Andhra Pradesh Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Constituency 1989 1991   1996   1998   1999   2004\n",
      "0           SRIKAKULAM  INC  INC    TDP    TDP    TDP    TDP\n",
      "1   PARVATHIPURAM (ST)  INC  INC    INC    TDP    TDP    INC\n",
      "2              BOBBILI  TDP  INC    TDP    TDP    INC    TDP\n",
      "3        VISAKHAPATNAM  INC  TDP    INC    INC    TDP    INC\n",
      "4    BHADRACHALAM (ST)  INC  INC    CPI    CPI    TDP    CPM\n",
      "5           ANAKAPALLI  INC  INC    TDP    INC    TDP    TDP\n",
      "6             KAKINADA  INC  TDP    TDP    BJP    TDP    INC\n",
      "7          RAJAHMUNDRY  INC  TDP    INC    BJP    BJP    INC\n",
      "8      AMALAPURAM (SC)  INC  TDP    INC    TDP    TDP    INC\n",
      "9            NARASAPUR  TDP  TDP    TDP    INC    BJP    INC\n",
      "10               ELURU  INC  TDP    TDP    INC    TDP    INC\n",
      "11       MACHILIPATNAM  INC  TDP    TDP    INC    TDP    INC\n",
      "12          VIJAYAWADA  INC  TDP    INC    INC    TDP    INC\n",
      "13              TENALI  INC  TDP    TDP    INC    TDP    INC\n",
      "14              GUNTUR  INC  TDP    INC    INC    TDP    INC\n",
      "15             BAPATLA  INC  TDP    TDP    INC    TDP    INC\n",
      "16        NARASARAOPET  INC  INC    TDP    INC    INC    INC\n",
      "17              ONGOLE  INC  INC    INC    INC    TDP    INC\n",
      "18        NELLORE (SC)  INC  INC    INC    INC    TDP    INC\n",
      "19      TIRUPATHI (SC)  INC  INC    INC    INC    BJP    INC\n",
      "20            CHITTOOR  INC  INC    TDP    TDP    TDP    TDP\n",
      "21            RAJAMPET  INC  INC    INC    INC    TDP    INC\n",
      "22            CUDDAPAH  INC  INC    INC    INC    INC    INC\n",
      "23            HINDUPUR  INC  INC    TDP    INC    TDP    INC\n",
      "24           ANANTAPUR  INC  INC    INC    INC    TDP    INC\n",
      "25             KURNOOL  INC  INC    INC    INC    TDP    INC\n",
      "26             NANDYAL  INC  INC    INC    TDP    TDP    INC\n",
      "27   NAGARKURNOOL (SC)  INC  INC    TDP    INC    TDP    TDP\n",
      "28        MAHABUBNAGAR  INC  INC    INC     JD    BJP    INC\n",
      "29           HYDERABAD  MIM  MIM  AIMIM  AIMIM  AIMIM  AIMIM\n",
      "30        SECUNDERABAD  INC  BJP    INC    BJP    BJP    INC\n",
      "31       SIDDIPET (SC)  INC  INC    INC    TDP    TDP    INC\n",
      "32               MEDAK  INC  INC    INC    INC    BJP    TRS\n",
      "33           NIZAMABAD  INC  TDP    INC    TDP    TDP    INC\n",
      "34            ADILABAD  INC  TDP    TDP    TDP    TDP    TRS\n",
      "35     PEDDAPALLI (SC)  INC  INC    INC    TDP    TDP    INC\n",
      "36          KARIMNAGAR  INC  INC    TDP    BJP    BJP    TRS\n",
      "37          HANAMKONDA  INC  INC    INC    TDP    TDP    TRS\n",
      "38            WARANGAL  INC  INC    TDP    TDP    TDP    TRS\n",
      "39             KHAMMAM  INC  INC    CPM    INC    INC    INC\n",
      "40            NALGONDA  INC  CPI    CPI    CPI    TDP    CPI\n",
      "41          MIRYALGUDA  INC  CPM    INC    INC    BJP    INC \n",
      "\n",
      "   Year  AIMIM  BJP  CPI  CPM  INC  JD  MIM  TDP  TRS\n",
      "0  1989      0    0    0    0   39   0    1    2    0\n",
      "1  1991      0    1    1    1   25   0    1   13    0\n",
      "2  1996      1    0    2    1   22   0    0   16    0\n",
      "3  1998      1    4    2    0   22   1    0   12    0\n",
      "4  1999      1    8    0    0    4   0    0   29    0\n",
      "5  2004      1    0    1    1   29   0    0    5    5\n"
     ]
    },
    {
     "data": {
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57cs4deqUVq1apRUrVujQoUOKiopy+2Kl8vYeT0xM1Ndff+2O0iRJW7Zs0bRp03Tw4EFZLBa5XC41atRIzz33nCHezRu5vvJO57NYLHryySerqZrKO3TokB566CFt3LjRLc/fsmVLNWvWTD179pTNVnYWcUdzMvqcsdHrQ2kk7csIDAzU6NGjNXr0aG3cuNHtSUwqf+/xiIgId5RVIjIyUv/85z+VmZmp7Oxs3XzzzZdcscrdjFzfL3+OZTF6027YsKFb9yG/ON+/bdu2Mu93197tRs9ERq8PpZG0AQAwCWPsQGBgubm5io+PV25urrtLKcXItUnUdy2MXJtEfdfCyLVJxq/P09G0y5Gbm6s5c+YY8hfYyLVJ1HctjFybRH3Xwsi1Scavz9PRtAEAMAmaNgAAJmGopn306FEVFxe7uwwAAAzJ7ad8FRQUaNGiRVq+fLlq1qypsLAwjRo1Su3atXN3aQAAGIrbT/l69913tXbtWk2YMEF169bVs88+qx9//FFPP/207HZ7qWvjVlSBMS4+VIqvzbi1Scauz/fnt5hGrq9mO+PuLnVu+xxDf+8kKa/wypcCdZfaPl46mW/cUcAgP6vhf7ZVoTr/3s5tv/IGSNWlWofH09PT9e2335Zczu/kyZNat26dWrdurYiICDVo0ECvvPKKsrOz9e6770py36YIAAAYTZUPj585c0bz5s3TypUrFRQUpMDAQAUHB2v27Nny8vLSqVOn1LRp05LH16hRQz4+Pvryyy+1c+dOhYeHX3XaBgDgRlJlTftio/3ss8/05ZdfaunSpWratKk2b96sRx55pKQhN2rUSO+9954CAwMVFRWltLQ0NW3aVHl5edq1a5fCw8Np2ACA0iyGWktdLa7bK3Y4HPrggw+UkZEh6b/D2osWLVLTpk1L0nTr1q3VvHlznThxQtKFqy+FhIRo9uzZuueee/TOO+9o9OjROnHiRMllJwEAwHVI2oWFhZo1a5ZWr16tJk2aKDc3V1FRURo+fLhCQ0PVpk0bHThwQP/+97/VvXt3xcfHKzAwUOHh4ZKkZs2aaebMmdq5c6d8fHzUokUL5ebmyuFwKCgo6JpfIADgBuWBo7BXnbQvLjrfuHGj9u/fr7S0NK1YsUJxcXFKT0/X3LlzJV24HN5vf/tbvfzyy2rVqpV27typ5s2bq3///lq2bJnOnz8vSQoPD1eLFi105swZzZgxQ3369FFkZKScTmOuJgUAoLpVuGlnZWXptddeU1pamiSVrABPT0+Xw+FQSEiInE6nYmJi1Lx5c61du1anT59WnTp1FBISorp162rFihVasWKF/vSnP2n48OFKSkrS+fPnVVRUpB07dmjw4MHq1q2bzpw5o6FDh14o0Mvz5iwAABVg8aq+D4Mod3i8qKhIKSkpWrhwoY4cOaKgoCD17dtXNWrUkMvlkpeXl4KDg3Xu3DnVrFlTkpSdnS2Hw6E1a9ZoxIgRev/993XkyBHdcccdJcetVauWXC6XbDabbDabQkJCNHr0aEVFRcnHx6fqXjEAACZV7tsHq9UqX19fTZgwQYsXL1Z2dra+//57SRcWm3Xo0EEHDx7UtGnTlJmZqcTERPn5+alPnz7atGmTJKlbt27av3+/kpOTtXv3bj3//PNKTk7WuHHj5O3tLUmqV6+e7rvvPho2AKBiLJbq+zCIcpu2xWJRr169FB0drZYtW6pNmzZasmRJyf09e/bUhAkTdP78eY0dO1bbtm1TXFycJCksLEzFxcXq1q2bJk+erM2bN+uPf/yjTp48qVdeeUW9e/euulcGAMANpkKrx/39/SVJvr6+6tevn+Lj4zV9+nRJF5p6jx49ZLfb5eXlJZvtwiEzMjLUpUsXWa1WSdKoUaN05syZkmMBAIDKqdTsusVikd1ul8Ph0Oeffy7pvwvSXC6X8vLytHXrVo0aNUoREREaOHDgJV9PwwYAXDcsRCtfvXr11LlzZyUlJclut8tms8npdCo/P1+xsbHKz89XdHS0Hn300ZKUDQAArl2lm3atWrX08MMPa8yYMYqPj1dycrKmTp2q6OhoJSQkKDQ0tCrqBADgUgZaIFZdKtW0XS6X0tLSNHv2bFmtVu3YsUOTJk1SdHS0JNGwAQCoQpVq2jk5Odq8ebMGDhyokSNHcnoWAMB9DDTXXF0q1bQDAwM1Y8aMqqoFAABcQZVfTxsAgCrhgXPanje2AACASZG0AQDm5IFz2p73igEAMCmSNgDAnJjTBgAARkXSBgCYE3PaAADAqEjaAABzYk4bAAAYFU0bAACTYHgcAGBOLEQDAABGRdIGAJgTSRsAABgVSRsAYE5enPIFAAAMiqQNADAn5rQBAIBRkbQBAObENqYAAMCoSNoAAHNiThsAABgVSRsAYE7MaQMAAKMiaQMAzIk5bQAAYFQ0bQAATILhcQCAObEQDQAAGBVJGwBgTixEAwAARkXSBgCYE3PaAADAqEjaAABzYk4bAAAY1Q2btH0N/MqMXJtEfdfi3PY57i7hioz8vZOk2j7GzRFBflZ3l3BFRv/ZVgkPnNO+YX/MBUXurqBsvjYpr9Dp7jIuq7aPl2Hru/gPupHr6/7K5+4u47L+/Ue7of8uJGP/bFN2HHV3GZf1/+6sb/ifLa4Pvp0AAHNiThsAABgVSRsAYE4kbQAAYFQkbQCAOXng6nGSNgAAJkHTBgDAJBgeBwCYEwvRAACAUZG0AQDmxEI0AABgVCRtAIA5MacNAACMiqQNADAn5rQBAIBRkbQBAKZkIWkDAACjImkDAEyJpA0AAAyLpA0AMCfPC9okbQAAzIKkDQAwJea0AQCAYdG0AQAwCYbHAQCmxPA4AAAwLJI2AMCUSNoAAMCwSNoAAFMiaQMAAMMiaQMAzMnzgjZJGwAAsyBpAwBMiTltAABgWCRtAIApkbQBAIBhkbQBAKZE0gYAAIZF0gYAmJInJm2aNgAA19HcuXP17rvv6tSpU7LZbGrbtq0mTZqkVq1a6T//+Y/mzp2rXbt2qaCgQLfffrsef/xx3XfffRU6NsPjAABcR3369NGqVau0bds2bdy4UXa7XWPGjJHT6dTp06cVHR2ttWvXauvWrYqLi9PEiRO1c+fOCh2bpA0AMKdqHB3Pzc1Vbm5uqdsDAgIUEBBwyW2NGze+5HMvLy/99NNPysvLU1RU1CX39e7dW4mJidq2bZvCw8PLrYOmDQBAORYtWqQ5c+aUun3cuHEaP358qds/+eQTTZo0SXl5ebJYLHrkkUd00003lXrc8ePHtX//frVs2bJCddC0AQCmVJ0L0WJjYzVgwIBSt/86ZV/0m9/8Rlu3blVOTo5SU1NVv379Uo/Jz8/X+PHj1aNHD3Xp0qVCddC0AQAoR1nD4BVRp04djRgxQh07dlSTJk3UrFkzSVJeXp4ee+wx3XLLLXrxxRcrfDwWogEATMlisVTbx7VwOp0qKipSZmamJOnUqVOKjY1V/fr19frrr8vb27vCx6JpAwBwHS1evFg//fSTJOnkyZN69tln5e3trYiICP30008aPny4WrRooVmzZslmq9yAN8PjAABTMurmKl988YUSExOVn58vf39/3XHHHVq4cKGCg4M1Z84c7d27V4cPH9Y///nPkq/p37+/nnvuuXKPTdMGAOA6mjt37mXvGzdunMaNG3fVx6ZpAwDMyZhBu0oxpw0AgEkYKmkXFRVVelIeAOCZjDqnXZXcnrRPnTqluXPnqnfv3vrwww/dXQ4AAIbl1qZ98uRJvfzyy9q5c6cKCgr0/vvvy+FwuLMkAIBJmOU87eupWseid+7cqdDQUAUFBUmSatSooXvvvVd33HGHdu3apd///vc6duyYbrvttuosCwAAU6jypp2fn68333xTy5cvV2BgoOrWrSu73a64uDj5+/ure/fuslgs6t69uyRp06ZNNG0AQLmMlICrS5UNj7tcLknSli1btH37diUnJ2vDhg3q2rWr4uPj9emnn5YMO1wcEr///vu1du1anT179pJjAACA69i0HQ6HPvjgA2VkZEj67zugefPmKTw8XI0bN5bT6dTYsWPVvn17vf322/rxxx8vFOF1oYzhw4dr+/bt2rdvn1wul0e+iwIA4HKueXi8sLBQs2bN0urVq9WkSRPl5uYqKipKsbGxatCggWrXrq2cnBxJF5pzXl6ecnJydOLECR04cEANGjSQzWZTUVGRWrdurVtuuUVfffWV7rjjDh0+fFg1atRQSEgITRwAcAlP7AlXnbQvDl1v3LhR+/fvV1pamlasWKG4uDilp6crISFBkhQTE6PU1FStWrVK586d0/r169W1a1cVFRXp4MGDJce6eH52r1699Le//U2dOnXSiBEjdOjQIUme+cMBAOCXKpy0s7KytGTJEjVr1kx9+/ZVUVGRatSoofT0dDkcDoWEhMjpdComJkZffPGF1qxZo0mTJql///7KyMjQunXr9MYbbyggIEAvvPCCvvvuOx0+fLjk+IcPH9aoUaN05MgR3Xffferbt6969epVJS8aAHAD8MAsV27TLioqUkpKihYuXKgjR44oKChIffv2VY0aNeRyueTl5aXg4GCdO3dONWvWlCRlZ2fL4XBozZo1GjFihKZMmaKsrCxJUnBwsHJycpSZmakRI0ZIupCi69Spo9GjR+uBBx6Qj49PFb5kAADMqdzhcavVKl9fX02YMEGLFy9Wdna2vv/+e0kXmm2HDh108OBBTZs2TZmZmUpMTJSfn5/69OmjTZs2Sbow/O3v76/g4GDt3btXM2fOVGRkpOx2e8nz+Pv7a9CgQTRsAECFeOLmKuU2bYvFol69eik6OlotW7ZUmzZttGTJkpL7e/bsqQkTJuj8+fMaO3astm3bpri4OElSWFhYyQKyr7/+WrGxsRo8eLBsNpueeOIJ2Ww2TusCAKCCKjSn7e/vL0ny9fVVv379FB8fr+nTp0u60NR79Oghu90uLy+vkgVlGRkZ6tKlS8k7lLCwME2ePFlt27a95NhGegcDADAPT+wflVo9brFYZLfb5XA49Pnnn0u6MOctXRgCz8vL09atWzVq1ChFRERo4MCBJV8bFBRUqmEDAICKq/R52vXq1VPnzp2VlJQku90um80mp9Op/Px8xcbGKj8/X9HR0Xr00UdltVqromYAADwyaVe6adeqVUsPP/ywxowZo/j4eCUnJ2vq1KmKjo5WQkKCQkNDq6JOAAA8XqWatsvlUlpammbPni2r1aodO3Zo0qRJio6OliQaNgCg+nhe0K5c087JydHmzZs1cOBAjRw5ktOzAACoRpVq2oGBgZoxY0ZV1QIAQIV54px2lV2aEwAAXF/XfJUvAADcgaQNAAAMi6YNAIBJMDwOADAlhscBAIBhkbQBAKZE0gYAAIZF0gYAmJPnBW2SNgAAZkHSBgCYEnPaAADAsEjaAABTImkDAADDImkDAEzJA4M2SRsAALMgaQMATIk5bQAAYFgkbQCAKXlg0CZpAwBgFiRtAIApMacNAAAMi6YNAIBJMDwOADAlDxwdJ2kDAGAWJG0AgCl5eXle1CZpAwBgEiRtAIApMacNAAAMi6QNADAlNlcBAACGRdIGAJiSBwZtkjYAAGZB0gYAmBJz2gAAwLBI2gAAU/LEpG1xuVwudxcBAEBl3TntX9X2XDue7Vltz3UlN2zSzit0uruEMtX28VJBkburuDxfm3Qyv9jdZZQpyM8qydg/2yGLtru7jMtKjm1n2N8935//JTJyfa9tPODuMi7rf7o1NvT3rqp4YNBmThsAALOgaQMAYBI37PA4AODG5okL0UjaAACYBEkbAGBKHhi0SdoAAJgFSRsAYErMaQMAAMMiaQMATMkDgzZJGwAAsyBpAwBMiTltAABgWCRtAIApeWDQJmkDAGAWJG0AgCkxpw0AAAyLpA0AMCUPDNokbQAAzIKmDQCASTA8DgAwJRaiAQAAwyJpAwBMyQODNkkbAACzIGkDAEyJOW0AAGBYJG0AgCl5YNAmaQMAYBYkbQCAKTGnDQAADIukDQAwJZI2AAAwLJI2AMCUPDBok7QBADALkjYAwJSY0wYAAIZF0wYAwCQYHgcAmJIHjo6TtAEAMAuSNgDAlFiIBgAADIukDQAwJQ8M2iRtAJFN1zgAACAASURBVACup5deekl9+/ZV+/bt1bVrV02dOlWnTp0q87G7du1SmzZtNHz48Aodm6YNADAlL4ul2j4qw2q16qWXXtKWLVu0Zs0aHTt2TM8880ypxxUWFuqZZ55Rx44dK/6aK1UJAAC4oj/+8Y9q3bq1atSooZtvvlnDhw/Xl19+Wepxr776qjp37qwOHTpU+NjMaQMATKk657Rzc3OVm5tb6vaAgAAFBARc8Ws3b96sli1bXnJbenq6Pv74Y6Wmpmr+/PkVroOmDQBAORYtWqQ5c+aUun3cuHEaP378Zb9u3bp1WrlypZYsWVJyW35+vqZOnaqZM2eqZs2alaqDpg0AMKXqPE87NjZWAwYMKHX7lVJ2Wlqapk+froSEBLVp06bk9hdffFFRUVGVmsu+iKYNAEA5KjIM/ksrV67USy+9pHnz5pWas/7ss8+Um5ur9957T5JUUFCgoqIiRUZGKiUlRQ0bNrzscWnaAABT8jLoedqLFy/WG2+8oQULFuiOO+4odX9ycrKKi4tLPv/73/+u//znP3r99dd1yy23XPHYNG0AAK6jGTNmyGazacSIEZfcnpaWpgYNGpRqzP7+/vL29la9evXKPbahmvbOnTv11VdfaejQofL19XV3OQAAAzPq3uPffvttpR5/pYVsv2aIpv3+++9r1qxZOnv2rLp166ZTp06pfv367i4LAABDcXvTPnnypNasWaMRI0bo4Ycfdnc5AAAYVrXuiLZz506dPHnyktvWrl2rzMxMPfzwwzp37pzef/997dy5szrLAgCYkMVSfR9GUeVJOz8/X2+++aaWL1+uwMBA1a1bV3a7XXFxcSoqKtLRo0d16623atGiRXrrrbd0++2369SpU+rSpYvGjBmjevXqyeVyGXbuAgCA6lJlSdvlckmStmzZou3btys5OVkbNmxQ165dFR8fr48//lg2m02FhYU6evSovvjiCy1atEhLly7Vk08+qQMHDigpKamqygMAmJylGv8ziuvWtB0Ohz744ANlZGRI+u+qvnnz5ik8PFyNGzeW0+nU2LFj1b59ey1YsEDnzp3Tvffeq++++06FhYUKCwtTcXGx+vbtqxYtWuj48eM6f/48KRsAAF2H4fHCwkLNmjVLq1evVpMmTZSbm6uoqCjFxsaqQYMGql27tnJyciRJXl5eysvLU05Ojo4dO6YdO3aoU6dOCg8P1/nz51VQUFByqtfu3bvVsmVL1ahR41pLBADcgIy6uUpVuuqkfXH4e+PGjdq/f7/S0tK0YsUKxcXFKT09XQkJCZKkmJgYpaamatWqVTp37pzWr1+vrl27yul06ttvv5XVatVDDz2krKwsPfPMM9q3b5/mz58vp9Op/v37X59XCQDADaDCSTsrK0tLlixRs2bN1LdvXxUVFalGjRpKT0+Xw+FQSEiInE6nYmJi9MUXX2jNmjWaNGmS+vfvr4yMDK1bt05vvPGGAgIC9MILL+i7777Tjz/+KEl68MEHVbduXa1Zs0Zjx45Vw4YN9eSTT5a5/RsAAJJxN1epSuU27aKiIqWkpGjhwoU6cuSIgoKC1LdvX9WoUUMul0teXl4KDg7WuXPnSi4xlp2dLYfDUXL+9ZQpU5SVlSVJCg4OVk5OjjIzM0u2eLNYLLr77rt11113ycvLSzab208fBwDAcModHrdarfL19dWECRO0ePFiZWdn6/vvv5d0odl26NBBBw8e1LRp05SZmanExET5+fmpT58+2rRpk6QLQ+n+/v4KDg7W3r17NXPmTEVGRsput1/yXN7e3jRsAECFeOJ52uU2bYvFol69eik6OlotW7ZUmzZtLrmYd8+ePTVhwgSdP39eY8eO1bZt2xQXFydJCgsLKznH+uuvv1ZsbKwGDx4sm82mJ554QjabrWRuHAAAXFmFYq2/v78kydfXV/369VN8fLymT58u6UJT79Gjh+x2+yVD2xkZGerSpUvJnENYWJgmT56stm3bXnJsT5yTAABcOy8P7B+VWj1usVhkt9vlcDj0+eefS7ow5y1dGALPy8vT1q1bNWrUKEVERGjgwIElXxsUFFSqYQMAgIqr9ARyvXr11LlzZyUlJclut8tms8npdCo/P1+xsbHKz89XdHS0Hn30UVmt1qqoGQAAQ801V5dKN+1atWrp4Ycf1pgxYxQfH6/k5GRNnTpV0dHRSkhIUGhoaFXUCQCAx6tU03a5XEpLS9Ps2bNltVq1Y8cOTZo0SdHR0ZJEwwYAVBtPXBNVqaadk5OjzZs3a+DAgRo5cqR8fHyqqi4AAPArlWragYGBmjFjRlXVAgAAroCdTAAApuSBo+NVdz1tAABwfZG0AQCmxOYqAADAsEjaAABT8rycTdIGAMA0SNoAAFPyxM1VSNoAAJgESRsAYEpenhe0SdoAAJgFSRsAYErMaQMAAMMiaQMATMkDgzZJGwAAsyBpAwBMiTltAABgWDRtAABMguFxAIApsbkKAAAwLJI2AMCUWIgGAAAMi6QNADAlz8vZJG0AAEyDpA0AMCUv5rQBAIBRkbQBAKbkgUGbpA0AgFmQtAEApsR52gAAwLBI2gAAU/LAoE3SBgDALEjaAABT4jxtAABgWDRtAABMguFxAIApeeDoOEkbAACzIGkDAEzJEzdXsbhcLpe7iwAAoLKeXL2n2p7rjQGtqu25ruSGTdp5hU53l1Cm2j5eOnyq0N1lXFZooI9O5he7u4wyBflZJUkFRW4u5DJ8bdKQRdvdXcZlJce2M/TfhWTsn23arhPuLuOy+rata+jvXVXxxPldT3zNAACY0g2btAEANzZPnNMmaQMAYBIkbQCAKXl5XtAmaQMAYBYkbQCAKZG0AQCAYZG0AQCmxOpxAABgWCRtAIApMacNAAAMi6YNAIBJMDwOADAlD1yHRtIGAMAsSNoAAFPy8sCoTdIGAMAkSNoAAFPyxNTpia8ZAABTImkDAEzJA6e0SdoAAJgFSRsAYEqsHgcAAIZF0gYAmJIHBm2SNgAAZkHSBgCYEpfmBAAAhkXSBgCYEqvHAQCAYdG0AQAwCYbHAQCm5IGj4yRtAADMgqQNADAlTvkCAACGRdIGAJiSRZ4XtUnaAACYBEkbAGBKzGkDAADDImkDAEyJpA0AAAyLpA0AMCWLB26JRtIGAMAkSNoAAFNiThsAABgWSRsAYEoeOKVN0gYA4HpKS0vTQw89pPbt26tFixal7i8oKNALL7yg7t27KyIiQvfee68+/fTTCh2bpA0AMCUvg0btgIAAPfTQQyooKNCf/vSnS+5zuVx68sknJUlLly5Vw4YNdezYMRUVFVXo2DRtAADKkZubq9zc3FK3BwQEKCAg4JLbunXrJknasmVLqcd//vnnSk9P1yeffKKgoCBJUr169SpcB00bAIByLFq0SHPmzCl1+7hx4zR+/PgKH+eLL75QaGioEhIStG7dOvn4+KhHjx764x//KD8/v3K/nqYNADCl6jzlKzY2VgMGDCh1+69TdnlOnTqlffv2yW6368MPP9SpU6c0btw4vfjii3ruuefK/XoWogEAUI6AgACFhoaW+qhs0/bz85PVatWkSZNUs2ZNNWjQQGPGjNGHH35Yoa83VNI+ePCgatasqZCQEHeXAgAwOIOuQ7ui1q1bS7p0C9bKbMfq9qR99uxZzZs3Tz169NDYsWP1+9//XqmpqXI4HO4uDQCASisuLlZhYaHOnz8vSSosLFRhYaGcTqfuvfde3XzzzXr11VflcDh0/PhxzZ8/X717967Qsd3etN9991199tlnmjVrljZs2KA777xTy5Yt0zfffOPu0gAABuYlS7V9VMaaNWsUHh6uUaNGSZLCw8MVHh6u9PR0+fn56e2339auXbsUGRmpQYMGqX379nrqqacqdOxqHR7fuXOnQkNDS5a55+Tk6OOPP1bjxo3VoUMHSdKwYcO0evVqhsgBAKb029/+Vr/97W8ve3+zZs30zjvvXNWxq7xp5+fn680339Ty5csVGBiounXr6u6779bjjz8um82m+vXrKzs7W2fPnlWtWrWUlpam7t27y9fXt6pLAwCYmBnntK9VlQ2Pu1wuSRdOLt++fbuSk5O1YcMGde3aVXPmzNHHH38sf39/TZw4UUFBQRo5cqRat26tlStXqqioSGPHjtWGDRsuORYAAJ7sujVth8OhDz74QBkZGZL+uxpu3rx5Cg8PV+PGjeV0OjV27Fi1b99eCxcu1KFDhxQYGKiYmBjddNNNWrVqlT755BO9+OKLJQ28oKDAIy90DgC4Mi9L9X0YxTUPjxcWFmrWrFlavXq1mjRpotzcXEVFRSk2NlYNGjRQ7dq1lZOTI0ny8vJSXl6ecnJydOLECf3www9q2LChlixZIj8/PzVr1kyS5OPjo/z8fPn5+cnpdF5riQAA3BCuOmlfHLLeuHGj9u/fr7S0NK1YsUJxcXFKT09XQkKCJCkmJkapqalatWqVzp07p/Xr16tr164qKirS999/L0kKDAxUdna2PvroI505c0ZvvfWWvvnmG0VHR6tWrVrX4WUCAG40XhZLtX0YRYWbdlZWll577TWlpaVJUskVSdLT0+VwOBQSEiKn06mYmBg1b95ca9as0enTp9W/f38NHz5c69atU9++fbVs2TI9+OCDioiI0OHDhyVd2B4uIiJCf//733X//fdr8+bNmjRpUoXPWwMAwBOUOzxeVFSklJQULVy4UEeOHFFQUJD69u2rGjVqyOVyycvLS8HBwTp37pxq1qwpScrOzpbD4dCaNWs0YsQITZkyRVlZWZKk4OBg5eTkKDMzUyNGjJAkNWrUSBMnTlRmZqbq168vb2/vKnzJAIAbgYECcLUpN2lbrVb5+vpqwoQJWrx4sbKzs0uGtS0Wizp06KCDBw9q2rRpyszMVGJiovz8/NSnTx9t2rRJ0oWhdH9/fwUHB2vv3r2aOXOmIiMjZbfbL3mu22+/nYYNAMBllNu0LRaLevXqpejoaLVs2VJt2rTRkiVLSu7v2bOnJkyYoPPnz2vs2LHatm2b4uLiJElhYWFyuVyyWCz6+uuvFRsbq8GDB8tms+mJJ56QzWbjdC4AwFXxxDntCq0e9/f3lyT5+vqqX79+io+P1/Tp0yVdaOo9evSQ3W6Xl5eXbLYLh8zIyFCXLl1KTtcKCwvT5MmT1bZt20uOzelcAABUTKVWj1ssFtntdjkcDn3++eeS/rsgzeVyKS8vT1u3btWoUaMUERGhgQMHlnxtUFBQqYYNAAAqrtLnaderV0+dO3dWUlKS7Ha7bDabnE6n8vPzFRsbq/z8fEVHR+vRRx+V1WqtipoBAPDIhWiVbtq1atXSww8/rDFjxig+Pl7JycmaOnWqoqOjlZCQoNDQ0KqoEwAAj1eppu1yuZSWlqbZs2fLarVqx44dmjRpkqKjoyWJhg0AqDZuv7a0G1Sqaefk5Gjz5s0aOHCgRo4cKR8fn6qqCwAA/EqlmnZgYKBmzJhRVbUAAFBhnnj2kSeOLgAAYErXfJUvAADcwfNyNkkbAADTIGkDAEzJSNuLVheSNgAAJkHSBgCYkuflbJI2AACmQdIGAJiSB05pk7QBADALkjYAwJTYEQ0AABgWTRsAAJNgeBwAYEqemDo98TUDAGBKJG0AgCmxEA0AABgWSRsAYEqel7NJ2gAAmAZJGwBgSsxpAwAAwyJpAwBMyRNTpye+ZgAATImkDQAwJea0AQCAYZG0AQCm5Hk5m6QNAIBpkLQBAKbkgVPaJG0AAMyCpg0AgEkwPA4AMCUvD1yKRtIGAMAkSNoAAFNiIRoAADAskjYAwJQszGkDAACjImkDAEzJE+e0LS6Xy+XuIgAAqKx1u09U23NFt6lbbc91JSRtAIApeeJ52jds084rdLq7hDLV9vHSf37Ic3cZlxVxW20VFLm7irL5/vzbauT6Dp8qdHcZlxUa6GPo751k7J9t2MT17i7jsva93MfQ3ztcP3w7AQCm5Ilz2qweBwDAJEjaAABTImkDAADDImkDAEyJHdEAAIBh0bQBADAJhscBAKbk5Xmj4yRtAADMgqQNADAlFqIBAADDImkDAEyJzVUAAIBhkbQBAKbEnDYAADAskjYAwJQ4TxsAABgWSRsAYErMaQMAAMMiaQMATInztAEAgGGRtAEApuSBQZukDQCAWdC0AQAwCYbHAQCm5OWBK9FI2gAAmARJGwBgSp6Xs0naAACYBkkbAGBOHhi1SdoAAJgESRsAYEpcMAQAABgWSRsAYEoeeJo2SRsAALMgaQMATMkDgzZJGwAAsyBpAwDMyQOjNkkbAACTIGkDAEyJ87QBAIBh0bQBADAJhscBAKbE5ioAAMCwSNoAAFPywKBN0gYAwCxI2gAAc/LAqE3SBgDgOsvKytLEiRPVpUsX3XXXXRo6dKjS09Ov+bg0bQCAKVmq8b/KevbZZ3XixAmlpaVpy5Ytuu+++/TYY48pNzf3ml4zTRsAgOssMzNT999/v4KCgmS1WjVkyBCdPXtWP/zwwzUdlzltAIApVed52rm5uWWm5ICAAAUEBJS6fcyYMUpJSdF9992nOnXqaOnSpWrUqJGaN29+TXUYqmkfPXpUdevWldVqdXcpAACUWLRokebMmVPq9nHjxmn8+PGlbm/Xrp1SU1PVtWtXWa1W1alTR2+88Ya8vb2vqQ63N+2CggItWrRIy5cvV82aNRUWFqZRo0apXbt27i4NAGBg1bl4PDY2VgMGDCh1e1kp2+l0auTIkYqMjNSXX34pPz8/ffLJJxozZoyWLl2qFi1aXHUdbp/TXrdunbZs2aLXXntNCxYs0Pnz5/XnP/9Zn3/+uSTJ5XK5uUIAgKcLCAhQaGhoqY+ymvbp06d16NAhjRgxQjfddJNsNpt69eqlhg0blvS2q1WtTXvnzp06efJkyecnT57UunXr1Lp1a0VERKhBgwZ65ZVXlJ2drXfffVeSZPHEzWUBAOWzVONHJQQGBiosLExLly7VmTNn5HQ69a9//Ut79+5VmzZtruUVV/3weH5+vt58800tX75cgYGBqlu3ru6++249/vjjslqtOnXqlJo2bVry+Bo1asjHx0dffvmldu7cqfDwcLlcLpo3AMA05s6dq7/97W+69957VVhYqFtvvVXTpk1TZGTkNR23ypr2xUa7ZcsWbd++XcnJyWrcuLESExM1e/ZstWjRQvfcc4+aNm2qf/zjHwoMDFRUVJTS0tLUtGlT5eXladeuXQoPD6dhAwBKuZrzp6tLo0aNNHfu3Ot+3Os2PO5wOPTBBx8oIyND0n+HtefNm6fw8HA1btxYTqdTY8eOVfv27fX222/rzJkzevLJJxUSEqLZs2frnnvu0TvvvKPRo0frxIkTCgwMvF7lAQBgetectAsLCzVr1iytXr1aTZo0UW5urqKiohQbG6sGDRqodu3aysnJkSR5eXkpLy9POTk5On78uHbs2CG73a7nn39eO3fulI+Pj1q0aKHc3Fw5HA4FBQVd8wsEAOBGcdVJ++Kq7o0bN2r//v1KS0vTihUrFBcXp/T0dCUkJEiSYmJilJqaqlWrVuncuXNav369unbtquLiYh08eFDSheXx4eHhatGihc6cOaMZM2aoT58+ioyMlNPpvPZXCQC44Vgs1fdhFBVu2llZWXrttdeUlpYmSSoqKpIkpaeny+FwKCQkRE6nUzExMWrevLnWrFmj06dPq3///ho+fLjWrVunvn37atmyZXrwwQcVERGhw4cPlxxrx44dGjx4sLp166YzZ85o6NChFwr0cvtZaQAAGEK5w+NFRUVKSUnRwoULdeTIEQUFBalv376qUaOGXC6XvLy8FBwcrHPnzqlmzZqSpOzsbDkcDq1Zs0YjRozQlClTlJWVJUkKDg5WTk6OMjMzNWLECEmSt7e3QkJCNHr0aEVFRcnHx6cKXzIA4EZgoABcbcqNsVarVb6+vpowYYIWL16s7Oxsff/995IuLDbr0KGDDh48qGnTpikzM1OJiYny8/NTnz59tGnTJkkXhtL9/f0VHBysvXv3aubMmYqMjJTdbi95nnr16um+++6jYQMAcBnlNm2LxaJevXopOjpaLVu2VJs2bbRkyZKS+3v27KkJEybo/PnzGjt2rLZt26a4uDhJUlhYWMmpX19//bViY2M1ePBg2Ww2PfHEE7LZbOx4BgC4OgbdXKUqVWj1uL+/vyTJ19dX/fr1U3x8vKZPny7pQlPv0aOH7Ha7vLy8ZLNdOGRGRoa6dOlScupXWFiYJk+erLZt215ybM7BBgCgYiq1ystischut8vhcJTsn3pxQZrL5VJeXp62bt2qUaNGKSIiQgMHDiz52qCgoFINGwCAq2Wpxv+MotLnaderV0+dO3dWUlKS7Ha7bDabnE6n8vPzFRsbq/z8fEVHR+vRRx/lEpsAAFxHlW7atWrV0sMPP6wxY8YoPj5eycnJmjp1qqKjo5WQkKDQ0NCqqBMAgEt44uxqpZq2y+VSWlqaZs+eLavVqh07dmjSpEmKjo6WJBo2AABVqFJNOycnR5s3b9bAgQM1cuRITs8CALiNBwbtyjXtwMBAzZgxo6pqAQAAV1Dl19MGAKBKeGDUZmNvAABMgqQNADAlI50/XV1I2gAAmARNGwAAk2B4HABgSp64uQpJGwAAkyBpAwBMyQODNkkbAACzIGkDAMzJA6M2SRsAAJMgaQMATInNVQAAgGGRtAEApsR52gAAwLBI2gAAU/LAoE3SBgDALEjaAABz8sCoTdIGAMAkSNoAAFPiPG0AAGBYNG0AAEyC4XEAgCmxuQoAADAskjYAwJQ8MGiTtAEAMAuSNgDAnDwwapO0AQAwCZI2AMCU2FwFAAAYFkkbAGBKnKcNAAAMi6QNADAlDwzaJG0AAMyCpA0AMCXmtAEAgGFZXC6Xy91FAABQWYdPOartuUIDvavtua7khh0ezyt0uruEMtX28VJBkburuDxfm7G/d5Kx6wubuN7dZVzWvpf7GPZ3z/fnf4mMXN+BrAJ3l3FZjYN9DVtf42Bfd5dwQ7lhmzYA4MbGnDYAADAsmjYAACbB8DgAwJQ8cHScpA0AgFmQtAEApsRCNAAAYFgkbQCAKVk8cFabpA0AgEmQtAEA5uR5QZukDQCAWZC0AQCm5IFBm6QNAIBZkLQBAKbEedoAAMCwSNoAAFPiPG0AAGBYJG0AgDl5XtAmaQMAYBY0bQAATILhcQCAKXng6DhJGwAAsyBpAwBMic1VAACAYZG0AQCmxOYqAADAsEjaAABTYk4bAAAYFk0bAACToGkDAGASzGkDAEyJOW0AAGBYJG0AgClxnjYAADAskjYAwJSY0wYAAIZF0wYAwCQYHgcAmJIHjo6TtAEAMAuSNgDAnDwwapO0AQAwCZI2AMCU2FwFAAAYFkkbAGBKbK4CAAAMi6QNADAlDwzaJG0AAMyCpA0AMCcPjNokbQAATIKkDQAwJc7TBgAA18zpdOqVV17R3XffrXbt2mnUqFE6cuTINR/XUE07KytLf/3rX7V69Wp3lwIAMDiLpfo+Kmv+/Plau3atlixZos8++0wNGjRQXFycnE7nNb1mQzXtd999V0uWLFF6erp++uknd5cDAIAkKTc3V4cPHy71kZubW+bjk5KSNHr0aDVp0kR+fn6aPHmyDhw4oG3btl1THYaY0y4qKpLNZtPhw4fVsWNH/fTTT/ruu+90yy23yOVyyXIVb3Nq+xjq/cglfA3xXb88I3/vJGPXt+/lPu4u4YqM/rtn5PoaB/u6u4QrMnp9VaE6f1/eWrRIc+bMKXX7uHHjNH78+Etuy8vL05EjR9S2bduS2wICAnT77bdrz5496tix41XXUa1/Iu+//762bt2q7t27KyIiQv7+/iouLpbNZtMPP/ygzMxMPfvss3r66ae1e/dude7cWVartTpLBACglNjYWA0YMKDU7QEBAaVuO3PmTJn31a5du+S+q1UtTfvEiROaMmWKfvzxR9111116+eWXFRwcrLfeequkKZ84cUI1a9ZUkyZN1KxZM+3Zs0dnz55V7dq1q6NEAAAuKyAgoMwGXRZ/f39JFxL3L+Xl5ZXcd7WqdJzx4oT75s2bdfr0aW3YsEEzZszQrFmztHXrVi1ZskQOh0OS9N5776lRo0aSpH79+uno0aOaP3++FixYUJUlAgBwXdWuXVu33nqrdu3aVXJbXl6efvjhB7Vq1eqajn3dmvb58+e1ZMkSPfHEE1q4cKFOnDghL68Lh//ggw/Utm1buVwuORwOhYWFafjw4UpNTdXhw4clSS6XS71799a+ffu0fv16ZWRkKCkpSbm5uXK5XNerTAAAqtzQoUO1YMECHThwQGfPntVLL72kRo0aqUOHDtd03OsyPF5YWKgJEyYoKytLAwYMUEpKijZt2qTRo0erU6dOCgwM1IEDBy5ZUDZo0CCtXLlShw4d0m233aYVK1boo48+ktVqVdeuXdWzZ0+dPn1aMTExslgsV70gDQCA6jZ69Gjl5eXpoYce0rlz59ShQwclJCSUhNmrdV2adlpamo4fP66lS5fKz89PnTt3VmJioubOnatOnTrp7rvvVmpqqoqLi+Xt7a3i4mI1bNhQAQEB2rNnj6KiojR58mQFBwcrKipKderUUXp6uhISEkrmBGjYAACz8PLy0sSJEzVx4sTretwKN+0TJ05o6dKl2r9/v+x2uzp16qQmTZpIunD+WnFxsfz8/FRcXKymTZvqwQcfVFxcnA4dOqRu3brJ399fSUlJ+t3vfqfi4mJZrVaFhYXp0KFDkqRRo0Zd8nwdO3a8pmXxAADcaMrN6YWFhZo1a5aGDBmiAwcOqEuXLlqzZo3+8Ic/lDzGYrGoVq1aOnr0aMlq8DZt2qh58+ZKSUmRv7+/Bg8erAULFmjbtm3y9vbWqVOn9NNPP+m+++6rulcHAMANpNyk7f3/27t/lkaiKAzjzyAzip1FZGITQRAbi6iINkEkjTb6sHd7PQAAAgJJREFUBWy0NJDO1i9gbxELwTLY2KWxdYSAIAhioVWEEMsU/kFnqw0sW2yCBsn6/Nq5xZ3qhXvPPSeKeHp64vDwkJmZGQDm5+fZ2dnh4uKC5eVlMpkMURRxdXVFNpsFYHR0lKWlpU713O7uLg8PD+zv7zM9PU2SJCwsLHz6Ul6SpJ/in6EdBAHlcpk4jnl7eyMMQ15eXhgfH2dsbAyAubk5arUa5+fnrK+v8/7+zvDwMI+Pj0xOTvL6+koURRwcHJAkCdfX12xtbZHP5/v+g5Ik/S+6KmPLZrMEQUAYhgDc3d3x8fHB1NQUAHEcs7q6Sr1e5/T0lKGhIdI0pdVqMTs72yk+i6KIQqFAqVQysCVJ6lHP1ePPz89UKhVKpRJhGHaKyjY2Nmg0GhwfH1OtVrm9vWVlZYVCoQBgO1JJkj4pSLvsXPL7nfTJyQlnZ2dUq9W/vqVpSrPZJEkSFhcXmZiY6NvGJUn6abp+5R0EAe12m3q9zt7eHu12m6OjI4rFIjc3N501cRyzublpYEuS9MV6Oh6/vLykVqtxf39Po9Egn89TLpf/GD8mSZL6o6fQTtOUXC7H9vY2a2trjIz8vPmtkiR9l67vtAH7f0uS9I166lxuYEuS9H36Ok9bkiR9HUNbkqQBYWhLkjQgDG1JkgaEoS1J0oAwtCVJGhCGtiRJA+IXxd+ZQH9sfp0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "#1989\n",
    "df=pd.read_csv('1989.csv')\n",
    "\n",
    "y1=df[df['TERRITORY']=='ANDHRA PRADESH']\n",
    "y1=y1[['No.', '%', 'CONSTITUENCY', 'PARTY']]\n",
    "\n",
    "j=-1\n",
    "unique=list(y1.CONSTITUENCY.unique())\n",
    "check=[]\n",
    "votes_percent=[]\n",
    "party89=[]\n",
    "\n",
    "for i in y1.index:\n",
    "    const=y1['CONSTITUENCY'][i]\n",
    "    if(const in unique and const not in check):\n",
    "        j+=1\n",
    "        check.append(const)\n",
    "        votes_percent.insert(j, y1['%'][i])\n",
    "        party89.insert(j, y1['PARTY'][i])\n",
    "        \n",
    "    if(const in unique and const in check):\n",
    "        if(y1['%'][i]>votes_percent[j]):\n",
    "            votes_percent[j]=y1['%'][i]\n",
    "            party89[j]=y1['PARTY'][i]\n",
    "\n",
    "#1991\n",
    "df=pd.read_csv('1991.csv')\n",
    "\n",
    "y1=df[df['TERRITORY']=='ANDHRA PRADESH']\n",
    "y1=y1[['No.', '%', 'CONSTITUENCY', 'PARTY']]\n",
    "\n",
    "j=-1\n",
    "unique=list(y1.CONSTITUENCY.unique())\n",
    "check=[]\n",
    "votes_percent=[]\n",
    "party91=[]\n",
    "\n",
    "for i in y1.index:\n",
    "    const=y1['CONSTITUENCY'][i]\n",
    "    if(const in unique and const not in check):\n",
    "        j+=1\n",
    "        check.append(const)\n",
    "        votes_percent.insert(j, y1['%'][i])\n",
    "        party91.insert(j, y1['PARTY'][i])\n",
    "        \n",
    "    if(const in unique and const in check):\n",
    "        if(y1['%'][i]>votes_percent[j]):\n",
    "            votes_percent[j]=y1['%'][i]\n",
    "            party91[j]=y1['PARTY'][i]\n",
    "\n",
    "#1996\n",
    "df=pd.read_csv('1996.csv')\n",
    "\n",
    "y1=df[df['TERRITORY']=='ANDHRA PRADESH']\n",
    "y1=y1[['No.', '%', 'CONSTITUENCY', 'PARTY']]\n",
    "\n",
    "j=-1\n",
    "unique=list(y1.CONSTITUENCY.unique())\n",
    "check=[]\n",
    "votes_percent=[]\n",
    "party96=[]\n",
    "\n",
    "for i in y1.index:\n",
    "    const=y1['CONSTITUENCY'][i]\n",
    "    if(const in unique and const not in check):\n",
    "        j+=1\n",
    "        check.append(const)\n",
    "        votes_percent.insert(j, y1['%'][i])\n",
    "        party96.insert(j, y1['PARTY'][i])\n",
    "        \n",
    "    if(const in unique and const in check):\n",
    "        if(y1['%'][i]>votes_percent[j]):\n",
    "            votes_percent[j]=y1['%'][i]\n",
    "            party96[j]=y1['PARTY'][i]\n",
    "\n",
    "#1998\n",
    "df=pd.read_csv('1998.csv')\n",
    "\n",
    "y1=df[df['TERRITORY']=='ANDHRA PRADESH']\n",
    "y1=y1[['No.', '%', 'CONSTITUENCY', 'PARTY']]\n",
    "\n",
    "j=-1\n",
    "unique=list(y1.CONSTITUENCY.unique())\n",
    "check=[]\n",
    "votes_percent=[]\n",
    "party98=[]\n",
    "\n",
    "for i in y1.index:\n",
    "    const=y1['CONSTITUENCY'][i]\n",
    "    if(const in unique and const not in check):\n",
    "        j+=1\n",
    "        check.append(const)\n",
    "        votes_percent.insert(j, y1['%'][i])\n",
    "        party98.insert(j, y1['PARTY'][i])\n",
    "        \n",
    "    if(const in unique and const in check):\n",
    "        if(y1['%'][i]>votes_percent[j]):\n",
    "            votes_percent[j]=y1['%'][i]\n",
    "            party98[j]=y1['PARTY'][i]\n",
    "\n",
    "#1999\n",
    "df=pd.read_csv('1999.csv')\n",
    "\n",
    "y1=df[df['TERRITORY']=='ANDHRA PRADESH']\n",
    "y1=y1[['No.', '%', 'CONSTITUENCY', 'PARTY']]\n",
    "\n",
    "j=-1\n",
    "unique=list(y1.CONSTITUENCY.unique())\n",
    "check=[]\n",
    "votes_percent=[]\n",
    "party99=[]\n",
    "\n",
    "for i in y1.index:\n",
    "    const=y1['CONSTITUENCY'][i]\n",
    "    if(const in unique and const not in check):\n",
    "        j+=1\n",
    "        check.append(const)\n",
    "        votes_percent.insert(j, y1['%'][i])\n",
    "        party99.insert(j, y1['PARTY'][i])\n",
    "        \n",
    "    if(const in unique and const in check):\n",
    "        if(y1['%'][i]>votes_percent[j]):\n",
    "            votes_percent[j]=y1['%'][i]\n",
    "            party99[j]=y1['PARTY'][i]\n",
    "\n",
    "#2004\n",
    "df=pd.read_csv('2004.csv')\n",
    "\n",
    "y1=df[df['TERRITORY']=='ANDHRA PRADESH']\n",
    "y1=y1[['No', '% OF TOTAL VOTES', 'CONSTITUENCY', 'PARTY']]\n",
    "\n",
    "j=-1\n",
    "unique=list(y1.CONSTITUENCY.unique())\n",
    "check=[]\n",
    "votes_percent=[]\n",
    "party04=[]\n",
    "\n",
    "for i in y1.index:\n",
    "    const=y1['CONSTITUENCY'][i]\n",
    "    if(const in unique and const not in check):\n",
    "        j+=1\n",
    "        check.append(const)\n",
    "        votes_percent.insert(j, y1['% OF TOTAL VOTES'][i])\n",
    "        party04.insert(j, y1['PARTY'][i])\n",
    "        \n",
    "    if(const in unique and const in check):\n",
    "        if(y1['% OF TOTAL VOTES'][i]>votes_percent[j]):\n",
    "            votes_percent[j]=y1['%'][i]\n",
    "            party04[j]=y1['PARTY'][i]\n",
    "\n",
    "final=pd.DataFrame({'Constituency': check, '1989': party89, '1991': party91, '1996': party96, '1998': party98, '1999': party99, '2004': party04})\n",
    "print(final, '\\n')\n",
    "\n",
    "years=list(final.columns)\n",
    "years.remove(years[0])\n",
    "parties=pd.unique(final[years].values.ravel())\n",
    "parties.sort()\n",
    "\n",
    "columns=list(parties)\n",
    "columns.insert(0, 'Year')\n",
    "\n",
    "ins=[]\n",
    "for i in years:\n",
    "    l=[0]*len(parties)\n",
    "    k=list(final[i])\n",
    "    for j in k:\n",
    "        for m in range(len(parties)):\n",
    "            if(j==parties[m]):\n",
    "                l[m]+=1\n",
    "    l.insert(0, i)\n",
    "    ins.append(l)\n",
    "    \n",
    "df=pd.DataFrame(ins, columns=columns)\n",
    "print(df)\n",
    "df.set_index('Year', inplace=True)\n",
    "df.index.name=\"\"\n",
    "\n",
    "sns.set(font_scale=1.2)\n",
    "ax = sns.heatmap(df, linewidths=0.3, cmap=\"Blues\")\n",
    "ax.xaxis.tick_top()\n",
    "plt.xticks(rotation=90)\n",
    "plt.yticks(rotation=20)\n",
    "fig = ax.get_figure()\n",
    "fig.set_size_inches(8, 10)\n",
    "fig.savefig('andhra_pradesh.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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