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.442194</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>23.765800</td>\n",
       "      <td>90.358400</td>\n",
       "      <td>250</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23.737286</td>\n",
       "      <td>90.371610</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>23.745173</td>\n",
       "      <td>90.386421</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>27.883743</td>\n",
       "      <td>79.912247</td>\n",
       "      <td>220</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>23.793993</td>\n",
       "      <td>90.404274</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24.098379</td>\n",
       "      <td>90.328712</td>\n",
       "      <td>50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>23.776604</td>\n",
       "      <td>90.382988</td>\n",
       "      <td>120</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23.734558</td>\n",
       "      <td>90.384998</td>\n",
       "      <td>200</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23.722109</td>\n",
       "      <td>90.426212</td>\n",
       "      <td>80</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>800</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.414300</td>\n",
       "      <td>140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23.730534</td>\n",
       "      <td>90.446159</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>23.828959</td>\n",
       "      <td>90.375672</td>\n",
       "      <td>500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23.759815</td>\n",
       "      <td>90.391319</td>\n",
       "      <td>120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>160</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415482</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>23.708022</td>\n",
       "      <td>90.417885</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>23.767600</td>\n",
       "      <td>90.414700</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>23.732598</td>\n",
       "      <td>90.402584</td>\n",
       "      <td>1500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>23.756107</td>\n",
       "      <td>90.387196</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>23.790579</td>\n",
       "      <td>90.365308</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>23.738078</td>\n",
       "      <td>90.372485</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>23.693846</td>\n",
       "      <td>90.455336</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>23.771800</td>\n",
       "      <td>90.363100</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>23.744602</td>\n",
       "      <td>90.411456</td>\n",
       "      <td>220</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>23.721389</td>\n",
       "      <td>90.429928</td>\n",
       "      <td>250</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>23.722595</td>\n",
       "      <td>90.429585</td>\n",
       "      <td>200</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.368613</td>\n",
       "      <td>250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415482</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>23.727835</td>\n",
       "      <td>90.374046</td>\n",
       "      <td>300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>23.763697</td>\n",
       "      <td>90.370691</td>\n",
       "      <td>120</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>23.778600</td>\n",
       "      <td>90.415600</td>\n",
       "      <td>200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>23.795600</td>\n",
       "      <td>90.353700</td>\n",
       "      <td>150</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>23.758353</td>\n",
       "      <td>90.416229</td>\n",
       "      <td>60</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>23.797911</td>\n",
       "      <td>90.414391</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.414300</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>23.780232</td>\n",
       "      <td>90.401148</td>\n",
       "      <td>600</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>23.746188</td>\n",
       "      <td>90.389870</td>\n",
       "      <td>160</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>23.751791</td>\n",
       "      <td>90.385936</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.368613</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>26.782930</td>\n",
       "      <td>82.150100</td>\n",
       "      <td>250</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             v           w     y   fc   RL\n",
       "0    23.750270   90.434130   150  1.0  1.0\n",
       "1    22.513531   88.301537   200  1.0  2.0\n",
       "2    23.707310   90.415480   200  0.0  2.0\n",
       "3    22.301411   70.822357    70  2.0  2.0\n",
       "4   -32.348610  151.924576  1000  2.0  2.0\n",
       "5    23.701443   90.442194   180  1.0  4.0\n",
       "6    23.697285   90.431817   250  1.0  2.0\n",
       "7    23.707310   90.415480   400  1.0  2.0\n",
       "8    23.761227   90.420769   150  1.0  0.0\n",
       "9    23.761227   90.420769   350  3.0  2.0\n",
       "10   23.701443   90.442194   150  1.0  1.0\n",
       "11   23.765800   90.358400   250  2.0  2.0\n",
       "12   23.737286   90.371610   100  3.0  2.0\n",
       "13   23.707310   90.415480   200  1.0  3.0\n",
       "14   23.745173   90.386421   200  1.0  2.0\n",
       "15   27.883743   79.912247   220  1.0  2.0\n",
       "16   23.707310   90.415480   150  1.0  1.0\n",
       "17   23.793993   90.404274    20  1.0  0.0\n",
       "18   24.098379   90.328712    50  2.0  1.0\n",
       "19   23.776604   90.382988   120  1.0  3.0\n",
       "20   23.734558   90.384998   200  3.0  0.0\n",
       "21   23.722109   90.426212    80  3.0  5.0\n",
       "22   23.761227   90.420769   800  3.0  0.0\n",
       "23   23.738200   90.414300   140  1.0  2.0\n",
       "24   23.707310   90.415480   250  1.0  5.0\n",
       "25   23.707310   90.415480   150  3.0  5.0\n",
       "26   23.730534   90.446159   100  3.0  4.0\n",
       "27   23.828959   90.375672   500  2.0  1.0\n",
       "28   23.759815   90.391319   120  0.0  2.0\n",
       "29   23.707310   90.415480   160  1.0  1.0\n",
       "..         ...         ...   ...  ...  ...\n",
       "73   23.707310   90.415482   300  2.0  5.0\n",
       "74   23.708022   90.417885   300  2.0  2.0\n",
       "75   23.767600   90.414700   300  2.0  0.0\n",
       "76   23.732598   90.402584  1500  2.0  1.0\n",
       "77   23.756107   90.387196   200  1.0  0.0\n",
       "78   23.790579   90.365308   150  1.0  2.0\n",
       "79   23.738078   90.372485   200  1.0  2.0\n",
       "80   23.693846   90.455336   400  1.0  2.0\n",
       "81   23.771800   90.363100   200  1.0  2.0\n",
       "82   23.707310   90.415480   150  0.0  0.0\n",
       "83   23.744602   90.411456   220  2.0  1.0\n",
       "84   23.721389   90.429928   250  4.0  2.0\n",
       "85   23.722595   90.429585   200  4.0  2.0\n",
       "86   23.707310   90.415480   500  1.0  0.0\n",
       "87   23.807085   90.368613   250  1.0  3.0\n",
       "88   23.707310   90.415482    50  1.0  5.0\n",
       "89   23.727835   90.374046   300  1.0  2.0\n",
       "90   23.763697   90.370691   120  1.0  2.0\n",
       "91   23.707310   90.415480   150  3.0  1.0\n",
       "92   23.707310   90.415480   180  1.0  5.0\n",
       "93   23.778600   90.415600   200  2.0  2.0\n",
       "94   23.795600   90.353700   150  2.0  2.0\n",
       "95   23.758353   90.416229    60  2.0  1.0\n",
       "96   23.797911   90.414391   300  2.0  1.0\n",
       "97   23.738200   90.414300   300  2.0  4.0\n",
       "98   23.780232   90.401148   600  2.0  2.0\n",
       "99   23.746188   90.389870   160  3.0  2.0\n",
       "100  23.751791   90.385936   180  1.0  2.0\n",
       "101  23.807085   90.368613   100  1.0  5.0\n",
       "102  26.782930   82.150100   250  3.0  5.0\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf[pd.to_numeric(fdf['w'],errors='coerce').notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>v</th>\n",
       "      <th>w</th>\n",
       "      <th>y</th>\n",
       "      <th>fc</th>\n",
       "      <th>RL</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>23.750270</td>\n",
       "      <td>90.434130</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.513531</td>\n",
       "      <td>88.301537</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22.301411</td>\n",
       "      <td>70.822357</td>\n",
       "      <td>70</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-32.348610</td>\n",
       "      <td>151.924576</td>\n",
       "      <td>1000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.442194</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.697285</td>\n",
       "      <td>90.431817</td>\n",
       "      <td>250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>350</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>23.701443</td>\n",
       "      <td>90.442194</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>23.765800</td>\n",
       "      <td>90.358400</td>\n",
       "      <td>250</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23.737286</td>\n",
       "      <td>90.371610</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>23.745173</td>\n",
       "      <td>90.386421</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>27.883743</td>\n",
       "      <td>79.912247</td>\n",
       "      <td>220</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>23.793993</td>\n",
       "      <td>90.404274</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>24.098379</td>\n",
       "      <td>90.328712</td>\n",
       "      <td>50</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>23.776604</td>\n",
       "      <td>90.382988</td>\n",
       "      <td>120</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23.734558</td>\n",
       "      <td>90.384998</td>\n",
       "      <td>200</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23.722109</td>\n",
       "      <td>90.426212</td>\n",
       "      <td>80</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.761227</td>\n",
       "      <td>90.420769</td>\n",
       "      <td>800</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.414300</td>\n",
       "      <td>140</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>23.730534</td>\n",
       "      <td>90.446159</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>23.828959</td>\n",
       "      <td>90.375672</td>\n",
       "      <td>500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23.759815</td>\n",
       "      <td>90.391319</td>\n",
       "      <td>120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>160</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415482</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>23.708022</td>\n",
       "      <td>90.417885</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>23.767600</td>\n",
       "      <td>90.414700</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>23.732598</td>\n",
       "      <td>90.402584</td>\n",
       "      <td>1500</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>23.756107</td>\n",
       "      <td>90.387196</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>23.790579</td>\n",
       "      <td>90.365308</td>\n",
       "      <td>150</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>23.738078</td>\n",
       "      <td>90.372485</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>23.693846</td>\n",
       "      <td>90.455336</td>\n",
       "      <td>400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>23.771800</td>\n",
       "      <td>90.363100</td>\n",
       "      <td>200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>23.744602</td>\n",
       "      <td>90.411456</td>\n",
       "      <td>220</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>23.721389</td>\n",
       "      <td>90.429928</td>\n",
       "      <td>250</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>23.722595</td>\n",
       "      <td>90.429585</td>\n",
       "      <td>200</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.368613</td>\n",
       "      <td>250</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415482</td>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>23.727835</td>\n",
       "      <td>90.374046</td>\n",
       "      <td>300</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>23.763697</td>\n",
       "      <td>90.370691</td>\n",
       "      <td>120</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>150</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>23.778600</td>\n",
       "      <td>90.415600</td>\n",
       "      <td>200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>23.795600</td>\n",
       "      <td>90.353700</td>\n",
       "      <td>150</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>23.758353</td>\n",
       "      <td>90.416229</td>\n",
       "      <td>60</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>23.797911</td>\n",
       "      <td>90.414391</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>23.738200</td>\n",
       "      <td>90.414300</td>\n",
       "      <td>300</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>23.780232</td>\n",
       "      <td>90.401148</td>\n",
       "      <td>600</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>23.746188</td>\n",
       "      <td>90.389870</td>\n",
       "      <td>160</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>23.751791</td>\n",
       "      <td>90.385936</td>\n",
       "      <td>180</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>23.807085</td>\n",
       "      <td>90.368613</td>\n",
       "      <td>100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>26.782930</td>\n",
       "      <td>82.150100</td>\n",
       "      <td>250</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             v           w     y   fc   RL\n",
       "0    23.750270   90.434130   150  1.0  1.0\n",
       "1    22.513531   88.301537   200  1.0  2.0\n",
       "2    23.707310   90.415480   200  0.0  2.0\n",
       "3    22.301411   70.822357    70  2.0  2.0\n",
       "4   -32.348610  151.924576  1000  2.0  2.0\n",
       "5    23.701443   90.442194   180  1.0  4.0\n",
       "6    23.697285   90.431817   250  1.0  2.0\n",
       "7    23.707310   90.415480   400  1.0  2.0\n",
       "8    23.761227   90.420769   150  1.0  0.0\n",
       "9    23.761227   90.420769   350  3.0  2.0\n",
       "10   23.701443   90.442194   150  1.0  1.0\n",
       "11   23.765800   90.358400   250  2.0  2.0\n",
       "12   23.737286   90.371610   100  3.0  2.0\n",
       "13   23.707310   90.415480   200  1.0  3.0\n",
       "14   23.745173   90.386421   200  1.0  2.0\n",
       "15   27.883743   79.912247   220  1.0  2.0\n",
       "16   23.707310   90.415480   150  1.0  1.0\n",
       "17   23.793993   90.404274    20  1.0  0.0\n",
       "18   24.098379   90.328712    50  2.0  1.0\n",
       "19   23.776604   90.382988   120  1.0  3.0\n",
       "20   23.734558   90.384998   200  3.0  0.0\n",
       "21   23.722109   90.426212    80  3.0  5.0\n",
       "22   23.761227   90.420769   800  3.0  0.0\n",
       "23   23.738200   90.414300   140  1.0  2.0\n",
       "24   23.707310   90.415480   250  1.0  5.0\n",
       "25   23.707310   90.415480   150  3.0  5.0\n",
       "26   23.730534   90.446159   100  3.0  4.0\n",
       "27   23.828959   90.375672   500  2.0  1.0\n",
       "28   23.759815   90.391319   120  0.0  2.0\n",
       "29   23.707310   90.415480   160  1.0  1.0\n",
       "..         ...         ...   ...  ...  ...\n",
       "73   23.707310   90.415482   300  2.0  5.0\n",
       "74   23.708022   90.417885   300  2.0  2.0\n",
       "75   23.767600   90.414700   300  2.0  0.0\n",
       "76   23.732598   90.402584  1500  2.0  1.0\n",
       "77   23.756107   90.387196   200  1.0  0.0\n",
       "78   23.790579   90.365308   150  1.0  2.0\n",
       "79   23.738078   90.372485   200  1.0  2.0\n",
       "80   23.693846   90.455336   400  1.0  2.0\n",
       "81   23.771800   90.363100   200  1.0  2.0\n",
       "82   23.707310   90.415480   150  0.0  0.0\n",
       "83   23.744602   90.411456   220  2.0  1.0\n",
       "84   23.721389   90.429928   250  4.0  2.0\n",
       "85   23.722595   90.429585   200  4.0  2.0\n",
       "86   23.707310   90.415480   500  1.0  0.0\n",
       "87   23.807085   90.368613   250  1.0  3.0\n",
       "88   23.707310   90.415482    50  1.0  5.0\n",
       "89   23.727835   90.374046   300  1.0  2.0\n",
       "90   23.763697   90.370691   120  1.0  2.0\n",
       "91   23.707310   90.415480   150  3.0  1.0\n",
       "92   23.707310   90.415480   180  1.0  5.0\n",
       "93   23.778600   90.415600   200  2.0  2.0\n",
       "94   23.795600   90.353700   150  2.0  2.0\n",
       "95   23.758353   90.416229    60  2.0  1.0\n",
       "96   23.797911   90.414391   300  2.0  1.0\n",
       "97   23.738200   90.414300   300  2.0  4.0\n",
       "98   23.780232   90.401148   600  2.0  2.0\n",
       "99   23.746188   90.389870   160  3.0  2.0\n",
       "100  23.751791   90.385936   180  1.0  2.0\n",
       "101  23.807085   90.368613   100  1.0  5.0\n",
       "102  26.782930   82.150100   250  3.0  5.0\n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf[pd.to_numeric(fdf['y'],errors='coerce').notnull()]\n",
    "fdf[pd.to_numeric(fdf['fc'],errors='coerce').notnull()]\n",
    "fdf[pd.to_numeric(fdf['RL'],errors='coerce').notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x223ca6145f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(fdf.corr())\n",
    "plt.xticks(range(len(fdf.columns)), fdf.columns)\n",
    "plt.yticks(range(len(fdf.columns)), fdf.columns)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x223ca666160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.scatter_matrix(fdf, figsize=(6, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf.to_csv('question2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "fdf = pd.read_csv('question2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>v</th>\n",
       "      <th>w</th>\n",
       "      <th>y</th>\n",
       "      <th>fc</th>\n",
       "      <th>RL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.750270</td>\n",
       "      <td>90.434130</td>\n",
       "      <td>150</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>22.513531</td>\n",
       "      <td>88.301537</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>23.707310</td>\n",
       "      <td>90.415480</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>22.301411</td>\n",
       "      <td>70.822357</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-32.348610</td>\n",
       "      <td>151.924576</td>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1          v           w     y  fc  RL\n",
       "0           0             0  23.750270   90.434130   150   1   1\n",
       "1           1             1  22.513531   88.301537   200   1   2\n",
       "2           2             2  23.707310   90.415480   200   0   2\n",
       "3           3             3  22.301411   70.822357    70   2   2\n",
       "4           4             4 -32.348610  151.924576  1000   2   2"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = fdf.iloc[:,2:6].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = fdf.iloc[:, 6].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler  \n",
    "scaler = StandardScaler()  \n",
    "scaler.fit(X_train)\n",
    "\n",
    "X_train = scaler.transform(X_train)  \n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=30, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "classifier = KNeighborsClassifier(n_neighbors=30)  \n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 1 0 0 0]\n",
      " [0 0 5 0 0 1]\n",
      " [0 1 6 0 0 1]\n",
      " [0 1 2 0 0 0]\n",
      " [0 0 3 0 0 0]\n",
      " [0 0 0 0 0 0]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.00      0.00      0.00         1\n",
      "          1       0.00      0.00      0.00         6\n",
      "          2       0.35      0.75      0.48         8\n",
      "          3       0.00      0.00      0.00         3\n",
      "          4       0.00      0.00      0.00         3\n",
      "          5       0.00      0.00      0.00         0\n",
      "\n",
      "avg / total       0.13      0.29      0.18        21\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1137: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.\n",
      "  'recall', 'true', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix  \n",
    "print(confusion_matrix(y_test, y_pred))  \n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc=classifier.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4024390243902439"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "exampe_measures = np.array([22.78,77.86,200,1])\n",
    "exampe_measures = exampe_measures.reshape(1,-1)\n",
    "prediction=classifier.predict(exampe_measures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2], dtype=int64)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_conf_mat(lTrue, lPred, title):\n",
    "    \n",
    "    cm = confusion_matrix(lTrue.astype(int), lPred.astype(int))\n",
    "    print(cm)\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    cax = ax.matshow(cm)\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 1 0 0 0]\n",
      " [0 0 5 0 0 1]\n",
      " [0 1 6 0 0 1]\n",
      " [0 1 2 0 0 0]\n",
      " [0 0 3 0 0 0]\n",
      " [0 0 0 0 0 0]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x223cae9f6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conf_mat(y_test, y_pred, 'K=5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "error = []\n",
    "\n",
    "\n",
    "for i in range(1, 40):  \n",
    "    knn = KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    error.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Mean Error')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x223caf30a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))  \n",
    "plt.plot(range(1, 40), error, color='red', linestyle='dashed', marker='o',  \n",
    "         markerfacecolor='blue', markersize=10)\n",
    "plt.title('Error Rate K Value')  \n",
    "plt.xlabel('K Value')  \n",
    "plt.ylabel('Mean Error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x223cafc91d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(fdf.corr())\n",
    "plt.xticks(range(len(fdf.columns)), fdf.columns)\n",
    "plt.yticks(range(len(fdf.columns)), fdf.columns)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: pandas.scatter_matrix is deprecated. Use pandas.plotting.scatter_matrix instead\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x223cafcfdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.scatter_matrix(fdf, figsize=(6, 6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}