nb1_por_estado.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  \n",
    "\n",
    "columns = ['uf', 'cidade', 'zona', 'secao', 'cargo', 'candidato', 'votos']\n",
    "df = pd.read_csv('./out.txt', sep=';', names=columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 28 artists>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = [12, 6]\n",
    "matplotlib.rcParams['axes.titlesize'] = 16\n",
    "\n",
    "votos_estado = df[df.cargo == 'Presidente'].groupby('uf').sum().sort_values('votos', ascending=False)\n",
    "total_votos = votos_estado.sum().votos\n",
    "votos_estado['porcent_votos'] = 100 * votos_estado.votos / total_votos\n",
    "plt.title(\"Porcentagem de votos por estado (Total = %s)\" % total_votos)\n",
    "plt.bar(votos_estado.index, votos_estado.porcent_votos)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f15d7d48590>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "presidente = df[df.cargo == 'Presidente']\n",
    "t = presidente.groupby('uf').sum()\n",
    "nomes = ['JAIR BOLSONARO', 'FERNANDO HADDAD', 'CIRO GOMES', 'Nulo', 'Branco', 'JOÃO AMOÊDO']\n",
    "colunas = ['bolsonaro', 'haddad', 'ciro', 'nulo', 'branco', 'amoedo']\n",
    "a_somas = [presidente[presidente.candidato == n].groupby('uf').sum() for n in nomes]\n",
    "s = a_somas[0]\n",
    "for c, a_soma in zip(colunas, a_somas):\n",
    "    s[c] = a_soma.votos\n",
    "s['total'] = t.votos\n",
    "for c in colunas:\n",
    "    s['porcent_%s' % c] = 100 * s[c] / s.total\n",
    "s = s.sort_values('porcent_bolsonaro', ascending=False)\n",
    "plt.title('Performance dos candidatos por estado')\n",
    "bottom = None\n",
    "plts = []\n",
    "for c in colunas:\n",
    "    plts.append(plt.bar(s.index, s['porcent_%s' % c], bottom=bottom))\n",
    "    bottom = s['porcent_%s' % c] if bottom is None else bottom + s['porcent_%s' % c]\n",
    "plt.legend([p[0] for p in plts], colunas)\n"
   ]
  }
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