Day 7 - 8 | Lok Sabha Candidate Data Analysis & Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lok Sabha Candidate Data Analysis & Visualization\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day 7 - 8 of 100 Days of Data Science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:14:41.467442Z",
     "start_time": "2020-08-19T14:14:39.068621Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:14:43.382445Z",
     "start_time": "2020-08-19T14:14:43.375941Z"
    }
   },
   "source": [
    "## Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:15:48.272462Z",
     "start_time": "2020-08-19T14:15:48.152071Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.8 ms, sys: 5.9 ms, total: 19.7 ms\n",
      "Wall time: 31.4 ms\n",
      "CPU times: user 19.4 ms, sys: 6.63 ms, total: 26 ms\n",
      "Wall time: 33 ms\n",
      "CPU times: user 14.4 ms, sys: 3.51 ms, total: 17.9 ms\n",
      "Wall time: 19.3 ms\n",
      "CPU times: user 13.6 ms, sys: 3.05 ms, total: 16.6 ms\n",
      "Wall time: 17.9 ms\n"
     ]
    }
   ],
   "source": [
    "%time candidates_2004 = pd.read_csv('./resources/data/lok-sabha-election-candidate-list-2004-to-2019/LokSabha2004.csv')\n",
    "%time candidates_2009 = pd.read_csv('./resources/data/lok-sabha-election-candidate-list-2004-to-2019/LokSabha2009.csv')\n",
    "%time candidates_2014 = pd.read_csv('./resources/data/lok-sabha-election-candidate-list-2004-to-2019/LokSabha2014.csv')\n",
    "%time candidates_2019 = pd.read_csv('./resources/data/lok-sabha-election-candidate-list-2004-to-2019/LokSabha2019.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have data file for each year. So let merge them in one file. before doing that I am going to add year column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:16:03.547496Z",
     "start_time": "2020-08-19T14:16:03.526237Z"
    }
   },
   "outputs": [],
   "source": [
    "candidates_2004['Year'] = 2004\n",
    "candidates_2009['Year'] = 2009\n",
    "candidates_2014['Year'] = 2014\n",
    "candidates_2019['Year'] = 2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have year column in each data frame. Let's merge them in once data frame using pd.concat method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:16:12.269055Z",
     "start_time": "2020-08-19T14:16:12.242190Z"
    }
   },
   "outputs": [],
   "source": [
    "candidates = pd.concat([candidates_2004, candidates_2009, candidates_2014, candidates_2019])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistics of Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:16:26.244522Z",
     "start_time": "2020-08-19T14:16:26.238318Z"
    }
   },
   "source": [
    "Let's have a look at example samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:16:34.772977Z",
     "start_time": "2020-08-19T14:16:34.743352Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Candidate</th>\n",
       "      <th>Party</th>\n",
       "      <th>Criminal Cases</th>\n",
       "      <th>Education</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MANORANJAN BHAKTA</td>\n",
       "      <td>INC</td>\n",
       "      <td>0</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>65</td>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A K BISWAS</td>\n",
       "      <td>BSP</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>61</td>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ASIT BARAN DUTTA</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>50</td>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BISHNU PADA RAY</td>\n",
       "      <td>BJP</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>54</td>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DEEPAK BISWAS</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Given</td>\n",
       "      <td>28</td>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DR SHANMUGAM</td>\n",
       "      <td>LJNSP</td>\n",
       "      <td>0</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>66</td>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GOUR CHANDRA MAJUMDER</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>37</td>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>K KALIMUTHU</td>\n",
       "      <td>IND</td>\n",
       "      <td>1</td>\n",
       "      <td>8th Pass</td>\n",
       "      <td>51</td>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>K PERUMAL</td>\n",
       "      <td>SWD</td>\n",
       "      <td>0</td>\n",
       "      <td>Literate</td>\n",
       "      <td>65</td>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>N K P NAIR</td>\n",
       "      <td>CPI(ML)(L)</td>\n",
       "      <td>0</td>\n",
       "      <td>Literate</td>\n",
       "      <td>49</td>\n",
       "      <td>Andaman &amp; Nicobar Islands</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Candidate       Party  Criminal Cases      Education  Age  \\\n",
       "0      MANORANJAN BHAKTA         INC               0  Post Graduate   65   \n",
       "1             A K BISWAS         BSP               0       Graduate   61   \n",
       "2       ASIT BARAN DUTTA         IND               0       Graduate   50   \n",
       "3        BISHNU PADA RAY         BJP               0       Graduate   54   \n",
       "4          DEEPAK BISWAS         IND               0      Not Given   28   \n",
       "5           DR SHANMUGAM       LJNSP               0      Doctorate   66   \n",
       "6  GOUR CHANDRA MAJUMDER         IND               0       Graduate   37   \n",
       "7            K KALIMUTHU         IND               1       8th Pass   51   \n",
       "8              K PERUMAL         SWD               0       Literate   65   \n",
       "9             N K P NAIR  CPI(ML)(L)               0       Literate   49   \n",
       "\n",
       "                        City  Year  \n",
       "0  Andaman & Nicobar Islands  2004  \n",
       "1  Andaman & Nicobar Islands  2004  \n",
       "2  Andaman & Nicobar Islands  2004  \n",
       "3  Andaman & Nicobar Islands  2004  \n",
       "4  Andaman & Nicobar Islands  2004  \n",
       "5  Andaman & Nicobar Islands  2004  \n",
       "6  Andaman & Nicobar Islands  2004  \n",
       "7  Andaman & Nicobar Islands  2004  \n",
       "8  Andaman & Nicobar Islands  2004  \n",
       "9  Andaman & Nicobar Islands  2004  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:16:43.075753Z",
     "start_time": "2020-08-19T14:16:43.044979Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 28014 entries, 0 to 7967\n",
      "Data columns (total 7 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   Candidate       28014 non-null  object\n",
      " 1   Party           28014 non-null  object\n",
      " 2   Criminal Cases  28014 non-null  int64 \n",
      " 3   Education       28014 non-null  object\n",
      " 4   Age             28014 non-null  int64 \n",
      " 5   City            28014 non-null  object\n",
      " 6   Year            28014 non-null  int64 \n",
      "dtypes: int64(3), object(4)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "candidates.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:16:48.534531Z",
     "start_time": "2020-08-19T14:16:48.456861Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Criminal Cases</th>\n",
       "      <th>Age</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>28014.000000</td>\n",
       "      <td>28014.000000</td>\n",
       "      <td>28014.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.483901</td>\n",
       "      <td>46.487078</td>\n",
       "      <td>2012.708503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.164205</td>\n",
       "      <td>12.546241</td>\n",
       "      <td>5.050962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>382.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Criminal Cases           Age          Year\n",
       "count    28014.000000  28014.000000  28014.000000\n",
       "mean         0.483901     46.487078   2012.708503\n",
       "std          4.164205     12.546241      5.050962\n",
       "min          0.000000      0.000000   2004.000000\n",
       "25%          0.000000     37.000000   2009.000000\n",
       "50%          0.000000     46.000000   2014.000000\n",
       "75%          0.000000     55.000000   2019.000000\n",
       "max        382.000000     94.000000   2019.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:16:52.877539Z",
     "start_time": "2020-08-19T14:16:52.861370Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Candidate         0\n",
       "Party             0\n",
       "Criminal Cases    0\n",
       "Education         0\n",
       "Age               0\n",
       "City              0\n",
       "Year              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:17:00.932944Z",
     "start_time": "2020-08-19T14:17:00.926882Z"
    }
   },
   "source": [
    "As we can see there is no null value in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:17:08.157007Z",
     "start_time": "2020-08-19T14:17:08.150517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28014, 7)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 28 thousand candidates details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Year wise Data Analysis and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Youngest Candidate who participated in Lok Sabha Election?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:17:41.148298Z",
     "start_time": "2020-08-19T14:17:41.132563Z"
    }
   },
   "source": [
    "Some entries have value lesser than 18 so I am ignoring those value because that values are false."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:17:50.392505Z",
     "start_time": "2020-08-19T14:17:50.371325Z"
    }
   },
   "outputs": [],
   "source": [
    "candidate_age_2004 = candidates_2004[candidates_2004['Age'] > 18]\n",
    "candidate_age_2009 = candidates_2009[candidates_2009['Age'] > 18]\n",
    "candidate_age_2014 = candidates_2014[candidates_2014['Age'] > 18]\n",
    "candidate_age_2019 = candidates_2019[candidates_2019['Age'] > 18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:17:59.237295Z",
     "start_time": "2020-08-19T14:17:59.212011Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Candidate</th>\n",
       "      <th>Party</th>\n",
       "      <th>Criminal Cases</th>\n",
       "      <th>Education</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>OM PRAKASH PASWAN</td>\n",
       "      <td>AD</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Given</td>\n",
       "      <td>25</td>\n",
       "      <td>Araria</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>SARJOON PASWAN</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Given</td>\n",
       "      <td>25</td>\n",
       "      <td>Aurangabad</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>SHAKTI RAJ</td>\n",
       "      <td>BSP</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Gaya</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>MONIKA TIWARI</td>\n",
       "      <td>BED</td>\n",
       "      <td>0</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Gopalganj</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>UTTAM CHOUDHARY UNNAT</td>\n",
       "      <td>JD(S)</td>\n",
       "      <td>0</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Hajipur</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>PREM KUMAR</td>\n",
       "      <td>SHS</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Katihar</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>ANURODH PRAGAT</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Muzaffarpur</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>724</th>\n",
       "      <td>KUNJ BIHARI</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Given</td>\n",
       "      <td>25</td>\n",
       "      <td>Patna</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>BADKURAM DHRUW</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Literate</td>\n",
       "      <td>25</td>\n",
       "      <td>Janjgir</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1214</th>\n",
       "      <td>SHAHZAD KHAN</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Given</td>\n",
       "      <td>25</td>\n",
       "      <td>Jammu</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1215</th>\n",
       "      <td>SHARAZ AHMAD</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Given</td>\n",
       "      <td>25</td>\n",
       "      <td>Jammu</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1389</th>\n",
       "      <td>SANGANNA</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Gulbarga</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>ALTAF AHMED</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Bhopal</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1800</th>\n",
       "      <td>JITENDRA ADKU RAUT</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Chimur</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1821</th>\n",
       "      <td>ADV. PUNJABRAO RAMDHAN CHAVAN</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Hingoli</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>RAJ SINGH</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Mumbai North West</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2199</th>\n",
       "      <td>SANJAY KUMAR YADAV</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2318</th>\n",
       "      <td>SARABJEET SINGH</td>\n",
       "      <td>SAD(A)(Simranjit Singh Mann)</td>\n",
       "      <td>2</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Bhatinda</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2419</th>\n",
       "      <td>COMRED SHYOPATRAM</td>\n",
       "      <td>CPM</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Ganganagar</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2712</th>\n",
       "      <td>VRAHASPATI GIRI</td>\n",
       "      <td>SHS</td>\n",
       "      <td>0</td>\n",
       "      <td>Illiterate</td>\n",
       "      <td>25</td>\n",
       "      <td>Bareilly</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2732</th>\n",
       "      <td>JAI MALA</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Given</td>\n",
       "      <td>25</td>\n",
       "      <td>Bijnor</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2982</th>\n",
       "      <td>VIVEK KUMAR SHARMA</td>\n",
       "      <td>AD</td>\n",
       "      <td>0</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Jalesar</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2994</th>\n",
       "      <td>ASHU SHARMA URF ASHWANI SHARMA</td>\n",
       "      <td>BJVP</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Given</td>\n",
       "      <td>25</td>\n",
       "      <td>Kairana</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3016</th>\n",
       "      <td>YOGESH</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Kairana</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3139</th>\n",
       "      <td>AMIRUDDIN</td>\n",
       "      <td>AD</td>\n",
       "      <td>0</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Padrauna</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3151</th>\n",
       "      <td>RAKESH CHANDRA PATEL</td>\n",
       "      <td>RSBP</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Phulpur</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3223</th>\n",
       "      <td>MANJU</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Given</td>\n",
       "      <td>25</td>\n",
       "      <td>Shahabad</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3232</th>\n",
       "      <td>DESH RAJ</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>8th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Shahjahanpur</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Candidate                         Party  \\\n",
       "393                OM PRAKASH PASWAN                            AD   \n",
       "420                   SARJOON PASWAN                           IND   \n",
       "550                       SHAKTI RAJ                           BSP   \n",
       "556                    MONIKA TIWARI                           BED   \n",
       "569            UTTAM CHOUDHARY UNNAT                         JD(S)   \n",
       "597                       PREM KUMAR                           SHS   \n",
       "679                   ANURODH PRAGAT                           IND   \n",
       "724                      KUNJ BIHARI                           IND   \n",
       "886                   BADKURAM DHRUW                           IND   \n",
       "1214                    SHAHZAD KHAN                           IND   \n",
       "1215                    SHARAZ AHMAD                           IND   \n",
       "1389                        SANGANNA                           IND   \n",
       "1597                     ALTAF AHMED                           IND   \n",
       "1800              JITENDRA ADKU RAUT                           IND   \n",
       "1821   ADV. PUNJABRAO RAMDHAN CHAVAN                           IND   \n",
       "1940                       RAJ SINGH                           IND   \n",
       "2199              SANJAY KUMAR YADAV                           IND   \n",
       "2318                 SARABJEET SINGH  SAD(A)(Simranjit Singh Mann)   \n",
       "2419               COMRED SHYOPATRAM                           CPM   \n",
       "2712                 VRAHASPATI GIRI                           SHS   \n",
       "2732                        JAI MALA                           IND   \n",
       "2982              VIVEK KUMAR SHARMA                            AD   \n",
       "2994  ASHU SHARMA URF ASHWANI SHARMA                          BJVP   \n",
       "3016                          YOGESH                           IND   \n",
       "3139                       AMIRUDDIN                            AD   \n",
       "3151            RAKESH CHANDRA PATEL                          RSBP   \n",
       "3223                           MANJU                           IND   \n",
       "3232                        DESH RAJ                           IND   \n",
       "\n",
       "      Criminal Cases      Education  Age               City  Year  \n",
       "393                0      Not Given   25             Araria  2004  \n",
       "420                0      Not Given   25         Aurangabad  2004  \n",
       "550                0       Graduate   25               Gaya  2004  \n",
       "556                0  Post Graduate   25          Gopalganj  2004  \n",
       "569                0  Post Graduate   25            Hajipur  2004  \n",
       "597                0       Graduate   25            Katihar  2004  \n",
       "679                0       Graduate   25        Muzaffarpur  2004  \n",
       "724                0      Not Given   25              Patna  2004  \n",
       "886                0       Literate   25            Janjgir  2004  \n",
       "1214               0      Not Given   25              Jammu  2004  \n",
       "1215               0      Not Given   25              Jammu  2004  \n",
       "1389               0  Post Graduate   25           Gulbarga  2004  \n",
       "1597               0      10th Pass   25             Bhopal  2004  \n",
       "1800               0       Graduate   25             Chimur  2004  \n",
       "1821               0  Post Graduate   25            Hingoli  2004  \n",
       "1940               0      10th Pass   25  Mumbai North West  2004  \n",
       "2199               0      10th Pass   25          New Delhi  2004  \n",
       "2318               2      12th Pass   25           Bhatinda  2004  \n",
       "2419               0       Graduate   25         Ganganagar  2004  \n",
       "2712               0     Illiterate   25           Bareilly  2004  \n",
       "2732               0      Not Given   25             Bijnor  2004  \n",
       "2982               0      10th Pass   25            Jalesar  2004  \n",
       "2994               0      Not Given   25            Kairana  2004  \n",
       "3016               0  Post Graduate   25            Kairana  2004  \n",
       "3139               0      12th Pass   25           Padrauna  2004  \n",
       "3151               0       Graduate   25            Phulpur  2004  \n",
       "3223               0      Not Given   25           Shahabad  2004  \n",
       "3232               0       8th Pass   25       Shahjahanpur  2004  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidate_age_2004[candidate_age_2004['Age'] == candidate_age_2004['Age'].min()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In 2004's Lok Sabha Election,yougest candidate age was 25 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:18:17.326994Z",
     "start_time": "2020-08-19T14:18:17.290162Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Candidate</th>\n",
       "      <th>Party</th>\n",
       "      <th>Criminal Cases</th>\n",
       "      <th>Education</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>TABA TAKU</td>\n",
       "      <td>LB</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Arunachal West</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>BINITA BAITHA</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Gopalganj</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>MOHAMMAD NISSAR ALAM</td>\n",
       "      <td>JMM</td>\n",
       "      <td>0</td>\n",
       "      <td>8th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Kishanganj</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>KUMAR RAJESH</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Nalanda</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1174</th>\n",
       "      <td>DURGESH NANDAN SINGH</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate Professional</td>\n",
       "      <td>25</td>\n",
       "      <td>Pataliputra</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1461</th>\n",
       "      <td>Santosh Banjare</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>5th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Korba</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1570</th>\n",
       "      <td>MULLA SALIM</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>South Goa</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1625</th>\n",
       "      <td>BHARATBHAI BABUBHAI SOLANKI</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Literate</td>\n",
       "      <td>25</td>\n",
       "      <td>Anand</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1848</th>\n",
       "      <td>MEHULKUMAR KARSANBHAI CHANDRAVADIYA</td>\n",
       "      <td>BSP</td>\n",
       "      <td>3</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Porbandar</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1878</th>\n",
       "      <td>MAHESHBHAI HIRABHAI HARSODA</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Literate</td>\n",
       "      <td>25</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>ALTAFBHAI VALIBHAI KORADIA</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Illiterate</td>\n",
       "      <td>25</td>\n",
       "      <td>Surendranagar</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>SATENDER SINGH THAKRAN</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2059</th>\n",
       "      <td>SANDEEP</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "      <td>25</td>\n",
       "      <td>Hisar</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2060</th>\n",
       "      <td>SANJAY KUMAR</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Hisar</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2224</th>\n",
       "      <td>ASHIQ HUSSAIN GANIE</td>\n",
       "      <td>BCDP</td>\n",
       "      <td>0</td>\n",
       "      <td>8th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Baramulla</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2341</th>\n",
       "      <td>PRADEEP YADAV</td>\n",
       "      <td>Samata Party</td>\n",
       "      <td>0</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Godda</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2423</th>\n",
       "      <td>Padma Baraik</td>\n",
       "      <td>IND</td>\n",
       "      <td>1</td>\n",
       "      <td>8th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Lohardaga</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2480</th>\n",
       "      <td>CHINCHOLLI SANTOSHKUMAR SAHEBGOUDA</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Bagalkot</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2971</th>\n",
       "      <td>Sajimon</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Attingal</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3204</th>\n",
       "      <td>SHAILENDRA SINGH ALIAS KALLU</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Bhind</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3274</th>\n",
       "      <td>SHIVRAJ BHAIYA BADE THAKUR</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>5th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Damoh</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421</th>\n",
       "      <td>DEV SINGH BHALAVI</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>5th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Mandla</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3577</th>\n",
       "      <td>BHAI MUNSHILAL SILAWAT</td>\n",
       "      <td>RPI(A)</td>\n",
       "      <td>0</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Vidisha</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3610</th>\n",
       "      <td>AMOL DEVIDASRAO JADHAV</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Literate</td>\n",
       "      <td>25</td>\n",
       "      <td>Amravati</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3638</th>\n",
       "      <td>KRISHNA DEVIDAS JADHAV</td>\n",
       "      <td>IND</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Aurangabad</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3728</th>\n",
       "      <td>GANESH ARJUN ZORE</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Buldhana</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3958</th>\n",
       "      <td>RAJKAMAL JAISINH YADAV</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Mumbai North Central</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4236</th>\n",
       "      <td>KOLI SANTOSH GOKUL</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Given</td>\n",
       "      <td>25</td>\n",
       "      <td>Raver</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4272</th>\n",
       "      <td>DHOTRE SUCHIT CHINTAMANI</td>\n",
       "      <td>KM</td>\n",
       "      <td>0</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Shirdi</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4633</th>\n",
       "      <td>Akshaya Kumar Sethi</td>\n",
       "      <td>Samruddha Odisha</td>\n",
       "      <td>0</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Jagatsinghpur</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4806</th>\n",
       "      <td>JAGMEET SINGH</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate Professional</td>\n",
       "      <td>25</td>\n",
       "      <td>Firozpur</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4947</th>\n",
       "      <td>DEVENDRA</td>\n",
       "      <td>JGP</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Alwar</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5476</th>\n",
       "      <td>KARUPPUSAMY. S</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Given</td>\n",
       "      <td>25</td>\n",
       "      <td>Dharmapuri</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5591</th>\n",
       "      <td>SATHEES C</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Kanniyakumari</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5815</th>\n",
       "      <td>EZHILARASU M</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Sriperumbudur</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5862</th>\n",
       "      <td>KAVITHA</td>\n",
       "      <td>BSP</td>\n",
       "      <td>0</td>\n",
       "      <td>Others</td>\n",
       "      <td>25</td>\n",
       "      <td>Theni</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6006</th>\n",
       "      <td>SURESHKUMAR P</td>\n",
       "      <td>IND</td>\n",
       "      <td>2</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Vellore</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6186</th>\n",
       "      <td>SWAMI NATH</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Given</td>\n",
       "      <td>25</td>\n",
       "      <td>Amethi</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6204</th>\n",
       "      <td>SUMAN</td>\n",
       "      <td>IVD</td>\n",
       "      <td>0</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Amroha</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6253</th>\n",
       "      <td>OMPAL</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Baghpat</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6322</th>\n",
       "      <td>DEPENDRA KUMAR RAWAT</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Barabanki</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6381</th>\n",
       "      <td>MONU KUMAR RAVI</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>8th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Bijnor</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6496</th>\n",
       "      <td>AJAY KUMAR</td>\n",
       "      <td>KKJHS</td>\n",
       "      <td>0</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Faizabad</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6590</th>\n",
       "      <td>LUBNA ASHIF</td>\n",
       "      <td>All India Minorities Front</td>\n",
       "      <td>0</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Gautam Budhha Nagar</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6747</th>\n",
       "      <td>PRABHA VERMA</td>\n",
       "      <td>AD</td>\n",
       "      <td>0</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>25</td>\n",
       "      <td>Jalaun</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6787</th>\n",
       "      <td>ABHISHEK</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Kairana</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6833</th>\n",
       "      <td>SANJEEV</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Kannauj</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6951</th>\n",
       "      <td>NITIN DWIVEDI</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Lucknow</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6955</th>\n",
       "      <td>RAJESH KUMAR</td>\n",
       "      <td>IND</td>\n",
       "      <td>1</td>\n",
       "      <td>8th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Lucknow</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7017</th>\n",
       "      <td>GOPAL SAINI</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>5th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Mathura</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7269</th>\n",
       "      <td>MOHD.ASIF</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Saharanpur</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7389</th>\n",
       "      <td>RAKESH</td>\n",
       "      <td>NYP</td>\n",
       "      <td>0</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Sultanpur</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7411</th>\n",
       "      <td>UMESHCHANDRA</td>\n",
       "      <td>AD</td>\n",
       "      <td>0</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Unnao</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7435</th>\n",
       "      <td>SUSHAMA</td>\n",
       "      <td>CPI(ML)(L)</td>\n",
       "      <td>0</td>\n",
       "      <td>Post Graduate</td>\n",
       "      <td>25</td>\n",
       "      <td>Almora</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7579</th>\n",
       "      <td>RAJU MALIK</td>\n",
       "      <td>JDP</td>\n",
       "      <td>0</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>25</td>\n",
       "      <td>Bardhaman Purba</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Candidate                       Party  \\\n",
       "570                             TABA TAKU                          LB   \n",
       "887                         BINITA BAITHA                         IND   \n",
       "1017                 MOHAMMAD NISSAR ALAM                         JMM   \n",
       "1128                         KUMAR RAJESH                         IND   \n",
       "1174                 DURGESH NANDAN SINGH                         IND   \n",
       "1461                      Santosh Banjare                         IND   \n",
       "1570                          MULLA SALIM                         IND   \n",
       "1625          BHARATBHAI BABUBHAI SOLANKI                         IND   \n",
       "1848  MEHULKUMAR KARSANBHAI CHANDRAVADIYA                         BSP   \n",
       "1878          MAHESHBHAI HIRABHAI HARSODA                         IND   \n",
       "1917           ALTAFBHAI VALIBHAI KORADIA                         IND   \n",
       "2025               SATENDER SINGH THAKRAN                         IND   \n",
       "2059                              SANDEEP                         IND   \n",
       "2060                         SANJAY KUMAR                         IND   \n",
       "2224                  ASHIQ HUSSAIN GANIE                        BCDP   \n",
       "2341                        PRADEEP YADAV                Samata Party   \n",
       "2423                         Padma Baraik                         IND   \n",
       "2480   CHINCHOLLI SANTOSHKUMAR SAHEBGOUDA                         IND   \n",
       "2971                              Sajimon                         IND   \n",
       "3204         SHAILENDRA SINGH ALIAS KALLU                         IND   \n",
       "3274           SHIVRAJ BHAIYA BADE THAKUR                         IND   \n",
       "3421                    DEV SINGH BHALAVI                         IND   \n",
       "3577               BHAI MUNSHILAL SILAWAT                      RPI(A)   \n",
       "3610               AMOL DEVIDASRAO JADHAV                         IND   \n",
       "3638               KRISHNA DEVIDAS JADHAV                         IND   \n",
       "3728                    GANESH ARJUN ZORE                         IND   \n",
       "3958               RAJKAMAL JAISINH YADAV                         IND   \n",
       "4236                   KOLI SANTOSH GOKUL                         IND   \n",
       "4272             DHOTRE SUCHIT CHINTAMANI                          KM   \n",
       "4633                  Akshaya Kumar Sethi            Samruddha Odisha   \n",
       "4806                        JAGMEET SINGH                         IND   \n",
       "4947                             DEVENDRA                         JGP   \n",
       "5476                       KARUPPUSAMY. S                         IND   \n",
       "5591                            SATHEES C                         IND   \n",
       "5815                         EZHILARASU M                         IND   \n",
       "5862                              KAVITHA                         BSP   \n",
       "6006                        SURESHKUMAR P                         IND   \n",
       "6186                           SWAMI NATH                         IND   \n",
       "6204                                SUMAN                         IVD   \n",
       "6253                                OMPAL                         IND   \n",
       "6322                 DEPENDRA KUMAR RAWAT                         IND   \n",
       "6381                      MONU KUMAR RAVI                         IND   \n",
       "6496                           AJAY KUMAR                       KKJHS   \n",
       "6590                          LUBNA ASHIF  All India Minorities Front   \n",
       "6747                         PRABHA VERMA                          AD   \n",
       "6787                             ABHISHEK                         IND   \n",
       "6833                              SANJEEV                         IND   \n",
       "6951                        NITIN DWIVEDI                         IND   \n",
       "6955                         RAJESH KUMAR                         IND   \n",
       "7017                          GOPAL SAINI                         IND   \n",
       "7269                            MOHD.ASIF                         IND   \n",
       "7389                               RAKESH                         NYP   \n",
       "7411                         UMESHCHANDRA                          AD   \n",
       "7435                              SUSHAMA                  CPI(ML)(L)   \n",
       "7579                           RAJU MALIK                         JDP   \n",
       "\n",
       "      Criminal Cases              Education  Age                  City  Year  \n",
       "570                0               Graduate   25        Arunachal West  2009  \n",
       "887                0               Graduate   25             Gopalganj  2009  \n",
       "1017               0               8th Pass   25            Kishanganj  2009  \n",
       "1128               0               Graduate   25               Nalanda  2009  \n",
       "1174               0  Graduate Professional   25           Pataliputra  2009  \n",
       "1461               0               5th Pass   25                 Korba  2009  \n",
       "1570               0              12th Pass   25             South Goa  2009  \n",
       "1625               0               Literate   25                 Anand  2009  \n",
       "1848               3               Graduate   25             Porbandar  2009  \n",
       "1878               0               Literate   25                Rajkot  2009  \n",
       "1917               0             Illiterate   25         Surendranagar  2009  \n",
       "2025               0              12th Pass   25               Gurgaon  2009  \n",
       "2059               0                 Others   25                 Hisar  2009  \n",
       "2060               0              12th Pass   25                 Hisar  2009  \n",
       "2224               0               8th Pass   25             Baramulla  2009  \n",
       "2341               0              12th Pass   25                 Godda  2009  \n",
       "2423               1               8th Pass   25             Lohardaga  2009  \n",
       "2480               0              12th Pass   25              Bagalkot  2009  \n",
       "2971               0              10th Pass   25              Attingal  2009  \n",
       "3204               0              12th Pass   25                 Bhind  2009  \n",
       "3274               0               5th Pass   25                 Damoh  2009  \n",
       "3421               0               5th Pass   25                Mandla  2009  \n",
       "3577               0              10th Pass   25               Vidisha  2009  \n",
       "3610               0               Literate   25              Amravati  2009  \n",
       "3638               1               Graduate   25            Aurangabad  2009  \n",
       "3728               0              12th Pass   25              Buldhana  2009  \n",
       "3958               0              10th Pass   25  Mumbai North Central  2009  \n",
       "4236               0              Not Given   25                 Raver  2009  \n",
       "4272               0              10th Pass   25                Shirdi  2009  \n",
       "4633               0              10th Pass   25         Jagatsinghpur  2009  \n",
       "4806               0  Graduate Professional   25              Firozpur  2009  \n",
       "4947               0               Graduate   25                 Alwar  2009  \n",
       "5476               0              Not Given   25            Dharmapuri  2009  \n",
       "5591               0              10th Pass   25         Kanniyakumari  2009  \n",
       "5815               0               Graduate   25         Sriperumbudur  2009  \n",
       "5862               0                 Others   25                 Theni  2009  \n",
       "6006               2              12th Pass   25               Vellore  2009  \n",
       "6186               0              Not Given   25                Amethi  2009  \n",
       "6204               0          Post Graduate   25                Amroha  2009  \n",
       "6253               0               Graduate   25               Baghpat  2009  \n",
       "6322               0              12th Pass   25             Barabanki  2009  \n",
       "6381               0               8th Pass   25                Bijnor  2009  \n",
       "6496               0              12th Pass   25              Faizabad  2009  \n",
       "6590               0          Post Graduate   25   Gautam Budhha Nagar  2009  \n",
       "6747               0              Doctorate   25                Jalaun  2009  \n",
       "6787               0              10th Pass   25               Kairana  2009  \n",
       "6833               0          Post Graduate   25               Kannauj  2009  \n",
       "6951               0              12th Pass   25               Lucknow  2009  \n",
       "6955               1               8th Pass   25               Lucknow  2009  \n",
       "7017               0               5th Pass   25               Mathura  2009  \n",
       "7269               0          Post Graduate   25            Saharanpur  2009  \n",
       "7389               0              10th Pass   25             Sultanpur  2009  \n",
       "7411               0          Post Graduate   25                 Unnao  2009  \n",
       "7435               0          Post Graduate   25                Almora  2009  \n",
       "7579               0              10th Pass   25       Bardhaman Purba  2009  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidate_age_2009[candidate_age_2009['Age'] == candidate_age_2009['Age'].min()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:18:26.156042Z",
     "start_time": "2020-08-19T14:18:26.136406Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Candidate</th>\n",
       "      <th>Party</th>\n",
       "      <th>Criminal Cases</th>\n",
       "      <th>Education</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7242</th>\n",
       "      <td>Ravikant Yadav</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>21</td>\n",
       "      <td>JAUNPUR</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Candidate Party  Criminal Cases Education  Age     City  Year\n",
       "7242  Ravikant Yadav   IND               0  Graduate   21  JAUNPUR  2014"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidate_age_2014[candidate_age_2014['Age'] == candidate_age_2014['Age'].min()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:18:32.092309Z",
     "start_time": "2020-08-19T14:18:32.073910Z"
    }
   },
   "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>Candidate</th>\n",
       "      <th>Party</th>\n",
       "      <th>Criminal Cases</th>\n",
       "      <th>Education</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5774</th>\n",
       "      <td>Ponnuthai G</td>\n",
       "      <td>IND</td>\n",
       "      <td>0</td>\n",
       "      <td>12th Pass</td>\n",
       "      <td>24</td>\n",
       "      <td>TENKASI</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Candidate Party  Criminal Cases  Education  Age     City  Year\n",
       "5774  Ponnuthai G   IND               0  12th Pass   24  TENKASI  2019"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidate_age_2019[candidate_age_2019['Age'] == candidate_age_2019['Age'].min()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ravikant Yadav is youngest candidate, 21 Age who participated in 2014 lok sabha election."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:18:57.027302Z",
     "start_time": "2020-08-19T14:18:54.166117Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,12))\n",
    "edgecolor=(0,0,0),\n",
    "sns.countplot(candidate_age_2004['Age'].sort_values(), palette = \"Dark2\", edgecolor=(0,0,0))\n",
    "plt.title(\"2004 Election's Candidate Age Count\",fontsize=20)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(fontsize=12,rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:19:02.708260Z",
     "start_time": "2020-08-19T14:18:59.985779Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,12))\n",
    "edgecolor=(0,0,0),\n",
    "sns.countplot(candidate_age_2009['Age'].sort_values(), palette = \"Dark2\", edgecolor=(0,0,0))\n",
    "plt.title(\"Candidate Age Count\",fontsize=20)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(fontsize=12,rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:19:09.550037Z",
     "start_time": "2020-08-19T14:19:06.479380Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,12))\n",
    "edgecolor=(0,0,0),\n",
    "sns.countplot(candidate_age_2014['Age'].sort_values(), palette = \"Dark2\", edgecolor=(0,0,0))\n",
    "plt.title(\"Candidate Age Count\",fontsize=20)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(fontsize=12,rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:19:14.370274Z",
     "start_time": "2020-08-19T14:19:12.318954Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,12))\n",
    "edgecolor=(0,0,0),\n",
    "sns.countplot(candidate_age_2019['Age'].sort_values(), palette = \"Dark2\", edgecolor=(0,0,0))\n",
    "plt.title(\"Candidate Age Count\",fontsize=20)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(fontsize=12,rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:22:03.449679Z",
     "start_time": "2020-08-19T14:22:03.386554Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38    904\n",
       "43    898\n",
       "42    875\n",
       "39    869\n",
       "40    859\n",
       "     ... \n",
       "88      1\n",
       "90      1\n",
       "93      1\n",
       "94      1\n",
       "24      1\n",
       "Name: Age, Length: 71, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There are 904 candidates who are 38 years old."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Candidate with Highest number of Criminal Cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:22:30.854592Z",
     "start_time": "2020-08-19T14:22:30.826800Z"
    }
   },
   "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>Candidate</th>\n",
       "      <th>Party</th>\n",
       "      <th>Criminal Cases</th>\n",
       "      <th>Education</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3144</th>\n",
       "      <td>ATIQUE AHAMAD</td>\n",
       "      <td>SP</td>\n",
       "      <td>36</td>\n",
       "      <td>8th Pass</td>\n",
       "      <td>41</td>\n",
       "      <td>Phulpur</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Candidate Party  Criminal Cases Education  Age     City  Year\n",
       "3144  ATIQUE AHAMAD    SP              36  8th Pass   41  Phulpur  2004"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates_2004[candidates_2004['Criminal Cases'] == candidates_2004['Criminal Cases'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:22:35.165321Z",
     "start_time": "2020-08-19T14:22:35.143500Z"
    }
   },
   "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>Candidate</th>\n",
       "      <th>Party</th>\n",
       "      <th>Criminal Cases</th>\n",
       "      <th>Education</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2427</th>\n",
       "      <td>KAMESHWAR BAITHA</td>\n",
       "      <td>JMM</td>\n",
       "      <td>46</td>\n",
       "      <td>10th Pass</td>\n",
       "      <td>56</td>\n",
       "      <td>Palamau</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Candidate Party  Criminal Cases  Education  Age     City  Year\n",
       "2427  KAMESHWAR BAITHA   JMM              46  10th Pass   56  Palamau  2009"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates_2009[candidates_2009['Criminal Cases'] == candidates_2009['Criminal Cases'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:22:39.271943Z",
     "start_time": "2020-08-19T14:22:39.255352Z"
    }
   },
   "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>Candidate</th>\n",
       "      <th>Party</th>\n",
       "      <th>Criminal Cases</th>\n",
       "      <th>Education</th>\n",
       "      <th>Age</th>\n",
       "      <th>City</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5714</th>\n",
       "      <td>Udayakumar S.p.</td>\n",
       "      <td>AAP</td>\n",
       "      <td>382</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>54</td>\n",
       "      <td>KANNIYAKUMARI</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Candidate Party  Criminal Cases  Education  Age           City  \\\n",
       "5714  Udayakumar S.p.   AAP             382  Doctorate   54  KANNIYAKUMARI   \n",
       "\n",
       "      Year  \n",
       "5714  2014  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidates_2014[candidates_2014['Criminal Cases'] == candidates_2014['Criminal Cases'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-08-19T14:22:43.254166Z",
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       "                        <th id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aelevel0_row0\" class=\"row_heading level0 row0\" >5714</th>\n",
       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow0_col0\" class=\"data row0 col0\" >Udayakumar S.p.</td>\n",
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       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow1_col1\" class=\"data row1 col1\" >AAP</td>\n",
       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow1_col2\" class=\"data row1 col2\" >380</td>\n",
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       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow1_col4\" class=\"data row1 col4\" >46</td>\n",
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       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow1_col6\" class=\"data row1 col6\" >2014</td>\n",
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       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow2_col6\" class=\"data row2 col6\" >2019</td>\n",
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       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow3_col0\" class=\"data row3 col0\" >Dean Kuriakose</td>\n",
       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow3_col1\" class=\"data row3 col1\" >INC</td>\n",
       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow3_col2\" class=\"data row3 col2\" >204</td>\n",
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       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow3_col4\" class=\"data row3 col4\" >37</td>\n",
       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow3_col5\" class=\"data row3 col5\" >IDUKKI</td>\n",
       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow3_col6\" class=\"data row3 col6\" >2019</td>\n",
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       "                        <th id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aelevel0_row4\" class=\"row_heading level0 row4\" >7432</th>\n",
       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow4_col0\" class=\"data row4 col0\" >Ateek Ahmad</td>\n",
       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow4_col1\" class=\"data row4 col1\" >IND</td>\n",
       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow4_col2\" class=\"data row4 col2\" >59</td>\n",
       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow4_col3\" class=\"data row4 col3\" >8th Pass</td>\n",
       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow4_col4\" class=\"data row4 col4\" >54</td>\n",
       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow4_col5\" class=\"data row4 col5\" >VARANASI</td>\n",
       "                        <td id=\"T_7623875c_e227_11ea_bc9e_2cf0ee25c1aerow4_col6\" class=\"data row4 col6\" >2019</td>\n",
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   "source": [
    "### Candidate Udayakumar S.p. has highest number of criminal cases which are 382."
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