Independent Candidates.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "ind=pd.DataFrame(columns=['CANDIDATE', '%', 'CONSTITUENCY', 'PARTY', 'STATE', 'YEAR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1989\n",
    "df=pd.read_csv('1989.csv')\n",
    "y1=df[['CANDIDATE', '%', 'CONSTITUENCY', 'PARTY', 'TERRITORY']]\n",
    "y1.sort_values(['%', 'CONSTITUENCY'], ascending=[False, True])\n",
    "\n",
    "unique=list(y1.CONSTITUENCY.unique())\n",
    "check=[]\n",
    "\n",
    "for i in y1.index:\n",
    "    const=y1['CONSTITUENCY'][i]\n",
    "    if(const in unique and const not in check):\n",
    "        check.append(const)\n",
    "        ind=ind.append({'CANDIDATE': y1['CANDIDATE'][i], '%': y1['%'][i], 'CONSTITUENCY': y1['CONSTITUENCY'][i], 'PARTY': y1['PARTY'][i], 'STATE': y1['TERRITORY'][i], 'YEAR': '1989'}, ignore_index=True)\n",
    "        \n",
    "    elif(const in unique and const in check):\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1991        \n",
    "df=pd.read_csv('1991.csv')\n",
    "y1=df[['CANDIDATE', '%', 'CONSTITUENCY', 'PARTY', 'TERRITORY']]\n",
    "y1.sort_values(['%', 'CONSTITUENCY'], ascending=[False, True])\n",
    "\n",
    "unique=list(y1.CONSTITUENCY.unique())\n",
    "check=[]\n",
    "\n",
    "for i in y1.index:\n",
    "    const=y1['CONSTITUENCY'][i]\n",
    "    if(const in unique and const not in check):\n",
    "        check.append(const)\n",
    "        ind=ind.append({'CANDIDATE': y1['CANDIDATE'][i], '%': y1['%'][i], 'CONSTITUENCY': y1['CONSTITUENCY'][i], 'PARTY': y1['PARTY'][i], 'STATE': y1['TERRITORY'][i], 'YEAR': '1991'}, ignore_index=True)\n",
    "        \n",
    "    elif(const in unique and const in check):\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1996\n",
    "df=pd.read_csv('1996.csv')\n",
    "y1=df[['CANDIDATE', '%', 'CONSTITUENCY', 'PARTY', 'TERRITORY']]\n",
    "y1.sort_values(['%', 'CONSTITUENCY'], ascending=[False, True])\n",
    "\n",
    "unique=list(y1.CONSTITUENCY.unique())\n",
    "check=[]\n",
    "\n",
    "for i in y1.index:\n",
    "    const=y1['CONSTITUENCY'][i]\n",
    "    if(const in unique and const not in check):\n",
    "        check.append(const)\n",
    "        ind=ind.append({'CANDIDATE': y1['CANDIDATE'][i], '%': y1['%'][i], 'CONSTITUENCY': y1['CONSTITUENCY'][i], 'PARTY': y1['PARTY'][i], 'STATE': y1['TERRITORY'][i], 'YEAR': '1996'}, ignore_index=True)\n",
    "        \n",
    "    elif(const in unique and const in check):\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1998        \n",
    "df=pd.read_csv('1998.csv')\n",
    "y1=df[['CANDIDATE', '%', 'CONSTITUENCY', 'PARTY', 'TERRITORY']]\n",
    "y1.sort_values(['%', 'CONSTITUENCY'], ascending=[False, True])\n",
    "\n",
    "unique=list(y1.CONSTITUENCY.unique())\n",
    "check=[]\n",
    "\n",
    "for i in y1.index:\n",
    "    const=y1['CONSTITUENCY'][i]\n",
    "    if(const in unique and const not in check):\n",
    "        check.append(const)\n",
    "        ind=ind.append({'CANDIDATE': y1['CANDIDATE'][i], '%': y1['%'][i], 'CONSTITUENCY': y1['CONSTITUENCY'][i], 'PARTY': y1['PARTY'][i], 'STATE': y1['TERRITORY'][i], 'YEAR': '1998'}, ignore_index=True)\n",
    "        \n",
    "    elif(const in unique and const in check):\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1999\n",
    "df=pd.read_csv('1999.csv')\n",
    "y1=df[['CANDIDATE', '%', 'CONSTITUENCY', 'PARTY', 'TERRITORY']]\n",
    "y1.sort_values(['%', 'CONSTITUENCY'], ascending=[False, True])\n",
    "\n",
    "unique=list(y1.CONSTITUENCY.unique())\n",
    "check=[]\n",
    "\n",
    "for i in y1.index:\n",
    "    const=y1['CONSTITUENCY'][i]\n",
    "    if(const in unique and const not in check):\n",
    "        check.append(const)\n",
    "        ind=ind.append({'CANDIDATE': y1['CANDIDATE'][i], '%': y1['%'][i], 'CONSTITUENCY': y1['CONSTITUENCY'][i], 'PARTY': y1['PARTY'][i], 'STATE': y1['TERRITORY'][i], 'YEAR': '1999'}, ignore_index=True)\n",
    "        \n",
    "    elif(const in unique and const in check):\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2004        \n",
    "df=pd.read_csv('2004.csv')\n",
    "y1=df[['CANDIDATES', '% OF TOTAL VOTES', 'CONSTITUENCY', 'PARTY', 'TERRITORY']]\n",
    "y1.sort_values(['% OF TOTAL VOTES', 'CONSTITUENCY'], ascending=[False, True])\n",
    "\n",
    "unique=list(y1.CONSTITUENCY.unique())\n",
    "check=[]\n",
    "\n",
    "for i in y1.index:\n",
    "    const=y1['CONSTITUENCY'][i]\n",
    "    if(const in unique and const not in check):\n",
    "        check.append(const)\n",
    "        ind=ind.append({'CANDIDATE': y1['CANDIDATES'][i], '%': y1['% OF TOTAL VOTES'][i], 'CONSTITUENCY': y1['CONSTITUENCY'][i], 'PARTY': y1['PARTY'][i], 'STATE': y1['TERRITORY'][i], 'YEAR': '2004'}, ignore_index=True)\n",
    "        \n",
    "    elif(const in unique and const in check):\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>%</th>\n",
       "      <th>CONSTITUENCY</th>\n",
       "      <th>PARTY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MOHD. HASSAN</td>\n",
       "      <td>52.650000</td>\n",
       "      <td>LADAKH</td>\n",
       "      <td>IND</td>\n",
       "      <td>JAMMU &amp; KASHMIR</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KANKAR MUNJARE</td>\n",
       "      <td>34.250000</td>\n",
       "      <td>BALAGHAT</td>\n",
       "      <td>IND</td>\n",
       "      <td>MADHYA PRADESH</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>VAMANRAO MAHADIK</td>\n",
       "      <td>35.910000</td>\n",
       "      <td>BOMBAY SOUTH CENTRAL</td>\n",
       "      <td>IND</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DESHMUKH ASHOK ANANDRAO</td>\n",
       "      <td>44.210000</td>\n",
       "      <td>PARBHANI</td>\n",
       "      <td>IND</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KIRPAL SINGH</td>\n",
       "      <td>47.030000</td>\n",
       "      <td>AMRITSAR</td>\n",
       "      <td>IND</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ATINDERPAL SINGH</td>\n",
       "      <td>46.590000</td>\n",
       "      <td>PATIALA</td>\n",
       "      <td>IND</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DHIAN SINGH</td>\n",
       "      <td>30.880000</td>\n",
       "      <td>FEROZEPUR</td>\n",
       "      <td>IND</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FASIURAAHMANALIYAS MUNNAN KHAN</td>\n",
       "      <td>34.970000</td>\n",
       "      <td>BALRAMPUR</td>\n",
       "      <td>IND</td>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>JAGDISH</td>\n",
       "      <td>40.150000</td>\n",
       "      <td>GHAZIPUR</td>\n",
       "      <td>IND</td>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DELKAR MOHANBHAI SANJIBHAI</td>\n",
       "      <td>42.660000</td>\n",
       "      <td>DADRA &amp; NAGAR HAVELI (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>DADRA &amp; NAGAR HAVELI</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>TANDEL DEVJI JOGIBHAI</td>\n",
       "      <td>42.820000</td>\n",
       "      <td>DAMAN AND DIU</td>\n",
       "      <td>IND</td>\n",
       "      <td>DAMAN &amp; DIU</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SATYENDRA NATH BROHMO CHOUDHURY</td>\n",
       "      <td>53.200000</td>\n",
       "      <td>KOKRAJHAR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>ASSAM</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TOMO RIBA</td>\n",
       "      <td>53.250000</td>\n",
       "      <td>ARUNACHAL WEST</td>\n",
       "      <td>IND</td>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>WANGCHA RAJKUMAR</td>\n",
       "      <td>33.730000</td>\n",
       "      <td>ARUNACHAL EAST</td>\n",
       "      <td>IND</td>\n",
       "      <td>ARUNACHAL PRADESH</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LOUIS ISLARY</td>\n",
       "      <td>24.360000</td>\n",
       "      <td>KOKRAJHAR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>ASSAM</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>RAMENDRA KUMAR</td>\n",
       "      <td>42.890000</td>\n",
       "      <td>BEGUSARAI</td>\n",
       "      <td>IND</td>\n",
       "      <td>BIHAR</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ARVIND KUMAR</td>\n",
       "      <td>33.070000</td>\n",
       "      <td>SONEPAT</td>\n",
       "      <td>IND</td>\n",
       "      <td>HARYANA</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>XAVIER ARRAKKAL</td>\n",
       "      <td>46.910000</td>\n",
       "      <td>ERNAKULAM</td>\n",
       "      <td>IND</td>\n",
       "      <td>KERALA</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MAHENDRA KARMA</td>\n",
       "      <td>32.150000</td>\n",
       "      <td>BASTAR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>MADHYA PRADESH</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>G.GILBERT SWELL</td>\n",
       "      <td>56.180000</td>\n",
       "      <td>SHILLONG</td>\n",
       "      <td>IND</td>\n",
       "      <td>MEGHALAYA</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>KALAPNATH RAI</td>\n",
       "      <td>32.620000</td>\n",
       "      <td>GHOSI</td>\n",
       "      <td>IND</td>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SANSUMA KHUNGGUR BWISWMUTHIARY</td>\n",
       "      <td>25.550000</td>\n",
       "      <td>KOKRAJHAR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>ASSAM</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>DR. H. LALLUNGMUANA</td>\n",
       "      <td>34.870000</td>\n",
       "      <td>MIZORAM (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>MIZORAM</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SATNAM SINGH KAINTH</td>\n",
       "      <td>52.040000</td>\n",
       "      <td>PHILLAUR (SC)</td>\n",
       "      <td>IND</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BUTA SINGH</td>\n",
       "      <td>54.320000</td>\n",
       "      <td>JALORE (SC)</td>\n",
       "      <td>IND</td>\n",
       "      <td>RAJASTHAN</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>RAMAMURTHY K.</td>\n",
       "      <td>55.470000</td>\n",
       "      <td>SALEM</td>\n",
       "      <td>IND</td>\n",
       "      <td>TAMIL NADU</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MANEKA GANDHI</td>\n",
       "      <td>55.990000</td>\n",
       "      <td>PILIBHIT</td>\n",
       "      <td>IND</td>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SANSUMA KHUNGGUR BWISMUTHIARY</td>\n",
       "      <td>37.570000</td>\n",
       "      <td>KOKRAJHAR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>ASSAM</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RAJESH RANJAN</td>\n",
       "      <td>63.170000</td>\n",
       "      <td>PURNEA</td>\n",
       "      <td>IND</td>\n",
       "      <td>BIHAR</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ATHAWALE RAMDAS BANDU</td>\n",
       "      <td>52.910000</td>\n",
       "      <td>PANDHARPUR (SC)</td>\n",
       "      <td>IND</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>VANLALZAWMA</td>\n",
       "      <td>58.800000</td>\n",
       "      <td>MIZORAM (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>MIZORAM</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>MANEKA GANDHI</td>\n",
       "      <td>57.940000</td>\n",
       "      <td>PILIBHIT</td>\n",
       "      <td>IND</td>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>DELKAR MOHANBHAI SANJIBHAI</td>\n",
       "      <td>41.520000</td>\n",
       "      <td>DADRA &amp; NAGAR HAVELI (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>DADRA &amp; NAGAR HAVELI</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>SANSUMA KHUNGGUR BWISWMUTHIARY</td>\n",
       "      <td>56.686392</td>\n",
       "      <td>KOKRAJHAR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>ASSAM</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>THUPSTAN CHHEWANG</td>\n",
       "      <td>38.026831</td>\n",
       "      <td>LADAKH</td>\n",
       "      <td>IND</td>\n",
       "      <td>JAMMU &amp; KASHMIR</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>DR SEBASTIAN PAUL</td>\n",
       "      <td>30.215381</td>\n",
       "      <td>ERNAKULAM</td>\n",
       "      <td>IND</td>\n",
       "      <td>KERALA</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>MANI CHARENAMEI</td>\n",
       "      <td>28.856033</td>\n",
       "      <td>OUTER MANIPUR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>MANIPUR</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>HARISH NAGPAL</td>\n",
       "      <td>21.416255</td>\n",
       "      <td>AMROHA</td>\n",
       "      <td>IND</td>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          CANDIDATE          %               CONSTITUENCY  \\\n",
       "0                      MOHD. HASSAN  52.650000                     LADAKH   \n",
       "1                    KANKAR MUNJARE  34.250000                   BALAGHAT   \n",
       "2                  VAMANRAO MAHADIK  35.910000       BOMBAY SOUTH CENTRAL   \n",
       "3           DESHMUKH ASHOK ANANDRAO  44.210000                   PARBHANI   \n",
       "4                      KIRPAL SINGH  47.030000                   AMRITSAR   \n",
       "5                  ATINDERPAL SINGH  46.590000                    PATIALA   \n",
       "6                       DHIAN SINGH  30.880000                  FEROZEPUR   \n",
       "7    FASIURAAHMANALIYAS MUNNAN KHAN  34.970000                  BALRAMPUR   \n",
       "8                           JAGDISH  40.150000                   GHAZIPUR   \n",
       "9        DELKAR MOHANBHAI SANJIBHAI  42.660000  DADRA & NAGAR HAVELI (ST)   \n",
       "10            TANDEL DEVJI JOGIBHAI  42.820000              DAMAN AND DIU   \n",
       "11  SATYENDRA NATH BROHMO CHOUDHURY  53.200000             KOKRAJHAR (ST)   \n",
       "12                        TOMO RIBA  53.250000             ARUNACHAL WEST   \n",
       "13                 WANGCHA RAJKUMAR  33.730000             ARUNACHAL EAST   \n",
       "14                     LOUIS ISLARY  24.360000             KOKRAJHAR (ST)   \n",
       "15                   RAMENDRA KUMAR  42.890000                  BEGUSARAI   \n",
       "16                     ARVIND KUMAR  33.070000                    SONEPAT   \n",
       "17                  XAVIER ARRAKKAL  46.910000                  ERNAKULAM   \n",
       "18                   MAHENDRA KARMA  32.150000                BASTAR (ST)   \n",
       "19                  G.GILBERT SWELL  56.180000                   SHILLONG   \n",
       "20                    KALAPNATH RAI  32.620000                      GHOSI   \n",
       "21   SANSUMA KHUNGGUR BWISWMUTHIARY  25.550000             KOKRAJHAR (ST)   \n",
       "22              DR. H. LALLUNGMUANA  34.870000               MIZORAM (ST)   \n",
       "23              SATNAM SINGH KAINTH  52.040000              PHILLAUR (SC)   \n",
       "24                       BUTA SINGH  54.320000                JALORE (SC)   \n",
       "25                    RAMAMURTHY K.  55.470000                      SALEM   \n",
       "26                    MANEKA GANDHI  55.990000                   PILIBHIT   \n",
       "27    SANSUMA KHUNGGUR BWISMUTHIARY  37.570000             KOKRAJHAR (ST)   \n",
       "28                    RAJESH RANJAN  63.170000                     PURNEA   \n",
       "29            ATHAWALE RAMDAS BANDU  52.910000            PANDHARPUR (SC)   \n",
       "30                      VANLALZAWMA  58.800000               MIZORAM (ST)   \n",
       "31                    MANEKA GANDHI  57.940000                   PILIBHIT   \n",
       "32       DELKAR MOHANBHAI SANJIBHAI  41.520000  DADRA & NAGAR HAVELI (ST)   \n",
       "33   SANSUMA KHUNGGUR BWISWMUTHIARY  56.686392             KOKRAJHAR (ST)   \n",
       "34                THUPSTAN CHHEWANG  38.026831                     LADAKH   \n",
       "35                DR SEBASTIAN PAUL  30.215381                  ERNAKULAM   \n",
       "36                  MANI CHARENAMEI  28.856033         OUTER MANIPUR (ST)   \n",
       "37                    HARISH NAGPAL  21.416255                     AMROHA   \n",
       "\n",
       "   PARTY                 STATE  YEAR  \n",
       "0    IND       JAMMU & KASHMIR  1989  \n",
       "1    IND        MADHYA PRADESH  1989  \n",
       "2    IND           MAHARASHTRA  1989  \n",
       "3    IND           MAHARASHTRA  1989  \n",
       "4    IND                PUNJAB  1989  \n",
       "5    IND                PUNJAB  1989  \n",
       "6    IND                PUNJAB  1989  \n",
       "7    IND         UTTAR PRADESH  1989  \n",
       "8    IND         UTTAR PRADESH  1989  \n",
       "9    IND  DADRA & NAGAR HAVELI  1989  \n",
       "10   IND           DAMAN & DIU  1989  \n",
       "11   IND                 ASSAM  1991  \n",
       "12   IND     ARUNACHAL PRADESH  1996  \n",
       "13   IND     ARUNACHAL PRADESH  1996  \n",
       "14   IND                 ASSAM  1996  \n",
       "15   IND                 BIHAR  1996  \n",
       "16   IND               HARYANA  1996  \n",
       "17   IND                KERALA  1996  \n",
       "18   IND        MADHYA PRADESH  1996  \n",
       "19   IND             MEGHALAYA  1996  \n",
       "20   IND         UTTAR PRADESH  1996  \n",
       "21   IND                 ASSAM  1998  \n",
       "22   IND               MIZORAM  1998  \n",
       "23   IND                PUNJAB  1998  \n",
       "24   IND             RAJASTHAN  1998  \n",
       "25   IND            TAMIL NADU  1998  \n",
       "26   IND         UTTAR PRADESH  1998  \n",
       "27   IND                 ASSAM  1999  \n",
       "28   IND                 BIHAR  1999  \n",
       "29   IND           MAHARASHTRA  1999  \n",
       "30   IND               MIZORAM  1999  \n",
       "31   IND         UTTAR PRADESH  1999  \n",
       "32   IND  DADRA & NAGAR HAVELI  1999  \n",
       "33   IND                 ASSAM  2004  \n",
       "34   IND       JAMMU & KASHMIR  2004  \n",
       "35   IND                KERALA  2004  \n",
       "36   IND               MANIPUR  2004  \n",
       "37   IND         UTTAR PRADESH  2004  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ind=ind[ind['PARTY']=='IND']\n",
    "ind.reset_index(drop=True, inplace=True)\n",
    "display(ind)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>CANDIDATE</th>\n",
       "      <th>%</th>\n",
       "      <th>CONSTITUENCY</th>\n",
       "      <th>PARTY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SATNAM SINGH KAINTH</td>\n",
       "      <td>52.04</td>\n",
       "      <td>PHILLAUR (SC)</td>\n",
       "      <td>IND</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BUTA SINGH</td>\n",
       "      <td>54.32</td>\n",
       "      <td>JALORE (SC)</td>\n",
       "      <td>IND</td>\n",
       "      <td>RAJASTHAN</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ATHAWALE RAMDAS BANDU</td>\n",
       "      <td>52.91</td>\n",
       "      <td>PANDHARPUR (SC)</td>\n",
       "      <td>IND</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                CANDIDATE      %     CONSTITUENCY PARTY        STATE  YEAR\n",
       "23    SATNAM SINGH KAINTH  52.04    PHILLAUR (SC)   IND       PUNJAB  1998\n",
       "24             BUTA SINGH  54.32      JALORE (SC)   IND    RAJASTHAN  1998\n",
       "29  ATHAWALE RAMDAS BANDU  52.91  PANDHARPUR (SC)   IND  MAHARASHTRA  1999"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#SC reserved constituencies with independent candidates as winners\n",
    "ind[ind['CONSTITUENCY'].str.contains(\"\\(SC\\)\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>CANDIDATE</th>\n",
       "      <th>%</th>\n",
       "      <th>CONSTITUENCY</th>\n",
       "      <th>PARTY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>DELKAR MOHANBHAI SANJIBHAI</td>\n",
       "      <td>42.660000</td>\n",
       "      <td>DADRA &amp; NAGAR HAVELI (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>DADRA &amp; NAGAR HAVELI</td>\n",
       "      <td>1989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SATYENDRA NATH BROHMO CHOUDHURY</td>\n",
       "      <td>53.200000</td>\n",
       "      <td>KOKRAJHAR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>ASSAM</td>\n",
       "      <td>1991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LOUIS ISLARY</td>\n",
       "      <td>24.360000</td>\n",
       "      <td>KOKRAJHAR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>ASSAM</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MAHENDRA KARMA</td>\n",
       "      <td>32.150000</td>\n",
       "      <td>BASTAR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>MADHYA PRADESH</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SANSUMA KHUNGGUR BWISWMUTHIARY</td>\n",
       "      <td>25.550000</td>\n",
       "      <td>KOKRAJHAR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>ASSAM</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>DR. H. LALLUNGMUANA</td>\n",
       "      <td>34.870000</td>\n",
       "      <td>MIZORAM (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>MIZORAM</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SANSUMA KHUNGGUR BWISMUTHIARY</td>\n",
       "      <td>37.570000</td>\n",
       "      <td>KOKRAJHAR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>ASSAM</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>VANLALZAWMA</td>\n",
       "      <td>58.800000</td>\n",
       "      <td>MIZORAM (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>MIZORAM</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>DELKAR MOHANBHAI SANJIBHAI</td>\n",
       "      <td>41.520000</td>\n",
       "      <td>DADRA &amp; NAGAR HAVELI (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>DADRA &amp; NAGAR HAVELI</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>SANSUMA KHUNGGUR BWISWMUTHIARY</td>\n",
       "      <td>56.686392</td>\n",
       "      <td>KOKRAJHAR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>ASSAM</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>MANI CHARENAMEI</td>\n",
       "      <td>28.856033</td>\n",
       "      <td>OUTER MANIPUR (ST)</td>\n",
       "      <td>IND</td>\n",
       "      <td>MANIPUR</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          CANDIDATE          %               CONSTITUENCY  \\\n",
       "9        DELKAR MOHANBHAI SANJIBHAI  42.660000  DADRA & NAGAR HAVELI (ST)   \n",
       "11  SATYENDRA NATH BROHMO CHOUDHURY  53.200000             KOKRAJHAR (ST)   \n",
       "14                     LOUIS ISLARY  24.360000             KOKRAJHAR (ST)   \n",
       "18                   MAHENDRA KARMA  32.150000                BASTAR (ST)   \n",
       "21   SANSUMA KHUNGGUR BWISWMUTHIARY  25.550000             KOKRAJHAR (ST)   \n",
       "22              DR. H. LALLUNGMUANA  34.870000               MIZORAM (ST)   \n",
       "27    SANSUMA KHUNGGUR BWISMUTHIARY  37.570000             KOKRAJHAR (ST)   \n",
       "30                      VANLALZAWMA  58.800000               MIZORAM (ST)   \n",
       "32       DELKAR MOHANBHAI SANJIBHAI  41.520000  DADRA & NAGAR HAVELI (ST)   \n",
       "33   SANSUMA KHUNGGUR BWISWMUTHIARY  56.686392             KOKRAJHAR (ST)   \n",
       "36                  MANI CHARENAMEI  28.856033         OUTER MANIPUR (ST)   \n",
       "\n",
       "   PARTY                 STATE  YEAR  \n",
       "9    IND  DADRA & NAGAR HAVELI  1989  \n",
       "11   IND                 ASSAM  1991  \n",
       "14   IND                 ASSAM  1996  \n",
       "18   IND        MADHYA PRADESH  1996  \n",
       "21   IND                 ASSAM  1998  \n",
       "22   IND               MIZORAM  1998  \n",
       "27   IND                 ASSAM  1999  \n",
       "30   IND               MIZORAM  1999  \n",
       "32   IND  DADRA & NAGAR HAVELI  1999  \n",
       "33   IND                 ASSAM  2004  \n",
       "36   IND               MANIPUR  2004  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ST reserved constituencies with independent candidates as winners\n",
    "ind[ind['CONSTITUENCY'].str.contains(\"\\(ST\\)\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KOKRAJHAR (ST)               5\n",
       "LADAKH                       2\n",
       "PILIBHIT                     2\n",
       "ERNAKULAM                    2\n",
       "MIZORAM (ST)                 2\n",
       "DADRA & NAGAR HAVELI (ST)    2\n",
       "BALAGHAT                     1\n",
       "PANDHARPUR (SC)              1\n",
       "PATIALA                      1\n",
       "FEROZEPUR                    1\n",
       "BEGUSARAI                    1\n",
       "BOMBAY SOUTH CENTRAL         1\n",
       "AMRITSAR                     1\n",
       "ARUNACHAL WEST               1\n",
       "SHILLONG                     1\n",
       "SALEM                        1\n",
       "PURNEA                       1\n",
       "AMROHA                       1\n",
       "OUTER MANIPUR (ST)           1\n",
       "GHAZIPUR                     1\n",
       "GHOSI                        1\n",
       "BASTAR (ST)                  1\n",
       "DAMAN AND DIU                1\n",
       "PARBHANI                     1\n",
       "PHILLAUR (SC)                1\n",
       "JALORE (SC)                  1\n",
       "BALRAMPUR                    1\n",
       "ARUNACHAL EAST               1\n",
       "SONEPAT                      1\n",
       "Name: CONSTITUENCY, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind['CONSTITUENCY'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "ind['CONSTITUENCY'].value_counts().sort_index().plot.bar()\n",
    "plt.title(\"Constituencies with the most number of Winning Independent candidates\")\n",
    "plt.xlabel(\"Constituencies\")\n",
    "plt.ylabel(\"No. of Winning Independent candidates\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UTTAR PRADESH           6\n",
       "ASSAM                   5\n",
       "PUNJAB                  4\n",
       "MAHARASHTRA             3\n",
       "MIZORAM                 2\n",
       "MADHYA PRADESH          2\n",
       "ARUNACHAL PRADESH       2\n",
       "BIHAR                   2\n",
       "KERALA                  2\n",
       "DADRA & NAGAR HAVELI    2\n",
       "JAMMU & KASHMIR         2\n",
       "MANIPUR                 1\n",
       "HARYANA                 1\n",
       "RAJASTHAN               1\n",
       "TAMIL NADU              1\n",
       "MEGHALAYA               1\n",
       "DAMAN & DIU             1\n",
       "Name: STATE, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind['STATE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "ind['STATE'].value_counts().sort_index().plot.bar()\n",
    "plt.title(\"States with the most number of Winning Independent candidates\")\n",
    "plt.xlabel(\"States\")\n",
    "plt.ylabel(\"No. of Winning Independent candidates\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1989    11\n",
       "1996     9\n",
       "1998     6\n",
       "1999     6\n",
       "2004     5\n",
       "1991     1\n",
       "Name: YEAR, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind['YEAR'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "ind['YEAR'].value_counts().sort_index().plot.bar()\n",
    "plt.title(\"Year-wise most number of Winning Independent candidates\")\n",
    "plt.xlabel(\"Years\")\n",
    "plt.ylabel(\"No. of Winning Independent candidates\")\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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}